diff --git a/.copier-answers.yml b/.copier-answers.yml new file mode 100644 index 0000000..0589b90 --- /dev/null +++ b/.copier-answers.yml @@ -0,0 +1,13 @@ +# Changes here will be overwritten by Copier; NEVER EDIT MANUALLY +_commit: 2024.08.19 +_src_path: gh:scientific-python/cookie +backend: hatch +email: alanlujan91@gmail.com +full_name: Alan Lujan +license: MIT +org: econ-ark +project_name: estimark +project_short_description: Estimating Microeconomic Dynamic Stochastic Optimization + Problems +url: https://github.com/econ-ark/EstimatingMicroDSOPs +vcs: true diff --git a/.git_archival.txt b/.git_archival.txt new file mode 100644 index 0000000..7c51009 --- /dev/null +++ b/.git_archival.txt @@ -0,0 +1,3 @@ +node: $Format:%H$ +node-date: $Format:%cI$ +describe-name: $Format:%(describe:tags=true,match=*[0-9]*)$ diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..00a7b00 --- /dev/null +++ b/.gitattributes @@ -0,0 +1 @@ +.git_archival.txt export-subst diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md new file mode 100644 index 0000000..0c1bfb4 --- /dev/null +++ b/.github/CONTRIBUTING.md @@ -0,0 +1,89 @@ +See the [Scientific Python Developer Guide][spc-dev-intro] for a detailed +description of best practices for developing scientific packages. + +[spc-dev-intro]: https://learn.scientific-python.org/development/ + +# Quick development + +The fastest way to start with development is to use nox. If you don't have nox, +you can use `pipx run nox` to run it without installing, or `pipx install nox`. +If you don't have pipx (pip for applications), then you can install with +`pip install pipx` (the only case were installing an application with regular +pip is reasonable). If you use macOS, then pipx and nox are both in brew, use +`brew install pipx nox`. + +To use, run `nox`. This will lint and test using every installed version of +Python on your system, skipping ones that are not installed. You can also run +specific jobs: + +```console +$ nox -s lint # Lint only +$ nox -s tests # Python tests +$ nox -s docs -- --serve # Build and serve the docs +$ nox -s build # Make an SDist and wheel +``` + +Nox handles everything for you, including setting up an temporary virtual +environment for each run. + +# Setting up a development environment manually + +You can set up a development environment by running: + +```bash +python3 -m venv .venv +source ./.venv/bin/activate +pip install -v -e .[dev] +``` + +If you have the +[Python Launcher for Unix](https://github.com/brettcannon/python-launcher), you +can instead do: + +```bash +py -m venv .venv +py -m install -v -e .[dev] +``` + +# Pre-commit + +You should prepare pre-commit, which will help you by checking that commits pass +required checks: + +```bash +pip install pre-commit # or brew install pre-commit on macOS +pre-commit install # Will install a pre-commit hook into the git repo +``` + +You can also/alternatively run `pre-commit run` (changes only) or +`pre-commit run --all-files` to check even without installing the hook. + +# Testing + +Use pytest to run the unit checks: + +```bash +pytest +``` + +# Coverage + +Use pytest-cov to generate coverage reports: + +```bash +pytest --cov=estimark +``` + +# Building docs + +You can build the docs using: + +```bash +nox -s docs +``` + +You can see a preview with: + +```bash +nox -s docs -- --serve +``` diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 0000000..6c4b369 --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,11 @@ +version: 2 +updates: + # Maintain dependencies for GitHub Actions + - package-ecosystem: "github-actions" + directory: "/" + schedule: + interval: "weekly" + groups: + actions: + patterns: + - "*" diff --git a/.github/release.yml b/.github/release.yml new file mode 100644 index 0000000..9d1e098 --- /dev/null +++ b/.github/release.yml @@ -0,0 +1,5 @@ +changelog: + exclude: + authors: + - dependabot + - pre-commit-ci diff --git a/.github/workflows/cd.yml b/.github/workflows/cd.yml new file mode 100644 index 0000000..85e2836 --- /dev/null +++ b/.github/workflows/cd.yml @@ -0,0 +1,60 @@ +name: CD + +on: + workflow_dispatch: + pull_request: + push: + branches: + - main + release: + types: + - published + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +env: + # Many color libraries just need this to be set to any value, but at least + # one distinguishes color depth, where "3" -> "256-bit color". + FORCE_COLOR: 3 + +jobs: + dist: + name: Distribution build + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - uses: hynek/build-and-inspect-python-package@v2 + + publish: + needs: [dist] + name: Publish to PyPI + environment: pypi + permissions: + id-token: write + attestations: write + contents: read + runs-on: ubuntu-latest + if: github.event_name == 'release' && github.event.action == 'published' + + steps: + - uses: actions/download-artifact@v4 + with: + name: Packages + path: dist + + - name: Generate artifact attestation for sdist and wheel + uses: actions/attest-build-provenance@v1.4.3 + with: + subject-path: "dist/*" + + - uses: pypa/gh-action-pypi-publish@release/v1 + with: + # Remember to tell (test-)pypi about this repo before publishing + # Remove this line to publish to PyPI + repository-url: https://test.pypi.org/legacy/ diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..98cc7c2 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,71 @@ +name: CI + +on: + workflow_dispatch: + pull_request: + push: + branches: + - main + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +env: + # Many color libraries just need this to be set to any value, but at least + # one distinguishes color depth, where "3" -> "256-bit color". + FORCE_COLOR: 3 + +jobs: + pre-commit: + name: Format + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + - uses: actions/setup-python@v5 + with: + python-version: "3.x" + - uses: pre-commit/action@v3.0.1 + with: + extra_args: --hook-stage manual --all-files + - name: Run PyLint + run: pipx run nox -s pylint -- --output-format=github + + checks: + name: Check Python ${{ matrix.python-version }} on ${{ matrix.runs-on }} + runs-on: ${{ matrix.runs-on }} + needs: [pre-commit] + strategy: + fail-fast: false + matrix: + python-version: ["3.8", "3.13"] + runs-on: [ubuntu-latest, windows-latest, macos-14] + + include: + - python-version: "pypy-3.10" + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + allow-prereleases: true + + - name: Install package + run: python -m pip install .[test] + + - name: Test package + run: >- + python -m pytest -ra --cov --cov-report=xml --cov-report=term + --durations=20 + + - name: Upload coverage report + uses: codecov/codecov-action@v4.5.0 + with: + token: ${{ secrets.CODECOV_TOKEN }} diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml index de0c122..d2effb2 100644 --- a/.github/workflows/deploy.yml +++ b/.github/workflows/deploy.yml @@ -18,7 +18,7 @@ permissions: # Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued. # However, do NOT cancel in-progress runs as we want to allow these production deployments to complete. concurrency: - group: 'pages' + group: "pages" cancel-in-progress: false jobs: deploy: @@ -29,7 +29,7 @@ jobs: steps: - uses: actions/checkout@v4 - name: Setup Pages - uses: actions/configure-pages@v3 + uses: actions/configure-pages@v5 - uses: actions/setup-node@v4 with: node-version: 18.x @@ -38,9 +38,9 @@ jobs: - name: Build HTML Assets run: myst build --html - name: Upload artifact - uses: actions/upload-pages-artifact@v1 + uses: actions/upload-pages-artifact@v3 with: - path: './_build/html' + path: "./_build/html" - name: Deploy to GitHub Pages id: deployment - uses: actions/deploy-pages@v2 + uses: actions/deploy-pages@v4 diff --git a/.gitignore b/.gitignore index a2b208f..f9bc3e4 100644 --- a/.gitignore +++ b/.gitignore @@ -473,5 +473,25 @@ TSWLatexianTemp* _build/ myst-gitlens.code-workspace +# setuptools_scm +src/*/_version.py + + +# ruff +.ruff_cache/ + +# OS specific stuff +.DS_Store +.DS_Store? +._* +.Spotlight-V100 +.Trashes +ehthumbs.db +Thumbs.db + +# Common editor files +*~ +*.swp + # MyST build outputs _build diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..90f6435 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,89 @@ +ci: + autoupdate_commit_msg: "chore: update pre-commit hooks" + autofix_commit_msg: "style: pre-commit fixes" + +exclude: ^.cruft.json|.copier-answers.yml$ + +repos: + - repo: https://github.com/adamchainz/blacken-docs + rev: "1.18.0" + hooks: + - id: blacken-docs + additional_dependencies: [black==24.*] + + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: "v4.6.0" + hooks: + - id: check-added-large-files + - id: check-case-conflict + - id: check-merge-conflict + - id: check-symlinks + - id: check-yaml + - id: debug-statements + - id: end-of-file-fixer + - id: mixed-line-ending + - id: name-tests-test + args: ["--pytest-test-first"] + - id: requirements-txt-fixer + - id: trailing-whitespace + + - repo: https://github.com/pre-commit/pygrep-hooks + rev: "v1.10.0" + hooks: + - id: rst-backticks + - id: rst-directive-colons + - id: rst-inline-touching-normal + + - repo: https://github.com/rbubley/mirrors-prettier + rev: "v3.3.3" + hooks: + - id: prettier + types_or: [yaml, markdown, html, css, scss, javascript, json] + args: [--prose-wrap=always] + + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: "v0.6.1" + hooks: + - id: ruff + args: ["--fix", "--show-fixes"] + - id: ruff-format + + - repo: https://github.com/pre-commit/mirrors-mypy + rev: "v1.11.1" + hooks: + - id: mypy + files: src|tests + args: [] + additional_dependencies: + - pytest + + - repo: https://github.com/codespell-project/codespell + rev: "v2.3.0" + hooks: + - id: codespell + + - repo: https://github.com/shellcheck-py/shellcheck-py + rev: "v0.10.0.1" + hooks: + - id: shellcheck + + - repo: local + hooks: + - id: disallow-caps + name: Disallow improper capitalization + language: pygrep + entry: PyBind|Numpy|Cmake|CCache|Github|PyTest + exclude: .pre-commit-config.yaml + + - repo: https://github.com/abravalheri/validate-pyproject + rev: "v0.19" + hooks: + - id: validate-pyproject + additional_dependencies: ["validate-pyproject-schema-store[all]"] + + - repo: https://github.com/python-jsonschema/check-jsonschema + rev: "0.29.1" + hooks: + - id: check-dependabot + - id: check-github-workflows + - id: check-readthedocs diff --git a/.readthedocs.yaml b/.readthedocs.yaml new file mode 100644 index 0000000..67c194c --- /dev/null +++ b/.readthedocs.yaml @@ -0,0 +1,17 @@ +# Read the Docs configuration file +# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details + +version: 2 + +build: + os: ubuntu-22.04 + tools: + python: "3.12" + commands: + - asdf plugin add uv + - asdf install uv latest + - asdf global uv latest + - uv venv + - uv pip install .[docs] + - .venv/bin/python -m sphinx -T -b html -d docs/_build/doctrees -D + language=en docs $READTHEDOCS_OUTPUT/html diff --git a/LICENSE b/LICENSE index 97049b6..a438ea6 100644 --- a/LICENSE +++ b/LICENSE @@ -1,13 +1,11 @@ -MIT License +Copyright 2024 Alan Lujan -Copyright (c) 2024 Alan Lujan - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies +of the Software, and to permit persons to whom the Software is furnished to do +so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. diff --git a/README.md b/README.md index f463dc1..69e9e9a 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,67 @@ -# TRP-wealth-in-utility +# Life-Cycle-Prime-Time https://econ-ark.github.io/Life-Cycle-Prime-Time/ -https://mybinder.org/v2/gh/econ-ark/EstimatingMicroDSOPs/HEAD +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/econ-ark/Life-Cycle-Prime-Time/HEAD) + +To reproduces all the results in the repository first clone this repository +locally: + +``` +# Clone this repository +$ git clone https://github.com/econ-ark/Life-Cycle-Prime-Time + +# Change working directory to Life-Cycle-Prime-Time +$ cd Life-Cycle-Prime-Time +``` + +Then you can either use a local virtual env(conda) or +[nbreproduce](https://github.com/econ-ark/nbreproduce) to reproduce to the +results. + +#### A local conda environment and execute the do_all.py file. + +``` +$ conda env create -f environment.yml +$ conda activate Life-Cycle-Prime-Time +# execute the script, select the appropriate option and use it to reproduce the data and figures. +$ python do_all.py +``` + +#### [nbreproduce](https://github.com/econ-ark/nbreproduce) (requires Docker to be installed on the machine). + +``` +# Install nbreproduce +$ pip install nbreproduce + +# Reproduce all results using nbreproduce +$ nbreproduce +``` + +## References + +[![Actions Status][actions-badge]][actions-link] +[![Documentation Status][rtd-badge]][rtd-link] + +[![PyPI version][pypi-version]][pypi-link] +[![Conda-Forge][conda-badge]][conda-link] +[![PyPI platforms][pypi-platforms]][pypi-link] + +[![GitHub Discussion][github-discussions-badge]][github-discussions-link] + + + + +[actions-badge]: https://github.com/econ-ark/Life-Cycle-Prime-Time/workflows/CI/badge.svg +[actions-link]: https://github.com/econ-ark/Life-Cycle-Prime-Time/actions +[conda-badge]: https://img.shields.io/conda/vn/conda-forge/estimark +[conda-link]: https://github.com/conda-forge/estimark-feedstock +[github-discussions-badge]: https://img.shields.io/static/v1?label=Discussions&message=Ask&color=blue&logo=github +[github-discussions-link]: https://github.com/econ-ark/Life-Cycle-Prime-Time/discussions +[pypi-link]: https://pypi.org/project/estimark/ +[pypi-platforms]: https://img.shields.io/pypi/pyversions/estimark +[pypi-version]: https://img.shields.io/pypi/v/estimark +[rtd-badge]: https://readthedocs.org/projects/estimark/badge/?version=latest +[rtd-link]: https://estimark.readthedocs.io/en/latest/?badge=latest + + diff --git a/_toc.yml b/_toc.yml new file mode 100644 index 0000000..79f3529 --- /dev/null +++ b/_toc.yml @@ -0,0 +1,23 @@ +# Table of Contents +# +# Myst will respect: +# 1. New pages +# - file: relative/path/to/page +# 2. New sections without an associated page +# - title: Folder Title +# sections: ... +# 3. New sections with an associated page +# - file: relative/path/to/page +# sections: ... +# +# Note: Titles defined on pages here are not recognized. +# +# This spec is based on the JupyterBook table of contents. +# Learn more at https://jupyterbook.org/customize/toc.html + +format: jb-book +root: README +chapters: + - title: Paper + sections: + - file: content/paper/01-paper diff --git a/content/figures/AllSMMcontour.pdf b/content/figures/AllSMMcontour.pdf new file mode 100644 index 0000000..07a64d8 Binary files /dev/null and b/content/figures/AllSMMcontour.pdf differ diff --git a/content/figures/AllSMMcontour.png b/content/figures/AllSMMcontour.png new file mode 100644 index 0000000..b61e17b Binary files /dev/null and b/content/figures/AllSMMcontour.png differ diff --git a/content/figures/AllSMMcontour.svg b/content/figures/AllSMMcontour.svg new file mode 100644 index 0000000..de239bc --- /dev/null +++ b/content/figures/AllSMMcontour.svg @@ -0,0 +1,18111 @@ + + + + + + + + 2023-06-18T14:40:02.090335 + image/svg+xml + + + Matplotlib v3.7.1, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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0000000..ed64d84 --- /dev/null +++ b/content/tables/TRP/Portfolio_estimate_results.csv @@ -0,0 +1,1673 @@ +CRRA,9.252286005027539 +time_to_estimate,60.753241539001465 +params,{'CRRA': 9.252286005027539} +criterion,0.6423582605057705 +start_criterion,0.6339648081630582 +start_params,{'CRRA': 9.252342476844415} +algorithm,multistart_tranquilo_ls +direction,minimize +n_free,1 +message,Absolute criterion change smaller than tolerance. +success, +n_criterion_evaluations, +n_derivative_evaluations, +n_iterations, +history,"{'params': [{'CRRA': 9.252342476844415}, {'CRRA': 8.327108229159974}, {'CRRA': 10.177576724528857}, {'CRRA': 8.327108229159974}, {'CRRA': 8.327108229159974}, {'CRRA': 10.177576724528857}, {'CRRA': 8.327108229159974}, {'CRRA': 8.327108229159974}, {'CRRA': 10.177576724528857}, {'CRRA': 8.327108229159974}, {'CRRA': 10.177576724528857}, {'CRRA': 8.327108229159974}, {'CRRA': 10.177576724528857}, {'CRRA': 8.327108229159974}, {'CRRA': 9.250458955049714}, {'CRRA': 8.789725353002193}, {'CRRA': 9.231195795349796}, {'CRRA': 9.23850082029189}, {'CRRA': 9.191470898591822}, {'CRRA': 9.310169617324693}, {'CRRA': 9.250657961285336}, {'CRRA': 9.250710524700677}, {'CRRA': 9.248963361277593}, {'CRRA': 9.253968442160518}, {'CRRA': 9.255956673124432}, {'CRRA': 9.254149574984424}, {'CRRA': 9.25143892777441}, {'CRRA': 9.251890702309414}, {'CRRA': 9.252568364111916}, {'CRRA': 9.252455420478165}, {'CRRA': 9.252286005027539}], 'criterion': [0.6423583236273489, 0.6726961956566793, 0.672466609452884, 0.6726961956566793, 0.6726961956566793, 0.672466609452884, 0.6726961956566793, 0.6726961956566793, 0.672466609452884, 0.6726961956566793, 0.672466609452884, 0.6726961956566793, 0.672466609452884, 0.6726961956566793, 0.642369169571296, 0.6492959495923358, 0.6423762404892832, 0.6423752797264956, 0.6425219143922716, 0.642514400144132, 0.6423685692715722, 0.6423684112220268, 0.6423805942108727, 0.6423699960365742, 0.6423863674367805, 0.6423725213049074, 0.6423641176751165, 0.6423603114677403, 0.6423590691348976, 0.642358466533662, 0.6423582605057704], 'runtime': [0.0, 3.3893006040002547, 3.7743795520000276, 3.9942729670001427, 4.308895564000068, 4.500098180999885, 4.797666575000221, 5.097781724000015, 5.30452481400016, 5.586473449000096, 5.8145816500000365, 6.019138186999953, 6.258510488999946, 21.4172058070003, 22.745588674999908, 24.07027555800005, 25.37667203000001, 26.848738280999896, 28.141902802999994, 29.463674151000305, 30.76145256100017, 32.056981691000146, 33.34122019300003, 34.749019994000264, 36.131662315000085, 37.520465677000175, 38.87502066600018, 40.19993009200016, 41.659364199000265, 43.079727706000085, 44.41924671800007], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]}" +convergence_report, +multistart_info,"{'start_parameters': [{'CRRA': 9.252342476844415}], 'local_optima': [Minimize with 1 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 9.827e-08* 9.827e-08* +relative_params_change 6.104e-06* 6.104e-06* +absolute_criterion_change 6.312e-08* 6.312e-08* +absolute_params_change 5.647e-05 5.647e-05 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 9.252342476844415}, {'CRRA': 8.1875}, {'CRRA': 10.549999999999999}, {'CRRA': 12.9125}, {'CRRA': 5.824999999999999}, {'CRRA': 14.093749999999998}, {'CRRA': 15.274999999999999}, {'CRRA': 4.64375}, {'CRRA': 17.6375}, {'CRRA': 3.4625}], 'exploration_results': array([0.64235832, 0.6831279 , 0.69939713, 1.02360977, 1.18227674, + 1.26376333, 1.54278366, 1.78141341, 2.1808234 , 2.89794431])}" +algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25234248]), radius=0.9252342476844415, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=[0], model=ScalarModel(intercept=0.6423583236273489, linear_terms=array([0.]), square_terms=array([[0.]]), scale=0.9252342476844415, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=0, candidate_x=array([9.25234248]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([9.25234248]), radius=0.9252342476844415, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]), model=ScalarModel(intercept=0.6366126588198241, linear_terms=array([0.00014855]), square_terms=array([[0.07297151]]), scale=0.9252342476844415, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=14, candidate_x=array([9.25045896]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-71.73087780998843, accepted=False, new_indices=array([ 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.46261712384222076, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 11, 12, 13, 14, 15]), model=ScalarModel(intercept=0.6391738833063486, linear_terms=array([0.0008387]), square_terms=array([[0.01834796]]), scale=0.46261712384222076, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + 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State(trustregion=Region(center=array([9.25234248]), radius=0.028913570240138797, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 16, 17, 19, 20]), model=ScalarModel(intercept=0.642364947951701, linear_terms=array([4.01212888e-06]), square_terms=array([[7.10835612e-05]]), scale=0.028913570240138797, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=21, candidate_x=array([9.25071052]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-89.09165927837773, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 16, 17, 19, 20]), old_indices_discarded=array([18]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.014456785120069399, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 16, 17, 20, 21]), model=ScalarModel(intercept=0.6423666277820237, linear_terms=array([4.21338439e-06]), square_terms=array([[1.80260164e-05]]), scale=0.014456785120069399, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, 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model_indices=array([ 0, 14, 17, 20, 21, 22]), model=ScalarModel(intercept=0.6423680016960622, linear_terms=array([-1.01758484e-06]), square_terms=array([[4.52377587e-06]]), scale=0.007228392560034699, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=23, candidate_x=array([9.25396844]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-101.98829389658573, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 17, 20, 21, 22]), old_indices_discarded=array([16]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.0036141962800173497, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 20, 21, 22, 23]), model=ScalarModel(intercept=0.6423664047173907, linear_terms=array([-8.15794512e-06]), square_terms=array([[1.06328797e-06]]), scale=0.0036141962800173497, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=24, candidate_x=array([9.25595667]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-3.6772491566976204, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 20, 21, 22, 23]), old_indices_discarded=array([17]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.0018070981400086748, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 20, 21, 22, 23]), model=ScalarModel(intercept=0.6423664047173909, linear_terms=array([-4.07897256e-06]), square_terms=array([[2.65821992e-07]]), scale=0.0018070981400086748, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=25, candidate_x=array([9.25414957]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-3.597936150162183, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 20, 21, 22, 23]), old_indices_discarded=array([24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.0009035490700043374, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 20, 21, 23, 25]), model=ScalarModel(intercept=0.6423678826985401, linear_terms=array([4.59211257e-07]), square_terms=array([[6.46936017e-08]]), scale=0.0009035490700043374, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=26, candidate_x=array([9.25143893]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-13.573507187210918, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 20, 21, 23, 25]), old_indices_discarded=array([22, 24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.0004517745350021687, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 20, 21, 23, 25, 26]), model=ScalarModel(intercept=0.6423670437711565, linear_terms=array([4.63065568e-07]), square_terms=array([[1.6122968e-08]]), scale=0.0004517745350021687, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=27, candidate_x=array([9.2518907]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-4.368840765358844, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 20, 21, 23, 25, 26]), old_indices_discarded=array([14]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.00022588726750108435, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 26, 27]), model=ScalarModel(intercept=0.6423580233416285, linear_terms=array([-1.44028832e-06]), square_terms=array([[4.10130588e-09]]), scale=0.00022588726750108435, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=28, candidate_x=array([9.25256836]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-0.5183479506363351, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.00011294363375054218, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 27, 28]), model=ScalarModel(intercept=0.6423590665246325, linear_terms=array([-2.47320622e-07]), square_terms=array([[1.00136317e-09]]), scale=0.00011294363375054218, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=29, candidate_x=array([9.25245542]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-0.5789901455335319, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 27, 28]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=5.647181687527109e-05, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 28, 29]), model=ScalarModel(intercept=0.6423582471741802, linear_terms=array([1.85886653e-07]), square_terms=array([[2.45317725e-10]]), scale=5.647181687527109e-05, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=30, candidate_x=array([9.25228601]), index=30, x=array([9.25228601]), fval=0.6423582605057705, rho=0.33979447206310537, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=5.647181687606917e-05, relative_step_length=1.0000000000141325, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 31 entries., 'multistart_info': {'start_parameters': [array([9.25234248])], 'local_optima': [{'solution_x': array([9.25228601]), 'solution_criterion': 0.6423582605057705, 'states': [State(trustregion=Region(center=array([9.25234248]), radius=0.9252342476844415, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=[0], model=ScalarModel(intercept=0.6423583236273489, linear_terms=array([0.]), square_terms=array([[0.]]), scale=0.9252342476844415, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=0, candidate_x=array([9.25234248]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([9.25234248]), radius=0.9252342476844415, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]), model=ScalarModel(intercept=0.6366126588198241, linear_terms=array([0.00014855]), square_terms=array([[0.07297151]]), scale=0.9252342476844415, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=14, candidate_x=array([9.25045896]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-71.73087780998843, accepted=False, new_indices=array([ 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.46261712384222076, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 11, 12, 13, 14, 15]), model=ScalarModel(intercept=0.6391738833063486, linear_terms=array([0.0008387]), square_terms=array([[0.01834796]]), scale=0.46261712384222076, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=16, candidate_x=array([9.2311958]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-0.9346804797475832, accepted=False, new_indices=array([15]), old_indices_used=array([ 0, 11, 12, 13, 14]), old_indices_discarded=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.23130856192111038, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 12, 13, 14, 15, 16]), model=ScalarModel(intercept=0.6400364245764906, linear_terms=array([0.00027196]), square_terms=array([[0.00454468]]), scale=0.23130856192111038, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=17, candidate_x=array([9.23850082]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-2.083816827569783, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 13, 14, 15, 16]), old_indices_discarded=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.11565428096055519, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 15, 16, 17]), model=ScalarModel(intercept=0.6424109502570059, linear_terms=array([0.00061503]), square_terms=array([[0.00116854]]), scale=0.11565428096055519, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=18, candidate_x=array([9.1914709]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-1.0107374685291592, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 15, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.057827140480277595, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 16, 17, 19]), model=ScalarModel(intercept=0.6423641764261762, linear_terms=array([8.28433083e-06]), square_terms=array([[0.00028439]]), scale=0.057827140480277595, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=20, candidate_x=array([9.25065796]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-84.9118930196347, accepted=False, new_indices=array([19]), old_indices_used=array([ 0, 14, 16, 17]), old_indices_discarded=array([18]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.028913570240138797, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 16, 17, 19, 20]), model=ScalarModel(intercept=0.642364947951701, linear_terms=array([4.01212888e-06]), square_terms=array([[7.10835612e-05]]), scale=0.028913570240138797, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=21, candidate_x=array([9.25071052]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-89.09165927837773, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 16, 17, 19, 20]), old_indices_discarded=array([18]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.014456785120069399, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 16, 17, 20, 21]), model=ScalarModel(intercept=0.6423666277820237, linear_terms=array([4.21338439e-06]), square_terms=array([[1.80260164e-05]]), scale=0.014456785120069399, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=22, candidate_x=array([9.24896336]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-45.22714683487297, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 16, 17, 20, 21]), old_indices_discarded=array([18, 19]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.007228392560034699, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 17, 20, 21, 22]), model=ScalarModel(intercept=0.6423680016960622, linear_terms=array([-1.01758484e-06]), square_terms=array([[4.52377587e-06]]), scale=0.007228392560034699, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=23, candidate_x=array([9.25396844]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-101.98829389658573, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 17, 20, 21, 22]), old_indices_discarded=array([16]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.0036141962800173497, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 20, 21, 22, 23]), model=ScalarModel(intercept=0.6423664047173907, linear_terms=array([-8.15794512e-06]), square_terms=array([[1.06328797e-06]]), scale=0.0036141962800173497, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=24, candidate_x=array([9.25595667]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-3.6772491566976204, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 20, 21, 22, 23]), old_indices_discarded=array([17]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.0018070981400086748, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 20, 21, 22, 23]), model=ScalarModel(intercept=0.6423664047173909, linear_terms=array([-4.07897256e-06]), square_terms=array([[2.65821992e-07]]), scale=0.0018070981400086748, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=25, candidate_x=array([9.25414957]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-3.597936150162183, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 20, 21, 22, 23]), old_indices_discarded=array([24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.0009035490700043374, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 20, 21, 23, 25]), model=ScalarModel(intercept=0.6423678826985401, linear_terms=array([4.59211257e-07]), square_terms=array([[6.46936017e-08]]), scale=0.0009035490700043374, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=26, candidate_x=array([9.25143893]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-13.573507187210918, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 20, 21, 23, 25]), old_indices_discarded=array([22, 24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.0004517745350021687, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 20, 21, 23, 25, 26]), model=ScalarModel(intercept=0.6423670437711565, linear_terms=array([4.63065568e-07]), square_terms=array([[1.6122968e-08]]), scale=0.0004517745350021687, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=27, candidate_x=array([9.2518907]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-4.368840765358844, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 20, 21, 23, 25, 26]), old_indices_discarded=array([14]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.00022588726750108435, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 26, 27]), model=ScalarModel(intercept=0.6423580233416285, linear_terms=array([-1.44028832e-06]), square_terms=array([[4.10130588e-09]]), scale=0.00022588726750108435, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=28, candidate_x=array([9.25256836]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-0.5183479506363351, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.00011294363375054218, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 27, 28]), model=ScalarModel(intercept=0.6423590665246325, linear_terms=array([-2.47320622e-07]), square_terms=array([[1.00136317e-09]]), scale=0.00011294363375054218, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=29, candidate_x=array([9.25245542]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-0.5789901455335319, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 27, 28]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=5.647181687527109e-05, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 28, 29]), model=ScalarModel(intercept=0.6423582471741802, linear_terms=array([1.85886653e-07]), square_terms=array([[2.45317725e-10]]), scale=5.647181687527109e-05, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=30, candidate_x=array([9.25228601]), index=30, x=array([9.25228601]), fval=0.6423582605057705, rho=0.33979447206310537, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=5.647181687606917e-05, relative_step_length=1.0000000000141325, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 31 entries., 'history': {'params': [{'CRRA': 9.252342476844415}, {'CRRA': 8.327108229159974}, {'CRRA': 10.177576724528857}, {'CRRA': 8.327108229159974}, {'CRRA': 8.327108229159974}, {'CRRA': 10.177576724528857}, {'CRRA': 8.327108229159974}, {'CRRA': 8.327108229159974}, {'CRRA': 10.177576724528857}, {'CRRA': 8.327108229159974}, {'CRRA': 10.177576724528857}, {'CRRA': 8.327108229159974}, {'CRRA': 10.177576724528857}, {'CRRA': 8.327108229159974}, {'CRRA': 9.250458955049714}, {'CRRA': 8.789725353002193}, {'CRRA': 9.231195795349796}, {'CRRA': 9.23850082029189}, {'CRRA': 9.191470898591822}, {'CRRA': 9.310169617324693}, {'CRRA': 9.250657961285336}, {'CRRA': 9.250710524700677}, {'CRRA': 9.248963361277593}, {'CRRA': 9.253968442160518}, {'CRRA': 9.255956673124432}, {'CRRA': 9.254149574984424}, {'CRRA': 9.25143892777441}, {'CRRA': 9.251890702309414}, {'CRRA': 9.252568364111916}, {'CRRA': 9.252455420478165}, {'CRRA': 9.252286005027539}], 'criterion': [0.6423583236273489, 0.6726961956566793, 0.672466609452884, 0.6726961956566793, 0.6726961956566793, 0.672466609452884, 0.6726961956566793, 0.6726961956566793, 0.672466609452884, 0.6726961956566793, 0.672466609452884, 0.6726961956566793, 0.672466609452884, 0.6726961956566793, 0.642369169571296, 0.6492959495923358, 0.6423762404892832, 0.6423752797264956, 0.6425219143922716, 0.642514400144132, 0.6423685692715722, 0.6423684112220268, 0.6423805942108727, 0.6423699960365742, 0.6423863674367805, 0.6423725213049074, 0.6423641176751165, 0.6423603114677403, 0.6423590691348976, 0.642358466533662, 0.6423582605057704], 'runtime': [0.0, 3.3893006040002547, 3.7743795520000276, 3.9942729670001427, 4.308895564000068, 4.500098180999885, 4.797666575000221, 5.097781724000015, 5.30452481400016, 5.586473449000096, 5.8145816500000365, 6.019138186999953, 6.258510488999946, 21.4172058070003, 22.745588674999908, 24.07027555800005, 25.37667203000001, 26.848738280999896, 28.141902802999994, 29.463674151000305, 30.76145256100017, 32.056981691000146, 33.34122019300003, 34.749019994000264, 36.131662315000085, 37.520465677000175, 38.87502066600018, 40.19993009200016, 41.659364199000265, 43.079727706000085, 44.41924671800007], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]}, 'multistart_info': {...}}], 'exploration_sample': array([[ 9.25234248], + [ 8.1875 ], + [10.55 ], + [12.9125 ], + [ 5.825 ], + [14.09375 ], + [15.275 ], + [ 4.64375 ], + [17.6375 ], + [ 3.4625 ]]), 'exploration_results': array([0.64235832, 0.6831279 , 0.69939713, 1.02360977, 1.18227674, + 1.26376333, 1.54278366, 1.78141341, 2.1808234 , 2.89794431])}}" diff --git a/content/tables/TRP/WarmGlowPortfolio_estimate_results.csv b/content/tables/TRP/WarmGlowPortfolio_estimate_results.csv new file mode 100644 index 0000000..4cd4e64 --- /dev/null +++ b/content/tables/TRP/WarmGlowPortfolio_estimate_results.csv @@ -0,0 +1,11062 @@ +CRRA,9.206775856414323 +BeqShift,45.64298427855443 +BeqFac,23.05054873023735 +time_to_estimate,236.11751127243042 +params,"{'CRRA': 9.206775856414323, 'BeqShift': 45.64298427855443, 'BeqFac': 23.05054873023735}" +criterion,0.6411981344087744 +start_criterion,0.6327696850981256 +start_params,"{'CRRA': 9.206778216146489, 'BeqShift': 50.64405071849033, 'BeqFac': 26.1368726540768}" +algorithm,multistart_tranquilo_ls +direction,minimize +n_free,3 +message,Absolute criterion change smaller than tolerance. +success, +n_criterion_evaluations, +n_derivative_evaluations, +n_iterations, +history,"{'params': [{'CRRA': 9.275230386313043, 'BeqShift': 45.88119053785236, 'BeqFac': 23.014811093019418}, {'CRRA': 10.556800566878689, 'BeqShift': 48.18211054060359, 'BeqFac': 19.257926550618446}, {'CRRA': 6.326950770325419, 'BeqShift': 48.028109248006054, 'BeqFac': 20.231068322997192}, {'CRRA': 5.376086840779585, 'BeqShift': 47.88380177945927, 'BeqFac': 24.370194536190226}, {'CRRA': 8.836842627116226, 'BeqShift': 41.31414348521892, 'BeqFac': 22.987721550281236}, {'CRRA': 8.588658189640931, 'BeqShift': 44.17145102421629, 'BeqFac': 27.216744679593038}, {'CRRA': 5.236533581737813, 'BeqShift': 43.73675712771144, 'BeqFac': 23.390536953045526}, {'CRRA': 11.326280642245544, 'BeqShift': 49.96975911041845, 'BeqFac': 22.657548826930118}, {'CRRA': 12.938221774324816, 'BeqShift': 44.69982951667233, 'BeqFac': 20.51726896510742}, {'CRRA': 8.667600751746502, 'BeqShift': 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79.23094035199938, 79.42576199999985, 79.6535652449993, 79.8579345169992, 80.10478922699986, 80.32257100899915, 80.57707195499916, 80.81765127299968, 81.04075872399972, 82.53920170899983], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 7, 8, 9, 10, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 29, 30, 31, 32, 33, 34, 35, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 37, 38, 39, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 41, 42, 43, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 45]}" +convergence_report,"{'one_step': {'relative_criterion_change': 3.5828565118959094e-08, 'relative_params_change': 0.1730113235913555, 'absolute_criterion_change': 2.297320911281986e-08, 'absolute_params_change': 5.876738890647083}, 'five_steps': {'relative_criterion_change': 3.5828565118959094e-08, 'relative_params_change': 0.1730113235913555, 'absolute_criterion_change': 2.297320911281986e-08, 'absolute_params_change': 5.876738890647083}}" +multistart_info,"{'start_parameters': [{'CRRA': 9.20677821614649, 'BeqShift': 50.64405071849033, 'BeqFac': 26.1368726540768}, {'CRRA': 9.275230386313043, 'BeqShift': 45.88119053785236, 'BeqFac': 23.014811093019418}, {'CRRA': 9.128116958674036, 'BeqShift': 48.90833875417502, 'BeqFac': 23.98172788815444}], 'local_optima': [Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Relative criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 1.182e-08* 2.384e-08* +relative_params_change 7.164e-07* 7.164e-07* +absolute_criterion_change 7.578e-09** 1.529e-08* +absolute_params_change 3.435e-05 3.435e-05 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 8.029e-09** 7.847e-06* +relative_params_change 2.558e-07* 0.0001336 +absolute_criterion_change 5.148e-09** 5.032e-06* +absolute_params_change 5.08e-06* 0.00307 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 5.493e-08* 6.761e-06* +relative_params_change 1.236e-05 5.445e-05 +absolute_criterion_change 3.522e-08* 4.335e-06* +absolute_params_change 0.0006154 0.000671 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 9.206778216146489, 'BeqShift': 50.64405071849033, 'BeqFac': 26.1368726540768}, {'CRRA': 9.368749999999999, 'BeqShift': 39.375, 'BeqFac': 18.75}, {'CRRA': 8.778125, 'BeqShift': 63.4375, 'BeqFac': 28.125}, {'CRRA': 9.959375, 'BeqShift': 6.5625, 'BeqFac': 84.375}, {'CRRA': 8.1875, 'BeqShift': 26.25, 'BeqFac': 62.5}, {'CRRA': 10.549999999999999, 'BeqShift': 35.0, 'BeqFac': 50.0}, {'CRRA': 7.596874999999999, 'BeqShift': 50.3125, 'BeqFac': 71.875}, {'CRRA': 7.00625, 'BeqShift': 13.125, 'BeqFac': 31.25}, {'CRRA': 11.73125, 'BeqShift': 30.625, 'BeqFac': 6.25}, {'CRRA': 12.321874999999999, 'BeqShift': 67.8125, 'BeqFac': 96.875}, {'CRRA': 6.415625, 'BeqShift': 19.6875, 'BeqFac': 15.625}, {'CRRA': 12.9125, 'BeqShift': 8.75, 'BeqFac': 87.5}, {'CRRA': 5.824999999999999, 'BeqShift': 52.5, 'BeqFac': 75.0}, {'CRRA': 13.503124999999999, 'BeqShift': 45.9375, 'BeqFac': 3.125}, {'CRRA': 5.234375, 'BeqShift': 59.0625, 'BeqFac': 9.375}, {'CRRA': 14.093749999999998, 'BeqShift': 56.875, 'BeqFac': 43.75}, {'CRRA': 14.684375, 'BeqShift': 24.0625, 'BeqFac': 59.375}, {'CRRA': 4.64375, 'BeqShift': 21.875, 'BeqFac': 93.75}, {'CRRA': 15.274999999999999, 'BeqShift': 17.5, 'BeqFac': 25.0}, {'CRRA': 4.053125, 'BeqShift': 10.9375, 'BeqFac': 53.125}, {'CRRA': 15.865624999999998, 'BeqShift': 54.6875, 'BeqFac': 65.625}, {'CRRA': 3.4625, 'BeqShift': 43.75, 'BeqFac': 37.5}, {'CRRA': 16.45625, 'BeqShift': 48.125, 'BeqFac': 81.25}, {'CRRA': 17.046875, 'BeqShift': 15.3125, 'BeqFac': 21.875}, {'CRRA': 2.871875, 'BeqShift': 32.8125, 'BeqFac': 46.875}, {'CRRA': 2.28125, 'BeqShift': 65.625, 'BeqFac': 56.25}, {'CRRA': 17.6375, 'BeqShift': 61.25, 'BeqFac': 12.5}, {'CRRA': 18.228125, 'BeqShift': 28.4375, 'BeqFac': 78.125}, {'CRRA': 18.81875, 'BeqShift': 4.375, 'BeqFac': 68.75}, {'CRRA': 19.409375, 'BeqShift': 41.5625, 'BeqFac': 34.375}], 'exploration_results': array([0.64119816, 0.64211564, 0.64842422, 0.66164008, 0.68191739, + 0.7044766 , 0.74714868, 0.84823283, 0.8626059 , 0.97951322, + 0.98996599, 1.12431839, 1.18221304, 1.30406645, 1.43892243, + 1.52011014, 1.78024917, 1.78131285, 2.0971452 , 2.24344059, + 2.48116605, 2.89787 , 2.94379242, 3.4976294 , 3.77080322, + 4.13701934, 4.16035282, 4.94978648, 5.89491246, 7.00125067])}" +algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.27523039, 45.88119054, 23.01481109]), radius=4.5881190537852365, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=[0], model=ScalarModel(intercept=0.6414954627541696, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 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model=ScalarModel(intercept=0.6413235030118442, linear_terms=array([3.04274084e-05, 4.74538518e-06, 1.23753508e-05]), square_terms=array([[ 2.91839759e-05, -9.35564134e-09, 7.99806758e-08], + [-9.35564134e-09, 2.75030515e-10, 1.05398303e-09], + [ 7.99806758e-08, 1.05398303e-09, 5.78042311e-09]]), scale=0.01792234005384858, shift=array([ 9.21052789, 45.64202864, 23.04766956])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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index=56, x=array([ 9.21052789, 45.64202864, 23.04766956]), fval=0.6412388217057458, rho=-2.034547178334105, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 38, 43, 46, 49, 50, 51, 52, 53, 54, 55, 56]), old_indices_discarded=array([31, 34, 36, 37, 39, 40, 41, 44, 45, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21052789, 45.64202864, 23.04766956]), radius=0.00896117002692429, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([35, 38, 43, 52, 53, 54, 55, 56, 57]), model=ScalarModel(intercept=0.641336784215586, linear_terms=array([ 3.37194784e-05, 1.47646655e-05, -4.94622101e-06]), square_terms=array([[ 7.27205053e-06, -8.46098600e-09, 2.41038200e-08], + [-8.46098600e-09, 7.14551454e-10, 5.67261002e-10], + [ 2.41038200e-08, 5.67261002e-10, 1.36351391e-09]]), scale=0.00896117002692429, shift=array([ 9.21052789, 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n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21052789, 45.64202864, 23.04766956]), radius=0.004480585013462145, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([52, 53, 56, 57, 58]), model=ScalarModel(intercept=0.6412438622916211, linear_terms=array([-9.52899239e-06, -1.52791403e-05, 4.25097244e-07]), square_terms=array([[1.82495925e-06, 6.38394061e-10, 4.11144666e-10], + [6.38394061e-10, 4.31291655e-10, 1.19384986e-10], + [4.11144666e-10, 1.19384986e-10, 5.63993449e-10]]), scale=0.004480585013462145, shift=array([ 9.21052789, 45.64202864, 23.04766956])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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old_indices_used=array([53, 56, 58, 59]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21052789, 45.64202864, 23.04766956]), radius=0.0011201462533655363, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([56, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72]), model=ScalarModel(intercept=0.6412388437802616, linear_terms=array([1.64312695e-05, 4.65279188e-07, 1.07229961e-07]), square_terms=array([[ 1.08773373e-07, 6.78393854e-10, -2.26235629e-10], + [ 6.78393854e-10, 2.77167620e-11, -8.32587980e-12], + [-2.26235629e-10, -8.32587980e-12, 3.11734525e-12]]), scale=0.0011201462533655363, shift=array([ 9.21052789, 45.64202864, 23.04766956])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + 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upper=array([ 20., 70., 100.]))), model_indices=array([56, 61, 62, 63, 64, 65, 67, 68, 69, 71, 72, 73]), model=ScalarModel(intercept=0.6412226294004633, linear_terms=array([ 3.24586330e-05, 5.92811032e-07, -3.43705302e-08]), square_terms=array([[4.28909220e-07, 2.16458658e-11, 5.94629399e-10], + [2.16458658e-11, 2.77111185e-12, 8.02947612e-13], + [5.94629399e-10, 8.02947612e-13, 7.42708381e-12]]), scale=0.0022402925067310725, shift=array([ 9.20940815, 45.64200068, 23.04765841])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20677822, 50.64405072, 26.13687265]), radius=0.6330506339811292, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.6403286802894971, linear_terms=array([-3.81858233e-04, 7.65007951e-05, -4.10301280e-05]), square_terms=array([[ 3.62463318e-02, -4.15487066e-06, -2.26071914e-05], + [-4.15487066e-06, 3.00843205e-08, 2.19045970e-08], + [-2.26071914e-05, 2.19045970e-08, 7.32549810e-08]]), scale=0.6330506339811292, shift=array([ 9.20677822, 50.64405072, 26.13687265])), vector_model=VectorModel(intercepts=array([ 0.04848823, 0.12360338, 0.14833629, 0.19329327, 0.2168361 , + 0.23171846, 0.23263925, 0.06582157, -0.08093788, -0.06795554, + -0.41009007, -0.4187331 , -0.12365488, -0.09727806, -0.08783687, + -0.09147742, -0.09757722]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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model=ScalarModel(intercept=0.6404706538725462, linear_terms=array([-5.67345184e-04, 2.74265158e-04, 9.56828002e-05]), square_terms=array([[9.01546888e-03, 1.04773684e-05, 2.47603794e-06], + [1.04773684e-05, 1.74310737e-07, 6.31897341e-08], + [2.47603794e-06, 6.31897341e-08, 2.70525471e-08]]), scale=0.3165253169905646, shift=array([ 9.20677822, 50.64405072, 26.13687265])), vector_model=VectorModel(intercepts=array([ 0.04848823, 0.12360338, 0.14833629, 0.19329327, 0.2168361 , + 0.23171846, 0.23263925, 0.06582157, -0.08093788, -0.06795554, + -0.41009007, -0.4187331 , -0.12365488, -0.09727806, -0.08783687, + -0.09147742, -0.09757722]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], 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index=0, x=array([ 9.20677822, 50.64405072, 26.13687265]), fval=0.6411981580830596, rho=-0.6661027359823584, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27]), old_indices_discarded=array([15, 24, 28]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20677822, 50.64405072, 26.13687265]), radius=0.1582626584952823, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 16, 17, 18, 19, 20, 21, 22, 23, 26, 27, 29]), model=ScalarModel(intercept=0.6404784832040219, linear_terms=array([-3.15520037e-04, 1.63853796e-04, 9.20223976e-05]), square_terms=array([[2.25226929e-03, 3.23332981e-06, 1.70120380e-06], + [3.23332981e-06, 6.16654786e-08, 3.52690733e-08], + [1.70120380e-06, 3.52690733e-08, 2.15873630e-08]]), scale=0.1582626584952823, shift=array([ 9.20677822, 50.64405072, 26.13687265])), vector_model=VectorModel(intercepts=array([ 0.04848823, 0.12360338, 0.14833629, 0.19329327, 0.2168361 , + 0.23171846, 0.23263925, 0.06582157, -0.08093788, -0.06795554, + -0.41009007, -0.4187331 , -0.12365488, -0.09727806, -0.08783687, + -0.09147742, -0.09757722]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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State(trustregion=Region(center=array([ 9.20677822, 50.64405072, 26.13687265]), radius=0.07913132924764114, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=0.6414058023823169, linear_terms=array([-5.22656412e-05, 8.88275468e-06, -2.13975506e-05]), square_terms=array([[ 5.67797994e-04, -1.08779162e-07, -6.52765214e-07], + [-1.08779162e-07, 1.75466701e-09, 2.24298365e-10], + [-6.52765214e-07, 2.24298365e-10, 3.40606226e-09]]), scale=0.07913132924764114, shift=array([ 9.20677822, 50.64405072, 26.13687265])), vector_model=VectorModel(intercepts=array([ 0.04848823, 0.12360338, 0.14833629, 0.19329327, 0.2168361 , + 0.23171846, 0.23263925, 0.06582157, -0.08093788, -0.06795554, + -0.41009007, -0.4187331 , -0.12365488, -0.09727806, -0.08783687, + -0.09147742, -0.09757722]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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50.64405072, 26.13687265]), fval=0.6411981580830596, rho=-0.5410025828097851, accepted=False, new_indices=array([ 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102]), old_indices_used=array([ 0, 89, 90]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20677822, 50.64405072, 26.13687265]), radius=9.659586089799944e-06, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 91, 92, 93, 94, 95, 96, 97, 98, 99, 101, 102]), model=ScalarModel(intercept=0.6411982194422793, linear_terms=array([ 7.59408746e-08, -1.57717395e-08, 2.68111814e-08]), square_terms=array([[ 8.61862000e-12, 2.46903340e-14, -4.19719973e-14], + [ 2.46903340e-14, 7.09360329e-16, -1.20587550e-15], + [-4.19719973e-14, -1.20587550e-15, 2.04992535e-15]]), scale=9.659586089799944e-06, shift=array([ 9.20677822, 50.64405072, 26.13687265])), 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78, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, + 120, 121]), model=ScalarModel(intercept=0.6411981649678726, linear_terms=array([-3.05612962e-08, 1.46809760e-13, -1.70649995e-13]), square_terms=array([[ 7.38373402e-12, 7.56759673e-19, -8.94606680e-19], + [ 7.56759673e-19, 1.33945952e-25, -1.57431020e-25], + [-8.94606680e-19, -1.57431020e-25, 1.85043423e-25]]), scale=8.751142604418252e-06, shift=array([ 9.20676711, 45.64298428, 23.05054873])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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23.01481109])), candidate_index=122, candidate_x=array([ 9.20677586, 45.64298428, 23.05054873]), index=122, x=array([ 9.20677586, 45.64298428, 23.05054873]), fval=0.6411981344087744, rho=1.0000555009711634, accepted=True, new_indices=array([110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121]), old_indices_used=array([ 78, 108, 109]), old_indices_discarded=array([], dtype=int64), step_length=8.75114260381017e-06, relative_step_length=0.999999999930514, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 123 entries., 'history': {'params': [{'CRRA': 9.275230386313043, 'BeqShift': 45.88119053785236, 'BeqFac': 23.014811093019418}, {'CRRA': 10.556800566878689, 'BeqShift': 48.18211054060359, 'BeqFac': 19.257926550618446}, {'CRRA': 6.326950770325419, 'BeqShift': 48.028109248006054, 'BeqFac': 20.231068322997192}, {'CRRA': 5.376086840779585, 'BeqShift': 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fval=0.6414177609144429, rho=-0.37962116512774374, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 9, 13, 14, 15, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.17594703, 50.79063701, 26.07002062]), radius=0.3056771172135939, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([14, 16, 17, 18]), model=ScalarModel(intercept=0.6414177609144434, linear_terms=array([-7.19667964e-04, 3.27673764e-06, -4.38632601e-06]), square_terms=array([[ 8.32991536e-03, -4.95785367e-06, -1.12565541e-06], + [-4.95785367e-06, 3.12359635e-08, 6.32213746e-09], + [-1.12565541e-06, 6.32213746e-09, 1.43003510e-09]]), scale=0.3056771172135939, shift=array([ 9.17594703, 50.79063701, 26.07002062])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 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bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 9, 13, 14, 15, 16, 17, 18, 19]), model=ScalarModel(intercept=0.6411417972530091, linear_terms=array([ 3.16072074e-03, 2.78118281e-05, -1.27986623e-04]), square_terms=array([[ 3.48228413e-02, 2.65152618e-05, -2.12954966e-05], + [ 2.65152618e-05, 5.39158692e-08, -3.83514408e-08], + [-2.12954966e-05, -3.83514408e-08, 4.64226978e-08]]), scale=0.6113542344271878, shift=array([ 9.20227374, 50.61564005, 26.34665317])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 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scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), candidate_index=20, candidate_x=array([ 9.14744387, 50.49524578, 26.94433444]), index=19, x=array([ 9.20227374, 50.61564005, 26.34665317]), fval=0.64127879028253, rho=-1.438980527261934, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 9, 13, 14, 15, 16, 17, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20227374, 50.61564005, 26.34665317]), radius=0.3056771172135939, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([14, 16, 17, 18, 19, 20]), model=ScalarModel(intercept=0.6413841070992645, linear_terms=array([-6.53726515e-04, -5.99067852e-05, -1.84848863e-05]), square_terms=array([[8.48604787e-03, 2.74233289e-06, 1.50336569e-07], + [2.74233289e-06, 1.06911664e-08, 2.45419451e-09], + [1.50336569e-07, 2.45419451e-09, 1.17491524e-09]]), scale=0.3056771172135939, shift=array([ 9.20227374, 50.61564005, 26.34665317])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], 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dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20227374, 50.61564005, 26.34665317]), radius=0.15283855860679696, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([14, 16, 18, 19, 20, 21]), model=ScalarModel(intercept=0.6413867749395041, linear_terms=array([-1.15334196e-04, -1.03339480e-05, 1.89663138e-05]), square_terms=array([[ 2.12361077e-03, 7.37109646e-07, -4.72884217e-07], + [ 7.37109646e-07, 2.13322450e-09, 4.55845370e-10], + [-4.72884217e-07, 4.55845370e-10, 3.49467194e-09]]), scale=0.15283855860679696, shift=array([ 9.20227374, 50.61564005, 26.34665317])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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model=ScalarModel(intercept=0.6413655868487599, linear_terms=array([-2.08473659e-04, -3.95761353e-05, -7.61346768e-06]), square_terms=array([[8.52016057e-03, 4.18394593e-06, 1.05230575e-06], + [4.18394593e-06, 1.37295451e-08, 5.81549864e-09], + [1.05230575e-06, 5.81549864e-09, 4.01248942e-09]]), scale=0.3056771172135939, shift=array([ 9.21043634, 50.68853093, 26.21255295])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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index=22, x=array([ 9.21043634, 50.68853093, 26.21255295]), fval=0.6412372943065084, rho=-1.6364205448497149, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 16, 17, 18, 19, 20, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21043634, 50.68853093, 26.21255295]), radius=0.15283855860679696, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([14, 16, 18, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=0.6413359796696954, linear_terms=array([-1.81109576e-05, -1.14485767e-05, 2.37026915e-05]), square_terms=array([[ 2.12663363e-03, 8.91621394e-07, -5.38885654e-07], + [ 8.91621394e-07, 2.74292807e-09, 6.77799196e-11], + [-5.38885654e-07, 6.77799196e-11, 3.59250963e-09]]), scale=0.15283855860679696, shift=array([ 9.21043634, 50.68853093, 26.21255295])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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State(trustregion=Region(center=array([ 9.21043634, 50.68853093, 26.21255295]), radius=0.07641927930339848, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([16, 19, 21, 22, 23, 24]), model=ScalarModel(intercept=0.6412542210909683, linear_terms=array([-2.23995338e-04, 2.54535201e-05, 2.11746359e-05]), square_terms=array([[ 5.33098874e-04, -5.21340692e-08, -6.08486471e-08], + [-5.21340692e-08, 2.75567603e-09, 1.19347943e-09], + [-6.08486471e-08, 1.19347943e-09, 8.72229962e-10]]), scale=0.07641927930339848, shift=array([ 9.21043634, 50.68853093, 26.21255295])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 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0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]]), scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), candidate_index=25, candidate_x=array([ 9.24063024, 50.63073733, 26.16448842]), index=22, x=array([ 9.21043634, 50.68853093, 26.21255295]), fval=0.6412372943065084, rho=-3.849990572292386, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([16, 19, 21, 22, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21043634, 50.68853093, 26.21255295]), radius=0.03820963965169924, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([16, 19, 22, 24, 25]), model=ScalarModel(intercept=0.641219258129736, linear_terms=array([-0.00032591, -0.00024411, -0.00013343]), square_terms=array([[1.34077944e-04, 5.43533915e-07, 3.15951718e-07], + [5.43533915e-07, 1.95813700e-07, 1.10514267e-07], + [3.15951718e-07, 1.10514267e-07, 6.24967287e-08]]), scale=0.03820963965169924, shift=array([ 9.21043634, 50.68853093, 26.21255295])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 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19, 22, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21043634, 50.68853093, 26.21255295]), radius=0.01910481982584962, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([22, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=0.6412969075158591, linear_terms=array([ 8.32111914e-05, -3.85237511e-06, -1.89858710e-05]), square_terms=array([[ 3.30302534e-05, -1.92352742e-09, 1.34216817e-08], + [-1.92352742e-09, 2.03912494e-10, 1.41492124e-10], + [ 1.34216817e-08, 1.41492124e-10, 7.26728056e-10]]), scale=0.01910481982584962, shift=array([ 9.21043634, 50.68853093, 26.21255295])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]]), scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), candidate_index=39, candidate_x=array([ 9.19299773, 50.68734198, 26.22026498]), index=22, x=array([ 9.21043634, 50.68853093, 26.21255295]), fval=0.6412372943065084, rho=-0.8735221855116919, accepted=False, new_indices=array([27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38]), old_indices_used=array([22, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21043634, 50.68853093, 26.21255295]), radius=0.00955240991292481, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([22, 27, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=0.6412994242831694, linear_terms=array([ 2.85233747e-05, -1.63095244e-05, -8.02189878e-07]), square_terms=array([[ 8.22961168e-06, 8.93803337e-09, 1.13412880e-08], + [ 8.93803337e-09, 5.36069130e-10, -1.49130908e-10], + [ 1.13412880e-08, -1.49130908e-10, 3.21289345e-10]]), scale=0.00955240991292481, shift=array([ 9.21043634, 50.68853093, 26.21255295])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]]), scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), candidate_index=40, candidate_x=array([ 9.20281168, 50.69426121, 26.21307923]), index=22, x=array([ 9.21043634, 50.68853093, 26.21255295]), fval=0.6412372943065084, rho=-1.1237580039409194, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 27, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39]), old_indices_discarded=array([26, 28, 34]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21043634, 50.68853093, 26.21255295]), radius=0.004776204956462405, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([22, 27, 29, 30, 31, 32, 33, 35, 36, 37, 38, 40]), model=ScalarModel(intercept=0.6412963629457158, linear_terms=array([ 1.62960573e-05, -7.58208714e-06, -1.23880890e-06]), square_terms=array([[ 2.06057449e-06, 2.04005372e-09, 2.61576683e-09], + [ 2.04005372e-09, 1.27885790e-10, -2.61931144e-11], + [ 2.61576683e-09, -2.61931144e-11, 6.68359271e-11]]), scale=0.004776204956462405, shift=array([ 9.21043634, 50.68853093, 26.21255295])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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step_length=0.004776204956462672, relative_step_length=1.000000000000056, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20624584, 50.69077625, 26.21301162]), radius=0.00955240991292481, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([22, 27, 28, 30, 32, 34, 35, 37, 38, 39, 40, 41]), model=ScalarModel(intercept=0.6412523356275223, linear_terms=array([-2.97875373e-05, -3.12914020e-06, -1.35700767e-05]), square_terms=array([[8.24295698e-06, 1.83108503e-08, 2.74598078e-08], + [1.83108503e-08, 1.20423462e-10, 1.79372343e-10], + [2.74598078e-08, 1.79372343e-10, 4.82089539e-10]]), scale=0.00955240991292481, shift=array([ 9.20624584, 50.69077625, 26.21301162])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + 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'runtime': [0.0, 1.6423095520003699, 2.040658591000465, 2.2376682650001385, 2.4299743769997804, 2.67046717500034, 2.862668802999906, 3.104374010000356, 3.32224695900004, 3.5381166859997393, 3.741620800000419, 4.010065631000543, 4.227599134000229, 5.809256223000375, 7.184767149999971, 8.550543121000374, 9.923031666000497, 11.329385642000489, 12.911365132000356, 14.307267012000011, 15.663889139000275, 17.065571911000006, 18.41535552300047, 19.819295285000408, 21.202764538999872, 22.709291078000206, 24.042428986000232, 25.732384420000017, 25.9312051349998, 26.183760038999935, 26.412906409999778, 26.61830315000043, 26.855693547000556, 27.050723016999655, 27.28677242699996, 27.52746980200027, 27.765533300000243, 27.970793123000476, 28.197342635000496, 29.677809333999903, 31.04888916300024, 32.42374823699993, 33.98980597300033, 35.40728008099995, 36.75439337300031, 38.18200040800002, 39.90860623499975, 40.13832715599983, 40.36971348000043, 40.58094771800006, 40.784019124000224, 40.990598760000466, 41.227546551999694, 41.53011763900031, 41.92065388799983, 42.16206820299976, 42.460215224999956, 42.67479388399988, 44.23093565199997, 45.70703041100023, 47.07414319700001, 48.44252813000003, 49.74495745600052], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 18, 19, 20, 21, 22, 23, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 25, 26, 27, 28, 29]}}], 'exploration_sample': array([[ 9.20677822, 50.64405072, 26.13687265], + [ 9.36875 , 39.375 , 18.75 ], + [ 8.778125 , 63.4375 , 28.125 ], + [ 9.959375 , 6.5625 , 84.375 ], + [ 8.1875 , 26.25 , 62.5 ], + [10.55 , 35. , 50. ], + [ 7.596875 , 50.3125 , 71.875 ], + [ 7.00625 , 13.125 , 31.25 ], + [11.73125 , 30.625 , 6.25 ], + [12.321875 , 67.8125 , 96.875 ], + [ 6.415625 , 19.6875 , 15.625 ], + [12.9125 , 8.75 , 87.5 ], + [ 5.825 , 52.5 , 75. ], + [13.503125 , 45.9375 , 3.125 ], + [ 5.234375 , 59.0625 , 9.375 ], + [14.09375 , 56.875 , 43.75 ], + [14.684375 , 24.0625 , 59.375 ], + [ 4.64375 , 21.875 , 93.75 ], + [15.275 , 17.5 , 25. ], + [ 4.053125 , 10.9375 , 53.125 ], + [15.865625 , 54.6875 , 65.625 ], + [ 3.4625 , 43.75 , 37.5 ], + [16.45625 , 48.125 , 81.25 ], + [17.046875 , 15.3125 , 21.875 ], + [ 2.871875 , 32.8125 , 46.875 ], + [ 2.28125 , 65.625 , 56.25 ], + [17.6375 , 61.25 , 12.5 ], + [18.228125 , 28.4375 , 78.125 ], + [18.81875 , 4.375 , 68.75 ], + [19.409375 , 41.5625 , 34.375 ]]), 'exploration_results': array([0.64119816, 0.64211564, 0.64842422, 0.66164008, 0.68191739, + 0.7044766 , 0.74714868, 0.84823283, 0.8626059 , 0.97951322, + 0.98996599, 1.12431839, 1.18221304, 1.30406645, 1.43892243, + 1.52011014, 1.78024917, 1.78131285, 2.0971452 , 2.24344059, + 2.48116605, 2.89787 , 2.94379242, 3.4976294 , 3.77080322, + 4.13701934, 4.16035282, 4.94978648, 5.89491246, 7.00125067])}}" diff --git a/content/tables/TRP/WealthPortfolio_estimate_results.csv b/content/tables/TRP/WealthPortfolio_estimate_results.csv new file mode 100644 index 0000000..49b3e99 --- /dev/null +++ b/content/tables/TRP/WealthPortfolio_estimate_results.csv @@ -0,0 +1,7386 @@ +CRRA,5.335577372664163 +WealthShare,0.1706005756625005 +time_to_estimate,202.92073488235474 +params,"{'CRRA': 5.335577372664163, 'WealthShare': 0.1706005756625005}" +criterion,0.2421983863534466 +start_criterion,0.23890510137815316 +start_params,"{'CRRA': 5.35399091577092, 'WealthShare': 0.1710302407154898}" +algorithm,multistart_tranquilo_ls +direction,minimize +n_free,2 +message,Absolute criterion change smaller than tolerance. +success, +n_criterion_evaluations, +n_derivative_evaluations, +n_iterations, +history,"{'params': [{'CRRA': 5.837945053873421, 'WealthShare': 0.17769838670536425}, {'CRRA': 5.521836423832173, 'WealthShare': 0.01}, {'CRRA': 6.355319463479058, 'WealthShare': 0.01}, {'CRRA': 5.320570644267783, 'WealthShare': 0.01}, {'CRRA': 6.288942410133527, 'WealthShare': 0.6950727963110019}, {'CRRA': 6.355319463479058, 'WealthShare': 0.34434498212577297}, {'CRRA': 6.227220953739785, 'WealthShare': 0.01}, {'CRRA': 5.320570644267783, 'WealthShare': 0.01}, {'CRRA': 5.871508721893411, 'WealthShare': 0.6950727963110019}, {'CRRA': 6.355319463479058, 'WealthShare': 0.6700100023631337}, {'CRRA': 5.428342638379923, 'WealthShare': 0.6950727963110019}, {'CRRA': 5.320570644267783, 'WealthShare': 0.39643867888963663}, {'CRRA': 5.320570644267783, 'WealthShare': 0.6950727963110019}, {'CRRA': 6.129876895883085, 'WealthShare': 0.14106398655854144}, {'CRRA': 5.5792578490706015, 'WealthShare': 0.1204690698151589}, {'CRRA': 5.696390372221354, 'WealthShare': 0.1330305333078522}, {'CRRA': 5.764661943417508, 'WealthShare': 0.16014227844012982}, {'CRRA': 5.618872311285, 'WealthShare': 0.1672901244784106}, {'CRRA': 5.360185106482181, 'WealthShare': 0.15621623896813125}, {'CRRA': 5.4728571815195926, 'WealthShare': 0.16421014778193593}, {'CRRA': 5.545825951300451, 'WealthShare': 0.16306458747685207}, {'CRRA': 5.582398435175709, 'WealthShare': 0.1685319632864777}, {'CRRA': 5.509355844762908, 'WealthShare': 0.16418872811097143}, {'CRRA': 5.545831963262839, 'WealthShare': 0.1638629137313917}, {'CRRA': 5.564154615154598, 'WealthShare': 0.16862293277010182}, {'CRRA': 5.527673129514142, 'WealthShare': 0.16928670293386877}, {'CRRA': 5.454701789923809, 'WealthShare': 0.16996692667547664}, {'CRRA': 5.308764908353464, 'WealthShare': 0.17181847436097414}, {'CRRA': 5.0970292962882615, 'WealthShare': 0.1601742594497508}, {'CRRA': 5.454606416226443, 'WealthShare': 0.16345692042672777}, {'CRRA': 5.381637011093981, 'WealthShare': 0.1642816207639356}, {'CRRA': 5.272807055506822, 'WealthShare': 0.16562610692660415}, {'CRRA': 5.326977544603738, 'WealthShare': 0.17075638690650644}, {'CRRA': 5.290536727275727, 'WealthShare': 0.1654939898644262}, {'CRRA': 5.34522111280142, 'WealthShare': 0.17069763426574053}, {'CRRA': 5.336100860393708, 'WealthShare': 0.17086100179753094}, {'CRRA': 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'WealthShare': 0.17053748476047093}, {'CRRA': 5.335633144546121, 'WealthShare': 0.17065220536865475}, {'CRRA': 5.335596501563392, 'WealthShare': 0.170572140462395}, {'CRRA': 5.335595445166626, 'WealthShare': 0.1705940558344041}, {'CRRA': 5.335583910614445, 'WealthShare': 0.17061205807039284}, {'CRRA': 5.335577372664163, 'WealthShare': 0.1706005756625005}], 'criterion': [0.2500249942408325, 1.1791007356386276, 0.9197651867022865, 1.2595025990222253, 33.4909498055013, 1.4882139243569137, 0.9530633832431904, 1.2595025990222253, 36.978934548688066, 26.44344185974536, 41.385698106382975, 3.0981667931180743, 42.608449629101216, 0.2728700599071999, 0.3342173205988753, 0.29198968043547885, 0.2481648865140715, 0.24373445213195505, 0.25114503099349234, 0.24406213167656582, 0.24489531582637777, 0.24315780951732555, 0.24426227832547462, 0.24453862473009094, 0.24306497226632034, 0.24285876672075696, 0.24259437399648875, 0.24243354455545987, 0.25079678704331854, 0.24441635199641626, 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14.032752316999904, 15.207649181999841, 16.429454532999898, 17.670099559999926, 18.8472972149998, 20.135531741999785, 21.277609068999936, 22.423084480999933, 23.553762927999742, 24.684132019000117, 25.817971458000102, 26.998730913000145, 28.1869263640001, 29.34812033800017, 30.50678410599994, 31.65761656199993, 32.799299537000024, 33.94402660300011, 35.26958909099994, 36.47545633799973, 37.66156821799996, 38.83101894899983, 39.99145682000017, 41.17240058000016, 42.34739114900003, 43.51756579399989, 44.678886351000074, 45.83479911199993, 46.989458055999876, 48.12758910999992, 49.42098368999996, 50.618441345000065, 51.84377754599973, 52.98982088799994, 54.21043696800007], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45]}" +convergence_report,"{'one_step': {'relative_criterion_change': 0.00012034146417080751, 'relative_params_change': 0.004344123651656158, 'absolute_criterion_change': 2.9146508433580687e-05, 'absolute_params_change': 0.01844539352010625}, 'five_steps': {'relative_criterion_change': 0.00012034146417080751, 'relative_params_change': 0.004344123651656158, 'absolute_criterion_change': 2.9146508433580687e-05, 'absolute_params_change': 0.01844539352010625}}" +multistart_info,"{'start_parameters': [{'CRRA': 5.35399091577092, 'WealthShare': 0.1710302407154898}, {'CRRA': 5.837945053873421, 'WealthShare': 0.17769838670536425}], 'local_optima': [Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 5.606e-08* 5.606e-08* +relative_params_change 0.0001132 0.0001132 +absolute_criterion_change 1.358e-08* 1.358e-08* +absolute_params_change 3.272e-05 3.272e-05 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 3.629e-07* 0.0001686 +relative_params_change 1.947e-05 0.00153 +absolute_criterion_change 8.791e-08* 4.083e-05 +absolute_params_change 4.454e-06* 0.0005847 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 5.35399091577092, 'WealthShare': 0.1710302407154898}, {'CRRA': 7.00625, 'WealthShare': 0.19375}, {'CRRA': 12.9125, 'WealthShare': 0.1325}, {'CRRA': 4.64375, 'WealthShare': 0.31625000000000003}, {'CRRA': 8.1875, 'WealthShare': 0.3775}, {'CRRA': 15.274999999999999, 'WealthShare': 0.255}, {'CRRA': 17.046875, 'WealthShare': 0.224375}, {'CRRA': 11.73125, 'WealthShare': 0.43875}, {'CRRA': 18.81875, 'WealthShare': 0.07125}, {'CRRA': 10.549999999999999, 'WealthShare': 0.5}, {'CRRA': 9.368749999999999, 'WealthShare': 0.56125}, {'CRRA': 16.45625, 'WealthShare': 0.68375}, {'CRRA': 2.871875, 'WealthShare': 0.469375}, {'CRRA': 7.596874999999999, 'WealthShare': 0.714375}, {'CRRA': 14.093749999999998, 'WealthShare': 0.80625}, {'CRRA': 3.4625, 'WealthShare': 0.6225}, {'CRRA': 17.6375, 'WealthShare': 0.8674999999999999}, {'CRRA': 5.824999999999999, 'WealthShare': 0.745}, {'CRRA': 12.321874999999999, 'WealthShare': 0.959375}, {'CRRA': 2.28125, 'WealthShare': 0.92875}], 'exploration_results': array([2.42227546e-01, 3.27384376e-01, 1.14034055e+00, 1.50312179e+00, + 1.79325436e+00, 2.08879824e+00, 2.54067782e+00, 2.64114350e+00, + 2.73814230e+00, 3.83228381e+00, 6.46248546e+00, 9.30118040e+00, + 1.59966394e+01, 3.03715662e+01, 3.38771067e+01, 4.26926083e+01, + 5.84656546e+01, 5.90505617e+01, 5.22500282e+02, 9.01924413e+02])}" +algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83794505, 0.17769839]), radius=0.5837945053873421, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=[0], model=ScalarModel(intercept=0.2500249942408325, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=0, candidate_x=array([5.83794505, 0.17769839]), index=0, x=array([5.83794505, 0.17769839]), fval=0.25002499424083247, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([5.83794505, 0.17769839]), radius=0.5837945053873421, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=5.323973668078372, linear_terms=array([-0.75583641, 15.97016397]), square_terms=array([[ 0.06851782, -1.1616573 ], + [-1.1616573 , 24.80625807]]), scale=array([0.51737441, 0.3425364 ]), shift=array([5.83794505, 0.3525364 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.83794505, 0.17769839]), radius=0.29189725269367106, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 2, 3, 5, 6, 7, 8, 13]), model=ScalarModel(intercept=1.020144031837767, linear_terms=array([-0.15036877, 3.82287309]), square_terms=array([[ 0.02811468, -0.37120958], + [-0.37120958, 8.70439816]]), scale=array([0.2586872, 0.2131928]), shift=array([5.83794505, 0.2231928 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.83794505, 0.17769839]), radius=0.14594862634683553, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 3, 5, 6, 7, 8, 13, 14]), model=ScalarModel(intercept=0.32337401785450964, linear_terms=array([-0.05509149, 1.09845952]), square_terms=array([[ 0.02085975, -0.26486803], + [-0.26486803, 4.42259018]]), scale=0.14594862634683553, shift=array([5.83794505, 0.17769839])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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State(trustregion=Region(center=array([5.83794505, 0.17769839]), radius=0.07297431317341777, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 13, 14, 15]), model=ScalarModel(intercept=0.26967457001726336, linear_terms=array([0.00305809, 0.10758911]), square_terms=array([[2.03063183e-04, 7.58408569e-03], + [7.58408569e-03, 4.14525606e-01]]), scale=0.07297431317341777, shift=array([5.83794505, 0.17769839])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 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radius=0.14594862634683553, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 3, 6, 7, 13, 14, 15, 16]), model=ScalarModel(intercept=0.24980518122836323, linear_terms=array([ 0.00949845, -0.02386839]), square_terms=array([[0.00257834, 0.05299243], + [0.05299243, 1.55867884]]), scale=0.14594862634683553, shift=array([5.76466194, 0.16014228])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 3, 7, 11, 14, 15, 16, 17]), model=ScalarModel(intercept=0.3762029190210623, linear_terms=array([0.00847171, 1.1355556 ]), square_terms=array([[ 0.0351543 , -0.12066294], + [-0.12066294, 4.22970628]]), scale=array([0.2586872 , 0.20798866]), shift=array([5.61887231, 0.21798866])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=18, candidate_x=array([5.36018511, 0.15621624]), index=17, x=array([5.61887231, 0.16729012]), fval=0.24373445213195502, rho=-0.28174894853494853, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 7, 11, 14, 15, 16, 17]), old_indices_discarded=array([ 2, 4, 5, 6, 8, 9, 10, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.61887231, 0.16729012]), radius=0.14594862634683553, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 3, 7, 14, 15, 16, 17, 18]), model=ScalarModel(intercept=0.24992252725033823, linear_terms=array([0.00316437, 0.06723442]), square_terms=array([[9.24265460e-04, 3.06288719e-02], + [3.06288719e-02, 1.73234651e+00]]), scale=0.14594862634683553, shift=array([5.61887231, 0.16729012])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=19, candidate_x=array([5.47285718, 0.16421015]), index=17, x=array([5.61887231, 0.16729012]), fval=0.24373445213195502, rho=-0.10605520364386403, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 7, 14, 15, 16, 17, 18]), old_indices_discarded=array([ 6, 8, 10, 11, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.61887231, 0.16729012]), radius=0.07297431317341777, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 7, 14, 15, 16, 17, 18, 19]), model=ScalarModel(intercept=0.24923278591953518, linear_terms=array([0.00140877, 0.03201995]), square_terms=array([[2.08936369e-04, 7.01345842e-03], + [7.01345842e-03, 4.30945644e-01]]), scale=0.07297431317341777, shift=array([5.61887231, 0.16729012])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=20, candidate_x=array([5.54582595, 0.16306459]), index=17, x=array([5.61887231, 0.16729012]), fval=0.24373445213195502, rho=-0.5716837122875763, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 7, 14, 15, 16, 17, 18, 19]), old_indices_discarded=array([3]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.61887231, 0.16729012]), radius=0.03648715658670888, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 15, 16, 17, 19, 20]), model=ScalarModel(intercept=0.24447966591289924, linear_terms=array([ 0.00057646, -0.00219486]), square_terms=array([[4.59383981e-05, 1.38128890e-03], + [1.38128890e-03, 1.04480430e-01]]), scale=0.03648715658670888, shift=array([5.61887231, 0.16729012])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=21, candidate_x=array([5.58239844, 0.16853196]), index=21, x=array([5.58239844, 0.16853196]), fval=0.24315780951732555, rho=0.938417610541786, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 15, 16, 17, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.03649501064612971, relative_step_length=1.0002152554530293, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.58239844, 0.16853196]), radius=0.07297431317341777, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 14, 15, 16, 17, 18, 19, 20, 21]), model=ScalarModel(intercept=0.2493403145907468, linear_terms=array([0.00206841, 0.03132845]), square_terms=array([[2.20612437e-04, 6.20169647e-03], + [6.20169647e-03, 4.20600928e-01]]), scale=0.07297431317341777, shift=array([5.58239844, 0.16853196])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=22, candidate_x=array([5.50935584, 0.16418873]), index=21, x=array([5.58239844, 0.16853196]), fval=0.24315780951732555, rho=-0.40755145676280513, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 1, 14, 15, 16, 17, 18, 19, 20, 21]), old_indices_discarded=array([ 0, 3, 7, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.58239844, 0.16853196]), radius=0.03648715658670888, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 14, 15, 17, 19, 20, 21, 22]), model=ScalarModel(intercept=0.24973373889649686, linear_terms=array([0.00287452, 0.0154878 ]), square_terms=array([[0.00011611, 0.00170606], + [0.00170606, 0.10513658]]), scale=0.03648715658670888, shift=array([5.58239844, 0.16853196])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=23, candidate_x=array([5.54583196, 0.16386291]), index=21, x=array([5.58239844, 0.16853196]), fval=0.24315780951732555, rho=-0.3707124284955786, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 1, 14, 15, 17, 19, 20, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.58239844, 0.16853196]), radius=0.01824357829335444, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 17, 20, 21, 22, 23]), model=ScalarModel(intercept=0.24348377086944023, linear_terms=array([5.04148881e-05, 1.91725030e-04]), square_terms=array([[9.67169504e-06, 3.22879765e-04], + [3.22879765e-04, 2.62610807e-02]]), scale=0.01824357829335444, shift=array([5.58239844, 0.16853196])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=24, candidate_x=array([5.56415462, 0.16862293]), index=24, x=array([5.56415462, 0.16862293]), fval=0.24306497226632034, rho=2.022284735880162, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 17, 20, 21, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.018244046821077237, relative_step_length=1.0000256817886963, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.56415462, 0.16862293]), radius=0.03648715658670888, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 15, 17, 19, 20, 21, 22, 23, 24]), model=ScalarModel(intercept=0.24352266720979443, linear_terms=array([ 0.00058615, -0.00048178]), square_terms=array([[4.77510213e-05, 1.43280356e-03], + [1.43280356e-03, 1.04666941e-01]]), scale=0.03648715658670888, shift=array([5.56415462, 0.16862293])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=25, candidate_x=array([5.52767313, 0.1692867 ]), index=25, x=array([5.52767313, 0.1692867 ]), fval=0.24285876672075696, rho=0.35571310395335953, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 15, 17, 19, 20, 21, 22, 23, 24]), old_indices_discarded=array([1]), step_length=0.03648752369461524, relative_step_length=1.0000100612911693, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.52767313, 0.1692867 ]), radius=0.07297431317341777, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 17, 19, 20, 21, 22, 23, 24, 25]), model=ScalarModel(intercept=0.24314216726921375, linear_terms=array([0.00031071, 0.00112818]), square_terms=array([[1.58692645e-04, 5.04893216e-03], + [5.04893216e-03, 4.20396152e-01]]), scale=0.07297431317341777, shift=array([5.52767313, 0.1692867 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=26, candidate_x=array([5.45470179, 0.16996693]), index=26, x=array([5.45470179, 0.16996693]), fval=0.24259437399648873, rho=1.0590927394756011, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 17, 19, 20, 21, 22, 23, 24, 25]), old_indices_discarded=array([ 0, 1, 3, 7, 11, 15, 16, 18]), step_length=0.07297450997400595, relative_step_length=1.0000026968474196, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.45470179, 0.16996693]), radius=0.14594862634683553, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 18, 19, 20, 21, 22, 24, 25, 26]), model=ScalarModel(intercept=0.2426437155645201, linear_terms=array([ 0.00066012, -0.0002101 ]), square_terms=array([[6.76799313e-04, 2.11994953e-02], + [2.11994953e-02, 1.68722879e+00]]), scale=0.14594862634683553, shift=array([5.45470179, 0.16996693])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=27, candidate_x=array([5.30876491, 0.17181847]), index=27, x=array([5.30876491, 0.17181847]), fval=0.24243354455545987, rho=0.35150966109351695, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 18, 19, 20, 21, 22, 24, 25, 26]), old_indices_discarded=array([ 0, 1, 3, 7, 8, 10, 11, 12, 13, 15, 16, 17, 23]), step_length=0.14594862668524397, relative_step_length=1.000000002318682, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.30876491, 0.17181847]), radius=0.29189725269367106, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 11, 14, 18, 19, 20, 21, 27]), model=ScalarModel(intercept=0.37246777346864934, linear_terms=array([0.00441854, 1.18206703]), square_terms=array([[ 0.02195466, -0.04742476], + [-0.04742476, 4.2726438 ]]), scale=array([0.2586872 , 0.21025284]), shift=array([5.30876491, 0.22025284])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=28, candidate_x=array([5.0970293 , 0.16017426]), index=27, x=array([5.30876491, 0.17181847]), fval=0.24243354455545987, rho=-0.7113501343654184, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 1, 3, 11, 14, 18, 19, 20, 21, 27]), old_indices_discarded=array([ 0, 2, 4, 5, 6, 7, 8, 9, 10, 12, 13, 15, 16, 17, 22, 23, 24, + 25, 26]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.30876491, 0.17181847]), radius=0.14594862634683553, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 3, 7, 18, 19, 22, 25, 26, 27, 28]), model=ScalarModel(intercept=0.2464194023851543, linear_terms=array([-0.00084176, 0.08200684]), square_terms=array([[6.47218524e-04, 2.11981078e-02], + [2.11981078e-02, 1.79973195e+00]]), scale=0.14594862634683553, shift=array([5.30876491, 0.17181847])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=29, candidate_x=array([5.45460642, 0.16345692]), index=27, x=array([5.30876491, 0.17181847]), fval=0.24243354455545987, rho=-0.5703889919895696, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 18, 19, 22, 25, 26, 27, 28]), old_indices_discarded=array([ 0, 1, 10, 11, 12, 14, 15, 16, 17, 20, 21, 23, 24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.30876491, 0.17181847]), radius=0.07297431317341777, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 3, 7, 18, 19, 22, 26, 27, 28, 29]), model=ScalarModel(intercept=0.24664926765654258, linear_terms=array([-0.00024156, 0.04118899]), square_terms=array([[1.70654218e-04, 5.36001424e-03], + [5.36001424e-03, 4.50004550e-01]]), scale=0.07297431317341777, shift=array([5.30876491, 0.17181847])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=30, candidate_x=array([5.38163701, 0.16428162]), index=27, x=array([5.30876491, 0.17181847]), fval=0.24243354455545987, rho=-0.668055965403151, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 18, 19, 22, 26, 27, 28, 29]), old_indices_discarded=array([ 1, 11, 14, 17, 20, 21, 23, 24, 25]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.30876491, 0.17181847]), radius=0.03648715658670888, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 3, 7, 18, 19, 26, 27, 29, 30]), model=ScalarModel(intercept=0.24521815600368138, linear_terms=array([0.00026426, 0.02038709]), square_terms=array([[4.74795190e-05, 1.30395809e-03], + [1.30395809e-03, 1.12532052e-01]]), scale=0.03648715658670888, shift=array([5.30876491, 0.17181847])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=31, candidate_x=array([5.27280706, 0.16562611]), index=27, x=array([5.30876491, 0.17181847]), fval=0.24243354455545987, rho=-0.8314331419387987, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 18, 19, 26, 27, 29, 30]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.30876491, 0.17181847]), radius=0.01824357829335444, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([18, 27, 30, 31]), model=ScalarModel(intercept=0.2425172780472681, linear_terms=array([1.04804479e-05, 1.37888168e-03]), square_terms=array([[1.12857867e-05, 2.91582077e-04], + [2.91582077e-04, 2.87671916e-02]]), scale=0.01824357829335444, shift=array([5.30876491, 0.17181847])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=32, candidate_x=array([5.32697754, 0.17075639]), index=32, x=array([5.32697754, 0.17075639]), fval=0.24225547458407035, rho=5.498532498656186, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([18, 27, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.018243578293354667, relative_step_length=1.0000000000000124, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.32697754, 0.17075639]), radius=0.03648715658670888, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 7, 18, 19, 26, 27, 29, 30, 31, 32]), model=ScalarModel(intercept=0.24442267125914174, linear_terms=array([0.00031052, 0.01755316]), square_terms=array([[4.95899758e-05, 1.31876158e-03], + [1.31876158e-03, 1.12502932e-01]]), scale=0.03648715658670888, shift=array([5.32697754, 0.17075639])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=33, candidate_x=array([5.29053673, 0.16549399]), index=32, x=array([5.32697754, 0.17075639]), fval=0.24225547458407035, rho=-1.1605097139907963, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 7, 18, 19, 26, 27, 29, 30, 31, 32]), old_indices_discarded=array([3]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.32697754, 0.17075639]), radius=0.01824357829335444, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([18, 27, 30, 31, 32, 33]), model=ScalarModel(intercept=0.24240138874383832, linear_terms=array([-1.34706482e-05, -2.00977445e-04]), square_terms=array([[1.12310877e-05, 2.93698037e-04], + [2.93698037e-04, 2.87879022e-02]]), scale=0.01824357829335444, shift=array([5.32697754, 0.17075639])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=34, candidate_x=array([5.34522111, 0.17069763]), index=32, x=array([5.32697754, 0.17075639]), fval=0.24225547458407035, rho=-2.4579564090648676, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([18, 27, 30, 31, 32, 33]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.32697754, 0.17075639]), radius=0.00912178914667722, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([18, 27, 32, 33, 34]), model=ScalarModel(intercept=0.24231372257835046, linear_terms=array([-3.67885812e-05, -1.56295915e-04]), square_terms=array([[2.74057907e-06, 7.34739042e-05], + [7.34739042e-05, 7.18730958e-03]]), scale=0.00912178914667722, shift=array([5.32697754, 0.17075639])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=35, candidate_x=array([5.33610086, 0.170861 ]), index=35, x=array([5.33610086, 0.170861 ]), fval=0.2422392115565381, rho=0.45299609567353283, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([18, 27, 32, 33, 34]), old_indices_discarded=array([], dtype=int64), step_length=0.009123915567284637, relative_step_length=1.000233114422316, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33610086, 0.170861 ]), radius=0.01824357829335444, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([18, 27, 30, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=0.2423307750427185, linear_terms=array([-1.78909546e-05, -1.98777282e-05]), square_terms=array([[1.11719188e-05, 2.91489798e-04], + [2.91489798e-04, 2.87505621e-02]]), scale=0.01824357829335444, shift=array([5.33610086, 0.170861 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=36, candidate_x=array([5.35434363, 0.17068872]), index=35, x=array([5.33610086, 0.170861 ]), fval=0.2422392115565381, rho=-2.963143065159759, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([18, 27, 30, 31, 32, 33, 34, 35]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33610086, 0.170861 ]), radius=0.00912178914667722, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([18, 27, 32, 34, 35, 36]), model=ScalarModel(intercept=0.24228466564231269, linear_terms=array([-2.24742123e-05, 1.60446770e-05]), square_terms=array([[2.72376463e-06, 6.51825427e-05], + [6.51825427e-05, 7.14572234e-03]]), scale=0.00912178914667722, shift=array([5.33610086, 0.170861 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33610086, 0.170861 ]), radius=0.00456089457333861, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([32, 34, 35, 36, 37]), model=ScalarModel(intercept=0.24224211188369624, linear_terms=array([ 2.56124148e-06, -7.33810091e-04]), square_terms=array([[6.58450434e-07, 1.62540985e-05], + [1.62540985e-05, 1.74025458e-03]]), scale=0.00456089457333861, shift=array([5.33610086, 0.170861 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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State(trustregion=Region(center=array([5.33610086, 0.170861 ]), radius=0.002280447286669305, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([32, 34, 35, 37, 38]), model=ScalarModel(intercept=0.2422610199987972, linear_terms=array([3.24076423e-06, 1.09212079e-04]), square_terms=array([[1.64689944e-07, 3.97469068e-06], + [3.97469068e-06, 4.42322743e-04]]), scale=0.002280447286669305, shift=array([5.33610086, 0.170861 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + 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bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 38, 39]), model=ScalarModel(intercept=0.2422392115565383, linear_terms=array([-5.63600427e-06, 5.10967489e-05]), square_terms=array([[4.70616627e-08, 8.13103968e-07], + [8.13103968e-07, 1.09902069e-04]]), scale=0.0011402236433346526, shift=array([5.33610086, 0.170861 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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model=ScalarModel(intercept=0.2422392115565383, linear_terms=array([2.67123864e-06, 2.32015646e-06]), square_terms=array([[1.01503457e-08, 2.55683066e-07], + [2.55683066e-07, 2.70609068e-05]]), scale=0.0005701118216673263, shift=array([5.33610086, 0.170861 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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linear_terms=array([-2.89843844e-07, 5.08377823e-05]), square_terms=array([[4.27453955e-08, 9.21556966e-07], + [9.21556966e-07, 1.10052928e-04]]), scale=0.0011402236433346526, shift=array([5.33553035, 0.17082138])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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square_terms=array([[1.00813101e-08, 2.51773556e-07], + [2.51773556e-07, 2.71406595e-05]]), scale=0.0005701118216673263, shift=array([5.33553035, 0.17082138])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=43, candidate_x=array([5.33496087, 0.17089262]), index=41, x=array([5.33553035, 0.17082138]), fval=0.2422306308030337, rho=-5.535264368026378, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 39, 40, 41, 42]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33553035, 0.17082138]), radius=0.00028505591083366315, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 41, 42, 43]), model=ScalarModel(intercept=0.24224023352414065, linear_terms=array([-1.87742787e-06, -5.21106061e-06]), square_terms=array([[3.04573962e-09, 8.14917983e-08], + [8.14917983e-08, 6.83492010e-06]]), scale=0.00028505591083366315, shift=array([5.33553035, 0.17082138])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=44, candidate_x=array([5.33576708, 0.17098362]), index=41, x=array([5.33553035, 0.17082138]), fval=0.2422306308030337, rho=-8.092327093834392, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 41, 42, 43]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33553035, 0.17082138]), radius=0.00014252795541683157, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 41, 43, 44]), model=ScalarModel(intercept=0.24223295230251193, linear_terms=array([-5.31736790e-07, 2.27975409e-05]), square_terms=array([[7.19415359e-10, 1.86503337e-08], + [1.86503337e-08, 1.61403964e-06]]), scale=0.00014252795541683157, shift=array([5.33553035, 0.17082138])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=45, candidate_x=array([5.33553886, 0.17067911]), index=45, x=array([5.33553886, 0.17067911]), fval=0.24220239688738698, rho=1.2842031311521878, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([35, 41, 43, 44]), old_indices_discarded=array([], dtype=int64), step_length=0.00014252795541681761, relative_step_length=0.9999999999999021, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33553886, 0.17067911]), radius=0.00028505591083366315, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.24222997040159935, linear_terms=array([5.08790955e-06, 9.87341516e-06]), square_terms=array([[2.55137299e-09, 5.67163305e-08], + [5.67163305e-08, 6.70555493e-06]]), scale=0.00028505591083366315, shift=array([5.33553886, 0.17067911])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=46, candidate_x=array([5.33533366, 0.17047473]), index=45, x=array([5.33553886, 0.17067911]), fval=0.24220239688738698, rho=-0.7071442609659108, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 41, 42, 43, 44, 45]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33553886, 0.17067911]), radius=0.00014252795541683157, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 41, 43, 44, 45, 46]), model=ScalarModel(intercept=0.24221820849154518, linear_terms=array([-8.49124020e-07, 1.52697526e-05]), square_terms=array([[7.31788970e-10, 1.94307162e-08], + [1.94307162e-08, 1.63646545e-06]]), scale=0.00014252795541683157, shift=array([5.33553886, 0.17067911])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=47, candidate_x=array([5.33555459, 0.17053745]), index=47, x=array([5.33555459, 0.17053745]), fval=0.24220041894287114, rho=0.1367487913232024, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([35, 41, 43, 44, 45, 46]), old_indices_discarded=array([], dtype=int64), step_length=0.00014252795541680403, relative_step_length=0.9999999999998068, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33555459, 0.17053745]), radius=0.00028505591083366315, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 41, 42, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=0.24221648568928017, linear_terms=array([7.61123169e-06, 1.35871487e-05]), square_terms=array([[2.37686475e-09, 4.53613218e-08], + [4.53613218e-08, 6.64587957e-06]]), scale=0.00028505591083366315, shift=array([5.33555459, 0.17053745])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=48, candidate_x=array([5.33534308, 0.17034634]), index=47, x=array([5.33555459, 0.17053745]), fval=0.24220041894287114, rho=-3.283522683589635, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 41, 42, 43, 44, 45, 46, 47]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33555459, 0.17053745]), radius=0.00014252795541683157, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 41, 43, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=0.2422186103167294, linear_terms=array([-1.17163675e-06, 6.22141261e-06]), square_terms=array([[7.43556893e-10, 1.99248388e-08], + [1.99248388e-08, 1.65585538e-06]]), scale=0.00014252795541683157, shift=array([5.33555459, 0.17053745])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=49, candidate_x=array([5.33559928, 0.17040211]), index=47, x=array([5.33555459, 0.17053745]), fval=0.24220041894287114, rho=-5.589036142419969, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 41, 43, 44, 45, 46, 47, 48]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33555459, 0.17053745]), radius=7.126397770841579e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([41, 45, 46, 47, 48, 49]), model=ScalarModel(intercept=0.2422175625561015, linear_terms=array([-1.08529643e-06, -2.15586658e-06]), square_terms=array([[2.13416381e-10, 6.09539586e-09], + [6.09539586e-09, 4.20627375e-07]]), scale=7.126397770841579e-05, shift=array([5.33555459, 0.17053745])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=50, candidate_x=array([5.33558034, 0.1706039 ]), index=50, x=array([5.33558034, 0.1706039 ]), fval=0.2421984742592525, rho=0.8769913650774368, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([41, 45, 46, 47, 48, 49]), old_indices_discarded=array([], dtype=int64), step_length=7.126397770841627e-05, relative_step_length=1.0000000000000069, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33558034, 0.1706039 ]), radius=0.00014252795541683157, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 41, 44, 45, 46, 47, 48, 49, 50]), model=ScalarModel(intercept=0.24222108854190627, linear_terms=array([2.91505429e-06, 2.31914584e-06]), square_terms=array([[6.76506500e-10, 1.48024983e-08], + [1.48024983e-08, 1.66662830e-06]]), scale=0.00014252795541683157, shift=array([5.33558034, 0.1706039 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, 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dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33558034, 0.1706039 ]), radius=1.7815994427103947e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([45, 47, 50, 52, 53]), model=ScalarModel(intercept=0.24219994234056103, linear_terms=array([-3.77226186e-07, 2.60098977e-07]), square_terms=array([[9.65464377e-12, 1.64344963e-10], + [1.64344963e-10, 2.60967257e-08]]), scale=1.7815994427103947e-05, shift=array([5.33558034, 0.1706039 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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n_evals_acceptance=1), State(trustregion=Region(center=array([5.33558034, 0.1706039 ]), radius=8.907997213551973e-06, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([50, 53, 54]), model=ScalarModel(intercept=0.2421984742592526, linear_terms=array([-9.96134608e-08, -2.19237776e-07]), square_terms=array([[3.52007307e-12, 5.01984429e-11], + [5.01984429e-11, 6.41022815e-09]]), scale=8.907997213551973e-06, shift=array([5.33558034, 0.1706039 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 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radius=4.453998606775987e-06, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([50, 54, 55]), model=ScalarModel(intercept=0.24219847425925245, linear_terms=array([7.44562175e-08, 8.11094797e-08]), square_terms=array([[7.34577640e-13, 2.02182033e-11], + [2.02182033e-11, 1.62644037e-09]]), scale=4.453998606775987e-06, shift=array([5.33558034, 0.1706039 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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0.17103024]), array([5.83794505, 0.17769839])], 'local_optima': [{'solution_x': array([5.3540173 , 0.17104959]), 'solution_criterion': 0.24222753286188017, 'states': [State(trustregion=Region(center=array([5.35399092, 0.17103024]), radius=0.535399091577092, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=[0], model=ScalarModel(intercept=0.24222754644165562, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.535399091577092, shift=array([5.35399092, 0.17103024])), vector_model=VectorModel(intercepts=array([ 0.039855 , 0.08727496, 0.08464579, 0.10728423, 0.11930841, + 0.1319508 , 0.14808462, 0.15979372, 0.07460467, 0.12637411, + -0.21040967, -0.24904034, -0.05785571, -0.0376285 , -0.03109732, + -0.03263715, -0.0247748 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=3.4201048201667734, linear_terms=array([-0.28177681, 11.21026201]), square_terms=array([[ 1.80970210e-02, -4.44098984e-01], + [-4.44098984e-01, 1.94933118e+01]]), scale=array([0.47448509, 0.31775767]), shift=array([5.35399092, 0.32775767])), vector_model=VectorModel(intercepts=array([ 0.039855 , 0.08727496, 0.08464579, 0.10728423, 0.11930841, + 0.1319508 , 0.14808462, 0.15979372, 0.07460467, 0.12637411, + -0.21040967, -0.24904034, -0.05785571, -0.0376285 , -0.03109732, + -0.03263715, -0.0247748 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 2, 3, 5, 6, 7, 11, 13]), model=ScalarModel(intercept=0.3453241266842719, linear_terms=array([0.02479079, 0.95756552]), square_terms=array([[0.00728976, 0.05998005], + [0.05998005, 3.79816812]]), scale=array([0.23724255, 0.19913639]), shift=array([5.35399092, 0.20913639])), vector_model=VectorModel(intercepts=array([ 0.039855 , 0.08727496, 0.08464579, 0.10728423, 0.11930841, + 0.1319508 , 0.14808462, 0.15979372, 0.07460467, 0.12637411, + -0.21040967, -0.24904034, -0.05785571, -0.0376285 , -0.03109732, + -0.03263715, -0.0247748 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.535399091577092, shift=array([5.35399092, 0.17103024])), candidate_index=14, candidate_x=array([5.11674837, 0.16207636]), index=0, x=array([5.35399092, 0.17103024]), fval=0.24222754644165564, rho=-0.46143358417508423, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 2, 3, 5, 6, 7, 11, 13]), old_indices_discarded=array([ 4, 8, 9, 10, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35399092, 0.17103024]), radius=0.133849772894273, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 2, 5, 6, 7, 11, 13, 14]), model=ScalarModel(intercept=0.2361989522624684, linear_terms=array([0.00800855, 0.11334879]), square_terms=array([[0.00196917, 0.02968529], + [0.02968529, 1.62675202]]), scale=0.133849772894273, shift=array([5.35399092, 0.17103024])), vector_model=VectorModel(intercepts=array([ 0.039855 , 0.08727496, 0.08464579, 0.10728423, 0.11930841, + 0.1319508 , 0.14808462, 0.15979372, 0.07460467, 0.12637411, + -0.21040967, -0.24904034, -0.05785571, -0.0376285 , -0.03109732, + -0.03263715, -0.0247748 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.535399091577092, shift=array([5.35399092, 0.17103024])), candidate_index=15, candidate_x=array([5.2200408 , 0.16416724]), index=0, x=array([5.35399092, 0.17103024]), fval=0.24222754644165564, rho=-0.3292785496260621, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 2, 5, 6, 7, 11, 13, 14]), old_indices_discarded=array([ 3, 4, 9, 10, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35399092, 0.17103024]), radius=0.0669248864471365, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 14, 15]), model=ScalarModel(intercept=0.24255126823870177, linear_terms=array([-0.00173985, 0.04550415]), square_terms=array([[1.04037898e-04, 4.03282410e-03], + [4.03282410e-03, 4.06667977e-01]]), scale=0.0669248864471365, shift=array([5.35399092, 0.17103024])), vector_model=VectorModel(intercepts=array([ 0.039855 , 0.08727496, 0.08464579, 0.10728423, 0.11930841, + 0.1319508 , 0.14808462, 0.15979372, 0.07460467, 0.12637411, + -0.21040967, -0.24904034, -0.05785571, -0.0376285 , -0.03109732, + -0.03263715, -0.0247748 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.535399091577092, shift=array([5.35399092, 0.17103024])), candidate_index=16, candidate_x=array([5.42083761, 0.16292119]), index=0, x=array([5.35399092, 0.17103024]), fval=0.24222754644165564, rho=-0.5254278897156385, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35399092, 0.17103024]), radius=0.03346244322356825, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 15, 16]), model=ScalarModel(intercept=0.24222754644165548, linear_terms=array([2.40076968e-05, 3.04590501e-05]), square_terms=array([[3.81907287e-05, 9.69902721e-04], + [9.69902721e-04, 9.61595844e-02]]), scale=0.03346244322356825, shift=array([5.35399092, 0.17103024])), vector_model=VectorModel(intercepts=array([ 0.039855 , 0.08727496, 0.08464579, 0.10728423, 0.11930841, + 0.1319508 , 0.14808462, 0.15979372, 0.07460467, 0.12637411, + -0.21040967, -0.24904034, -0.05785571, -0.0376285 , -0.03109732, + -0.03263715, -0.0247748 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.535399091577092, shift=array([5.35399092, 0.17103024])), candidate_index=17, candidate_x=array([5.32053009, 0.17135876]), index=0, x=array([5.35399092, 0.17103024]), fval=0.24222754644165564, rho=-12.331804806172741, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 15, 16]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35399092, 0.17103024]), radius=0.016731221611784124, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 16, 17]), model=ScalarModel(intercept=0.24222754644165576, linear_terms=array([-5.69494381e-05, -5.53184385e-04]), square_terms=array([[9.07291274e-06, 2.18209656e-04], + [2.18209656e-04, 2.36720867e-02]]), scale=0.016731221611784124, shift=array([5.35399092, 0.17103024])), vector_model=VectorModel(intercepts=array([ 0.039855 , 0.08727496, 0.08464579, 0.10728423, 0.11930841, + 0.1319508 , 0.14808462, 0.15979372, 0.07460467, 0.12637411, + -0.21040967, -0.24904034, -0.05785571, -0.0376285 , -0.03109732, + -0.03263715, -0.0247748 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.535399091577092, shift=array([5.35399092, 0.17103024])), candidate_index=18, candidate_x=array([5.3707248 , 0.17126653]), index=0, x=array([5.35399092, 0.17103024]), fval=0.24222754644165564, rho=-2.7558354422098033, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35399092, 0.17103024]), radius=0.008365610805892062, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=0.2422275464416555, linear_terms=array([1.38880687e-05, 4.03818545e-03]), square_terms=array([[2.16087810e-06, 4.68859000e-05], + [4.68859000e-05, 5.46996031e-03]]), scale=0.008365610805892062, shift=array([5.35399092, 0.17103024])), vector_model=VectorModel(intercepts=array([ 0.039855 , 0.08727496, 0.08464579, 0.10728423, 0.11930841, + 0.1319508 , 0.14808462, 0.15979372, 0.07460467, 0.12637411, + -0.21040967, -0.24904034, -0.05785571, -0.0376285 , -0.03109732, + -0.03263715, -0.0247748 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.535399091577092, shift=array([5.35399092, 0.17103024])), candidate_index=19, candidate_x=array([5.36002884, 0.16483287]), index=0, x=array([5.35399092, 0.17103024]), fval=0.24222754644165564, rho=-1.151992301822513, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35399092, 0.17103024]), radius=0.004182805402946031, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 18, 19]), model=ScalarModel(intercept=0.24222754644165576, linear_terms=array([ 3.59888783e-05, -3.78793239e-05]), square_terms=array([[5.09713542e-07, 1.18397453e-05], + [1.18397453e-05, 1.50374245e-03]]), scale=0.004182805402946031, shift=array([5.35399092, 0.17103024])), vector_model=VectorModel(intercepts=array([ 0.039855 , 0.08727496, 0.08464579, 0.10728423, 0.11930841, + 0.1319508 , 0.14808462, 0.15979372, 0.07460467, 0.12637411, + -0.21040967, -0.24904034, -0.05785571, -0.0376285 , -0.03109732, + -0.03263715, -0.0247748 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.535399091577092, shift=array([5.35399092, 0.17103024])), candidate_index=20, candidate_x=array([5.34980904, 0.17116531]), index=0, x=array([5.35399092, 0.17103024]), fval=0.24222754644165564, rho=-1.286789678419487, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35399092, 0.17103024]), radius=0.0020914027014730155, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 19, 20]), model=ScalarModel(intercept=0.24222754644165584, linear_terms=array([-2.51820360e-05, -6.10052937e-05]), square_terms=array([[1.51629548e-07, 3.66995116e-06], + [3.66995116e-06, 3.77296137e-04]]), scale=0.0020914027014730155, shift=array([5.35399092, 0.17103024])), vector_model=VectorModel(intercepts=array([ 0.039855 , 0.08727496, 0.08464579, 0.10728423, 0.11930841, + 0.1319508 , 0.14808462, 0.15979372, 0.07460467, 0.12637411, + -0.21040967, -0.24904034, -0.05785571, -0.0376285 , -0.03109732, + -0.03263715, -0.0247748 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.535399091577092, shift=array([5.35399092, 0.17103024])), candidate_index=21, candidate_x=array([5.35608532, 0.17132867]), index=0, x=array([5.35399092, 0.17103024]), fval=0.24222754644165564, rho=-1.568071751203749, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35399092, 0.17103024]), radius=0.0010457013507365078, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 20, 21]), model=ScalarModel(intercept=0.24222754644165548, linear_terms=array([-5.59040445e-06, 1.86243050e-04]), square_terms=array([[3.53347737e-08, 8.32607267e-07], + [8.32607267e-07, 9.15341276e-05]]), scale=0.0010457013507365078, shift=array([5.35399092, 0.17103024])), vector_model=VectorModel(intercepts=array([ 0.039855 , 0.08727496, 0.08464579, 0.10728423, 0.11930841, + 0.1319508 , 0.14808462, 0.15979372, 0.07460467, 0.12637411, + -0.21040967, -0.24904034, -0.05785571, -0.0376285 , -0.03109732, + -0.03263715, -0.0247748 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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State(trustregion=Region(center=array([5.35399092, 0.17103024]), radius=0.00026142533768412694, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 22, 23]), model=ScalarModel(intercept=0.24222754644165576, linear_terms=array([-4.74482435e-05, -2.44726668e-05]), square_terms=array([[1.41323716e-08, 1.52966509e-07], + [1.52966509e-07, 5.98698669e-06]]), scale=0.00026142533768412694, shift=array([5.35399092, 0.17103024])), vector_model=VectorModel(intercepts=array([ 0.039855 , 0.08727496, 0.08464579, 0.10728423, 0.11930841, + 0.1319508 , 0.14808462, 0.15979372, 0.07460467, 0.12637411, + -0.21040967, -0.24904034, -0.05785571, -0.0376285 , -0.03109732, + -0.03263715, -0.0247748 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + 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model=ScalarModel(intercept=0.24222754644165567, linear_terms=array([ 8.98967423e-06, -8.17920862e-06]), square_terms=array([[3.56744076e-10, 2.72970406e-09], + [2.72970406e-09, 3.66603832e-07]]), scale=6.535633442103174e-05, shift=array([5.35399092, 0.17103024])), vector_model=VectorModel(intercepts=array([ 0.039855 , 0.08727496, 0.08464579, 0.10728423, 0.11930841, + 0.1319508 , 0.14808462, 0.15979372, 0.07460467, 0.12637411, + -0.21040967, -0.24904034, -0.05785571, -0.0376285 , -0.03109732, + -0.03263715, -0.0247748 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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fval=0.24373445213195502, rho=-0.28174894853494853, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 7, 11, 14, 15, 16, 17]), old_indices_discarded=array([ 2, 4, 5, 6, 8, 9, 10, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.61887231, 0.16729012]), radius=0.14594862634683553, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 3, 7, 14, 15, 16, 17, 18]), model=ScalarModel(intercept=0.24992252725033823, linear_terms=array([0.00316437, 0.06723442]), square_terms=array([[9.24265460e-04, 3.06288719e-02], + [3.06288719e-02, 1.73234651e+00]]), scale=0.14594862634683553, shift=array([5.61887231, 0.16729012])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, 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rho=-0.10605520364386403, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 7, 14, 15, 16, 17, 18]), old_indices_discarded=array([ 6, 8, 10, 11, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.61887231, 0.16729012]), radius=0.07297431317341777, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 7, 14, 15, 16, 17, 18, 19]), model=ScalarModel(intercept=0.24923278591953518, linear_terms=array([0.00140877, 0.03201995]), square_terms=array([[2.08936369e-04, 7.01345842e-03], + [7.01345842e-03, 4.30945644e-01]]), scale=0.07297431317341777, shift=array([5.61887231, 0.16729012])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=20, candidate_x=array([5.54582595, 0.16306459]), index=17, x=array([5.61887231, 0.16729012]), fval=0.24373445213195502, rho=-0.5716837122875763, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 7, 14, 15, 16, 17, 18, 19]), old_indices_discarded=array([3]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.61887231, 0.16729012]), radius=0.03648715658670888, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 15, 16, 17, 19, 20]), model=ScalarModel(intercept=0.24447966591289924, linear_terms=array([ 0.00057646, -0.00219486]), square_terms=array([[4.59383981e-05, 1.38128890e-03], + [1.38128890e-03, 1.04480430e-01]]), scale=0.03648715658670888, shift=array([5.61887231, 0.16729012])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=21, candidate_x=array([5.58239844, 0.16853196]), index=21, x=array([5.58239844, 0.16853196]), fval=0.24315780951732555, rho=0.938417610541786, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 15, 16, 17, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.03649501064612971, relative_step_length=1.0002152554530293, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.58239844, 0.16853196]), radius=0.07297431317341777, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 14, 15, 16, 17, 18, 19, 20, 21]), model=ScalarModel(intercept=0.2493403145907468, linear_terms=array([0.00206841, 0.03132845]), square_terms=array([[2.20612437e-04, 6.20169647e-03], + [6.20169647e-03, 4.20600928e-01]]), scale=0.07297431317341777, shift=array([5.58239844, 0.16853196])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=22, candidate_x=array([5.50935584, 0.16418873]), index=21, x=array([5.58239844, 0.16853196]), fval=0.24315780951732555, rho=-0.40755145676280513, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 1, 14, 15, 16, 17, 18, 19, 20, 21]), old_indices_discarded=array([ 0, 3, 7, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.58239844, 0.16853196]), radius=0.03648715658670888, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 14, 15, 17, 19, 20, 21, 22]), model=ScalarModel(intercept=0.24973373889649686, linear_terms=array([0.00287452, 0.0154878 ]), square_terms=array([[0.00011611, 0.00170606], + [0.00170606, 0.10513658]]), scale=0.03648715658670888, shift=array([5.58239844, 0.16853196])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=23, candidate_x=array([5.54583196, 0.16386291]), index=21, x=array([5.58239844, 0.16853196]), fval=0.24315780951732555, rho=-0.3707124284955786, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 1, 14, 15, 17, 19, 20, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.58239844, 0.16853196]), radius=0.01824357829335444, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 17, 20, 21, 22, 23]), model=ScalarModel(intercept=0.24348377086944023, linear_terms=array([5.04148881e-05, 1.91725030e-04]), square_terms=array([[9.67169504e-06, 3.22879765e-04], + [3.22879765e-04, 2.62610807e-02]]), scale=0.01824357829335444, shift=array([5.58239844, 0.16853196])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=24, candidate_x=array([5.56415462, 0.16862293]), index=24, x=array([5.56415462, 0.16862293]), fval=0.24306497226632034, rho=2.022284735880162, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 17, 20, 21, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.018244046821077237, relative_step_length=1.0000256817886963, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.56415462, 0.16862293]), radius=0.03648715658670888, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 15, 17, 19, 20, 21, 22, 23, 24]), model=ScalarModel(intercept=0.24352266720979443, linear_terms=array([ 0.00058615, -0.00048178]), square_terms=array([[4.77510213e-05, 1.43280356e-03], + [1.43280356e-03, 1.04666941e-01]]), scale=0.03648715658670888, shift=array([5.56415462, 0.16862293])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=25, candidate_x=array([5.52767313, 0.1692867 ]), index=25, x=array([5.52767313, 0.1692867 ]), fval=0.24285876672075696, rho=0.35571310395335953, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 15, 17, 19, 20, 21, 22, 23, 24]), old_indices_discarded=array([1]), step_length=0.03648752369461524, relative_step_length=1.0000100612911693, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.52767313, 0.1692867 ]), radius=0.07297431317341777, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 17, 19, 20, 21, 22, 23, 24, 25]), model=ScalarModel(intercept=0.24314216726921375, linear_terms=array([0.00031071, 0.00112818]), square_terms=array([[1.58692645e-04, 5.04893216e-03], + [5.04893216e-03, 4.20396152e-01]]), scale=0.07297431317341777, shift=array([5.52767313, 0.1692867 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 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State(trustregion=Region(center=array([5.45470179, 0.16996693]), radius=0.14594862634683553, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 18, 19, 20, 21, 22, 24, 25, 26]), model=ScalarModel(intercept=0.2426437155645201, linear_terms=array([ 0.00066012, -0.0002101 ]), square_terms=array([[6.76799313e-04, 2.11994953e-02], + [2.11994953e-02, 1.68722879e+00]]), scale=0.14594862634683553, shift=array([5.45470179, 0.16996693])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + 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State(trustregion=Region(center=array([5.30876491, 0.17181847]), radius=0.29189725269367106, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 11, 14, 18, 19, 20, 21, 27]), model=ScalarModel(intercept=0.37246777346864934, linear_terms=array([0.00441854, 1.18206703]), square_terms=array([[ 0.02195466, -0.04742476], + [-0.04742476, 4.2726438 ]]), scale=array([0.2586872 , 0.21025284]), shift=array([5.30876491, 0.22025284])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 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State(trustregion=Region(center=array([5.30876491, 0.17181847]), radius=0.14594862634683553, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 3, 7, 18, 19, 22, 25, 26, 27, 28]), model=ScalarModel(intercept=0.2464194023851543, linear_terms=array([-0.00084176, 0.08200684]), square_terms=array([[6.47218524e-04, 2.11981078e-02], + [2.11981078e-02, 1.79973195e+00]]), scale=0.14594862634683553, shift=array([5.30876491, 0.17181847])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 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State(trustregion=Region(center=array([5.30876491, 0.17181847]), radius=0.07297431317341777, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 3, 7, 18, 19, 22, 26, 27, 28, 29]), model=ScalarModel(intercept=0.24664926765654258, linear_terms=array([-0.00024156, 0.04118899]), square_terms=array([[1.70654218e-04, 5.36001424e-03], + [5.36001424e-03, 4.50004550e-01]]), scale=0.07297431317341777, shift=array([5.30876491, 0.17181847])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 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0.17181847]), radius=0.03648715658670888, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 3, 7, 18, 19, 26, 27, 29, 30]), model=ScalarModel(intercept=0.24521815600368138, linear_terms=array([0.00026426, 0.02038709]), square_terms=array([[4.74795190e-05, 1.30395809e-03], + [1.30395809e-03, 1.12532052e-01]]), scale=0.03648715658670888, shift=array([5.30876491, 0.17181847])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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0.01]), upper=array([20. , 0.99]))), model_indices=array([18, 27, 30, 31]), model=ScalarModel(intercept=0.2425172780472681, linear_terms=array([1.04804479e-05, 1.37888168e-03]), square_terms=array([[1.12857867e-05, 2.91582077e-04], + [2.91582077e-04, 2.87671916e-02]]), scale=0.01824357829335444, shift=array([5.30876491, 0.17181847])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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26, 27, 29, 30, 31, 32]), model=ScalarModel(intercept=0.24442267125914174, linear_terms=array([0.00031052, 0.01755316]), square_terms=array([[4.95899758e-05, 1.31876158e-03], + [1.31876158e-03, 1.12502932e-01]]), scale=0.03648715658670888, shift=array([5.32697754, 0.17075639])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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linear_terms=array([-1.34706482e-05, -2.00977445e-04]), square_terms=array([[1.12310877e-05, 2.93698037e-04], + [2.93698037e-04, 2.87879022e-02]]), scale=0.01824357829335444, shift=array([5.32697754, 0.17075639])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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x=array([5.33610086, 0.170861 ]), fval=0.2422392115565381, rho=-0.01817829767899888, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([32, 34, 35, 37, 38]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33610086, 0.170861 ]), radius=0.0011402236433346526, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 38, 39]), model=ScalarModel(intercept=0.2422392115565383, linear_terms=array([-5.63600427e-06, 5.10967489e-05]), square_terms=array([[4.70616627e-08, 8.13103968e-07], + [8.13103968e-07, 1.09902069e-04]]), scale=0.0011402236433346526, shift=array([5.33610086, 0.170861 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, 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old_indices_discarded=array([], dtype=int64), step_length=0.0005718859490511061, relative_step_length=1.0031118936958565, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33553035, 0.17082138]), radius=0.0011402236433346526, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 38, 39, 40, 41]), model=ScalarModel(intercept=0.24224726707979996, linear_terms=array([-2.89843844e-07, 5.08377823e-05]), square_terms=array([[4.27453955e-08, 9.21556966e-07], + [9.21556966e-07, 1.10052928e-04]]), scale=0.0011402236433346526, shift=array([5.33553035, 0.17082138])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33553035, 0.17082138]), radius=0.0005701118216673263, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 39, 40, 41, 42]), model=ScalarModel(intercept=0.24223360243650394, linear_terms=array([ 2.91278438e-06, -3.50722253e-06]), square_terms=array([[1.00813101e-08, 2.51773556e-07], + [2.51773556e-07, 2.71406595e-05]]), scale=0.0005701118216673263, shift=array([5.33553035, 0.17082138])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=43, candidate_x=array([5.33496087, 0.17089262]), index=41, x=array([5.33553035, 0.17082138]), fval=0.2422306308030337, rho=-5.535264368026378, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 39, 40, 41, 42]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33553035, 0.17082138]), radius=0.00028505591083366315, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 41, 42, 43]), model=ScalarModel(intercept=0.24224023352414065, linear_terms=array([-1.87742787e-06, -5.21106061e-06]), square_terms=array([[3.04573962e-09, 8.14917983e-08], + [8.14917983e-08, 6.83492010e-06]]), scale=0.00028505591083366315, shift=array([5.33553035, 0.17082138])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=44, candidate_x=array([5.33576708, 0.17098362]), index=41, x=array([5.33553035, 0.17082138]), fval=0.2422306308030337, rho=-8.092327093834392, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 41, 42, 43]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33553035, 0.17082138]), radius=0.00014252795541683157, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 41, 43, 44]), model=ScalarModel(intercept=0.24223295230251193, linear_terms=array([-5.31736790e-07, 2.27975409e-05]), square_terms=array([[7.19415359e-10, 1.86503337e-08], + [1.86503337e-08, 1.61403964e-06]]), scale=0.00014252795541683157, shift=array([5.33553035, 0.17082138])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.24222997040159935, linear_terms=array([5.08790955e-06, 9.87341516e-06]), square_terms=array([[2.55137299e-09, 5.67163305e-08], + [5.67163305e-08, 6.70555493e-06]]), scale=0.00028505591083366315, shift=array([5.33553886, 0.17067911])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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model_indices=array([35, 41, 43, 44, 45, 46]), model=ScalarModel(intercept=0.24221820849154518, linear_terms=array([-8.49124020e-07, 1.52697526e-05]), square_terms=array([[7.31788970e-10, 1.94307162e-08], + [1.94307162e-08, 1.63646545e-06]]), scale=0.00014252795541683157, shift=array([5.33553886, 0.17067911])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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46, 47]), model=ScalarModel(intercept=0.24221648568928017, linear_terms=array([7.61123169e-06, 1.35871487e-05]), square_terms=array([[2.37686475e-09, 4.53613218e-08], + [4.53613218e-08, 6.64587957e-06]]), scale=0.00028505591083366315, shift=array([5.33555459, 0.17053745])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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linear_terms=array([-1.17163675e-06, 6.22141261e-06]), square_terms=array([[7.43556893e-10, 1.99248388e-08], + [1.99248388e-08, 1.65585538e-06]]), scale=0.00014252795541683157, shift=array([5.33555459, 0.17053745])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], 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'WealthShare': 0.17082137941624043}, {'CRRA': 5.336665303591373, 'WealthShare': 0.17028845981607194}, {'CRRA': 5.334960873512694, 'WealthShare': 0.17089262349762813}, {'CRRA': 5.335767080396652, 'WealthShare': 0.170983623728545}, {'CRRA': 5.3355388649474635, 'WealthShare': 0.1706791061179343}, {'CRRA': 5.335333663634032, 'WealthShare': 0.17047473426234255}, {'CRRA': 5.335554589018792, 'WealthShare': 0.17053744817902458}, {'CRRA': 5.335343081312356, 'WealthShare': 0.17034634254745087}, {'CRRA': 5.33559928332507, 'WealthShare': 0.17040210920806037}, {'CRRA': 5.335580340581636, 'WealthShare': 0.1706038967420634}, {'CRRA': 5.335454088688302, 'WealthShare': 0.17053748476047093}, {'CRRA': 5.335633144546121, 'WealthShare': 0.17065220536865475}, {'CRRA': 5.335596501563392, 'WealthShare': 0.170572140462395}, {'CRRA': 5.335595445166626, 'WealthShare': 0.1705940558344041}, {'CRRA': 5.335583910614445, 'WealthShare': 0.17061205807039284}, {'CRRA': 5.335577372664163, 'WealthShare': 0.1706005756625005}], 'criterion': [0.2500249942408325, 1.1791007356386276, 0.9197651867022865, 1.2595025990222253, 33.4909498055013, 1.4882139243569137, 0.9530633832431904, 1.2595025990222253, 36.978934548688066, 26.44344185974536, 41.385698106382975, 3.0981667931180743, 42.608449629101216, 0.2728700599071999, 0.3342173205988753, 0.29198968043547885, 0.2481648865140715, 0.24373445213195505, 0.25114503099349234, 0.24406213167656582, 0.24489531582637777, 0.24315780951732555, 0.24426227832547462, 0.24453862473009094, 0.24306497226632034, 0.24285876672075696, 0.24259437399648875, 0.24243354455545987, 0.25079678704331854, 0.24441635199641626, 0.24414569958048554, 0.2439788766246561, 0.24225547458407035, 0.24394629290622924, 0.24227514910123488, 0.24223921155653808, 0.24227947343936865, 0.24227295597179582, 0.24250574148919085, 0.24223949661361607, 0.24225288167948408, 0.2422306308030337, 0.2422548267052162, 0.24224813492764274, 0.24225797040581512, 0.242202396887387, 0.24220875273143883, 0.2422004189428712, 0.24224389187159454, 0.24223135088965633, 0.2421984742592525, 0.24219915623837365, 0.24220019073252083, 0.24219911551794257, 0.2421985513731777, 0.24219868532057515, 0.24219838635344662], 'runtime': [0.0, 1.296167903999958, 1.3449491520000265, 1.3858779940001114, 1.4279043609999462, 1.4686698719997366, 1.514770218999729, 1.5592568910001319, 1.6084964980000223, 1.6590743809997548, 1.7047839209999438, 1.761857082000006, 1.8163866790000611, 3.181013745999735, 4.325823927999863, 5.600623542999983, 6.755316591999872, 7.917994205000014, 9.077396046000104, 10.294875745999889, 11.503319416000068, 12.720127907000006, 14.032752316999904, 15.207649181999841, 16.429454532999898, 17.670099559999926, 18.8472972149998, 20.135531741999785, 21.277609068999936, 22.423084480999933, 23.553762927999742, 24.684132019000117, 25.817971458000102, 26.998730913000145, 28.1869263640001, 29.34812033800017, 30.50678410599994, 31.65761656199993, 32.799299537000024, 33.94402660300011, 35.26958909099994, 36.47545633799973, 37.66156821799996, 38.83101894899983, 39.99145682000017, 41.17240058000016, 42.34739114900003, 43.51756579399989, 44.678886351000074, 45.83479911199993, 46.989458055999876, 48.12758910999992, 49.42098368999996, 50.618441345000065, 51.84377754599973, 52.98982088799994, 54.21043696800007], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45]}, 'multistart_info': {...}}], 'exploration_sample': array([[ 5.35399092, 0.17103024], + [ 7.00625 , 0.19375 ], + [12.9125 , 0.1325 ], + [ 4.64375 , 0.31625 ], + [ 8.1875 , 0.3775 ], + [15.275 , 0.255 ], + [17.046875 , 0.224375 ], + [11.73125 , 0.43875 ], + [18.81875 , 0.07125 ], + [10.55 , 0.5 ], + [ 9.36875 , 0.56125 ], + [16.45625 , 0.68375 ], + [ 2.871875 , 0.469375 ], + [ 7.596875 , 0.714375 ], + [14.09375 , 0.80625 ], + [ 3.4625 , 0.6225 ], + [17.6375 , 0.8675 ], + [ 5.825 , 0.745 ], + [12.321875 , 0.959375 ], + [ 2.28125 , 0.92875 ]]), 'exploration_results': array([2.42227546e-01, 3.27384376e-01, 1.14034055e+00, 1.50312179e+00, + 1.79325436e+00, 2.08879824e+00, 2.54067782e+00, 2.64114350e+00, + 2.73814230e+00, 3.83228381e+00, 6.46248546e+00, 9.30118040e+00, + 1.59966394e+01, 3.03715662e+01, 3.38771067e+01, 4.26926083e+01, + 5.84656546e+01, 5.90505617e+01, 5.22500282e+02, 9.01924413e+02])}}" diff --git a/content/tables/min/IndShockSub(Labor)Market_estimate_results.csv b/content/tables/min/IndShockSub(Labor)Market_estimate_results.csv new file mode 100644 index 0000000..4178f42 --- /dev/null +++ b/content/tables/min/IndShockSub(Labor)Market_estimate_results.csv @@ -0,0 +1,3432 @@ +CRRA,7.130704194162657 +DiscFac,1.1 +time_to_estimate,125.13451099395752 +params,"{'CRRA': 7.130704194162657, 'DiscFac': 1.1}" +criterion,328.6857256480938 +start_criterion,326.59347458169844 +start_params,"{'CRRA': 7.130705399496962, 'DiscFac': 1.1}" +algorithm,multistart_tranquilo_ls +direction,minimize +n_free,2 +message,Absolute criterion change smaller than tolerance. +success, +n_criterion_evaluations, +n_derivative_evaluations, +n_iterations, +history,"{'params': [{'CRRA': 7.1307053994969625, 'DiscFac': 1.1}, {'CRRA': 6.510767198704628, 'DiscFac': 0.5}, {'CRRA': 7.76264771174752, 'DiscFac': 0.667509824412047}, {'CRRA': 6.498763087246405, 'DiscFac': 0.7646705193953846}, {'CRRA': 7.7572821825655724, 'DiscFac': 1.1}, {'CRRA': 7.76264771174752, 'DiscFac': 0.5136853258814519}, {'CRRA': 7.273282253690742, 'DiscFac': 0.5}, {'CRRA': 6.563689963434692, 'DiscFac': 1.1}, {'CRRA': 7.76264771174752, 'DiscFac': 0.8576010401343696}, {'CRRA': 7.541774270837221, 'DiscFac': 1.1}, {'CRRA': 6.498763087246405, 'DiscFac': 0.9393508821937643}, {'CRRA': 6.8257661935251335, 'DiscFac': 0.5}, {'CRRA': 6.975591610867178, 'DiscFac': 1.1}, {'CRRA': 7.76264771174752, 'DiscFac': 1.1}, {'CRRA': 7.446676555622242, 'DiscFac': 1.1}, {'CRRA': 7.288690977559602, 'DiscFac': 1.1}, {'CRRA': 7.1307053994969625, 'DiscFac': 1.0210072109686805}, {'CRRA': 7.091209004981303, 'DiscFac': 1.1}, {'CRRA': 7.150453596754792, 'DiscFac': 1.1}, {'CRRA': 7.1307053994969625, 'DiscFac': 1.0901259013710851}, {'CRRA': 7.125768350182505, 'DiscFac': 1.1}, {'CRRA': 7.133173924154192, 'DiscFac': 1.1}, {'CRRA': 7.1307053994969625, 'DiscFac': 1.0987657376713857}, {'CRRA': 7.130088268332655, 'DiscFac': 1.1}, {'CRRA': 7.1310139650791164, 'DiscFac': 1.1}, {'CRRA': 7.1307054098923786, 'DiscFac': 1.0998457172089233}, {'CRRA': 7.130628258101424, 'DiscFac': 1.1}, {'CRRA': 7.1307439701947315, 'DiscFac': 1.1}, {'CRRA': 7.130705399615011, 'DiscFac': 1.0999807146511156}, {'CRRA': 7.13069575682252, 'DiscFac': 1.1}, {'CRRA': 7.1307102208341835, 'DiscFac': 1.1}, {'CRRA': 7.130705399497495, 'DiscFac': 1.0999975893313896}, {'CRRA': 7.130704194162657, 'DiscFac': 1.1}], 'criterion': [328.68572765353184, 1206.2706415190628, 1148.075585346323, 1156.4087165998333, 331.973305021638, 1171.8842022006597, 1185.2077132955035, 331.393555989452, 1079.6701064572155, 330.3765261616103, 1034.509095781267, 1196.921365706314, 328.7368186327918, 332.02270492207424, 329.8967246379625, 329.18440465332003, 637.2317168949542, 328.7279849397648, 328.7538868285242, 336.2720412596524, 328.71228088422583, 328.69313869584244, 329.0616996890574, 328.68663665216843, 328.68620013808686, 328.722382669286, 328.6858270336297, 328.68579184091817, 328.69048423225576, 328.685735803517, 328.6857356755372, 328.6861565104462, 328.6857256480938], 'runtime': [0.0, 1.8174836779999168, 1.8582274160000907, 1.8944267730000774, 1.9308879210000214, 1.9681783720000112, 2.0083805479998773, 2.047968998999977, 2.087882699999909, 2.1254185349998806, 2.164312073000019, 2.2041665560000183, 2.2673407219999717, 4.470447659000001, 6.200013716000058, 7.911075711999956, 9.640005885999926, 11.525267746000054, 13.279075794999926, 15.050836525000022, 16.736254687999917, 18.404302440000038, 20.087218322999888, 21.787123633999954, 23.55556847799994, 25.282935239999915, 26.979193439000028, 28.675701849999996, 30.349664291999943, 32.026717970999925, 33.69924636699989, 35.48895722299994, 37.13772394700004], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21]}" +convergence_report, +multistart_info,"{'start_parameters': [{'CRRA': 7.1307053994969625, 'DiscFac': 1.1}, {'CRRA': 8.65116292089478, 'DiscFac': 1.0945082521472478}], 'local_optima': [Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 6.101e-09** 6.101e-09** +relative_params_change 1.69e-07* 1.69e-07* +absolute_criterion_change 2.005e-06* 2.005e-06* +absolute_params_change 1.205e-06* 1.205e-06* + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 1.804e-06* 0.01593 +relative_params_change 0.0001065 0.1347 +absolute_criterion_change 0.000593 5.236 +absolute_params_change 0.0007487 0.9471 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 7.130705399496962, 'DiscFac': 1.1}, {'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 17.6375, 'DiscFac': 1.0250000000000001}, {'CRRA': 14.093749999999998, 'DiscFac': 0.9875}, {'CRRA': 16.45625, 'DiscFac': 0.9125000000000001}, {'CRRA': 2.28125, 'DiscFac': 1.0625}, {'CRRA': 18.81875, 'DiscFac': 0.5375}, {'CRRA': 17.046875, 'DiscFac': 0.6312500000000001}, {'CRRA': 15.274999999999999, 'DiscFac': 0.65}, {'CRRA': 7.596874999999999, 'DiscFac': 0.9312500000000001}, {'CRRA': 11.73125, 'DiscFac': 0.7625000000000001}, {'CRRA': 10.549999999999999, 'DiscFac': 0.8}, {'CRRA': 12.9125, 'DiscFac': 0.575}, {'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 9.368749999999999, 'DiscFac': 0.8375}, {'CRRA': 8.1875, 'DiscFac': 0.7250000000000001}, {'CRRA': 7.00625, 'DiscFac': 0.6125}, {'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 2.871875, 'DiscFac': 0.78125}], 'exploration_results': array([ 328.68572765, 420.32021458, 528.04271383, 663.30843919, + 755.01356649, 860.62318106, 905.15922637, 916.6930808 , + 958.0124336 , 1004.84993533, 1006.66630029, 1027.0399295 , + 1032.30721934, 1038.25662345, 1043.08294325, 1119.86544023, + 1173.59414907, 1217.73888335, 1230.5461544 , 1263.48004797])}" +algorithm_output,"{'states': [State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=0.7130705399496963, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=328.68572765353184, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=0, candidate_x=array([7.1307054, 1.1 ]), index=0, x=array([7.1307054, 1.1 ]), fval=328.68572765353184, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=0.7130705399496963, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=684.6285593668615, linear_terms=array([ -22.9819671 , -547.66412791]), square_terms=array([[ 0.6439968 , 16.18455499], + [ 16.18455499, 423.45480142]]), scale=array([0.63194231, 0.3 ]), shift=array([7.1307054, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=13, candidate_x=array([7.76264771, 1.1 ]), index=0, x=array([7.1307054, 1.1 ]), fval=328.68572765353184, rho=-0.5153303589217099, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=0.35653526997484813, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 4, 6, 7, 9, 10, 11, 12, 13]), model=ScalarModel(intercept=430.1275837731621, linear_terms=array([ -32.41979258, -147.64037513]), square_terms=array([[ 4.91529106, 23.07088099], + [ 23.07088099, 109.76970458]]), scale=array([0.31597116, 0.15798558]), shift=array([7.1307054 , 0.94201442])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=14, candidate_x=array([7.44667656, 1.1 ]), index=0, x=array([7.1307054, 1.1 ]), fval=328.68572765353184, rho=-0.17572924528129888, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 4, 6, 7, 9, 10, 11, 12, 13]), old_indices_discarded=array([1, 2, 3, 5, 8]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=0.17826763498742407, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 4, 6, 7, 9, 10, 12, 13, 14]), model=ScalarModel(intercept=373.9288270957216, linear_terms=array([-11.86946609, -53.28346797]), square_terms=array([[ 1.46182094, 6.88656047], + [ 6.88656047, 32.81336042]]), scale=array([0.15798558, 0.07899279]), shift=array([7.1307054 , 1.02100721])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=15, candidate_x=array([7.28869098, 1.1 ]), index=0, x=array([7.1307054, 1.1 ]), fval=328.68572765353184, rho=-0.11728070749441931, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 4, 6, 7, 9, 10, 12, 13, 14]), old_indices_discarded=array([ 1, 2, 3, 5, 8, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=0.08913381749371203, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 9, 12, 14, 15]), model=ScalarModel(intercept=82.18005561986612, linear_terms=array([4.37309415e-02, 1.64360111e+02]), square_terms=array([[9.69642782e-02, 4.37309415e-02], + [4.37309415e-02, 1.64360111e+02]]), scale=array([0.07899279, 0.03949639]), shift=array([7.1307054 , 1.06050361])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=16, candidate_x=array([7.1307054 , 1.02100721]), index=0, x=array([7.1307054, 1.1 ]), fval=328.68572765353184, rho=-0.9386279521050687, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 9, 12, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=0.04456690874685602, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 15, 16]), model=ScalarModel(intercept=351.29343542327797, linear_terms=array([ 1.03610956, -40.75215842]), square_terms=array([[ 2.66252145e-02, -9.79726420e-01], + [-9.79726420e-01, 3.63737347e+01]]), scale=array([0.03949639, 0.0197482 ]), shift=array([7.1307054, 1.0802518])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=17, candidate_x=array([7.091209, 1.1 ]), index=0, x=array([7.1307054, 1.1 ]), fval=328.68572765353184, rho=-0.9811182562540376, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 15, 16]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=0.02228345437342801, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 16, 17]), model=ScalarModel(intercept=335.45431480648267, linear_terms=array([ 0.23079179, -11.31139594]), square_terms=array([[ 6.66914167e-03, -2.45251292e-01], + [-2.45251292e-01, 9.08561757e+00]]), scale=array([0.0197482, 0.0098741]), shift=array([7.1307054, 1.0901259])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=18, candidate_x=array([7.1504536, 1.1 ]), index=0, x=array([7.1307054, 1.1 ]), fval=328.68572765353184, rho=-6.126705629830687, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=0.011141727186714004, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=82.17991947367543, linear_terms=array([2.05037269e-03, 1.64359839e+02]), square_terms=array([[1.67645145e-03, 2.05037269e-03], + [2.05037269e-03, 1.64359839e+02]]), scale=array([0.0098741 , 0.00493705]), shift=array([7.1307054 , 1.09506295])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=19, candidate_x=array([7.1307054, 1.0901259]), index=0, x=array([7.1307054, 1.1 ]), fval=328.68572765353184, rho=-0.02307836772872069, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=0.005570863593357002, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 18, 19]), model=ScalarModel(intercept=329.4218198142641, linear_terms=array([ 0.0341575 , -1.12292091]), square_terms=array([[ 4.25689746e-04, -1.79690890e-02], + [-1.79690890e-02, 7.73657494e-01]]), scale=array([0.00493705, 0.00246852]), shift=array([7.1307054 , 1.09753148])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=20, candidate_x=array([7.12576835, 1.1 ]), index=0, x=array([7.1307054, 1.1 ]), fval=328.68572765353184, rho=-1.6621148239306327, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=0.002785431796678501, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 19, 20]), model=ScalarModel(intercept=328.95706654716514, linear_terms=array([-0.0087333 , -0.36804608]), square_terms=array([[ 1.04746291e-04, -4.43856766e-03], + [-4.43856766e-03, 1.93414373e-01]]), scale=array([0.00246852, 0.00123426]), shift=array([7.1307054 , 1.09876574])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=21, candidate_x=array([7.13317392, 1.1 ]), index=0, x=array([7.1307054, 1.1 ]), fval=328.68572765353184, rho=-0.5648877335859794, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=0.0013927158983392505, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 20, 21]), model=ScalarModel(intercept=82.17363166997947, linear_terms=array([-1.82653515e-03, 1.64347263e+02]), square_terms=array([[ 2.60901900e-05, -1.82653515e-03], + [-1.82653515e-03, 1.64347263e+02]]), scale=array([0.00123426, 0.00061713]), shift=array([7.1307054 , 1.09938287])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=22, candidate_x=array([7.1307054 , 1.09876574]), index=0, x=array([7.1307054, 1.1 ]), fval=328.68572765353184, rho=-0.0011438341834382178, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=0.0006963579491696253, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 21, 22]), model=ScalarModel(intercept=328.7606960199903, linear_terms=array([ 0.00212365, -0.08130991]), square_terms=array([[ 6.50959970e-06, -2.83906053e-04], + [-2.83906053e-04, 1.26830949e-02]]), scale=array([0.00061713, 0.00030857]), shift=array([7.1307054 , 1.09969143])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=23, candidate_x=array([7.13008827, 1.1 ]), index=0, x=array([7.1307054, 1.1 ]), fval=328.68572765353184, rho=-0.4949661202782044, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=0.00034817897458481263, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 22, 23]), model=ScalarModel(intercept=328.72162644989186, linear_terms=array([-0.0003794 , -0.03748418]), square_terms=array([[ 1.74425143e-06, -7.33599165e-05], + [-7.33599165e-05, 3.17077374e-03]]), scale=array([0.00030857, 0.00015428]), shift=array([7.1307054 , 1.09984572])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=24, candidate_x=array([7.13101397, 1.1 ]), index=0, x=array([7.1307054, 1.1 ]), fval=328.68572765353184, rho=-1.0455905987879714, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=0.00017408948729240632, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 23, 24]), model=ScalarModel(intercept=82.17153095327862, linear_terms=array([-4.71627572e-05, 1.64343062e+02]), square_terms=array([[ 4.35279396e-07, -4.71627572e-05], + [-4.71627572e-05, 1.64343062e+02]]), scale=array([1.54282791e-04, 7.71413955e-05]), shift=array([7.1307054 , 1.09992286])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), 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State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=8.704474364620316e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 24, 25]), model=ScalarModel(intercept=328.69458979956903, linear_terms=array([ 0.00012252, -0.00896268]), square_terms=array([[ 1.08524278e-07, -4.61317406e-06], + [-4.61317406e-06, 2.01068872e-04]]), scale=array([7.71413955e-05, 3.85706978e-05]), shift=array([7.1307054 , 1.09996143])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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fval=328.68572765353184, rho=-1.2928017748452014, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=2.176118591155079e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 26, 27]), model=ScalarModel(intercept=82.17144411021586, linear_terms=array([-4.28457592e-06, 1.64342888e+02]), square_terms=array([[ 6.71814178e-09, -4.28457592e-06], + [-4.28457592e-06, 1.64342888e+02]]), scale=array([1.92853489e-05, 9.64267444e-06]), shift=array([7.1307054 , 1.09999036])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 27, 28]), model=ScalarModel(intercept=328.6869119178632, linear_terms=array([ 1.61165492e-05, -1.18589110e-03]), square_terms=array([[ 1.69027365e-09, -7.30831108e-08], + [-7.30831108e-08, 3.25353312e-06]]), scale=array([9.64267444e-06, 4.82133722e-06]), shift=array([7.1307054 , 1.09999518])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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1.09999759])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=30, candidate_x=array([7.13071022, 1.1 ]), index=0, x=array([7.1307054, 1.1 ]), fval=328.68572765353184, rho=-1.9688977200541027, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=2.7201482389438487e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 29, 30]), model=ScalarModel(intercept=82.17143320942247, linear_terms=array([-1.54616964e-07, 1.64342866e+02]), square_terms=array([[ 1.04999460e-10, -1.54616964e-07], + [-1.54616964e-07, 1.64342866e+02]]), scale=array([2.41066861e-06, 1.20533431e-06]), shift=array([7.1307054 , 1.09999879])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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model=ScalarModel(intercept=328.68583478989285, linear_terms=array([ 2.00660539e-06, -1.07162317e-04]), square_terms=array([[ 2.64108489e-11, -1.15674817e-09], + [-1.15674817e-09, 5.19115862e-08]]), scale=array([1.20533431e-06, 6.02667153e-07]), shift=array([7.1307054, 1.0999994])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=32, candidate_x=array([7.13070419, 1.1 ]), index=32, x=array([7.13070419, 1.1 ]), fval=328.6857256480938, rho=1.000001303855134, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=1.2053343052542687e-06, relative_step_length=0.8862269254283461, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 33 entries., 'multistart_info': {'start_parameters': [array([7.1307054, 1.1 ]), array([8.65116292, 1.09450825])], 'local_optima': [{'solution_x': array([7.13070419, 1.1 ]), 'solution_criterion': 328.6857256480938, 'states': [State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=0.7130705399496963, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=328.68572765353184, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=0, candidate_x=array([7.1307054, 1.1 ]), index=0, x=array([7.1307054, 1.1 ]), fval=328.68572765353184, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=0.7130705399496963, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=684.6285593668615, linear_terms=array([ -22.9819671 , -547.66412791]), square_terms=array([[ 0.6439968 , 16.18455499], + [ 16.18455499, 423.45480142]]), scale=array([0.63194231, 0.3 ]), shift=array([7.1307054, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=13, candidate_x=array([7.76264771, 1.1 ]), index=0, x=array([7.1307054, 1.1 ]), fval=328.68572765353184, rho=-0.5153303589217099, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=0.35653526997484813, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 4, 6, 7, 9, 10, 11, 12, 13]), model=ScalarModel(intercept=430.1275837731621, linear_terms=array([ -32.41979258, -147.64037513]), square_terms=array([[ 4.91529106, 23.07088099], + [ 23.07088099, 109.76970458]]), scale=array([0.31597116, 0.15798558]), shift=array([7.1307054 , 0.94201442])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=14, candidate_x=array([7.44667656, 1.1 ]), index=0, x=array([7.1307054, 1.1 ]), fval=328.68572765353184, rho=-0.17572924528129888, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 4, 6, 7, 9, 10, 11, 12, 13]), old_indices_discarded=array([1, 2, 3, 5, 8]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=0.17826763498742407, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 4, 6, 7, 9, 10, 12, 13, 14]), model=ScalarModel(intercept=373.9288270957216, linear_terms=array([-11.86946609, -53.28346797]), square_terms=array([[ 1.46182094, 6.88656047], + [ 6.88656047, 32.81336042]]), scale=array([0.15798558, 0.07899279]), shift=array([7.1307054 , 1.02100721])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=15, candidate_x=array([7.28869098, 1.1 ]), index=0, x=array([7.1307054, 1.1 ]), fval=328.68572765353184, rho=-0.11728070749441931, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 4, 6, 7, 9, 10, 12, 13, 14]), old_indices_discarded=array([ 1, 2, 3, 5, 8, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=0.08913381749371203, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 9, 12, 14, 15]), model=ScalarModel(intercept=82.18005561986612, linear_terms=array([4.37309415e-02, 1.64360111e+02]), square_terms=array([[9.69642782e-02, 4.37309415e-02], + [4.37309415e-02, 1.64360111e+02]]), scale=array([0.07899279, 0.03949639]), shift=array([7.1307054 , 1.06050361])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=16, candidate_x=array([7.1307054 , 1.02100721]), index=0, x=array([7.1307054, 1.1 ]), fval=328.68572765353184, rho=-0.9386279521050687, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 9, 12, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=0.04456690874685602, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 15, 16]), model=ScalarModel(intercept=351.29343542327797, linear_terms=array([ 1.03610956, -40.75215842]), square_terms=array([[ 2.66252145e-02, -9.79726420e-01], + [-9.79726420e-01, 3.63737347e+01]]), scale=array([0.03949639, 0.0197482 ]), shift=array([7.1307054, 1.0802518])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=17, candidate_x=array([7.091209, 1.1 ]), index=0, x=array([7.1307054, 1.1 ]), fval=328.68572765353184, rho=-0.9811182562540376, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 15, 16]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=0.02228345437342801, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 16, 17]), model=ScalarModel(intercept=335.45431480648267, linear_terms=array([ 0.23079179, -11.31139594]), square_terms=array([[ 6.66914167e-03, -2.45251292e-01], + [-2.45251292e-01, 9.08561757e+00]]), scale=array([0.0197482, 0.0098741]), shift=array([7.1307054, 1.0901259])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=18, candidate_x=array([7.1504536, 1.1 ]), index=0, x=array([7.1307054, 1.1 ]), fval=328.68572765353184, rho=-6.126705629830687, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=0.011141727186714004, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=82.17991947367543, linear_terms=array([2.05037269e-03, 1.64359839e+02]), square_terms=array([[1.67645145e-03, 2.05037269e-03], + [2.05037269e-03, 1.64359839e+02]]), scale=array([0.0098741 , 0.00493705]), shift=array([7.1307054 , 1.09506295])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=19, candidate_x=array([7.1307054, 1.0901259]), index=0, x=array([7.1307054, 1.1 ]), fval=328.68572765353184, rho=-0.02307836772872069, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=0.005570863593357002, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 18, 19]), model=ScalarModel(intercept=329.4218198142641, linear_terms=array([ 0.0341575 , -1.12292091]), square_terms=array([[ 4.25689746e-04, -1.79690890e-02], + [-1.79690890e-02, 7.73657494e-01]]), scale=array([0.00493705, 0.00246852]), shift=array([7.1307054 , 1.09753148])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=20, candidate_x=array([7.12576835, 1.1 ]), index=0, x=array([7.1307054, 1.1 ]), fval=328.68572765353184, rho=-1.6621148239306327, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=0.002785431796678501, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 19, 20]), model=ScalarModel(intercept=328.95706654716514, linear_terms=array([-0.0087333 , -0.36804608]), square_terms=array([[ 1.04746291e-04, -4.43856766e-03], + [-4.43856766e-03, 1.93414373e-01]]), scale=array([0.00246852, 0.00123426]), shift=array([7.1307054 , 1.09876574])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=21, candidate_x=array([7.13317392, 1.1 ]), index=0, x=array([7.1307054, 1.1 ]), fval=328.68572765353184, rho=-0.5648877335859794, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=0.0013927158983392505, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 20, 21]), model=ScalarModel(intercept=82.17363166997947, linear_terms=array([-1.82653515e-03, 1.64347263e+02]), square_terms=array([[ 2.60901900e-05, -1.82653515e-03], + [-1.82653515e-03, 1.64347263e+02]]), scale=array([0.00123426, 0.00061713]), shift=array([7.1307054 , 1.09938287])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=22, candidate_x=array([7.1307054 , 1.09876574]), index=0, x=array([7.1307054, 1.1 ]), fval=328.68572765353184, rho=-0.0011438341834382178, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=0.0006963579491696253, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 21, 22]), model=ScalarModel(intercept=328.7606960199903, linear_terms=array([ 0.00212365, -0.08130991]), square_terms=array([[ 6.50959970e-06, -2.83906053e-04], + [-2.83906053e-04, 1.26830949e-02]]), scale=array([0.00061713, 0.00030857]), shift=array([7.1307054 , 1.09969143])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=23, candidate_x=array([7.13008827, 1.1 ]), index=0, x=array([7.1307054, 1.1 ]), fval=328.68572765353184, rho=-0.4949661202782044, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=0.00034817897458481263, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 22, 23]), model=ScalarModel(intercept=328.72162644989186, linear_terms=array([-0.0003794 , -0.03748418]), square_terms=array([[ 1.74425143e-06, -7.33599165e-05], + [-7.33599165e-05, 3.17077374e-03]]), scale=array([0.00030857, 0.00015428]), shift=array([7.1307054 , 1.09984572])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=24, candidate_x=array([7.13101397, 1.1 ]), index=0, x=array([7.1307054, 1.1 ]), fval=328.68572765353184, rho=-1.0455905987879714, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=0.00017408948729240632, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 23, 24]), model=ScalarModel(intercept=82.17153095327862, linear_terms=array([-4.71627572e-05, 1.64343062e+02]), square_terms=array([[ 4.35279396e-07, -4.71627572e-05], + [-4.71627572e-05, 1.64343062e+02]]), scale=array([1.54282791e-04, 7.71413955e-05]), shift=array([7.1307054 , 1.09992286])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=25, candidate_x=array([7.13070541, 1.09984572]), index=0, x=array([7.1307054, 1.1 ]), fval=328.68572765353184, rho=-0.00011151981510175064, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=8.704474364620316e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 24, 25]), model=ScalarModel(intercept=328.69458979956903, linear_terms=array([ 0.00012252, -0.00896268]), square_terms=array([[ 1.08524278e-07, -4.61317406e-06], + [-4.61317406e-06, 2.01068872e-04]]), scale=array([7.71413955e-05, 3.85706978e-05]), shift=array([7.1307054 , 1.09996143])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=26, candidate_x=array([7.13062826, 1.1 ]), index=0, x=array([7.1307054, 1.1 ]), fval=328.68572765353184, rho=-0.8432774106898498, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=4.352237182310158e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 25, 26]), model=ScalarModel(intercept=328.6901335966009, linear_terms=array([-4.85159641e-05, -4.43107668e-03]), square_terms=array([[ 2.68136204e-08, -1.14727110e-06], + [-1.14727110e-06, 5.02672183e-05]]), scale=array([3.85706978e-05, 1.92853489e-05]), shift=array([7.1307054 , 1.09998071])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=27, candidate_x=array([7.13074397, 1.1 ]), index=0, x=array([7.1307054, 1.1 ]), fval=328.68572765353184, rho=-1.2928017748452014, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=2.176118591155079e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 26, 27]), model=ScalarModel(intercept=82.17144411021586, linear_terms=array([-4.28457592e-06, 1.64342888e+02]), square_terms=array([[ 6.71814178e-09, -4.28457592e-06], + [-4.28457592e-06, 1.64342888e+02]]), scale=array([1.92853489e-05, 9.64267444e-06]), shift=array([7.1307054 , 1.09999036])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7130705399496963, shift=array([7.1307054, 1.1 ])), candidate_index=28, candidate_x=array([7.1307054 , 1.09998071]), index=0, x=array([7.1307054, 1.1 ]), fval=328.68572765353184, rho=-1.4471507636928303e-05, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=1.0880592955775395e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 27, 28]), model=ScalarModel(intercept=328.6869119178632, linear_terms=array([ 1.61165492e-05, -1.18589110e-03]), square_terms=array([[ 1.69027365e-09, -7.30831108e-08], + [-7.30831108e-08, 3.25353312e-06]]), scale=array([9.64267444e-06, 4.82133722e-06]), shift=array([7.1307054 , 1.09999518])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 28, 29]), model=ScalarModel(intercept=328.68631937904274, linear_terms=array([-4.05637097e-06, -5.92132203e-04]), square_terms=array([[ 4.19075878e-10, -1.82023087e-08], + [-1.82023087e-08, 8.13383279e-07]]), scale=array([4.82133722e-06, 2.41066861e-06]), shift=array([7.1307054 , 1.09999759])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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old_indices_used=array([ 0, 29, 30]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.1307054, 1.1 ]), radius=1.3600741194719243e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 30, 31]), model=ScalarModel(intercept=328.68583478989285, linear_terms=array([ 2.00660539e-06, -1.07162317e-04]), square_terms=array([[ 2.64108489e-11, -1.15674817e-09], + [-1.15674817e-09, 5.19115862e-08]]), scale=array([1.20533431e-06, 6.02667153e-07]), shift=array([7.1307054, 1.0999994])), vector_model=VectorModel(intercepts=array([ 0.18971506, 0.64530636, 0.88631615, 1.6998268 , + 2.52794186, 3.6447253 , 4.19605438, 1.8984428 , + -0.13233767, -2.54017195, -9.17924131, -13.89044349]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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31.0156482 ], + [ 31.0156482 , 283.64709579]]), scale=array([1.5333787, 0.3 ]), shift=array([8.26781825, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.16466094, 0.38553561, 0.39344118, 0.98377146, + 1.55037222, 2.35627331, 2.5554994 , -0.42663176, + -2.29184792, -4.28626857, -10.06557688, -14.49445188]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.865116292089478, shift=array([8.65116292, 1.09450825])), candidate_index=16, candidate_x=array([9.80119695, 1.1 ]), index=15, x=array([8.26781825, 1.1 ]), fval=337.1356979156533, rho=-0.6621094135020358, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 4, 5, 6, 7, 8, 9, 15]), old_indices_discarded=array([ 2, 3, 10, 11, 12, 13, 14]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.26781825, 1.1 ]), radius=0.865116292089478, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 3, 4, 6, 7, 12, 14, 15]), model=ScalarModel(intercept=667.0619227892128, linear_terms=array([-138.82460985, -425.84473678]), square_terms=array([[ 26.78795585, 85.02464739], + [ 85.02464739, 276.6898722 ]]), scale=array([0.76668935, 0.3 ]), shift=array([8.26781825, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.16466094, 0.38553561, 0.39344118, 0.98377146, + 1.55037222, 2.35627331, 2.5554994 , -0.42663176, + -2.29184792, -4.28626857, -10.06557688, -14.49445188]), linear_terms=array([[0., 0.], + [0., 0.], + 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radius=0.432558146044739, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 3, 6, 7, 10, 11, 12, 15]), model=ScalarModel(intercept=518.531761813198, linear_terms=array([ -96.35860558, -189.44144632]), square_terms=array([[ 22.75383694, 47.30491434], + [ 47.30491434, 100.88841846]]), scale=array([0.38334468, 0.19167234]), shift=array([8.26781825, 0.90832766])), vector_model=VectorModel(intercepts=array([ 0.16466094, 0.38553561, 0.39344118, 0.98377146, + 1.55037222, 2.35627331, 2.5554994 , -0.42663176, + -2.29184792, -4.28626857, -10.06557688, -14.49445188]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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scale=array([0.19167234, 0.09583617]), shift=array([8.26781825, 1.00416383])), vector_model=VectorModel(intercepts=array([ 0.16466094, 0.38553561, 0.39344118, 0.98377146, + 1.55037222, 2.35627331, 2.5554994 , -0.42663176, + -2.29184792, -4.28626857, -10.06557688, -14.49445188]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.865116292089478, shift=array([8.65116292, 1.09450825])), candidate_index=19, candidate_x=array([8.45949058, 1.1 ]), index=15, x=array([8.26781825, 1.1 ]), fval=337.1356979156533, rho=-0.09328886716788783, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 3, 6, 7, 10, 11, 12, 15, 18]), old_indices_discarded=array([ 1, 4, 14, 17]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.26781825, 1.1 ]), radius=0.10813953651118476, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 7, 10, 12, 15, 18, 19]), model=ScalarModel(intercept=465.6908146663486, linear_terms=array([ 2.34840297, -198.24386739]), square_terms=array([[ 2.33813772e-02, -1.77226688e+00], + [-1.77226688e+00, 1.42140315e+02]]), scale=array([0.09583617, 0.04791808]), shift=array([8.26781825, 1.05208192])), vector_model=VectorModel(intercepts=array([ 0.16466094, 0.38553561, 0.39344118, 0.98377146, + 1.55037222, 2.35627331, 2.5554994 , -0.42663176, + -2.29184792, -4.28626857, -10.06557688, -14.49445188]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], 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]), radius=0.2162790730223695, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 7, 10, 12, 15, 18, 19, 20]), model=ScalarModel(intercept=446.8884033608795, linear_terms=array([-50.69148589, -80.13196328]), square_terms=array([[ 9.83245025, 17.0582945 ], + [17.0582945 , 30.87002433]]), scale=array([0.19167234, 0.09583617]), shift=array([8.17198208, 1.00416383])), vector_model=VectorModel(intercepts=array([ 0.16466094, 0.38553561, 0.39344118, 0.98377146, + 1.55037222, 2.35627331, 2.5554994 , -0.42663176, + -2.29184792, -4.28626857, -10.06557688, -14.49445188]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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1.45186265e+02]]), scale=array([0.09583617, 0.04791808]), shift=array([8.17198208, 1.05208192])), vector_model=VectorModel(intercepts=array([ 0.16466094, 0.38553561, 0.39344118, 0.98377146, + 1.55037222, 2.35627331, 2.5554994 , -0.42663176, + -2.29184792, -4.28626857, -10.06557688, -14.49445188]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.865116292089478, shift=array([8.65116292, 1.09450825])), candidate_index=22, candidate_x=array([8.07614591, 1.1 ]), index=22, x=array([8.07614591, 1.1 ]), fval=335.0379978894075, rho=1.5910112912334122, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 7, 10, 12, 15, 18, 19, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.09583616896219382, relative_step_length=0.8862269254527607, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.07614591, 1.1 ]), radius=0.2162790730223695, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 12, 15, 19, 20, 21, 22]), model=ScalarModel(intercept=450.0658505748284, linear_terms=array([-88.59696893, -76.20809182]), square_terms=array([[29.52819021, 27.95116491], + [27.95116491, 27.65884117]]), scale=array([0.19167234, 0.09583617]), shift=array([8.07614591, 1.00416383])), vector_model=VectorModel(intercepts=array([ 0.16466094, 0.38553561, 0.39344118, 0.98377146, + 1.55037222, 2.35627331, 2.5554994 , -0.42663176, + -2.29184792, -4.28626857, -10.06557688, -14.49445188]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.865116292089478, shift=array([8.65116292, 1.09450825])), candidate_index=23, candidate_x=array([8.26781825, 1.1 ]), index=22, x=array([8.07614591, 1.1 ]), fval=335.0379978894075, rho=-0.045719744886912835, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 10, 12, 15, 19, 20, 21, 22]), old_indices_discarded=array([ 0, 1, 4, 6, 11, 14, 17, 18]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.07614591, 1.1 ]), radius=0.10813953651118476, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 12, 15, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=458.8313103700649, linear_terms=array([ 1.51391966, -193.65407848]), square_terms=array([[ 1.30594814e-02, -1.20163564e+00], + [-1.20163564e+00, 1.42908571e+02]]), scale=array([0.09583617, 0.04791808]), shift=array([8.07614591, 1.05208192])), vector_model=VectorModel(intercepts=array([ 0.16466094, 0.38553561, 0.39344118, 0.98377146, + 1.55037222, 2.35627331, 2.5554994 , -0.42663176, + -2.29184792, -4.28626857, -10.06557688, -14.49445188]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.865116292089478, shift=array([8.65116292, 1.09450825])), candidate_index=24, candidate_x=array([7.98030974, 1.1 ]), index=24, x=array([7.98030974, 1.1 ]), fval=333.9565001654597, rho=3.537146752181767, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 7, 10, 12, 15, 19, 20, 21, 22, 23]), old_indices_discarded=array([3]), step_length=0.09583616896219382, relative_step_length=0.8862269254527607, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.98030974, 1.1 ]), radius=0.2162790730223695, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 12, 15, 20, 21, 22, 23, 24]), model=ScalarModel(intercept=722.4278442360151, linear_terms=array([ 7.34516855, -678.26881272]), square_terms=array([[ 8.14141338e-02, -6.45484828e+00], + [-6.45484828e+00, 5.82624548e+02]]), scale=array([0.19167234, 0.09583617]), shift=array([7.98030974, 1.00416383])), vector_model=VectorModel(intercepts=array([ 0.16466094, 0.38553561, 0.39344118, 0.98377146, + 1.55037222, 2.35627331, 2.5554994 , -0.42663176, + -2.29184792, -4.28626857, -10.06557688, -14.49445188]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.865116292089478, shift=array([8.65116292, 1.09450825])), candidate_index=25, candidate_x=array([7.7886374, 1.1 ]), index=25, x=array([7.7886374, 1.1 ]), fval=332.27529049642715, rho=1.9787942109411345, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 7, 10, 12, 15, 20, 21, 22, 23, 24]), old_indices_discarded=array([ 0, 1, 3, 6, 11, 14, 17, 18, 19]), step_length=0.19167233792438676, relative_step_length=0.8862269254527566, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.7886374, 1.1 ]), radius=0.432558146044739, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 12, 15, 20, 22, 23, 24, 25]), model=ScalarModel(intercept=1691.9509202183522, linear_terms=array([ 46.61506721, -2557.30936158]), square_terms=array([[ 8.28500190e-01, -4.40279990e+01], + [-4.40279990e+01, 2.39704782e+03]]), scale=array([0.38334468, 0.19167234]), shift=array([7.7886374 , 0.90832766])), vector_model=VectorModel(intercepts=array([ 0.16466094, 0.38553561, 0.39344118, 0.98377146, + 1.55037222, 2.35627331, 2.5554994 , -0.42663176, + -2.29184792, -4.28626857, -10.06557688, -14.49445188]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.865116292089478, shift=array([8.65116292, 1.09450825])), candidate_index=26, candidate_x=array([7.40529272, 1.1 ]), index=26, x=array([7.40529272, 1.1 ]), fval=329.61834100665794, rho=1.222812647522843, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 7, 10, 12, 15, 20, 22, 23, 24, 25]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 8, 9, 11, 13, 14, 16, 17, 18, 19, 21]), step_length=0.3833446758487744, relative_step_length=0.8862269254527586, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.40529272, 1.1 ]), radius=0.865116292089478, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 11, 12, 15, 22, 24, 25, 26]), model=ScalarModel(intercept=643.567304264121, linear_terms=array([ 10.29942563, -504.83456548]), square_terms=array([[ 2.33922797e-01, -8.85959873e+00], + [-8.85959873e+00, 4.11114335e+02]]), scale=array([0.76668935, 0.3 ]), shift=array([7.40529272, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.16466094, 0.38553561, 0.39344118, 0.98377146, + 1.55037222, 2.35627331, 2.5554994 , -0.42663176, + -2.29184792, -4.28626857, -10.06557688, -14.49445188]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.865116292089478, shift=array([8.65116292, 1.09450825])), candidate_index=27, candidate_x=array([6.63860337, 1.1 ]), index=26, x=array([7.40529272, 1.1 ]), fval=329.61834100665794, rho=-0.6853713078207403, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 11, 12, 15, 22, 24, 25, 26]), old_indices_discarded=array([ 0, 1, 2, 4, 5, 6, 8, 9, 10, 13, 14, 16, 17, 18, 19, 20, 21, + 23]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.40529272, 1.1 ]), radius=0.432558146044739, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 1, 3, 7, 10, 22, 24, 25, 26, 27]), model=ScalarModel(intercept=459.34754309772336, linear_terms=array([ 54.12204115, -193.49155259]), square_terms=array([[ 10.33456741, -38.99556157], + [-38.99556157, 148.70365672]]), scale=array([0.38334468, 0.19167234]), shift=array([7.40529272, 0.90832766])), vector_model=VectorModel(intercepts=array([ 0.16466094, 0.38553561, 0.39344118, 0.98377146, + 1.55037222, 2.35627331, 2.5554994 , -0.42663176, + -2.29184792, -4.28626857, -10.06557688, -14.49445188]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.865116292089478, shift=array([8.65116292, 1.09450825])), candidate_index=28, candidate_x=array([7.02194805, 1.1 ]), index=28, x=array([7.02194805, 1.1 ]), fval=328.72551916138025, rho=0.0896479852836265, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 1, 3, 7, 10, 22, 24, 25, 26, 27]), old_indices_discarded=array([ 0, 2, 4, 5, 6, 8, 9, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, + 23]), step_length=0.3833446758487744, relative_step_length=0.8862269254527586, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.02194805, 1.1 ]), radius=0.2162790730223695, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 1, 3, 7, 10, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=344.68498320341723, linear_terms=array([ 14.50456677, -31.58487069]), square_terms=array([[ 4.74363424, -12.33704645], + [-12.33704645, 32.58384172]]), scale=array([0.19167234, 0.09583617]), shift=array([7.02194805, 1.00416383])), vector_model=VectorModel(intercepts=array([ 0.16466094, 0.38553561, 0.39344118, 0.98377146, + 1.55037222, 2.35627331, 2.5554994 , -0.42663176, + -2.29184792, -4.28626857, -10.06557688, -14.49445188]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.865116292089478, shift=array([8.65116292, 1.09450825])), candidate_index=29, candidate_x=array([6.83027571, 1.06077587]), index=28, x=array([7.02194805, 1.1 ]), fval=328.72551916138025, rho=-33.90897314919695, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 1, 3, 7, 10, 24, 25, 26, 27, 28]), old_indices_discarded=array([22]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.02194805, 1.1 ]), radius=0.10813953651118476, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([26, 27, 28, 29]), model=ScalarModel(intercept=472.8555269072373, linear_terms=array([ 6.88520604, -288.69818864]), square_terms=array([[ 1.70714674e-01, -6.98249164e+00], + [-6.98249164e+00, 2.89107224e+02]]), scale=array([0.09583617, 0.04791808]), shift=array([7.02194805, 1.05208192])), vector_model=VectorModel(intercepts=array([ 0.16466094, 0.38553561, 0.39344118, 0.98377146, + 1.55037222, 2.35627331, 2.5554994 , -0.42663176, + -2.29184792, -4.28626857, -10.06557688, -14.49445188]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.865116292089478, shift=array([8.65116292, 1.09450825])), candidate_index=30, candidate_x=array([7.07656245, 1.1 ]), index=28, x=array([7.02194805, 1.1 ]), fval=328.72551916138025, rho=-0.1305571607560506, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 27, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.02194805, 1.1 ]), radius=0.05406976825559238, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([28, 29, 30]), model=ScalarModel(intercept=364.8494895641686, linear_terms=array([ 1.67827189, -71.77579257]), square_terms=array([[ 4.10272702e-02, -1.69847688e+00], + [-1.69847688e+00, 7.13036443e+01]]), scale=array([0.04791808, 0.02395904]), shift=array([7.02194805, 1.07604096])), vector_model=VectorModel(intercepts=array([ 0.16466094, 0.38553561, 0.39344118, 0.98377146, + 1.55037222, 2.35627331, 2.5554994 , -0.42663176, + -2.29184792, -4.28626857, -10.06557688, -14.49445188]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.865116292089478, shift=array([8.65116292, 1.09450825])), candidate_index=31, candidate_x=array([7.0455466, 1.1 ]), index=28, x=array([7.02194805, 1.1 ]), fval=328.72551916138025, rho=-1.4185831981745718, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([28, 29, 30]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.02194805, 1.1 ]), radius=0.02703488412779619, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([28, 30, 31]), model=ScalarModel(intercept=82.18251293128704, linear_terms=array([-5.29166152e-03, 1.64365026e+02]), square_terms=array([[ 1.02579323e-02, -5.29166152e-03], + [-5.29166152e-03, 1.64365026e+02]]), scale=array([0.02395904, 0.01197952]), shift=array([7.02194805, 1.08802048])), vector_model=VectorModel(intercepts=array([ 0.16466094, 0.38553561, 0.39344118, 0.98377146, + 1.55037222, 2.35627331, 2.5554994 , -0.42663176, + -2.29184792, -4.28626857, -10.06557688, -14.49445188]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.865116292089478, shift=array([8.65116292, 1.09450825])), candidate_index=32, candidate_x=array([7.02194805, 1.07604096]), index=28, x=array([7.02194805, 1.1 ]), fval=328.72551916138025, rho=-0.10796958207563324, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([28, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.02194805, 1.1 ]), radius=0.013517442063898094, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([28, 30, 31, 32]), model=ScalarModel(intercept=330.9096992755648, linear_terms=array([ 0.10081694, -4.41045785]), square_terms=array([[ 2.56448307e-03, -1.06108599e-01], + [-1.06108599e-01, 4.46162059e+00]]), scale=array([0.01197952, 0.00598976]), shift=array([7.02194805, 1.09401024])), vector_model=VectorModel(intercepts=array([ 0.16466094, 0.38553561, 0.39344118, 0.98377146, + 1.55037222, 2.35627331, 2.5554994 , -0.42663176, + -2.29184792, -4.28626857, -10.06557688, -14.49445188]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.865116292089478, shift=array([8.65116292, 1.09450825])), candidate_index=33, candidate_x=array([7.03392757, 1.1 ]), index=33, x=array([7.03392757, 1.1 ]), fval=328.72149242354726, rho=1.0043192903172269, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([28, 30, 31, 32]), old_indices_discarded=array([], dtype=int64), step_length=0.011979521120274228, relative_step_length=0.8862269254527607, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.03392757, 1.1 ]), radius=0.02703488412779619, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([28, 30, 31, 32, 33]), model=ScalarModel(intercept=337.7586531621453, linear_terms=array([ 0.41964668, -17.95725485]), square_terms=array([[ 1.02541614e-02, -4.24398631e-01], + [-4.24398631e-01, 1.78463967e+01]]), scale=array([0.02395904, 0.01197952]), shift=array([7.03392757, 1.08802048])), vector_model=VectorModel(intercepts=array([ 0.16466094, 0.38553561, 0.39344118, 0.98377146, + 1.55037222, 2.35627331, 2.5554994 , -0.42663176, + -2.29184792, -4.28626857, -10.06557688, -14.49445188]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.03392757, 1.1 ]), radius=0.006758721031949047, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([28, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=329.31215250046296, linear_terms=array([ 0.028537 , -1.14367376]), square_terms=array([[ 6.39508336e-04, -2.64406249e-02], + [-2.64406249e-02, 1.11536573e+00]]), scale=array([0.00598976, 0.00299488]), shift=array([7.03392757, 1.09700512])), vector_model=VectorModel(intercepts=array([ 0.16466094, 0.38553561, 0.39344118, 0.98377146, + 1.55037222, 2.35627331, 2.5554994 , -0.42663176, + -2.29184792, -4.28626857, -10.06557688, -14.49445188]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], 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1156.1295319954902, 785.6173065198678, 1060.505414009956, 361.2431556777584, 571.0421242025416, 1169.5142048600862, 336.07619323885825, 351.20668606488493, 346.42767854398153, 337.1356979156533, 355.8313545359116, 346.42767854398153, 341.61633648106147, 339.28453805742834, 336.07568759259027, 338.21715032862573, 335.0379978894075, 337.1356979156533, 333.9565001654597, 332.27529049642715, 329.61834100665794, 330.5249950650412, 328.72551916138025, 414.33912647339173, 328.7291382315541, 328.73257695737027, 364.2183654618458, 328.72149242354726, 328.73182598744506, 328.7316552362415, 328.72610277857893, 329.3509739636462, 328.7224226866329, 328.72089942436855], 'runtime': [0.0, 1.727137932000005, 1.7634881000001315, 1.8007889259999956, 1.8387122760000238, 1.8739716889999727, 1.919525539999995, 1.9487520860000132, 1.983478089000073, 2.019379148000098, 2.062711801999967, 2.1234359350000886, 2.1681270500000664, 4.314270990000068, 6.025481959999979, 7.716149045000066, 9.449394989999973, 11.12918414800015, 12.826702885000032, 14.505306856000061, 16.165513319999945, 17.827433128999928, 19.492043205000073, 21.157377778999944, 22.975806641999952, 24.676801362000106, 26.346183448000147, 28.026251385000023, 29.700532247999945, 31.36276561700015, 33.03013676399996, 34.709746659000075, 36.37672618600004, 38.06136919200003, 39.754508002999955, 41.45337039800006, 43.128914240000086, 44.82997982799998, 46.48745151100002, 48.26309048500002], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28]}}], 'exploration_sample': array([[ 7.1307054, 1.1 ], + [12.321875 , 1.08125 ], + [17.6375 , 1.025 ], + [14.09375 , 0.9875 ], + [16.45625 , 0.9125 ], + [ 2.28125 , 1.0625 ], + [18.81875 , 0.5375 ], + [17.046875 , 0.63125 ], + [15.275 , 0.65 ], + [ 7.596875 , 0.93125 ], + [11.73125 , 0.7625 ], + [10.55 , 0.8 ], + [12.9125 , 0.575 ], + [ 5.825 , 0.95 ], + [ 9.36875 , 0.8375 ], + [ 8.1875 , 0.725 ], + [ 7.00625 , 0.6125 ], + [ 3.4625 , 0.875 ], + [ 4.64375 , 0.6875 ], + [ 2.871875 , 0.78125 ]]), 'exploration_results': array([ 328.68572765, 420.32021458, 528.04271383, 663.30843919, + 755.01356649, 860.62318106, 905.15922637, 916.6930808 , + 958.0124336 , 1004.84993533, 1006.66630029, 1027.0399295 , + 1032.30721934, 1038.25662345, 1043.08294325, 1119.86544023, + 1173.59414907, 1217.73888335, 1230.5461544 , 1263.48004797])}}" diff --git a/content/tables/min/IndShockSub(Stock)(Labor)Market_estimate_results.csv b/content/tables/min/IndShockSub(Stock)(Labor)Market_estimate_results.csv new file mode 100644 index 0000000..062a68d --- /dev/null +++ b/content/tables/min/IndShockSub(Stock)(Labor)Market_estimate_results.csv @@ -0,0 +1,3721 @@ +CRRA,6.039369571191269 +DiscFac,1.1 +time_to_estimate,142.97382497787476 +params,"{'CRRA': 6.039369571191269, 'DiscFac': 1.1}" +criterion,327.98396253621803 +start_criterion,325.62672656303187 +start_params,"{'CRRA': 6.039365487749873, 'DiscFac': 1.1}" +algorithm,multistart_tranquilo_ls +direction,minimize +n_free,2 +message,Absolute criterion change smaller than tolerance. +success, +n_criterion_evaluations, +n_derivative_evaluations, +n_iterations, +history,"{'params': [{'CRRA': 6.039365487749873, 'DiscFac': 1.1}, {'CRRA': 5.514307563653458, 'DiscFac': 0.5647751692105935}, {'CRRA': 6.574590318539279, 'DiscFac': 0.7142008649214286}, {'CRRA': 5.504140656960466, 'DiscFac': 0.8008722258078252}, {'CRRA': 6.570045972650687, 'DiscFac': 1.1}, {'CRRA': 6.574590318539279, 'DiscFac': 0.5769830462592568}, {'CRRA': 6.1601212453261995, 'DiscFac': 0.5647751692105935}, {'CRRA': 5.559130607226342, 'DiscFac': 1.1}, {'CRRA': 6.574590318539279, 'DiscFac': 0.883770096203983}, {'CRRA': 6.387521107085344, 'DiscFac': 1.1}, {'CRRA': 5.504140656960466, 'DiscFac': 0.9566943385094835}, {'CRRA': 5.781096605047464, 'DiscFac': 0.5647751692105935}, {'CRRA': 5.907991547972339, 'DiscFac': 1.1}, {'CRRA': 6.574590318539279, 'DiscFac': 1.1}, {'CRRA': 6.306977903144576, 'DiscFac': 1.1}, {'CRRA': 6.173171695447224, 'DiscFac': 1.1}, {'CRRA': 6.039365487749873, 'DiscFac': 1.0330968961513243}, {'CRRA': 6.07281703967421, 'DiscFac': 1.1}, {'CRRA': 6.028731043723087, 'DiscFac': 1.1}, {'CRRA': 6.027928979667826, 'DiscFac': 1.1}, {'CRRA': 6.039361622061221, 'DiscFac': 1.0916371120189157}, {'CRRA': 6.043546931740415, 'DiscFac': 1.1}, {'CRRA': 6.041456209745144, 'DiscFac': 1.1}, {'CRRA': 6.039365487749873, 'DiscFac': 1.0989546390023646}, {'CRRA': 6.038842807251055, 'DiscFac': 1.1}, {'CRRA': 6.039626827999282, 'DiscFac': 1.1}, {'CRRA': 6.039365497432717, 'DiscFac': 1.0998693298752957}, {'CRRA': 6.03930015268752, 'DiscFac': 1.1}, {'CRRA': 6.039398155281049, 'DiscFac': 1.1}, {'CRRA': 6.039365487829561, 'DiscFac': 1.099983666234412}, {'CRRA': 6.0393573208670785, 'DiscFac': 1.1}, {'CRRA': 6.039369571191269, 'DiscFac': 1.1}], 'criterion': [327.98396556352463, 1228.9150329049723, 1168.3472017064682, 1177.9817288188055, 330.8717181120478, 1192.5000375134468, 1207.3141152065677, 331.15203360269527, 1113.3654262066202, 329.346768098511, 1064.643722382122, 1220.9186466524695, 328.1806911330513, 330.90965387351446, 328.7510110443769, 328.1891563382183, 644.4055514457284, 328.03739692493264, 328.0274414332465, 328.0253666491021, 338.8317868940389, 327.99236994618195, 327.98650331588243, 328.73050303716934, 327.9847748806261, 327.98424933631094, 328.1086092400093, 327.98401405708523, 327.9839784217286, 327.9938739215477, 327.9839716193877, 327.98396253621803], 'runtime': [0.0, 1.7629111660003218, 1.7969741930000964, 1.8328278670001055, 1.8675967369999853, 1.9038407150001149, 1.9360275830003957, 1.9721233080003913, 2.0105504640000618, 2.0486393700002736, 2.0854981720003707, 2.125488069000312, 2.1669091100002333, 4.268970466000155, 5.939877997000167, 7.606953285000145, 9.403391789000125, 11.076275230000192, 12.79517630600003, 14.58376199200029, 16.296275266000066, 18.030881056999988, 19.788515587000347, 21.50479367800017, 23.194084481000118, 24.86841848100039, 26.535735132000354, 28.18876280700033, 29.846337465000033, 31.526947226000175, 33.37655216200028, 35.10128562199998], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]}" +convergence_report, +multistart_info,"{'start_parameters': [{'CRRA': 6.039365487749873, 'DiscFac': 1.1}, {'CRRA': 7.879472808448048, 'DiscFac': 1.0945082521472478}], 'local_optima': [Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 9.23e-09** 9.23e-09** +relative_params_change 6.761e-07* 6.761e-07* +absolute_criterion_change 3.027e-06* 3.027e-06* +absolute_params_change 4.083e-06* 4.083e-06* + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 8.88e-07* 0.0001055 +relative_params_change 5.646e-05 0.006636 +absolute_criterion_change 0.0002913 0.03461 +absolute_params_change 0.000341 0.04007 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 6.039365487749873, 'DiscFac': 1.1}, {'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 2.28125, 'DiscFac': 1.0625}, {'CRRA': 17.6375, 'DiscFac': 1.0250000000000001}, {'CRRA': 14.093749999999998, 'DiscFac': 0.9875}, {'CRRA': 16.45625, 'DiscFac': 0.9125000000000001}, {'CRRA': 18.81875, 'DiscFac': 0.5375}, {'CRRA': 17.046875, 'DiscFac': 0.6312500000000001}, {'CRRA': 15.274999999999999, 'DiscFac': 0.65}, {'CRRA': 11.73125, 'DiscFac': 0.7625000000000001}, {'CRRA': 7.596874999999999, 'DiscFac': 0.9312500000000001}, {'CRRA': 10.549999999999999, 'DiscFac': 0.8}, {'CRRA': 12.9125, 'DiscFac': 0.575}, {'CRRA': 9.368749999999999, 'DiscFac': 0.8375}, {'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 8.1875, 'DiscFac': 0.7250000000000001}, {'CRRA': 7.00625, 'DiscFac': 0.6125}, {'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 2.871875, 'DiscFac': 0.78125}], 'exploration_results': array([ 327.98396556, 444.08066157, 540.65943804, 544.96572798, + 689.47008041, 769.45866328, 909.59665878, 921.36892514, + 962.43294355, 1011.74494613, 1024.37390697, 1032.75469874, + 1035.57943478, 1049.83280992, 1065.59145403, 1123.108822 , + 1175.63544527, 1221.64398624, 1232.43946077, 1265.07252504])}" +algorithm_output,"{'states': [State(trustregion=Region(center=array([6.03936549, 1.1 ]), radius=0.6039365487749873, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=327.98396556352463, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.6039365487749873, shift=array([6.03936549, 1.1 ])), vector_model=VectorModel(intercepts=array([ 0.29039072, 0.99271422, 1.39732246, 2.3121125 , + 3.18792659, 4.27665212, 4.69902694, 1.38743729, + -0.98655574, -3.47119021, -8.67868612, -13.37218272]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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9, 10, 11, 12]), model=ScalarModel(intercept=696.5309985139809, linear_terms=array([ -24.53423506, -561.26319031]), square_terms=array([[ 0.67185156, 16.6323865 ], + [ 16.6323865 , 431.2650042 ]]), scale=array([0.53522483, 0.26761242]), shift=array([6.03936549, 0.83238758])), vector_model=VectorModel(intercepts=array([ 0.29039072, 0.99271422, 1.39732246, 2.3121125 , + 3.18792659, 4.27665212, 4.69902694, 1.38743729, + -0.98655574, -3.47119021, -8.67868612, -13.37218272]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.03936549, 1.1 ]), radius=0.15098413719374681, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 4, 6, 7, 9, 10, 12, 13, 14]), model=ScalarModel(intercept=374.02545387845635, linear_terms=array([-12.77816255, -50.95359 ]), square_terms=array([[ 1.53083186, 6.75843346], + [ 6.75843346, 30.16030439]]), scale=array([0.13380621, 0.0669031 ]), shift=array([6.03936549, 1.0330969 ])), vector_model=VectorModel(intercepts=array([ 0.29039072, 0.99271422, 1.39732246, 2.3121125 , + 3.18792659, 4.27665212, 4.69902694, 1.38743729, + -0.98655574, -3.47119021, -8.67868612, -13.37218272]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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1.63992464e+02]), square_terms=array([[9.48148041e-02, 6.00642694e-03], + [6.00642694e-03, 1.63992464e+02]]), scale=array([0.0669031 , 0.03345155]), shift=array([6.03936549, 1.06654845])), vector_model=VectorModel(intercepts=array([ 0.29039072, 0.99271422, 1.39732246, 2.3121125 , + 3.18792659, 4.27665212, 4.69902694, 1.38743729, + -0.98655574, -3.47119021, -8.67868612, -13.37218272]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6039365487749873, shift=array([6.03936549, 1.1 ])), candidate_index=16, candidate_x=array([6.03936549, 1.0330969 ]), index=0, x=array([6.03936549, 1.1 ]), fval=327.98396556352463, rho=-0.9647442858617606, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 9, 12, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.03936549, 1.1 ]), radius=0.037746034298436704, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 16, 17]), model=ScalarModel(intercept=355.5690312460679, linear_terms=array([ 0.96745994, -44.74534031]), square_terms=array([[ 2.71211892e-02, -9.58837955e-01], + [-9.58837955e-01, 3.43556665e+01]]), scale=array([0.03345155, 0.01672578]), shift=array([6.03936549, 1.08327422])), vector_model=VectorModel(intercepts=array([ 0.29039072, 0.99271422, 1.39732246, 2.3121125 , + 3.18792659, 4.27665212, 4.69902694, 1.38743729, + -0.98655574, -3.47119021, 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State(trustregion=Region(center=array([6.03936549, 1.1 ]), radius=0.018873017149218352, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 16, 17, 18]), model=ScalarModel(intercept=337.5037817416159, linear_terms=array([ 0.23793178, -13.78785468]), square_terms=array([[ 6.44286666e-03, -2.33526375e-01], + [-2.33526375e-01, 8.58721579e+00]]), scale=array([0.01672578, 0.00836289]), shift=array([6.03936549, 1.09163711])), vector_model=VectorModel(intercepts=array([ 0.29039072, 0.99271422, 1.39732246, 2.3121125 , + 3.18792659, 4.27665212, 4.69902694, 1.38743729, + -0.98655574, -3.47119021, -8.67868612, -13.37218272]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6039365487749873, shift=array([6.03936549, 1.1 ])), candidate_index=19, candidate_x=array([6.02792898, 1.1 ]), index=0, x=array([6.03936549, 1.1 ]), fval=327.98396556352463, rho=-27.4883232237418, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.03936549, 1.1 ]), radius=0.009436508574609176, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18, 19]), model=ScalarModel(intercept=82.00359936243075, linear_terms=array([3.22891594e-04, 1.64007199e+02]), square_terms=array([[1.61137378e-03, 3.22891594e-04], + [3.22891594e-04, 1.64007199e+02]]), scale=array([0.00836289, 0.00418144]), shift=array([6.03936549, 1.09581856])), vector_model=VectorModel(intercepts=array([ 0.29039072, 0.99271422, 1.39732246, 2.3121125 , + 3.18792659, 4.27665212, 4.69902694, 1.38743729, + -0.98655574, -3.47119021, -8.67868612, -13.37218272]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6039365487749873, shift=array([6.03936549, 1.1 ])), candidate_index=20, candidate_x=array([6.03936162, 1.09163711]), index=0, x=array([6.03936549, 1.1 ]), fval=327.98396556352463, rho=-0.03307117435960311, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 17, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.03936549, 1.1 ]), radius=0.004718254287304588, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 18, 19, 20]), model=ScalarModel(intercept=329.67519625480406, linear_terms=array([ 1.00101667e-03, -2.03133325e+00]), square_terms=array([[ 4.05448268e-04, -1.64373484e-02], + [-1.64373484e-02, 6.80586092e-01]]), scale=array([0.00418144, 0.00209072]), shift=array([6.03936549, 1.09790928])), vector_model=VectorModel(intercepts=array([ 0.29039072, 0.99271422, 1.39732246, 2.3121125 , + 3.18792659, 4.27665212, 4.69902694, 1.38743729, + -0.98655574, -3.47119021, -8.67868612, -13.37218272]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6039365487749873, shift=array([6.03936549, 1.1 ])), candidate_index=21, candidate_x=array([6.04354693, 1.1 ]), index=0, x=array([6.03936549, 1.1 ]), fval=327.98396556352463, rho=-0.5517000871442037, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 18, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.03936549, 1.1 ]), radius=0.002359127143652294, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 18, 19, 20, 21]), model=ScalarModel(intercept=328.7541308447501, linear_terms=array([-0.00126429, -0.84418061]), square_terms=array([[ 1.01603097e-04, -4.11444119e-03], + [-4.11444119e-03, 1.70140517e-01]]), scale=array([0.00209072, 0.00104536]), shift=array([6.03936549, 1.09895464])), vector_model=VectorModel(intercepts=array([ 0.29039072, 0.99271422, 1.39732246, 2.3121125 , + 3.18792659, 4.27665212, 4.69902694, 1.38743729, + -0.98655574, -3.47119021, -8.67868612, -13.37218272]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6039365487749873, shift=array([6.03936549, 1.1 ])), candidate_index=22, candidate_x=array([6.04145621, 1.1 ]), index=0, x=array([6.03936549, 1.1 ]), fval=327.98396556352463, rho=-0.47631152639781554, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 18, 19, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.03936549, 1.1 ]), radius=0.001179563571826147, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 21, 22]), model=ScalarModel(intercept=81.99585687999274, linear_terms=array([1.02492387e-03, 1.63991714e+02]), square_terms=array([[2.57162142e-05, 1.02492387e-03], + [1.02492387e-03, 1.63991714e+02]]), scale=array([0.00104536, 0.00052268]), shift=array([6.03936549, 1.09947732])), vector_model=VectorModel(intercepts=array([ 0.29039072, 0.99271422, 1.39732246, 2.3121125 , + 3.18792659, 4.27665212, 4.69902694, 1.38743729, + -0.98655574, -3.47119021, -8.67868612, -13.37218272]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6039365487749873, shift=array([6.03936549, 1.1 ])), candidate_index=23, candidate_x=array([6.03936549, 1.09895464]), index=0, x=array([6.03936549, 1.1 ]), fval=327.98396556352463, rho=-0.002276143887176283, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.03936549, 1.1 ]), radius=0.0005897817859130735, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 22, 23]), model=ScalarModel(intercept=328.1545372959133, linear_terms=array([ 0.00088326, -0.17592594]), square_terms=array([[ 6.57063367e-06, -2.61966221e-04], + [-2.61966221e-04, 1.07084240e-02]]), scale=array([0.00052268, 0.00026134]), shift=array([6.03936549, 1.09973866])), vector_model=VectorModel(intercepts=array([ 0.29039072, 0.99271422, 1.39732246, 2.3121125 , + 3.18792659, 4.27665212, 4.69902694, 1.38743729, + -0.98655574, -3.47119021, -8.67868612, -13.37218272]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6039365487749873, shift=array([6.03936549, 1.1 ])), candidate_index=24, candidate_x=array([6.03884281, 1.1 ]), index=0, x=array([6.03936549, 1.1 ]), fval=327.98396556352463, rho=-1.3095502183058638, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.03936549, 1.1 ]), radius=0.00029489089295653675, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 23, 24]), model=ScalarModel(intercept=328.06791287671706, linear_terms=array([-0.00033776, -0.08528587]), square_terms=array([[ 1.63289767e-06, -6.52683395e-05], + [-6.52683395e-05, 2.67710600e-03]]), scale=array([0.00026134, 0.00013067]), shift=array([6.03936549, 1.09986933])), vector_model=VectorModel(intercepts=array([ 0.29039072, 0.99271422, 1.39732246, 2.3121125 , + 3.18792659, 4.27665212, 4.69902694, 1.38743729, + -0.98655574, -3.47119021, -8.67868612, -13.37218272]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6039365487749873, shift=array([6.03936549, 1.1 ])), candidate_index=25, candidate_x=array([6.03962683, 1.1 ]), index=0, x=array([6.03936549, 1.1 ]), fval=327.98396556352463, rho=-0.7055352872670753, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.03936549, 1.1 ]), radius=0.00014744544647826837, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 24, 25]), model=ScalarModel(intercept=81.99606488811281, linear_terms=array([-5.1757451e-05, 1.6399213e+02]), square_terms=array([[ 4.09301525e-07, -5.17574510e-05], + [-5.17574510e-05, 1.63992130e+02]]), scale=array([1.30670125e-04, 6.53350624e-05]), shift=array([6.03936549, 1.09993466])), vector_model=VectorModel(intercepts=array([ 0.29039072, 0.99271422, 1.39732246, 2.3121125 , + 3.18792659, 4.27665212, 4.69902694, 1.38743729, + -0.98655574, -3.47119021, -8.67868612, -13.37218272]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6039365487749873, shift=array([6.03936549, 1.1 ])), candidate_index=26, candidate_x=array([6.0393655 , 1.09986933]), index=0, x=array([6.03936549, 1.1 ]), fval=327.98396556352463, rho=-0.0003800294460921199, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.03936549, 1.1 ]), radius=7.372272323913419e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 25, 26]), model=ScalarModel(intercept=328.01486951589106, linear_terms=array([ 7.48970521e-05, -3.09896069e-02]), square_terms=array([[ 1.03088600e-07, -4.16003286e-06], + [-4.16003286e-06, 1.71309011e-04]]), scale=array([6.53350624e-05, 3.26675312e-05]), shift=array([6.03936549, 1.09996733])), vector_model=VectorModel(intercepts=array([ 0.29039072, 0.99271422, 1.39732246, 2.3121125 , + 3.18792659, 4.27665212, 4.69902694, 1.38743729, + -0.98655574, -3.47119021, -8.67868612, -13.37218272]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6039365487749873, shift=array([6.03936549, 1.1 ])), candidate_index=27, candidate_x=array([6.03930015, 1.1 ]), index=0, x=array([6.03936549, 1.1 ]), fval=327.98396556352463, rho=-0.6860470368189644, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.03936549, 1.1 ]), radius=3.6861361619567093e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 26, 27]), model=ScalarModel(intercept=327.99939612828905, linear_terms=array([-2.31799670e-05, -1.54519784e-02]), square_terms=array([[ 2.58647370e-08, -1.04094853e-06], + [-1.04094853e-06, 4.28272528e-05]]), scale=array([3.26675312e-05, 1.63337656e-05]), shift=array([6.03936549, 1.09998367])), vector_model=VectorModel(intercepts=array([ 0.29039072, 0.99271422, 1.39732246, 2.3121125 , + 3.18792659, 4.27665212, 4.69902694, 1.38743729, + -0.98655574, -3.47119021, -8.67868612, -13.37218272]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6039365487749873, shift=array([6.03936549, 1.1 ])), candidate_index=28, candidate_x=array([6.03939816, 1.1 ]), index=0, x=array([6.03936549, 1.1 ]), fval=327.98396556352463, rho=-0.5311555206890006, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.03936549, 1.1 ]), radius=1.8430680809783547e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 27, 28]), model=ScalarModel(intercept=81.9959953622531, linear_terms=array([-3.40764237e-06, 1.63991991e+02]), square_terms=array([[ 6.47325828e-09, -3.40764237e-06], + [-3.40764237e-06, 1.63991991e+02]]), scale=array([1.63337656e-05, 8.16688279e-06]), shift=array([6.03936549, 1.09999183])), vector_model=VectorModel(intercepts=array([ 0.29039072, 0.99271422, 1.39732246, 2.3121125 , + 3.18792659, 4.27665212, 4.69902694, 1.38743729, + -0.98655574, -3.47119021, 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State(trustregion=Region(center=array([6.03936549, 1.1 ]), radius=9.215340404891773e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 28, 29]), model=ScalarModel(intercept=327.9864386827784, linear_terms=array([ 3.27627114e-06, -2.47444267e-03]), square_terms=array([[ 1.62290726e-09, -6.49659870e-08], + [-6.49659870e-08, 2.64682914e-06]]), scale=array([8.16688279e-06, 4.08344140e-06]), shift=array([6.03936549, 1.09999592])), vector_model=VectorModel(intercepts=array([ 0.29039072, 0.99271422, 1.39732246, 2.3121125 , + 3.18792659, 4.27665212, 4.69902694, 1.38743729, + -0.98655574, -3.47119021, -8.67868612, -13.37218272]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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6.61707286e-07]]), scale=array([4.0834414e-06, 2.0417207e-06]), shift=array([6.03936549, 1.09999796])), vector_model=VectorModel(intercepts=array([ 0.29039072, 0.99271422, 1.39732246, 2.3121125 , + 3.18792659, 4.27665212, 4.69902694, 1.38743729, + -0.98655574, -3.47119021, -8.67868612, -13.37218272]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6039365487749873, shift=array([6.03936549, 1.1 ])), candidate_index=31, candidate_x=array([6.03936957, 1.1 ]), index=31, x=array([6.03936957, 1.1 ]), fval=327.98396253621803, rho=0.9999938036629777, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 29, 30]), old_indices_discarded=array([], dtype=int64), step_length=4.083441396574017e-06, relative_step_length=0.8862269253572893, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 32 entries., 'multistart_info': {'start_parameters': [array([6.03936549, 1.1 ]), array([7.87947281, 1.09450825])], 'local_optima': [{'solution_x': array([6.03936957, 1.1 ]), 'solution_criterion': 327.98396253621803, 'states': [State(trustregion=Region(center=array([6.03936549, 1.1 ]), radius=0.6039365487749873, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=327.98396556352463, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.6039365487749873, shift=array([6.03936549, 1.1 ])), vector_model=VectorModel(intercepts=array([ 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[-9.58837955e-01, 3.43556665e+01]]), scale=array([0.03345155, 0.01672578]), shift=array([6.03936549, 1.08327422])), vector_model=VectorModel(intercepts=array([ 0.29039072, 0.99271422, 1.39732246, 2.3121125 , + 3.18792659, 4.27665212, 4.69902694, 1.38743729, + -0.98655574, -3.47119021, -8.67868612, -13.37218272]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6039365487749873, shift=array([6.03936549, 1.1 ])), candidate_index=18, candidate_x=array([6.02873104, 1.1 ]), index=0, x=array([6.03936549, 1.1 ]), fval=327.98396556352463, rho=-31.72287269530175, accepted=False, new_indices=array([17]), old_indices_used=array([ 0, 12, 16]), old_indices_discarded=array([15]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.03936549, 1.1 ]), radius=0.018873017149218352, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 16, 17, 18]), model=ScalarModel(intercept=337.5037817416159, linear_terms=array([ 0.23793178, -13.78785468]), square_terms=array([[ 6.44286666e-03, -2.33526375e-01], + [-2.33526375e-01, 8.58721579e+00]]), scale=array([0.01672578, 0.00836289]), shift=array([6.03936549, 1.09163711])), vector_model=VectorModel(intercepts=array([ 0.29039072, 0.99271422, 1.39732246, 2.3121125 , + 3.18792659, 4.27665212, 4.69902694, 1.38743729, + -0.98655574, -3.47119021, -8.67868612, -13.37218272]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6039365487749873, shift=array([6.03936549, 1.1 ])), candidate_index=19, candidate_x=array([6.02792898, 1.1 ]), index=0, x=array([6.03936549, 1.1 ]), fval=327.98396556352463, rho=-27.4883232237418, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.03936549, 1.1 ]), radius=0.009436508574609176, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18, 19]), model=ScalarModel(intercept=82.00359936243075, linear_terms=array([3.22891594e-04, 1.64007199e+02]), square_terms=array([[1.61137378e-03, 3.22891594e-04], + [3.22891594e-04, 1.64007199e+02]]), scale=array([0.00836289, 0.00418144]), shift=array([6.03936549, 1.09581856])), vector_model=VectorModel(intercepts=array([ 0.29039072, 0.99271422, 1.39732246, 2.3121125 , + 3.18792659, 4.27665212, 4.69902694, 1.38743729, + -0.98655574, -3.47119021, -8.67868612, -13.37218272]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6039365487749873, shift=array([6.03936549, 1.1 ])), candidate_index=20, candidate_x=array([6.03936162, 1.09163711]), index=0, x=array([6.03936549, 1.1 ]), fval=327.98396556352463, rho=-0.03307117435960311, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 17, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.03936549, 1.1 ]), radius=0.004718254287304588, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 18, 19, 20]), model=ScalarModel(intercept=329.67519625480406, linear_terms=array([ 1.00101667e-03, -2.03133325e+00]), square_terms=array([[ 4.05448268e-04, -1.64373484e-02], + [-1.64373484e-02, 6.80586092e-01]]), scale=array([0.00418144, 0.00209072]), shift=array([6.03936549, 1.09790928])), vector_model=VectorModel(intercepts=array([ 0.29039072, 0.99271422, 1.39732246, 2.3121125 , + 3.18792659, 4.27665212, 4.69902694, 1.38743729, + -0.98655574, -3.47119021, -8.67868612, -13.37218272]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6039365487749873, shift=array([6.03936549, 1.1 ])), candidate_index=21, candidate_x=array([6.04354693, 1.1 ]), index=0, x=array([6.03936549, 1.1 ]), fval=327.98396556352463, rho=-0.5517000871442037, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 18, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.03936549, 1.1 ]), radius=0.002359127143652294, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 18, 19, 20, 21]), model=ScalarModel(intercept=328.7541308447501, linear_terms=array([-0.00126429, -0.84418061]), square_terms=array([[ 1.01603097e-04, -4.11444119e-03], + [-4.11444119e-03, 1.70140517e-01]]), scale=array([0.00209072, 0.00104536]), shift=array([6.03936549, 1.09895464])), vector_model=VectorModel(intercepts=array([ 0.29039072, 0.99271422, 1.39732246, 2.3121125 , + 3.18792659, 4.27665212, 4.69902694, 1.38743729, + -0.98655574, -3.47119021, -8.67868612, -13.37218272]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6039365487749873, shift=array([6.03936549, 1.1 ])), candidate_index=22, candidate_x=array([6.04145621, 1.1 ]), index=0, x=array([6.03936549, 1.1 ]), fval=327.98396556352463, rho=-0.47631152639781554, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 18, 19, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.03936549, 1.1 ]), radius=0.001179563571826147, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 21, 22]), model=ScalarModel(intercept=81.99585687999274, linear_terms=array([1.02492387e-03, 1.63991714e+02]), square_terms=array([[2.57162142e-05, 1.02492387e-03], + [1.02492387e-03, 1.63991714e+02]]), scale=array([0.00104536, 0.00052268]), shift=array([6.03936549, 1.09947732])), vector_model=VectorModel(intercepts=array([ 0.29039072, 0.99271422, 1.39732246, 2.3121125 , + 3.18792659, 4.27665212, 4.69902694, 1.38743729, + -0.98655574, -3.47119021, -8.67868612, -13.37218272]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6039365487749873, shift=array([6.03936549, 1.1 ])), candidate_index=23, candidate_x=array([6.03936549, 1.09895464]), index=0, x=array([6.03936549, 1.1 ]), fval=327.98396556352463, rho=-0.002276143887176283, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.03936549, 1.1 ]), radius=0.0005897817859130735, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 22, 23]), model=ScalarModel(intercept=328.1545372959133, linear_terms=array([ 0.00088326, -0.17592594]), square_terms=array([[ 6.57063367e-06, -2.61966221e-04], + [-2.61966221e-04, 1.07084240e-02]]), scale=array([0.00052268, 0.00026134]), shift=array([6.03936549, 1.09973866])), vector_model=VectorModel(intercepts=array([ 0.29039072, 0.99271422, 1.39732246, 2.3121125 , + 3.18792659, 4.27665212, 4.69902694, 1.38743729, + -0.98655574, -3.47119021, -8.67868612, -13.37218272]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6039365487749873, shift=array([6.03936549, 1.1 ])), candidate_index=24, candidate_x=array([6.03884281, 1.1 ]), index=0, x=array([6.03936549, 1.1 ]), fval=327.98396556352463, rho=-1.3095502183058638, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.03936549, 1.1 ]), radius=0.00029489089295653675, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 23, 24]), model=ScalarModel(intercept=328.06791287671706, linear_terms=array([-0.00033776, -0.08528587]), square_terms=array([[ 1.63289767e-06, -6.52683395e-05], + [-6.52683395e-05, 2.67710600e-03]]), scale=array([0.00026134, 0.00013067]), shift=array([6.03936549, 1.09986933])), vector_model=VectorModel(intercepts=array([ 0.29039072, 0.99271422, 1.39732246, 2.3121125 , + 3.18792659, 4.27665212, 4.69902694, 1.38743729, + -0.98655574, -3.47119021, -8.67868612, -13.37218272]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6039365487749873, shift=array([6.03936549, 1.1 ])), candidate_index=25, candidate_x=array([6.03962683, 1.1 ]), index=0, x=array([6.03936549, 1.1 ]), fval=327.98396556352463, rho=-0.7055352872670753, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.03936549, 1.1 ]), radius=0.00014744544647826837, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 24, 25]), model=ScalarModel(intercept=81.99606488811281, linear_terms=array([-5.1757451e-05, 1.6399213e+02]), square_terms=array([[ 4.09301525e-07, -5.17574510e-05], + [-5.17574510e-05, 1.63992130e+02]]), scale=array([1.30670125e-04, 6.53350624e-05]), shift=array([6.03936549, 1.09993466])), vector_model=VectorModel(intercepts=array([ 0.29039072, 0.99271422, 1.39732246, 2.3121125 , + 3.18792659, 4.27665212, 4.69902694, 1.38743729, + -0.98655574, -3.47119021, -8.67868612, -13.37218272]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6039365487749873, shift=array([6.03936549, 1.1 ])), candidate_index=26, candidate_x=array([6.0393655 , 1.09986933]), index=0, x=array([6.03936549, 1.1 ]), fval=327.98396556352463, rho=-0.0003800294460921199, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.03936549, 1.1 ]), radius=7.372272323913419e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 25, 26]), model=ScalarModel(intercept=328.01486951589106, linear_terms=array([ 7.48970521e-05, -3.09896069e-02]), square_terms=array([[ 1.03088600e-07, -4.16003286e-06], + [-4.16003286e-06, 1.71309011e-04]]), scale=array([6.53350624e-05, 3.26675312e-05]), shift=array([6.03936549, 1.09996733])), vector_model=VectorModel(intercepts=array([ 0.29039072, 0.99271422, 1.39732246, 2.3121125 , + 3.18792659, 4.27665212, 4.69902694, 1.38743729, + -0.98655574, -3.47119021, -8.67868612, 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State(trustregion=Region(center=array([6.03936549, 1.1 ]), radius=3.6861361619567093e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 26, 27]), model=ScalarModel(intercept=327.99939612828905, linear_terms=array([-2.31799670e-05, -1.54519784e-02]), square_terms=array([[ 2.58647370e-08, -1.04094853e-06], + [-1.04094853e-06, 4.28272528e-05]]), scale=array([3.26675312e-05, 1.63337656e-05]), shift=array([6.03936549, 1.09998367])), vector_model=VectorModel(intercepts=array([ 0.29039072, 0.99271422, 1.39732246, 2.3121125 , + 3.18792659, 4.27665212, 4.69902694, 1.38743729, + -0.98655574, -3.47119021, -8.67868612, -13.37218272]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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shift=array([7.87947281, 1.09450825])), candidate_index=15, candidate_x=array([7.53032276, 1.1 ]), index=15, x=array([7.53032276, 1.1 ]), fval=343.61078177168645, rho=0.3897527141119779, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 2, 4, 5, 6, 8, 9, 12, 14]), old_indices_discarded=array([ 1, 3, 7, 10, 11, 13]), step_length=0.6983000961219519, relative_step_length=0.8862269254527576, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.53032276, 1.1 ]), radius=1.5758945616896096, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 4, 5, 6, 7, 8, 9, 15]), model=ScalarModel(intercept=741.96560799635, linear_terms=array([ -81.17490217, -456.86793398]), square_terms=array([[ 6.2118656 , 37.41354298], + [ 37.41354298, 262.61099621]]), scale=array([1.39660019, 0.3 ]), shift=array([7.53032276, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=16, candidate_x=array([8.92692295, 1.1 ]), index=15, x=array([7.53032276, 1.1 ]), fval=343.61078177168645, rho=-0.5197566873306682, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 4, 5, 6, 7, 8, 9, 15]), old_indices_discarded=array([ 2, 3, 10, 11, 12, 13, 14]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.53032276, 1.1 ]), radius=0.7879472808448048, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 3, 4, 6, 7, 12, 14, 15]), model=ScalarModel(intercept=696.7489995883543, linear_terms=array([-156.00233823, -422.415263 ]), square_terms=array([[ 29.90717633, 85.76296971], + [ 85.76296971, 251.92073865]]), scale=array([0.6983001, 0.3 ]), shift=array([7.53032276, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=17, candidate_x=array([8.22862286, 1.1 ]), index=15, x=array([7.53032276, 1.1 ]), fval=343.61078177168645, rho=-0.19308136249134683, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 4, 6, 7, 12, 14, 15]), old_indices_discarded=array([ 2, 5, 8, 9, 10, 11, 13, 16]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.53032276, 1.1 ]), radius=0.3939736404224024, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 3, 6, 7, 10, 11, 12, 15]), model=ScalarModel(intercept=532.6627393859355, linear_terms=array([-107.3513404 , -166.70287139]), square_terms=array([[24.828587 , 42.60005527], + [42.60005527, 75.03119316]]), scale=array([0.34915005, 0.17457502]), shift=array([7.53032276, 0.92542498])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=18, candidate_x=array([7.87947281, 1.1 ]), index=15, x=array([7.53032276, 1.1 ]), fval=343.61078177168645, rho=-0.1008027544343953, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 6, 7, 10, 11, 12, 15]), old_indices_discarded=array([ 2, 4, 5, 8, 9, 13, 14, 16, 17]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.53032276, 1.1 ]), radius=0.1969868202112012, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 6, 7, 10, 11, 12, 15, 18]), model=ScalarModel(intercept=460.1437147096384, linear_terms=array([-47.06325937, -68.55904581]), square_terms=array([[ 7.32151626, 12.11173414], + [12.11173414, 20.54517742]]), scale=array([0.17457502, 0.08728751]), shift=array([7.53032276, 1.01271249])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=19, candidate_x=array([7.70489778, 1.1 ]), index=15, x=array([7.53032276, 1.1 ]), fval=343.61078177168645, rho=-0.08350368497772914, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 3, 6, 7, 10, 11, 12, 15, 18]), old_indices_discarded=array([ 1, 4, 14, 17]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.53032276, 1.1 ]), radius=0.0984934101056006, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 7, 10, 12, 15, 18, 19]), model=ScalarModel(intercept=484.24067620553103, linear_terms=array([ 1.0644039 , -197.58980592]), square_terms=array([[ 1.25819630e-02, -1.13285371e+00], + [-1.13285371e+00, 1.21597578e+02]]), scale=array([0.08728751, 0.04364376]), shift=array([7.53032276, 1.05635624])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + 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linear_terms=array([ 0.31541082, 171.80675932]), square_terms=array([[1.35745386e-02, 3.15410823e-01], + [3.15410823e-01, 1.71806759e+02]]), scale=array([0.04364376, 0.02182188]), shift=array([7.53032276, 1.07817812])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=21, candidate_x=array([7.53032276, 1.05635624]), index=15, x=array([7.53032276, 1.1 ]), fval=343.61078177168645, rho=-0.4842496873250061, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([12, 15, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.53032276, 1.1 ]), radius=0.02462335252640015, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([12, 15, 20, 21]), model=ScalarModel(intercept=370.3749860727118, linear_terms=array([ 0.50457648, -31.70198232]), square_terms=array([[ 3.50286658e-03, -1.84087878e-01], + [-1.84087878e-01, 9.89435283e+00]]), scale=array([0.02182188, 0.01091094]), shift=array([7.53032276, 1.08908906])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=22, candidate_x=array([7.50850088, 1.1 ]), index=22, x=array([7.50850088, 1.1 ]), fval=343.28224909887444, rho=1.030732234655116, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([12, 15, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.02182187800381108, relative_step_length=0.8862269254527609, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.50850088, 1.1 ]), radius=0.0492467050528003, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([12, 15, 19, 20, 21, 22]), model=ScalarModel(intercept=406.34011345197075, linear_terms=array([ 1.34943418, -82.83589813]), square_terms=array([[ 1.35463583e-02, -7.24364450e-01], + [-7.24364450e-01, 3.95818331e+01]]), scale=array([0.04364376, 0.02182188]), shift=array([7.50850088, 1.07817812])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=23, candidate_x=array([7.46485713, 1.1 ]), index=23, x=array([7.46485713, 1.1 ]), fval=342.67788390884755, rho=0.9774681554682292, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([12, 15, 19, 20, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.04364375600762216, relative_step_length=0.8862269254527609, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.46485713, 1.1 ]), radius=0.0984934101056006, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 12, 15, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=485.2798968490961, linear_terms=array([ 2.10790896, -203.95653243]), square_terms=array([[ 2.49785511e-02, -1.75495211e+00], + [-1.75495211e+00, 1.28169197e+02]]), scale=array([0.08728751, 0.04364376]), shift=array([7.46485713, 1.05635624])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=24, candidate_x=array([7.37756961, 1.1 ]), index=24, x=array([7.37756961, 1.1 ]), fval=341.3356764476497, rho=3.9422476031380995, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 7, 10, 12, 15, 19, 20, 21, 22, 23]), old_indices_discarded=array([ 0, 18]), step_length=0.08728751201524432, relative_step_length=0.8862269254527609, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.37756961, 1.1 ]), radius=0.1969868202112012, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 12, 15, 20, 21, 22, 23, 24]), model=ScalarModel(intercept=752.8426020338497, linear_terms=array([ 11.7913993 , -670.97057512]), square_terms=array([[ 1.94255131e-01, -9.99673672e+00], + [-9.99673672e+00, 5.23616394e+02]]), scale=array([0.17457502, 0.08728751]), shift=array([7.37756961, 1.01271249])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=25, candidate_x=array([7.20299459, 1.1 ]), index=25, x=array([7.20299459, 1.1 ]), fval=338.7295161935011, rho=1.5352615607065352, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 7, 10, 12, 15, 20, 21, 22, 23, 24]), old_indices_discarded=array([ 0, 1, 3, 4, 6, 11, 14, 17, 18, 19]), step_length=0.17457502403048775, relative_step_length=0.8862269254527565, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.20299459, 1.1 ]), radius=0.3939736404224024, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 12, 15, 21, 22, 23, 24, 25]), model=ScalarModel(intercept=1672.4520919741888, linear_terms=array([ 59.20275575, -2396.40015865]), square_terms=array([[ 1.34997550e+00, -5.33739278e+01], + [-5.33739278e+01, 2.12746527e+03]]), scale=array([0.34915005, 0.17457502]), shift=array([7.20299459, 0.92542498])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=26, candidate_x=array([6.85384454, 1.1 ]), index=26, x=array([6.85384454, 1.1 ]), fval=334.0684862360313, rho=0.9043799998208798, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 7, 10, 12, 15, 21, 22, 23, 24, 25]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 8, 9, 11, 13, 14, 16, 17, 18, 19, 20]), step_length=0.3491500480609764, relative_step_length=0.8862269254527587, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.85384454, 1.1 ]), radius=0.7879472808448048, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 11, 20, 21, 23, 24, 25, 26]), model=ScalarModel(intercept=687.9675100097688, linear_terms=array([ 27.86324984, -497.24927831]), square_terms=array([[ 1.04186318, -18.63808543], + [-18.63808543, 351.96003495]]), scale=array([0.6983001, 0.3 ]), shift=array([6.85384454, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=27, candidate_x=array([6.15554445, 1.1 ]), index=27, x=array([6.15554445, 1.1 ]), fval=328.1547443088514, rho=0.6794098978593791, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 11, 20, 21, 23, 24, 25, 26]), old_indices_discarded=array([ 0, 1, 2, 4, 5, 6, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, + 22]), step_length=0.6983000961219519, relative_step_length=0.8862269254527576, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.15554445, 1.1 ]), radius=1.5758945616896096, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 11, 19, 21, 24, 25, 26, 27]), model=ScalarModel(intercept=541.024874876126, linear_terms=array([ 183.74810222, -385.25100262]), square_terms=array([[ 71.61201793, -157.85600624], + [-157.85600624, 351.48172557]]), scale=array([1.39660019, 0.3 ]), shift=array([6.15554445, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=28, candidate_x=array([4.75894425, 0.99408832]), index=27, x=array([6.15554445, 1.1 ]), fval=328.1547443088514, rho=-51.441844650874955, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 11, 19, 21, 24, 25, 26, 27]), old_indices_discarded=array([ 0, 1, 2, 4, 5, 6, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 20, + 22, 23]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.15554445, 1.1 ]), radius=0.7879472808448048, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 1, 3, 7, 10, 11, 24, 25, 26, 27]), model=ScalarModel(intercept=474.6518866289607, linear_terms=array([ 129.29833356, -282.36003754]), square_terms=array([[ 48.05094448, -113.81690171], + [-113.81690171, 273.4429247 ]]), scale=array([0.6983001, 0.3 ]), shift=array([6.15554445, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=29, candidate_x=array([5.45724435, 0.98491223]), index=27, x=array([6.15554445, 1.1 ]), fval=328.1547443088514, rho=-55.08218130865472, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 1, 3, 7, 10, 11, 24, 25, 26, 27]), old_indices_discarded=array([ 0, 2, 4, 5, 6, 8, 9, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, + 22, 23, 28]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.15554445, 1.1 ]), radius=0.3939736404224024, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 1, 3, 7, 10, 24, 25, 26, 27, 29]), model=ScalarModel(intercept=673.0196062200134, linear_terms=array([ -48.84501593, -283.59249035]), square_terms=array([[ 3.16938894, 19.7566563 ], + [ 19.7566563 , 125.67239874]]), scale=array([0.34915005, 0.17457502]), shift=array([6.15554445, 0.92542498])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=30, candidate_x=array([6.50469449, 1.1 ]), index=27, x=array([6.15554445, 1.1 ]), fval=328.1547443088514, rho=-0.0754593477356818, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 1, 3, 7, 10, 24, 25, 26, 27, 29]), old_indices_discarded=array([ 0, 6, 11, 12, 15, 18, 19, 20, 21, 22, 23, 28]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.15554445, 1.1 ]), radius=0.1969868202112012, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([26, 27, 29, 30]), model=ScalarModel(intercept=764.1130434811344, linear_terms=array([ 20.06484004, -826.75640293]), square_terms=array([[ 4.93284740e-01, -1.95514687e+01], + [-1.95514687e+01, 7.81578372e+02]]), scale=array([0.17457502, 0.08728751]), shift=array([6.15554445, 1.01271249])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=31, candidate_x=array([5.98096942, 1.1 ]), index=31, x=array([5.98096942, 1.1 ]), fval=328.0443136638892, rho=0.41401820841177545, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([26, 27, 29, 30]), old_indices_discarded=array([], dtype=int64), step_length=0.17457502403048775, relative_step_length=0.8862269254527565, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.98096942, 1.1 ]), radius=0.3939736404224024, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 25, 26, 27, 28, 29, 30, 31]), model=ScalarModel(intercept=1781.353556601766, linear_terms=array([ 10.93716932, -2746.79789078]), square_terms=array([[ 5.27680505e-02, -1.13800463e+01], + [-1.13800463e+01, 2.59482090e+03]]), scale=array([0.34915005, 0.17457502]), shift=array([5.98096942, 0.92542498])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=32, candidate_x=array([6.33011947, 1.1 ]), index=31, x=array([5.98096942, 1.1 ]), fval=328.0443136638892, rho=-2.151335538932381, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 7, 10, 25, 26, 27, 28, 29, 30, 31]), old_indices_discarded=array([ 0, 1, 3, 11, 12, 15, 18, 19, 20, 21, 22, 23, 24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.98096942, 1.1 ]), radius=0.1969868202112012, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([26, 27, 29, 30, 31, 32]), model=ScalarModel(intercept=745.2066316366386, linear_terms=array([ 20.20317251, -811.00927104]), square_terms=array([[ 5.25594199e-01, -2.02581872e+01], + [-2.02581872e+01, 7.87518596e+02]]), scale=array([0.17457502, 0.08728751]), shift=array([5.98096942, 1.01271249])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=33, candidate_x=array([5.99924245, 1.1 ]), index=33, x=array([5.99924245, 1.1 ]), fval=328.0186137820637, rho=8.925939431058925, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([26, 27, 29, 30, 31, 32]), old_indices_discarded=array([], dtype=int64), step_length=0.01827302390022645, relative_step_length=0.09276267255156899, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.99924245, 1.1 ]), radius=0.1969868202112012, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([27, 29, 30, 31, 32, 33, 34]), model=ScalarModel(intercept=582.0194811496058, linear_terms=array([ -28.85282245, -443.47361967]), square_terms=array([[ 1.2267104 , 21.48342558], + [ 21.48342558, 395.80538633]]), scale=array([0.17457502, 0.08728751]), shift=array([5.99924245, 1.01271249])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=35, candidate_x=array([6.17381747, 1.1 ]), index=33, x=array([5.99924245, 1.1 ]), fval=328.0186137820637, rho=-0.025063369162145882, accepted=False, new_indices=array([34]), old_indices_used=array([27, 29, 30, 31, 32, 33]), old_indices_discarded=array([26]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.99924245, 1.1 ]), radius=0.0984934101056006, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([27, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=373.9527215685082, linear_terms=array([ 3.76666514, -91.66730808]), square_terms=array([[ 0.16148106, -3.82858358], + [-3.82858358, 91.45176386]]), scale=array([0.08728751, 0.04364376]), shift=array([5.99924245, 1.05635624])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=36, candidate_x=array([6.03271205, 1.1 ]), index=36, x=array([6.03271205, 1.1 ]), fval=328.0044517781902, rho=1.1929883954298723, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([27, 31, 32, 33, 34, 35]), old_indices_discarded=array([], dtype=int64), step_length=0.03346960318564207, relative_step_length=0.33981566025338483, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.03271205, 1.1 ]), radius=0.0984934101056006, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([27, 30, 31, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=375.50251992573067, linear_terms=array([ 3.71849567, -93.26586519]), square_terms=array([[ 0.15120155, -3.70612343], + [-3.70612343, 91.51845029]]), scale=array([0.08728751, 0.04364376]), shift=array([6.03271205, 1.05635624])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=37, candidate_x=array([6.02556965, 1.1 ]), index=36, x=array([6.03271205, 1.1 ]), fval=328.0044517781902, rho=-41.29603041690138, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 30, 31, 32, 33, 34, 35, 36]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.03271205, 1.1 ]), radius=0.0492467050528003, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([27, 31, 33, 34, 35, 36, 37]), model=ScalarModel(intercept=340.27567010709225, linear_terms=array([ 0.97003223, -23.69091442]), square_terms=array([[ 0.04231341, -0.97941127], + [-0.97941127, 22.84408401]]), scale=array([0.04364376, 0.02182188]), shift=array([6.03271205, 1.07817812])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=38, candidate_x=array([6.04238597, 1.1 ]), index=38, x=array([6.04238597, 1.1 ]), fval=327.9885858870018, rho=15.263550557757792, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([27, 31, 33, 34, 35, 36, 37]), old_indices_discarded=array([], dtype=int64), step_length=0.009673921240452188, relative_step_length=0.1964379389459702, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.04238597, 1.1 ]), radius=0.0492467050528003, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([27, 31, 33, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=82.00226854986404, linear_terms=array([1.12355056e-03, 1.64004537e+02]), square_terms=array([[4.23535962e-02, 1.12355056e-03], + [1.12355056e-03, 1.64004537e+02]]), scale=array([0.04364376, 0.02182188]), shift=array([6.04238597, 1.07817812])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=40, candidate_x=array([6.04238597, 1.05635624]), index=38, x=array([6.04238597, 1.1 ]), fval=327.9885858870018, rho=-0.494329253043876, accepted=False, new_indices=array([39]), old_indices_used=array([27, 31, 33, 35, 36, 37, 38]), old_indices_discarded=array([34]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.04238597, 1.1 ]), radius=0.02462335252640015, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([27, 31, 33, 36, 37, 38, 39, 40]), model=ScalarModel(intercept=343.91263589535333, linear_terms=array([ 0.41921903, -24.11112863]), square_terms=array([[ 1.06977552e-02, -4.15677541e-01], + [-4.15677541e-01, 1.64193433e+01]]), scale=array([0.02182188, 0.01091094]), shift=array([6.04238597, 1.08908906])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=41, candidate_x=array([6.03516184, 1.1 ]), index=38, x=array([6.04238597, 1.1 ]), fval=327.9885858870018, rho=-8.610781824853877, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 31, 33, 36, 37, 38, 39, 40]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.04238597, 1.1 ]), radius=0.012311676263200075, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 33, 36, 37, 38, 39, 40, 41]), model=ScalarModel(intercept=333.9166749762653, linear_terms=array([ 0.11087176, -7.95349274]), square_terms=array([[ 2.77123252e-03, -1.05793252e-01], + [-1.05793252e-01, 4.10375275e+00]]), scale=array([0.01091094, 0.00545547]), shift=array([6.04238597, 1.09454453])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=42, candidate_x=array([6.03147503, 1.1 ]), index=38, x=array([6.04238597, 1.1 ]), fval=327.9885858870018, rho=-5.532076896558681, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 36, 37, 38, 39, 40, 41]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.04238597, 1.1 ]), radius=0.006155838131600038, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([36, 37, 38, 41, 42]), model=ScalarModel(intercept=81.99616915231834, linear_terms=array([-5.57837938e-03, 1.63992338e+02]), square_terms=array([[ 6.95313444e-04, -5.57837938e-03], + [-5.57837938e-03, 1.63992338e+02]]), scale=array([0.00545547, 0.00272773]), shift=array([6.04238597, 1.09727227])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=43, candidate_x=array([6.04238597, 1.09454453]), index=38, x=array([6.04238597, 1.1 ]), fval=327.9885858870018, rho=-0.017449574377679796, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([36, 37, 38, 41, 42]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.04238597, 1.1 ]), radius=0.003077919065800019, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([36, 38, 41, 42, 43]), model=ScalarModel(intercept=328.98107778773453, linear_terms=array([ 0.00257064, -1.13993963]), square_terms=array([[ 1.73497189e-04, -7.02696057e-03], + [-7.02696057e-03, 2.91307159e-01]]), scale=array([0.00272773, 0.00136387]), shift=array([6.04238597, 1.09863613])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=44, candidate_x=array([6.04511371, 1.1 ]), index=38, x=array([6.04238597, 1.1 ]), fval=327.9885858870018, rho=-1.9565035599151293, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([36, 38, 41, 42, 43]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.04238597, 1.1 ]), radius=0.0015389595329000094, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([38, 41, 43, 44]), model=ScalarModel(intercept=328.45309645909305, linear_terms=array([ 0.0020121 , -0.49633079]), square_terms=array([[ 4.29178526e-05, -1.74592843e-03], + [-1.74592843e-03, 7.28313036e-02]]), scale=array([0.00136387, 0.00068193]), shift=array([6.04238597, 1.09931807])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=45, candidate_x=array([6.0410221, 1.1 ]), index=45, x=array([6.0410221, 1.1 ]), fval=327.98556983184415, rho=12.32505267649563, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([38, 41, 43, 44]), old_indices_discarded=array([], dtype=int64), step_length=0.0013638673752378594, relative_step_length=0.8862269254525446, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.0410221, 1.1 ]), radius=0.003077919065800019, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([36, 38, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=328.98235930232534, linear_terms=array([ 0.00410544, -1.13528318]), square_terms=array([[ 1.72838051e-04, -7.01449154e-03], + [-7.01449154e-03, 2.91327554e-01]]), scale=array([0.00272773, 0.00136387]), shift=array([6.0410221 , 1.09863613])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=46, candidate_x=array([6.04374984, 1.1 ]), index=45, x=array([6.0410221, 1.1 ]), fval=327.98556983184415, rho=-2.667493300675556, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([36, 38, 41, 42, 43, 44, 45]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.0410221, 1.1 ]), radius=0.0015389595329000094, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([38, 41, 43, 44, 45, 46]), model=ScalarModel(intercept=328.44972430739466, linear_terms=array([ 0.00202841, -0.49477126]), square_terms=array([[ 4.29037016e-05, -1.74571541e-03], + [-1.74571541e-03, 7.28320789e-02]]), scale=array([0.00136387, 0.00068193]), shift=array([6.0410221 , 1.09931807])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=47, candidate_x=array([6.03965824, 1.1 ]), index=47, x=array([6.03965824, 1.1 ]), fval=327.98429259556826, rho=4.889085736765836, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([38, 41, 43, 44, 45, 46]), old_indices_discarded=array([], dtype=int64), step_length=0.0013638673752378594, relative_step_length=0.8862269254525446, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.03965824, 1.1 ]), radius=0.003077919065800019, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([36, 38, 41, 42, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=328.97968288575123, linear_terms=array([ 0.00413303, -1.13194477]), square_terms=array([[ 1.72725488e-04, -7.01224971e-03], + [-7.01224971e-03, 2.91334569e-01]]), scale=array([0.00272773, 0.00136387]), shift=array([6.03965824, 1.09863613])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=48, candidate_x=array([6.04238597, 1.1 ]), index=47, x=array([6.03965824, 1.1 ]), fval=327.98429259556826, rho=-1.5372396672850397, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([36, 38, 41, 42, 43, 44, 45, 46, 47]), old_indices_discarded=array([37]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.03965824, 1.1 ]), radius=0.0015389595329000094, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([36, 38, 41, 43, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=328.4488892463763, linear_terms=array([ 0.00084455, -0.49316821]), square_terms=array([[ 4.31680626e-05, -1.75188159e-03], + [-1.75188159e-03, 7.28330788e-02]]), scale=array([0.00136387, 0.00068193]), shift=array([6.03965824, 1.09931807])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=49, candidate_x=array([6.0410221, 1.1 ]), index=47, x=array([6.03965824, 1.1 ]), fval=327.98429259556826, rho=-1.4419895015300965, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([36, 38, 41, 43, 44, 45, 46, 47, 48]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.03965824, 1.1 ]), radius=0.0007694797664500047, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([38, 45, 47, 48, 49]), model=ScalarModel(intercept=81.99595206365382, linear_terms=array([5.55897644e-04, 1.63991904e+02]), square_terms=array([[1.10604556e-05, 5.55897644e-04], + [5.55897644e-04, 1.63991904e+02]]), scale=array([0.00068193, 0.00034097]), shift=array([6.03965824, 1.09965903])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + 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model=ScalarModel(intercept=328.0990818934895, linear_terms=array([ 0.00042599, -0.11708902]), square_terms=array([[ 2.81678772e-06, -1.12313719e-04], + [-1.12313719e-04, 4.59943641e-03]]), scale=array([0.00034097, 0.00017048]), shift=array([6.03965824, 1.09982952])), vector_model=VectorModel(intercepts=array([ 0.16523807, 0.51247424, 0.62394851, 1.25726166, + 1.80642194, 2.52072732, 2.53217272, -1.3933831 , + -3.46208642, -5.39227137, -9.89407466, -14.24933131]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7879472808448048, shift=array([7.87947281, 1.09450825])), candidate_index=51, candidate_x=array([6.03931727, 1.1 ]), index=51, x=array([6.03931727, 1.1 ]), fval=327.984001342669, rho=0.9327044045903453, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([45, 47, 49, 50]), old_indices_discarded=array([], dtype=int64), step_length=0.00034096684380990894, relative_step_length=0.8862269254536987, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 52 entries., 'history': {'params': [{'CRRA': 7.879472808448048, 'DiscFac': 1.0945082521472478}, {'CRRA': 7.181172712326096, 'DiscFac': 0.5084104012442524}, {'CRRA': 8.540392915867926, 'DiscFac': 0.5}, {'CRRA': 7.181172712326096, 'DiscFac': 0.5970983532003942}, {'CRRA': 8.317580122737366, 'DiscFac': 1.1}, {'CRRA': 8.57777290457, 'DiscFac': 0.5}, {'CRRA': 7.85919300418376, 'DiscFac': 0.5}, {'CRRA': 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{'CRRA': 4.758944253903075, 'DiscFac': 0.9940883236648268}, {'CRRA': 5.457244350025028, 'DiscFac': 0.9849122291338899}, {'CRRA': 6.504694494207956, 'DiscFac': 1.1}, {'CRRA': 5.980969422116492, 'DiscFac': 1.1}, {'CRRA': 6.330119470177468, 'DiscFac': 1.1}, {'CRRA': 5.999242446016718, 'DiscFac': 1.1}, {'CRRA': 6.173817470047206, 'DiscFac': 0.925424975969512}, {'CRRA': 6.173817470047206, 'DiscFac': 1.1}, {'CRRA': 6.0327120492023605, 'DiscFac': 1.1}, {'CRRA': 6.0255696487355195, 'DiscFac': 1.1}, {'CRRA': 6.042385970442813, 'DiscFac': 1.1}, {'CRRA': 6.086029726450435, 'DiscFac': 1.1}, {'CRRA': 6.042385970442813, 'DiscFac': 1.0563562439923782}, {'CRRA': 6.035161837634803, 'DiscFac': 1.1}, {'CRRA': 6.031475031440907, 'DiscFac': 1.1}, {'CRRA': 6.042385970442813, 'DiscFac': 1.0945445304990473}, {'CRRA': 6.045113705193289, 'DiscFac': 1.1}, {'CRRA': 6.041022103067575, 'DiscFac': 1.1}, {'CRRA': 6.043749837818051, 'DiscFac': 1.1}, {'CRRA': 6.039658235692337, 'DiscFac': 1.1}, {'CRRA': 6.042385970442814, 'DiscFac': 1.1}, {'CRRA': 6.041022103067575, 'DiscFac': 1.1}, {'CRRA': 6.039657692952983, 'DiscFac': 1.099318066313301}, {'CRRA': 6.039317268848527, 'DiscFac': 1.1}], 'criterion': [362.2293826755968, 1187.743679800527, 1159.7095068806466, 1174.0418141897962, 355.68144300647845, 1158.9666410651712, 1173.5778504276218, 864.5109614488913, 1088.0491210454918, 373.08167200198454, 642.3720812043641, 1187.1737226708901, 342.372356550768, 359.56273854626926, 354.28543556924024, 343.61078177168645, 364.74171151073244, 354.28543556924024, 348.88649468702897, 346.2236761294166, 344.9346473761243, 510.0055207370543, 343.28224909887444, 342.67788390884755, 341.3356764476497, 338.7295161935011, 334.0684862360313, 328.1547443088514, 944.9326629116758, 965.8465398719559, 330.23015294186956, 328.0443136638892, 328.9403298556242, 328.01861378206365, 1091.3153770562494, 328.1879429485059, 328.0044517781902, 328.0253552638186, 327.9885858870018, 328.07914476813323, 490.1330665276323, 327.9936335742644, 328.00901523323273, 333.71177889623465, 327.9971349694465, 327.98556983184415, 327.99309918135714, 327.98429259556826, 327.9885858870018, 327.98556983184415, 328.47104519062555, 327.984001342669], 'runtime': [0.0, 1.7765155639999648, 1.8151272790000803, 1.8593051980001292, 1.8948058890000539, 1.9326183160001165, 1.9705387630001496, 2.0124783999999636, 2.0458206460002657, 2.096703477000119, 2.1292357160000392, 2.176972142000068, 2.2070135070002834, 4.35327257300014, 6.049714535000021, 7.726529704999848, 9.414594586000021, 11.075412772000163, 12.730456283999956, 14.406838406000134, 16.128083006999987, 17.833465021999928, 19.535505804999957, 21.230333686999984, 23.028694962999907, 24.711374977000105, 26.380975154000225, 28.050134140999944, 29.71698349799999, 31.416988460000084, 33.12656330100026, 34.8171338950001, 36.516174843000044, 38.212346643000274, 39.884359208000205, 41.56874796200009, 43.223786692999965, 44.90209642800028, 46.58122632100003, 48.384702152000045, 50.08727036900018, 51.832894017999934, 53.547060395000244, 55.24626221800008, 56.92538110700025, 58.679952487000264, 60.40381566099995, 62.06925174800017, 63.74787286599985, 65.45953768300024, 67.17978708300006, 68.86361415100009], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40]}}], 'exploration_sample': array([[ 6.03936549, 1.1 ], + [12.321875 , 1.08125 ], + [ 2.28125 , 1.0625 ], + [17.6375 , 1.025 ], + [14.09375 , 0.9875 ], + [16.45625 , 0.9125 ], + [18.81875 , 0.5375 ], + [17.046875 , 0.63125 ], + [15.275 , 0.65 ], + [11.73125 , 0.7625 ], + [ 7.596875 , 0.93125 ], + [10.55 , 0.8 ], + [12.9125 , 0.575 ], + [ 9.36875 , 0.8375 ], + [ 5.825 , 0.95 ], + [ 8.1875 , 0.725 ], + [ 7.00625 , 0.6125 ], + [ 3.4625 , 0.875 ], + [ 4.64375 , 0.6875 ], + [ 2.871875 , 0.78125 ]]), 'exploration_results': array([ 327.98396556, 444.08066157, 540.65943804, 544.96572798, + 689.47008041, 769.45866328, 909.59665878, 921.36892514, + 962.43294355, 1011.74494613, 1024.37390697, 1032.75469874, + 1035.57943478, 1049.83280992, 1065.59145403, 1123.108822 , + 1175.63544527, 1221.64398624, 1232.43946077, 1265.07252504])}}" diff --git a/content/tables/min/IndShockSub(Stock)Market_estimate_results.csv b/content/tables/min/IndShockSub(Stock)Market_estimate_results.csv new file mode 100644 index 0000000..6e78c41 --- /dev/null +++ b/content/tables/min/IndShockSub(Stock)Market_estimate_results.csv @@ -0,0 +1,2278 @@ +CRRA,7.14171909860584 +DiscFac,0.9702277827402849 +time_to_estimate,73.5582926273346 +params,"{'CRRA': 7.14171909860584, 'DiscFac': 0.9702277827402849}" +criterion,533.1537602800875 +start_criterion,533.15737536874 +start_params,"{'CRRA': 7.14177124762114, 'DiscFac': 0.9700614043534316}" +algorithm,multistart_tranquilo_ls +direction,minimize +n_free,2 +message,Absolute params change smaller than tolerance. +success, +n_criterion_evaluations, +n_derivative_evaluations, +n_iterations, +history,"{'params': [{'CRRA': 7.14177124762114, 'DiscFac': 0.9700614043534316}, {'CRRA': 6.7868466809109, 'DiscFac': 0.5}, {'CRRA': 7.774694245127759, 'DiscFac': 0.5}, {'CRRA': 6.508848250114521, 'DiscFac': 0.5822476891055735}, {'CRRA': 7.1224518033555935, 'DiscFac': 0.5}, {'CRRA': 6.508848250114521, 'DiscFac': 1.1}, {'CRRA': 7.774694245127759, 'DiscFac': 1.0997076620875013}, {'CRRA': 7.774694245127759, 'DiscFac': 0.5}, {'CRRA': 7.774694245127759, 'DiscFac': 0.8015740448404156}, {'CRRA': 6.508848250114521, 'DiscFac': 0.86701952781014}, {'CRRA': 6.643685659085663, 'DiscFac': 1.1}, {'CRRA': 6.7385739033781045, 'DiscFac': 0.5}, {'CRRA': 6.952085805903453, 'DiscFac': 1.1}, {'CRRA': 6.508848250114521, 'DiscFac': 0.8589548110844714}, {'CRRA': 7.458232746374449, 'DiscFac': 0.7931436368201807}, {'CRRA': 6.983540498244485, 'DiscFac': 0.895173793706215}, {'CRRA': 7.228788139736241, 'DiscFac': 0.9428726530440706}, {'CRRA': 7.097118053919887, 'DiscFac': 0.9665399827624269}, {'CRRA': 7.164075291790564, 'DiscFac': 0.9691220965048899}, {'CRRA': 7.130654871032354, 'DiscFac': 0.9728334071792328}, {'CRRA': 7.136152673415857, 'DiscFac': 0.9676722997627686}, {'CRRA': 7.144569087997866, 'DiscFac': 0.9705858692354545}, {'CRRA': 7.141216169840549, 'DiscFac': 0.9713410805045143}, {'CRRA': 7.141768129279196, 'DiscFac': 0.9693639727263295}, {'CRRA': 7.1421045480761505, 'DiscFac': 0.9701699258755666}, {'CRRA': 7.14171909860584, 'DiscFac': 0.9702277827402849}], 'criterion': [533.15737536874, 1161.6255596324959, 1125.3550485373012, 1148.0325263179116, 1148.9622366552176, 1115.600102107612, 1072.682807744145, 1125.3550485373012, 814.3281527350895, 762.7520449523593, 1109.3316900326913, 1163.50801717681, 1096.7008859842308, 785.9518656250486, 860.9640525487661, 648.6439339079293, 548.8324203759328, 533.8638831122132, 533.4640229418753, 533.537825382944, 533.5621230706874, 533.2947213944947, 533.3675712639503, 533.4630789324145, 533.1576972061284, 533.1537602800875], 'runtime': [0.0, 1.3984181969999554, 1.4335604479999802, 1.467527397999902, 1.5055896680000842, 1.5399199890000546, 1.574796397, 1.6134832329998972, 1.6568872610000653, 1.7109269620000305, 1.750221017000058, 1.7807563960000152, 1.84237283199991, 3.529250513000079, 4.813397466999959, 6.06371923100005, 7.346313510000073, 8.639765625999871, 9.935525670000061, 11.23562790699998, 12.586682291999978, 13.944498423999903, 15.273930696999969, 16.61528579600008, 17.93765505500005, 19.28534189800007], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]}" +convergence_report, +multistart_info,"{'start_parameters': [{'CRRA': 7.14177124762114, 'DiscFac': 0.9700614043534316}, {'CRRA': 7.275031175627114, 'DiscFac': 0.9588114544912715}], 'local_optima': [Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute params change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 6.781e-06* 6.781e-06* +relative_params_change 0.0001716 0.0001716 +absolute_criterion_change 0.003615 0.003615 +absolute_params_change 0.0001744 0.0001744 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute params change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 7.135e-06* 0.002522 +relative_params_change 0.0007284 0.01496 +absolute_criterion_change 0.003805 1.345 +absolute_params_change 0.0007105 0.09367 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 7.14177124762114, 'DiscFac': 0.9700614043534316}, {'CRRA': 7.596874999999999, 'DiscFac': 0.9312500000000001}, {'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 16.45625, 'DiscFac': 0.9125000000000001}, {'CRRA': 17.046875, 'DiscFac': 0.6312500000000001}, {'CRRA': 9.368749999999999, 'DiscFac': 0.8375}, {'CRRA': 10.549999999999999, 'DiscFac': 0.8}, {'CRRA': 11.73125, 'DiscFac': 0.7625000000000001}, {'CRRA': 15.274999999999999, 'DiscFac': 0.65}, {'CRRA': 18.81875, 'DiscFac': 0.5375}, {'CRRA': 14.093749999999998, 'DiscFac': 0.9875}, {'CRRA': 12.9125, 'DiscFac': 0.575}, {'CRRA': 17.6375, 'DiscFac': 1.0250000000000001}, {'CRRA': 8.1875, 'DiscFac': 0.7250000000000001}, {'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 7.00625, 'DiscFac': 0.6125}, {'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 2.871875, 'DiscFac': 0.78125}, {'CRRA': 2.28125, 'DiscFac': 1.0625}], 'exploration_results': array([ 533.15737537, 554.91392947, 580.45087475, 623.67135041, + 630.89444653, 637.20876422, 645.36720654, 650.8682765 , + 661.36967475, 665.67712442, 701.74175737, 833.17265466, + 899.56232297, 924.72647063, 987.48155993, 1111.46554906, + 1124.06873967, 1196.97223495, 1247.86777405, 1292.92183431])}" +algorithm_output,"{'states': [State(trustregion=Region(center=array([7.14177125, 0.9700614 ]), radius=0.714177124762114, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=533.15737536874, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.714177124762114, shift=array([7.14177125, 0.9700614 ])), vector_model=VectorModel(intercepts=array([ 1.55437059, 3.34214162, 4.09540253, 5.14811998, + 6.18204764, 7.58497401, 7.99989814, -1.17627655, + -4.69083048, -6.78876609, -9.69389851, -12.37341873]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.714177124762114, shift=array([7.14177125, 0.9700614 ])), candidate_index=0, candidate_x=array([7.14177125, 0.9700614 ]), index=0, x=array([7.14177125, 0.9700614 ]), fval=533.15737536874, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([7.14177125, 0.9700614 ]), radius=0.714177124762114, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=442.1495797287485, linear_terms=array([ 1.41452419, -238.64908191]), square_terms=array([[8.82252168e-01, 2.88981448e+01], + [2.88981448e+01, 1.36145238e+03]]), scale=array([0.632923, 0.3 ]), shift=array([7.14177125, 0.8 ])), vector_model=VectorModel(intercepts=array([ 1.55437059, 3.34214162, 4.09540253, 5.14811998, + 6.18204764, 7.58497401, 7.99989814, -1.17627655, + -4.69083048, -6.78876609, -9.69389851, -12.37341873]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.714177124762114, shift=array([7.14177125, 0.9700614 ])), candidate_index=13, candidate_x=array([6.50884825, 0.85895481]), index=0, x=array([7.14177125, 0.9700614 ]), fval=533.15737536874, rho=-2.28307560842845, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.14177125, 0.9700614 ]), radius=0.357088562381057, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 4, 6, 9, 10, 11, 12, 13]), model=ScalarModel(intercept=422.6414371114164, linear_terms=array([ -4.184942 , 114.47022955]), square_terms=array([[ 28.6033514 , 131.8146705 ], + [131.8146705 , 657.10282209]]), scale=array([0.3164615 , 0.22320005]), shift=array([7.14177125, 0.87679995])), vector_model=VectorModel(intercepts=array([ 1.55437059, 3.34214162, 4.09540253, 5.14811998, + 6.18204764, 7.58497401, 7.99989814, -1.17627655, + -4.69083048, -6.78876609, -9.69389851, -12.37341873]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.714177124762114, shift=array([7.14177125, 0.9700614 ])), candidate_index=14, candidate_x=array([7.45823275, 0.79314364]), index=0, x=array([7.14177125, 0.9700614 ]), fval=533.15737536874, rho=-2.321101997346237, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 4, 6, 9, 10, 11, 12, 13]), old_indices_discarded=array([2, 3, 5, 7, 8]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.14177125, 0.9700614 ]), radius=0.1785442811905285, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 4, 9, 10, 11, 12, 13, 14]), model=ScalarModel(intercept=484.33037893683667, linear_terms=array([ 3.99179309, 119.65638941]), square_terms=array([[7.78942951e-02, 3.82963431e+00], + [3.82963431e+00, 2.74752036e+02]]), scale=array([0.15823075, 0.14408467]), shift=array([7.14177125, 0.95591533])), vector_model=VectorModel(intercepts=array([ 1.55437059, 3.34214162, 4.09540253, 5.14811998, + 6.18204764, 7.58497401, 7.99989814, -1.17627655, + -4.69083048, -6.78876609, -9.69389851, -12.37341873]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.714177124762114, shift=array([7.14177125, 0.9700614 ])), candidate_index=15, candidate_x=array([6.9835405 , 0.89517379]), index=0, x=array([7.14177125, 0.9700614 ]), fval=533.15737536874, rho=-2.78689113089774, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 4, 9, 10, 11, 12, 13, 14]), old_indices_discarded=array([2, 3, 5, 6, 7, 8]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.14177125, 0.9700614 ]), radius=0.08927214059526425, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 14, 15]), model=ScalarModel(intercept=499.0532038884674, linear_terms=array([9.04855388e-02, 1.02982568e+02]), square_terms=array([[ 4.87329587, 47.47303548], + [ 47.47303548, 480.20443204]]), scale=0.08927214059526425, shift=array([7.14177125, 0.9700614 ])), vector_model=VectorModel(intercepts=array([ 1.55437059, 3.34214162, 4.09540253, 5.14811998, + 6.18204764, 7.58497401, 7.99989814, -1.17627655, + -4.69083048, -6.78876609, -9.69389851, -12.37341873]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.714177124762114, shift=array([7.14177125, 0.9700614 ])), candidate_index=16, candidate_x=array([7.22878814, 0.94287265]), index=0, x=array([7.14177125, 0.9700614 ]), fval=533.15737536874, rho=-0.7542207154863827, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.14177125, 0.9700614 ]), radius=0.04463607029763213, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 15, 16]), model=ScalarModel(intercept=533.1573753687401, linear_terms=array([1.83566489, 8.0288301 ]), square_terms=array([[8.68328033e-03, 4.45498440e-01], + [4.45498440e-01, 9.43298357e+01]]), scale=0.04463607029763213, shift=array([7.14177125, 0.9700614 ])), vector_model=VectorModel(intercepts=array([ 1.55437059, 3.34214162, 4.09540253, 5.14811998, + 6.18204764, 7.58497401, 7.99989814, -1.17627655, + -4.69083048, -6.78876609, -9.69389851, -12.37341873]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.714177124762114, shift=array([7.14177125, 0.9700614 ])), candidate_index=17, candidate_x=array([7.09711805, 0.96653998]), index=0, x=array([7.14177125, 0.9700614 ]), fval=533.15737536874, rho=-0.33065015869998954, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 15, 16]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.14177125, 0.9700614 ]), radius=0.022318035148816064, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 16, 17]), model=ScalarModel(intercept=533.1573753687401, linear_terms=array([-0.14937063, 0.59885935]), square_terms=array([[9.78342324e-03, 5.02961858e-01], + [5.02961858e-01, 2.60110124e+01]]), scale=0.022318035148816064, shift=array([7.14177125, 0.9700614 ])), vector_model=VectorModel(intercepts=array([ 1.55437059, 3.34214162, 4.09540253, 5.14811998, + 6.18204764, 7.58497401, 7.99989814, -1.17627655, + -4.69083048, -6.78876609, -9.69389851, -12.37341873]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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-1.65056925]), square_terms=array([[1.77756833e-03, 1.10987896e-01], + [1.10987896e-01, 6.98434777e+00]]), scale=0.011159017574408032, shift=array([7.14177125, 0.9700614 ])), vector_model=VectorModel(intercepts=array([ 1.55437059, 3.34214162, 4.09540253, 5.14811998, + 6.18204764, 7.58497401, 7.99989814, -1.17627655, + -4.69083048, -6.78876609, -9.69389851, -12.37341873]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.714177124762114, shift=array([7.14177125, 0.9700614 ])), candidate_index=19, candidate_x=array([7.13065487, 0.97283341]), index=0, x=array([7.14177125, 0.9700614 ]), fval=533.15737536874, rho=-1.2685892957313123, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.14177125, 0.9700614 ]), radius=0.005579508787204016, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 18, 19]), model=ScalarModel(intercept=533.1573753687384, linear_terms=array([0.10886642, 0.82071015]), square_terms=array([[4.64204757e-04, 2.81652064e-02], + [2.81652064e-02, 1.75477789e+00]]), scale=0.005579508787204016, shift=array([7.14177125, 0.9700614 ])), vector_model=VectorModel(intercepts=array([ 1.55437059, 3.34214162, 4.09540253, 5.14811998, + 6.18204764, 7.58497401, 7.99989814, -1.17627655, + -4.69083048, -6.78876609, -9.69389851, 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State(trustregion=Region(center=array([7.14177125, 0.9700614 ]), radius=0.002789754393602008, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 19, 20]), model=ScalarModel(intercept=533.1573753687398, linear_terms=array([-0.07319973, -0.10148276]), square_terms=array([[1.16420387e-04, 6.43434400e-03], + [6.43434400e-03, 4.33820453e-01]]), scale=0.002789754393602008, shift=array([7.14177125, 0.9700614 ])), vector_model=VectorModel(intercepts=array([ 1.55437059, 3.34214162, 4.09540253, 5.14811998, + 6.18204764, 7.58497401, 7.99989814, -1.17627655, + -4.69083048, -6.78876609, -9.69389851, -12.37341873]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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scale=0.001394877196801004, shift=array([7.14177125, 0.9700614 ])), vector_model=VectorModel(intercepts=array([ 1.55437059, 3.34214162, 4.09540253, 5.14811998, + 6.18204764, 7.58497401, 7.99989814, -1.17627655, + -4.69083048, -6.78876609, -9.69389851, -12.37341873]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.714177124762114, shift=array([7.14177125, 0.9700614 ])), candidate_index=22, candidate_x=array([7.14121617, 0.97134108]), index=0, x=array([7.14177125, 0.9700614 ]), fval=533.15737536874, rho=-0.4305118795095497, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.14177125, 0.9700614 ]), radius=0.000697438598400502, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 21, 22]), model=ScalarModel(intercept=533.1573753687398, linear_terms=array([0.01410848, 0.09622588]), square_terms=array([[3.52531352e-06, 2.52424871e-04], + [2.52424871e-04, 2.68727971e-02]]), scale=0.000697438598400502, shift=array([7.14177125, 0.9700614 ])), vector_model=VectorModel(intercepts=array([ 1.55437059, 3.34214162, 4.09540253, 5.14811998, + 6.18204764, 7.58497401, 7.99989814, -1.17627655, + -4.69083048, -6.78876609, -9.69389851, -12.37341873]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.714177124762114, shift=array([7.14177125, 0.9700614 ])), candidate_index=23, candidate_x=array([7.14176813, 0.96936397]), index=0, x=array([7.14177125, 0.9700614 ]), fval=533.15737536874, rho=-3.6898109781146147, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.14177125, 0.9700614 ]), radius=0.000348719299200251, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 22, 23]), model=ScalarModel(intercept=533.1573753687397, linear_terms=array([-0.43619623, -0.14415327]), square_terms=array([[0.00042869, 0.00019034], + [0.00019034, 0.00674808]]), scale=0.000348719299200251, shift=array([7.14177125, 0.9700614 ])), vector_model=VectorModel(intercepts=array([ 1.55437059, 3.34214162, 4.09540253, 5.14811998, + 6.18204764, 7.58497401, 7.99989814, -1.17627655, + -4.69083048, -6.78876609, -9.69389851, -12.37341873]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.714177124762114, shift=array([7.14177125, 0.9700614 ])), candidate_index=24, candidate_x=array([7.14210455, 0.97016993]), index=0, x=array([7.14177125, 0.9700614 ]), fval=533.15737536874, rho=-0.0006978398706696238, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.14177125, 0.9700614 ]), radius=0.0001743596496001255, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 23, 24]), model=ScalarModel(intercept=533.1573753687401, linear_terms=array([ 0.0238168 , -0.07315828]), square_terms=array([[ 1.33108636e-06, -9.62454114e-07], + [-9.62454114e-07, 1.68745639e-03]]), scale=0.0001743596496001255, shift=array([7.14177125, 0.9700614 ])), vector_model=VectorModel(intercepts=array([ 1.55437059, 3.34214162, 4.09540253, 5.14811998, + 6.18204764, 7.58497401, 7.99989814, -1.17627655, + -4.69083048, -6.78876609, -9.69389851, -12.37341873]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.714177124762114, shift=array([7.14177125, 0.9700614 ])), candidate_index=25, candidate_x=array([7.1417191 , 0.97022778]), index=25, x=array([7.1417191 , 0.97022778]), fval=533.1537602800875, rho=0.04746438706449571, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.00017435964960002963, relative_step_length=0.9999999999994501, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 26 entries., 'multistart_info': {'start_parameters': [array([7.14177125, 0.9700614 ]), array([7.27503118, 0.95881145])], 'local_optima': [{'solution_x': array([7.1417191 , 0.97022778]), 'solution_criterion': 533.1537602800875, 'states': [State(trustregion=Region(center=array([7.14177125, 0.9700614 ]), radius=0.714177124762114, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=533.15737536874, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.714177124762114, shift=array([7.14177125, 0.9700614 ])), vector_model=VectorModel(intercepts=array([ 1.55437059, 3.34214162, 4.09540253, 5.14811998, + 6.18204764, 7.58497401, 7.99989814, -1.17627655, + -4.69083048, -6.78876609, -9.69389851, -12.37341873]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.714177124762114, shift=array([7.14177125, 0.9700614 ])), candidate_index=0, candidate_x=array([7.14177125, 0.9700614 ]), index=0, x=array([7.14177125, 0.9700614 ]), fval=533.15737536874, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([7.14177125, 0.9700614 ]), radius=0.714177124762114, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=442.1495797287485, linear_terms=array([ 1.41452419, -238.64908191]), square_terms=array([[8.82252168e-01, 2.88981448e+01], + [2.88981448e+01, 1.36145238e+03]]), scale=array([0.632923, 0.3 ]), shift=array([7.14177125, 0.8 ])), vector_model=VectorModel(intercepts=array([ 1.55437059, 3.34214162, 4.09540253, 5.14811998, + 6.18204764, 7.58497401, 7.99989814, -1.17627655, + -4.69083048, -6.78876609, -9.69389851, -12.37341873]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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shift=array([7.14177125, 0.87679995])), vector_model=VectorModel(intercepts=array([ 1.55437059, 3.34214162, 4.09540253, 5.14811998, + 6.18204764, 7.58497401, 7.99989814, -1.17627655, + -4.69083048, -6.78876609, -9.69389851, -12.37341873]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.714177124762114, shift=array([7.14177125, 0.9700614 ])), candidate_index=14, candidate_x=array([7.45823275, 0.79314364]), index=0, x=array([7.14177125, 0.9700614 ]), fval=533.15737536874, rho=-2.321101997346237, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 4, 6, 9, 10, 11, 12, 13]), old_indices_discarded=array([2, 3, 5, 7, 8]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.14177125, 0.9700614 ]), radius=0.1785442811905285, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 4, 9, 10, 11, 12, 13, 14]), model=ScalarModel(intercept=484.33037893683667, linear_terms=array([ 3.99179309, 119.65638941]), square_terms=array([[7.78942951e-02, 3.82963431e+00], + [3.82963431e+00, 2.74752036e+02]]), scale=array([0.15823075, 0.14408467]), shift=array([7.14177125, 0.95591533])), vector_model=VectorModel(intercepts=array([ 1.55437059, 3.34214162, 4.09540253, 5.14811998, + 6.18204764, 7.58497401, 7.99989814, -1.17627655, + -4.69083048, -6.78876609, -9.69389851, -12.37341873]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 14, 15]), model=ScalarModel(intercept=499.0532038884674, linear_terms=array([9.04855388e-02, 1.02982568e+02]), square_terms=array([[ 4.87329587, 47.47303548], + [ 47.47303548, 480.20443204]]), scale=0.08927214059526425, shift=array([7.14177125, 0.9700614 ])), vector_model=VectorModel(intercepts=array([ 1.55437059, 3.34214162, 4.09540253, 5.14811998, + 6.18204764, 7.58497401, 7.99989814, -1.17627655, + -4.69083048, -6.78876609, -9.69389851, -12.37341873]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.714177124762114, shift=array([7.14177125, 0.9700614 ])), candidate_index=16, candidate_x=array([7.22878814, 0.94287265]), index=0, x=array([7.14177125, 0.9700614 ]), fval=533.15737536874, rho=-0.7542207154863827, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.14177125, 0.9700614 ]), radius=0.04463607029763213, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 15, 16]), model=ScalarModel(intercept=533.1573753687401, linear_terms=array([1.83566489, 8.0288301 ]), square_terms=array([[8.68328033e-03, 4.45498440e-01], + [4.45498440e-01, 9.43298357e+01]]), scale=0.04463607029763213, shift=array([7.14177125, 0.9700614 ])), vector_model=VectorModel(intercepts=array([ 1.55437059, 3.34214162, 4.09540253, 5.14811998, + 6.18204764, 7.58497401, 7.99989814, -1.17627655, + -4.69083048, -6.78876609, -9.69389851, -12.37341873]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.714177124762114, shift=array([7.14177125, 0.9700614 ])), candidate_index=17, candidate_x=array([7.09711805, 0.96653998]), index=0, x=array([7.14177125, 0.9700614 ]), fval=533.15737536874, rho=-0.33065015869998954, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 15, 16]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.14177125, 0.9700614 ]), radius=0.022318035148816064, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 16, 17]), model=ScalarModel(intercept=533.1573753687401, linear_terms=array([-0.14937063, 0.59885935]), square_terms=array([[9.78342324e-03, 5.02961858e-01], + [5.02961858e-01, 2.60110124e+01]]), scale=0.022318035148816064, shift=array([7.14177125, 0.9700614 ])), vector_model=VectorModel(intercepts=array([ 1.55437059, 3.34214162, 4.09540253, 5.14811998, + 6.18204764, 7.58497401, 7.99989814, -1.17627655, + -4.69083048, -6.78876609, -9.69389851, -12.37341873]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.714177124762114, shift=array([7.14177125, 0.9700614 ])), candidate_index=18, candidate_x=array([7.16407529, 0.9691221 ]), index=0, x=array([7.14177125, 0.9700614 ]), fval=533.15737536874, rho=-1.8283997882836927, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.14177125, 0.9700614 ]), radius=0.011159017574408032, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=533.1573753687401, linear_terms=array([ 0.07909469, -1.65056925]), square_terms=array([[1.77756833e-03, 1.10987896e-01], + [1.10987896e-01, 6.98434777e+00]]), scale=0.011159017574408032, shift=array([7.14177125, 0.9700614 ])), vector_model=VectorModel(intercepts=array([ 1.55437059, 3.34214162, 4.09540253, 5.14811998, + 6.18204764, 7.58497401, 7.99989814, -1.17627655, + -4.69083048, -6.78876609, -9.69389851, -12.37341873]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.714177124762114, shift=array([7.14177125, 0.9700614 ])), candidate_index=19, candidate_x=array([7.13065487, 0.97283341]), index=0, x=array([7.14177125, 0.9700614 ]), fval=533.15737536874, rho=-1.2685892957313123, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.14177125, 0.9700614 ]), radius=0.005579508787204016, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 18, 19]), model=ScalarModel(intercept=533.1573753687384, linear_terms=array([0.10886642, 0.82071015]), square_terms=array([[4.64204757e-04, 2.81652064e-02], + [2.81652064e-02, 1.75477789e+00]]), scale=0.005579508787204016, shift=array([7.14177125, 0.9700614 ])), vector_model=VectorModel(intercepts=array([ 1.55437059, 3.34214162, 4.09540253, 5.14811998, + 6.18204764, 7.58497401, 7.99989814, -1.17627655, + -4.69083048, -6.78876609, -9.69389851, -12.37341873]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.714177124762114, shift=array([7.14177125, 0.9700614 ])), candidate_index=20, candidate_x=array([7.13615267, 0.9676723 ]), index=0, x=array([7.14177125, 0.9700614 ]), fval=533.15737536874, rho=-1.4063386870873529, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.14177125, 0.9700614 ]), radius=0.002789754393602008, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 19, 20]), model=ScalarModel(intercept=533.1573753687398, linear_terms=array([-0.07319973, -0.10148276]), square_terms=array([[1.16420387e-04, 6.43434400e-03], + [6.43434400e-03, 4.33820453e-01]]), scale=0.002789754393602008, shift=array([7.14177125, 0.9700614 ])), vector_model=VectorModel(intercepts=array([ 1.55437059, 3.34214162, 4.09540253, 5.14811998, + 6.18204764, 7.58497401, 7.99989814, -1.17627655, + -4.69083048, -6.78876609, -9.69389851, -12.37341873]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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0.9700614 ]), fval=533.15737536874, rho=-0.4305118795095497, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.14177125, 0.9700614 ]), radius=0.000697438598400502, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 21, 22]), model=ScalarModel(intercept=533.1573753687398, linear_terms=array([0.01410848, 0.09622588]), square_terms=array([[3.52531352e-06, 2.52424871e-04], + [2.52424871e-04, 2.68727971e-02]]), scale=0.000697438598400502, shift=array([7.14177125, 0.9700614 ])), vector_model=VectorModel(intercepts=array([ 1.55437059, 3.34214162, 4.09540253, 5.14811998, + 6.18204764, 7.58497401, 7.99989814, -1.17627655, + -4.69083048, -6.78876609, -9.69389851, -12.37341873]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 22, 23]), model=ScalarModel(intercept=533.1573753687397, linear_terms=array([-0.43619623, -0.14415327]), square_terms=array([[0.00042869, 0.00019034], + [0.00019034, 0.00674808]]), scale=0.000348719299200251, shift=array([7.14177125, 0.9700614 ])), vector_model=VectorModel(intercepts=array([ 1.55437059, 3.34214162, 4.09540253, 5.14811998, + 6.18204764, 7.58497401, 7.99989814, -1.17627655, + -4.69083048, -6.78876609, -9.69389851, -12.37341873]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], 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vector_model=VectorModel(intercepts=array([ 1.55437059, 3.34214162, 4.09540253, 5.14811998, + 6.18204764, 7.58497401, 7.99989814, -1.17627655, + -4.69083048, -6.78876609, -9.69389851, -12.37341873]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.714177124762114, shift=array([7.14177125, 0.9700614 ])), candidate_index=25, candidate_x=array([7.1417191 , 0.97022778]), index=25, x=array([7.1417191 , 0.97022778]), fval=533.1537602800875, rho=0.04746438706449571, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.00017435964960002963, relative_step_length=0.9999999999994501, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute params change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 26 entries., 'history': {'params': [{'CRRA': 7.14177124762114, 'DiscFac': 0.9700614043534316}, {'CRRA': 6.7868466809109, 'DiscFac': 0.5}, {'CRRA': 7.774694245127759, 'DiscFac': 0.5}, {'CRRA': 6.508848250114521, 'DiscFac': 0.5822476891055735}, {'CRRA': 7.1224518033555935, 'DiscFac': 0.5}, {'CRRA': 6.508848250114521, 'DiscFac': 1.1}, {'CRRA': 7.774694245127759, 'DiscFac': 1.0997076620875013}, {'CRRA': 7.774694245127759, 'DiscFac': 0.5}, {'CRRA': 7.774694245127759, 'DiscFac': 0.8015740448404156}, {'CRRA': 6.508848250114521, 'DiscFac': 0.86701952781014}, {'CRRA': 6.643685659085663, 'DiscFac': 1.1}, {'CRRA': 6.7385739033781045, 'DiscFac': 0.5}, {'CRRA': 6.952085805903453, 'DiscFac': 1.1}, {'CRRA': 6.508848250114521, 'DiscFac': 0.8589548110844714}, {'CRRA': 7.458232746374449, 'DiscFac': 0.7931436368201807}, {'CRRA': 6.983540498244485, 'DiscFac': 0.895173793706215}, {'CRRA': 7.228788139736241, 'DiscFac': 0.9428726530440706}, {'CRRA': 7.097118053919887, 'DiscFac': 0.9665399827624269}, {'CRRA': 7.164075291790564, 'DiscFac': 0.9691220965048899}, {'CRRA': 7.130654871032354, 'DiscFac': 0.9728334071792328}, {'CRRA': 7.136152673415857, 'DiscFac': 0.9676722997627686}, {'CRRA': 7.144569087997866, 'DiscFac': 0.9705858692354545}, {'CRRA': 7.141216169840549, 'DiscFac': 0.9713410805045143}, {'CRRA': 7.141768129279196, 'DiscFac': 0.9693639727263295}, {'CRRA': 7.1421045480761505, 'DiscFac': 0.9701699258755666}, {'CRRA': 7.14171909860584, 'DiscFac': 0.9702277827402849}], 'criterion': [533.15737536874, 1161.6255596324959, 1125.3550485373012, 1148.0325263179116, 1148.9622366552176, 1115.600102107612, 1072.682807744145, 1125.3550485373012, 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0.96990146]), 'solution_criterion': 533.2440439987661, 'states': [State(trustregion=Region(center=array([7.27503118, 0.95881145]), radius=0.7275031175627115, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=535.8336101105399, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.7275031175627115, shift=array([7.27503118, 0.95881145])), vector_model=VectorModel(intercepts=array([ 1.52500338, 3.24924761, 3.93209507, 4.90414442, + 5.84131877, 7.11596859, 7.37627124, -1.95398176, + -5.32763855, -7.27033929, -10.02191597, -12.60049836]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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shift=array([7.27503118, 0.8 ])), vector_model=VectorModel(intercepts=array([ 1.52500338, 3.24924761, 3.93209507, 4.90414442, + 5.84131877, 7.11596859, 7.37627124, -1.95398176, + -5.32763855, -7.27033929, -10.02191597, -12.60049836]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7275031175627115, shift=array([7.27503118, 0.95881145])), candidate_index=13, candidate_x=array([6.63029832, 0.81703549]), index=0, x=array([7.27503118, 0.95881145]), fval=535.8336101105399, rho=-2.1996865150337928, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.27503118, 0.95881145]), radius=0.36375155878135573, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 2, 5, 6, 10, 11, 12, 13]), model=ScalarModel(intercept=417.31221688662185, linear_terms=array([-0.54948831, 87.98913779]), square_terms=array([[ 1.47910127, 36.96601123], + [ 36.96601123, 942.9518399 ]]), scale=array([0.32236643, 0.23177749]), shift=array([7.27503118, 0.86822251])), vector_model=VectorModel(intercepts=array([ 1.52500338, 3.24924761, 3.93209507, 4.90414442, + 5.84131877, 7.11596859, 7.37627124, -1.95398176, + -5.32763855, -7.27033929, -10.02191597, -12.60049836]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7275031175627115, shift=array([7.27503118, 0.95881145])), candidate_index=14, candidate_x=array([7.5973976 , 0.83750855]), index=0, x=array([7.27503118, 0.95881145]), fval=535.8336101105399, rho=-1.8353928289069765, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 2, 5, 6, 10, 11, 12, 13]), old_indices_discarded=array([3, 4, 7, 8, 9]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.27503118, 0.95881145]), radius=0.18187577939067787, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 5, 6, 10, 11, 12, 13, 14]), model=ScalarModel(intercept=479.25633430231835, linear_terms=array([ -0.9605358, 198.4344603]), square_terms=array([[ 3.53736626e-01, -1.25212887e+01], + [-1.25212887e+01, 5.27262999e+02]]), scale=array([0.16118321, 0.15118588]), shift=array([7.27503118, 0.94881412])), vector_model=VectorModel(intercepts=array([ 1.52500338, 3.24924761, 3.93209507, 4.90414442, + 5.84131877, 7.11596859, 7.37627124, -1.95398176, + -5.32763855, -7.27033929, -10.02191597, -12.60049836]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7275031175627115, shift=array([7.27503118, 0.95881145])), candidate_index=15, candidate_x=array([7.11384796, 0.88832527]), index=0, x=array([7.27503118, 0.95881145]), fval=535.8336101105399, rho=-2.1919235286030267, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 5, 6, 10, 11, 12, 13, 14]), old_indices_discarded=array([2, 3, 4, 7, 8, 9]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.27503118, 0.95881145]), radius=0.09093788969533893, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 14, 15]), model=ScalarModel(intercept=535.83361011054, linear_terms=array([-2.60296232, -6.42359962]), square_terms=array([[ 0.62558151, 13.75829417], + [ 13.75829417, 305.6691718 ]]), scale=0.09093788969533893, shift=array([7.27503118, 0.95881145])), vector_model=VectorModel(intercepts=array([ 1.52500338, 3.24924761, 3.93209507, 4.90414442, + 5.84131877, 7.11596859, 7.37627124, -1.95398176, + -5.32763855, -7.27033929, -10.02191597, -12.60049836]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7275031175627115, shift=array([7.27503118, 0.95881145])), candidate_index=16, candidate_x=array([7.36596367, 0.95664593]), index=0, x=array([7.27503118, 0.95881145]), fval=535.8336101105399, rho=-0.17714922152722917, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.27503118, 0.95881145]), radius=0.04546894484766947, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 15, 16]), model=ScalarModel(intercept=535.8336101105402, linear_terms=array([ 0.05515596, -6.31406938]), square_terms=array([[2.36105391e-02, 1.41653415e+00], + [1.41653415e+00, 8.63639556e+01]]), scale=0.04546894484766947, shift=array([7.27503118, 0.95881145])), vector_model=VectorModel(intercepts=array([ 1.52500338, 3.24924761, 3.93209507, 4.90414442, + 5.84131877, 7.11596859, 7.37627124, -1.95398176, + -5.32763855, -7.27033929, -10.02191597, -12.60049836]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7275031175627115, shift=array([7.27503118, 0.95881145])), candidate_index=17, candidate_x=array([7.22962273, 0.96287303]), index=17, x=array([7.22962273, 0.96287303]), fval=534.588843709447, rho=3.198849368892046, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 15, 16]), old_indices_discarded=array([], dtype=int64), step_length=0.04558972329311975, relative_step_length=1.002656284324497, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.22962273, 0.96287303]), radius=0.09093788969533893, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 14, 15, 16, 17]), model=ScalarModel(intercept=536.8953875202508, linear_terms=array([-2.01628042, 0.31734197]), square_terms=array([[ 0.53816169, 12.68599992], + [ 12.68599992, 301.6904951 ]]), scale=0.09093788969533893, shift=array([7.22962273, 0.96287303])), vector_model=VectorModel(intercepts=array([ 1.52500338, 3.24924761, 3.93209507, 4.90414442, + 5.84131877, 7.11596859, 7.37627124, -1.95398176, + -5.32763855, -7.27033929, -10.02191597, -12.60049836]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7275031175627115, shift=array([7.27503118, 0.95881145])), candidate_index=18, candidate_x=array([7.32047741, 0.95898305]), index=17, x=array([7.22962273, 0.96287303]), fval=534.588843709447, rho=-0.5149227125446602, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 15, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.22962273, 0.96287303]), radius=0.04546894484766947, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 15, 16, 17, 18]), model=ScalarModel(intercept=534.7951734150251, linear_terms=array([ 0.37660787, -0.24339639]), square_terms=array([[1.90879144e-02, 1.26918342e+00], + [1.26918342e+00, 8.74666070e+01]]), scale=0.04546894484766947, shift=array([7.22962273, 0.96287303])), vector_model=VectorModel(intercepts=array([ 1.52500338, 3.24924761, 3.93209507, 4.90414442, + 5.84131877, 7.11596859, 7.37627124, -1.95398176, + -5.32763855, -7.27033929, -10.02191597, -12.60049836]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7275031175627115, shift=array([7.27503118, 0.95881145])), candidate_index=19, candidate_x=array([7.18416036, 0.96365584]), index=19, x=array([7.18416036, 0.96365584]), fval=534.4961590726019, rho=0.24385078198095397, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 15, 16, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.04546911152108367, relative_step_length=1.0000036656538822, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.18416036, 0.96365584]), radius=0.09093788969533893, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 14, 15, 16, 17, 18, 19]), model=ScalarModel(intercept=537.1732111300869, linear_terms=array([-1.8486363 , -1.37306878]), square_terms=array([[ 0.48035219, 12.01319527], + [ 12.01319527, 302.72448626]]), scale=0.09093788969533893, shift=array([7.18416036, 0.96365584])), vector_model=VectorModel(intercepts=array([ 1.52500338, 3.24924761, 3.93209507, 4.90414442, + 5.84131877, 7.11596859, 7.37627124, -1.95398176, + -5.32763855, -7.27033929, -10.02191597, -12.60049836]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7275031175627115, shift=array([7.27503118, 0.95881145])), candidate_index=20, candidate_x=array([7.27504382, 0.96048049]), index=19, x=array([7.18416036, 0.96365584]), fval=534.4961590726019, rho=-0.4234582842544629, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 15, 16, 17, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.18416036, 0.96365584]), radius=0.04546894484766947, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 15, 16, 17, 18, 19, 20]), model=ScalarModel(intercept=534.462302941555, linear_terms=array([0.36485571, 0.02907571]), square_terms=array([[1.91627349e-02, 1.27347557e+00], + [1.27347557e+00, 8.74511060e+01]]), scale=0.04546894484766947, shift=array([7.18416036, 0.96365584])), vector_model=VectorModel(intercepts=array([ 1.52500338, 3.24924761, 3.93209507, 4.90414442, + 5.84131877, 7.11596859, 7.37627124, -1.95398176, + -5.32763855, -7.27033929, -10.02191597, -12.60049836]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7275031175627115, shift=array([7.27503118, 0.95881145])), candidate_index=21, candidate_x=array([7.13869598, 0.9643001 ]), index=21, x=array([7.13869598, 0.9643001 ]), fval=534.4830529160092, rho=0.035996887089534295, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 15, 16, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.04546894833187833, relative_step_length=1.0000000766283201, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.13869598, 0.9643001 ]), radius=0.022734472423834733, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([15, 17, 19, 21]), model=ScalarModel(intercept=534.4696686133611, linear_terms=array([0.02723782, 0.05853844]), square_terms=array([[6.08273978e-03, 3.62360068e-01], + [3.62360068e-01, 2.17695548e+01]]), scale=0.022734472423834733, shift=array([7.13869598, 0.9643001 ])), vector_model=VectorModel(intercepts=array([ 1.52500338, 3.24924761, 3.93209507, 4.90414442, + 5.84131877, 7.11596859, 7.37627124, -1.95398176, + -5.32763855, -7.27033929, -10.02191597, -12.60049836]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7275031175627115, shift=array([7.27503118, 0.95881145])), candidate_index=22, candidate_x=array([7.11596363, 0.96461698]), index=21, x=array([7.13869598, 0.9643001 ]), fval=534.4830529160092, rho=-0.8752257584770998, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([15, 17, 19, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.13869598, 0.9643001 ]), radius=0.011367236211917367, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 21, 22]), model=ScalarModel(intercept=534.4830529160088, linear_terms=array([ -0.89246836, -63.20417025]), square_terms=array([[1.81314397e-02, 1.25119504e+00], + [1.25119504e+00, 8.65432178e+01]]), scale=0.011367236211917367, shift=array([7.13869598, 0.9643001 ])), vector_model=VectorModel(intercepts=array([ 1.52500338, 3.24924761, 3.93209507, 4.90414442, + 5.84131877, 7.11596859, 7.37627124, -1.95398176, + -5.32763855, -7.27033929, -10.02191597, -12.60049836]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7275031175627115, shift=array([7.27503118, 0.95881145])), candidate_index=23, candidate_x=array([7.13048926, 0.97271764]), index=23, x=array([7.13048926, 0.97271764]), fval=533.4954041768699, rho=0.04276465971658145, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([19, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.011756065045185197, relative_step_length=1.034206101291375, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.13048926, 0.97271764]), radius=0.005683618105958683, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([21, 22, 23]), model=ScalarModel(intercept=533.4954041768697, linear_terms=array([0.00978471, 0.64478173]), square_terms=array([[4.94174323e-04, 2.97949841e-02], + [2.97949841e-02, 1.81603321e+00]]), scale=0.005683618105958683, shift=array([7.13048926, 0.97271764])), vector_model=VectorModel(intercepts=array([ 1.52500338, 3.24924761, 3.93209507, 4.90414442, + 5.84131877, 7.11596859, 7.37627124, -1.95398176, + -5.32763855, -7.27033929, -10.02191597, -12.60049836]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7275031175627115, shift=array([7.27503118, 0.95881145])), candidate_index=24, candidate_x=array([7.13613898, 0.9706079 ]), index=24, x=array([7.13613898, 0.9706079 ]), fval=533.2478486405716, rho=2.147962831805317, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([21, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.006030784295875672, relative_step_length=1.061081899495151, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.13613898, 0.9706079 ]), radius=0.011367236211917367, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 21, 22, 23, 24]), model=ScalarModel(intercept=533.334653237849, linear_terms=array([ 0.00603185, -0.09455541]), square_terms=array([[1.95693258e-03, 1.18563575e-01], + [1.18563575e-01, 7.24180756e+00]]), scale=0.011367236211917367, shift=array([7.13613898, 0.9706079 ])), vector_model=VectorModel(intercepts=array([ 1.52500338, 3.24924761, 3.93209507, 4.90414442, + 5.84131877, 7.11596859, 7.37627124, -1.95398176, + -5.32763855, -7.27033929, -10.02191597, -12.60049836]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7275031175627115, shift=array([7.27503118, 0.95881145])), candidate_index=25, candidate_x=array([7.12477569, 0.97094201]), index=24, x=array([7.13613898, 0.9706079 ]), fval=533.2478486405716, rho=-1.1569477008317235, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([19, 21, 22, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.13613898, 0.9706079 ]), radius=0.005683618105958683, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([21, 22, 23, 24, 25]), model=ScalarModel(intercept=533.3075686464457, linear_terms=array([-0.00388246, -0.05444873]), square_terms=array([[4.74346475e-04, 2.91295366e-02], + [2.91295366e-02, 1.81095403e+00]]), scale=0.005683618105958683, shift=array([7.13613898, 0.9706079 ])), vector_model=VectorModel(intercepts=array([ 1.52500338, 3.24924761, 3.93209507, 4.90414442, + 5.84131877, 7.11596859, 7.37627124, -1.95398176, + -5.32763855, -7.27033929, -10.02191597, -12.60049836]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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linear_terms=array([-0.00015112, -0.02425646]), square_terms=array([[1.01944703e-04, 6.71780488e-03], + [6.71780488e-03, 4.51232908e-01]]), scale=0.0028418090529793417, shift=array([7.13613898, 0.9706079 ])), vector_model=VectorModel(intercepts=array([ 1.52500338, 3.24924761, 3.93209507, 4.90414442, + 5.84131877, 7.11596859, 7.37627124, -1.95398176, + -5.32763855, -7.27033929, -10.02191597, -12.60049836]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7275031175627115, shift=array([7.27503118, 0.95881145])), candidate_index=27, candidate_x=array([7.13329976, 0.97080284]), index=24, x=array([7.13613898, 0.9706079 ]), fval=533.2478486405716, rho=-17.652484632017263, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([21, 23, 24, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.13613898, 0.9706079 ]), radius=0.0014209045264896708, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([21, 23, 24, 26, 27]), model=ScalarModel(intercept=533.3025575826287, linear_terms=array([-0.00970311, -0.01838657]), square_terms=array([[2.37642951e-05, 1.50731850e-03], + [1.50731850e-03, 1.12591971e-01]]), scale=0.0014209045264896708, shift=array([7.13613898, 0.9706079 ])), vector_model=VectorModel(intercepts=array([ 1.52500338, 3.24924761, 3.93209507, 4.90414442, + 5.84131877, 7.11596859, 7.37627124, -1.95398176, + -5.32763855, -7.27033929, -10.02191597, -12.60049836]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7275031175627115, shift=array([7.27503118, 0.95881145])), candidate_index=28, candidate_x=array([7.13756265, 0.97080438]), index=24, x=array([7.13613898, 0.9706079 ]), fval=533.2478486405716, rho=-3.639125843439522, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([21, 23, 24, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.13613898, 0.9706079 ]), radius=0.0007104522632448354, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([24, 27, 28]), model=ScalarModel(intercept=533.2478486405727, linear_terms=array([0.00394943, 0.11045841]), square_terms=array([[7.94592327e-06, 4.89630637e-04], + [4.89630637e-04, 3.03637816e-02]]), scale=0.0007104522632448354, shift=array([7.13613898, 0.9706079 ])), vector_model=VectorModel(intercepts=array([ 1.52500338, 3.24924761, 3.93209507, 4.90414442, + 5.84131877, 7.11596859, 7.37627124, -1.95398176, + -5.32763855, -7.27033929, -10.02191597, -12.60049836]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7275031175627115, shift=array([7.27503118, 0.95881145])), candidate_index=29, candidate_x=array([7.13621442, 0.96990146]), index=29, x=array([7.13621442, 0.96990146]), fval=533.2440439987661, rho=0.04027969930951135, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([24, 27, 28]), old_indices_discarded=array([], dtype=int64), step_length=0.0007104522632448244, relative_step_length=0.9999999999999845, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute params change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 30 entries., 'history': {'params': [{'CRRA': 7.275031175627114, 'DiscFac': 0.9588114544912715}, {'CRRA': 7.919764026762012, 'DiscFac': 1.1}, {'CRRA': 7.919734534674724, 'DiscFac': 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533.2478486405718, 533.2573202084424, 533.2901960706058, 533.2630440267309, 533.2877597361326, 533.2440439987661], 'runtime': [0.0, 1.4024723219999942, 1.4388733560001583, 1.4760291270001744, 1.5204015250001248, 1.557460337000066, 1.5933073060000424, 1.6330519420000655, 1.6700915129999885, 1.7067589840000892, 1.7568892110000434, 1.798135848000129, 1.8419761540001218, 3.5102549680000266, 4.88248653200003, 6.172515729999986, 7.455305024000154, 8.86595460600006, 10.179171131999965, 11.506471603000136, 12.812378022000075, 14.153470921999997, 15.470118244000105, 16.785496700000067, 18.08746162500006, 19.39189258700003, 20.683552956000085, 21.973715585000036, 23.292128334000154, 24.603098476000014], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]}}], 'exploration_sample': array([[ 7.14177125, 0.9700614 ], + [ 7.596875 , 0.93125 ], + [ 5.825 , 0.95 ], + [16.45625 , 0.9125 ], + [17.046875 , 0.63125 ], + [ 9.36875 , 0.8375 ], + [10.55 , 0.8 ], + [11.73125 , 0.7625 ], + [15.275 , 0.65 ], + [18.81875 , 0.5375 ], + [14.09375 , 0.9875 ], + [12.9125 , 0.575 ], + [17.6375 , 1.025 ], + [ 8.1875 , 0.725 ], + [12.321875 , 1.08125 ], + [ 7.00625 , 0.6125 ], + [ 3.4625 , 0.875 ], + [ 4.64375 , 0.6875 ], + [ 2.871875 , 0.78125 ], + [ 2.28125 , 1.0625 ]]), 'exploration_results': array([ 533.15737537, 554.91392947, 580.45087475, 623.67135041, + 630.89444653, 637.20876422, 645.36720654, 650.8682765 , + 661.36967475, 665.67712442, 701.74175737, 833.17265466, + 899.56232297, 924.72647063, 987.48155993, 1111.46554906, + 1124.06873967, 1196.97223495, 1247.86777405, 1292.92183431])}}" diff --git a/content/tables/min/IndShock_estimate_results.csv b/content/tables/min/IndShock_estimate_results.csv new file mode 100644 index 0000000..126a30b --- /dev/null +++ b/content/tables/min/IndShock_estimate_results.csv @@ -0,0 +1,4148 @@ +CRRA,6.518738923982261 +DiscFac,0.9740861364280193 +time_to_estimate,232.447092294693 +params,"{'CRRA': 6.518738923982261, 'DiscFac': 0.9740861364280193}" +criterion,501.1269763500226 +start_criterion,501.1275163802119 +start_params,"{'CRRA': 6.518758193778226, 'DiscFac': 0.9740911850209896}" +algorithm,multistart_tranquilo_ls +direction,minimize +n_free,2 +message,Absolute criterion change smaller than tolerance. +success, +n_criterion_evaluations, +n_derivative_evaluations, +n_iterations, +history,"{'params': [{'CRRA': 6.518758193778226, 'DiscFac': 0.9740911850209896}, {'CRRA': 6.930435776304124, 'DiscFac': 0.5}, {'CRRA': 7.096468096962432, 'DiscFac': 0.5}, {'CRRA': 5.941048290594021, 'DiscFac': 0.5}, {'CRRA': 6.705153615535426, 'DiscFac': 0.5}, {'CRRA': 5.941048290594021, 'DiscFac': 1.1}, {'CRRA': 7.096468096962432, 'DiscFac': 1.1}, {'CRRA': 7.096468096962432, 'DiscFac': 0.5}, {'CRRA': 7.096468096962432, 'DiscFac': 0.7833377741815668}, {'CRRA': 5.941048290594021, 'DiscFac': 0.5799959927689777}, {'CRRA': 5.941048290594021, 'DiscFac': 1.0332928909664387}, {'CRRA': 5.941048290594021, 'DiscFac': 0.5}, {'CRRA': 6.2297845747911476, 'DiscFac': 1.1}, {'CRRA': 5.941048290594021, 'DiscFac': 0.8214633948443593}, {'CRRA': 6.807613145370329, 'DiscFac': 0.8543317905352251}, {'CRRA': 6.3743307179821755, 'DiscFac': 0.8955781211319409}, {'CRRA': 6.599493668467294, 'DiscFac': 0.9521187924148061}, {'CRRA': 6.478038657071983, 'DiscFac': 0.9690127400527637}, {'CRRA': 6.539101636686653, 'DiscFac': 0.9722812493535758}, {'CRRA': 6.508570517122245, 'DiscFac': 0.973998315531643}, {'CRRA': 6.523866400568332, 'DiscFac': 0.9752096738990378}, {'CRRA': 6.521296875753758, 'DiscFac': 0.9734910546050374}, {'CRRA': 6.517488876385145, 'DiscFac': 0.974439643595108}, {'CRRA': 6.51861333285585, 'DiscFac': 0.973471288525973}, {'CRRA': 6.518993872594638, 'DiscFac': 0.9743093353778189}, {'CRRA': 6.518624583903896, 'DiscFac': 0.9741816112149284}, {'CRRA': 6.518803789449013, 'DiscFac': 0.9740259687247471}, {'CRRA': 6.518784589267384, 'DiscFac': 0.9741209559930034}, {'CRRA': 6.518738923982261, 'DiscFac': 0.9740861364280193}], 'criterion': [501.1275163802119, 1154.3726351101354, 1148.09766251957, 1195.974792701822, 1163.2375608777152, 1428.5522412431983, 1294.4542058703007, 1148.09766251957, 902.0945418161405, 1173.9905874325736, 648.3248639009156, 1195.974792701822, 1385.7533024422844, 931.9425069323898, 744.4865312872281, 656.740266680918, 513.8743925303191, 501.95725779536633, 501.33148551773866, 501.16451799386607, 501.2133690844172, 501.19054144598135, 501.1597756573809, 501.1801324132359, 501.13805630451577, 501.15422481249345, 501.14233384565057, 501.14511952364694, 501.1269763500226], 'runtime': [0.0, 3.3772133320001103, 3.3739613640000243, 3.508898283999997, 3.5765015290000974, 3.5974512459999914, 3.667596008000146, 3.6805465340000865, 3.726354085000139, 3.8215960679999625, 3.8228244819999873, 3.8050569389999964, 3.8416639150000265, 102.65433508000001, 103.93067918199995, 105.23330888400005, 115.1877147560001, 116.5051563510001, 117.81345196899997, 119.11422845400011, 120.4153765850001, 121.71612788200014, 123.01899637600013, 127.92256343100007, 129.22530063, 130.54040302700014, 131.88101467599995, 133.22392327700004, 134.55956812399995], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]}" +convergence_report, +multistart_info,"{'start_parameters': [{'CRRA': 6.518758193778226, 'DiscFac': 0.9740911850209896}, {'CRRA': 6.834517669606002, 'DiscFac': 0.9615397225480846}], 'local_optima': [Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 1.078e-06* 1.078e-06* +relative_params_change 5.967e-06* 5.967e-06* +absolute_criterion_change 0.00054 0.00054 +absolute_params_change 1.992e-05 1.992e-05 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 3.829e-05 0.00369 +relative_params_change 0.003188 0.01772 +absolute_criterion_change 0.01919 1.85 +absolute_params_change 0.02136 0.107 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 6.518758193778226, 'DiscFac': 0.9740911850209896}, {'CRRA': 7.596874999999999, 'DiscFac': 0.9312500000000001}, {'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 16.45625, 'DiscFac': 0.9125000000000001}, {'CRRA': 9.368749999999999, 'DiscFac': 0.8375}, {'CRRA': 17.046875, 'DiscFac': 0.6312500000000001}, {'CRRA': 10.549999999999999, 'DiscFac': 0.8}, {'CRRA': 11.73125, 'DiscFac': 0.7625000000000001}, {'CRRA': 15.274999999999999, 'DiscFac': 0.65}, {'CRRA': 18.81875, 'DiscFac': 0.5375}, {'CRRA': 14.093749999999998, 'DiscFac': 0.9875}, {'CRRA': 12.9125, 'DiscFac': 0.575}, {'CRRA': 17.6375, 'DiscFac': 1.0250000000000001}, {'CRRA': 8.1875, 'DiscFac': 0.7250000000000001}, {'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 7.00625, 'DiscFac': 0.6125}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 2.871875, 'DiscFac': 0.78125}, {'CRRA': 2.28125, 'DiscFac': 1.0625}], 'exploration_results': array([ 501.12751638, 519.11435204, 538.30979579, 597.0207673 , + 611.52511798, 615.89547778, 622.99770198, 631.34502173, + 647.27786212, 656.04258415, 694.37527482, 830.0300439 , + 914.55694912, 917.36098753, 1057.62479346, 1104.333468 , + 1108.21046316, 1193.90258834, 1244.39797507, 2082.87264995])}" +algorithm_output,"{'states': [State(trustregion=Region(center=array([6.51875819, 0.97409119]), radius=0.6518758193778227, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=501.1275163802119, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.6518758193778227, shift=array([6.51875819, 0.97409119])), vector_model=VectorModel(intercepts=array([ 1.4283287 , 3.07288925, 3.75060056, 4.78539882, + 5.86452219, 7.39510824, 8.03652346, -0.25397907, + -3.91381883, -6.25547607, -9.48322028, -12.43249019]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + 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1.1]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=400.52572423834147, linear_terms=array([ 11.90696049, -154.78556577]), square_terms=array([[ 1.76232264, -45.06920125], + [ -45.06920125, 1533.53696351]]), scale=array([0.5777099, 0.3 ]), shift=array([6.51875819, 0.8 ])), vector_model=VectorModel(intercepts=array([ 1.4283287 , 3.07288925, 3.75060056, 4.78539882, + 5.86452219, 7.39510824, 8.03652346, -0.25397907, + -3.91381883, -6.25547607, -9.48322028, -12.43249019]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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old_indices_used=array([ 0, 1, 4, 5, 6, 8, 10, 12, 13]), old_indices_discarded=array([ 2, 3, 7, 9, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.51875819, 0.97409119]), radius=0.16296895484445567, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 4, 5, 6, 8, 10, 12, 13, 14]), model=ScalarModel(intercept=487.99821575861523, linear_terms=array([ 1.00329653, 243.69886722]), square_terms=array([[7.80065934e-03, 6.88926991e-01], + [6.88926991e-01, 4.74302269e+02]]), scale=array([0.14442748, 0.13516815]), shift=array([6.51875819, 0.96483185])), vector_model=VectorModel(intercepts=array([ 1.4283287 , 3.07288925, 3.75060056, 4.78539882, + 5.86452219, 7.39510824, 8.03652346, -0.25397907, + -3.91381883, -6.25547607, -9.48322028, -12.43249019]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 14, 15]), model=ScalarModel(intercept=486.0712828893975, linear_terms=array([ 4.15090305, 139.51579954]), square_terms=array([[ 0.79922532, 21.81534971], + [ 21.81534971, 596.60341244]]), scale=0.08148447742222784, shift=array([6.51875819, 0.97409119])), vector_model=VectorModel(intercepts=array([ 1.4283287 , 3.07288925, 3.75060056, 4.78539882, + 5.86452219, 7.39510824, 8.03652346, -0.25397907, + -3.91381883, -6.25547607, -9.48322028, -12.43249019]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6518758193778227, shift=array([6.51875819, 0.97409119])), candidate_index=16, candidate_x=array([6.59949367, 0.95211879]), index=0, x=array([6.51875819, 0.97409119]), fval=501.1275163802119, rho=-0.7387791904497933, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.51875819, 0.97409119]), radius=0.04074223871111392, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 15, 16]), model=ScalarModel(intercept=501.12751638021234, linear_terms=array([ 2.06914883, 12.57107207]), square_terms=array([[ 1.32670528e-02, -4.66031303e-01], + [-4.66031303e-01, 1.02473984e+02]]), scale=0.04074223871111392, shift=array([6.51875819, 0.97409119])), vector_model=VectorModel(intercepts=array([ 1.4283287 , 3.07288925, 3.75060056, 4.78539882, + 5.86452219, 7.39510824, 8.03652346, -0.25397907, + -3.91381883, -6.25547607, -9.48322028, -12.43249019]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6518758193778227, shift=array([6.51875819, 0.97409119])), candidate_index=17, candidate_x=array([6.47803866, 0.96901274]), index=0, x=array([6.51875819, 0.97409119]), fval=501.1275163802119, rho=-0.2870768838823704, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 15, 16]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.51875819, 0.97409119]), radius=0.02037111935555696, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 16, 17]), model=ScalarModel(intercept=501.1275163802114, linear_terms=array([-0.08268818, 2.18026048]), square_terms=array([[8.70976750e-03, 5.09399469e-01], + [5.09399469e-01, 3.01453577e+01]]), scale=0.02037111935555696, shift=array([6.51875819, 0.97409119])), vector_model=VectorModel(intercepts=array([ 1.4283287 , 3.07288925, 3.75060056, 4.78539882, + 5.86452219, 7.39510824, 8.03652346, -0.25397907, + -3.91381883, -6.25547607, -9.48322028, -12.43249019]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6518758193778227, shift=array([6.51875819, 0.97409119])), candidate_index=18, candidate_x=array([6.53910164, 0.97228125]), index=0, x=array([6.51875819, 0.97409119]), fval=501.1275163802119, rho=-1.029320734318488, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.51875819, 0.97409119]), radius=0.01018555967777848, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=501.12751638021166, linear_terms=array([0.07240624, 0.1780674 ]), square_terms=array([[1.42712941e-03, 1.04982487e-01], + [1.04982487e-01, 7.94326537e+00]]), scale=0.01018555967777848, shift=array([6.51875819, 0.97409119])), vector_model=VectorModel(intercepts=array([ 1.4283287 , 3.07288925, 3.75060056, 4.78539882, + 5.86452219, 7.39510824, 8.03652346, -0.25397907, + -3.91381883, -6.25547607, -9.48322028, -12.43249019]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6518758193778227, shift=array([6.51875819, 0.97409119])), candidate_index=19, candidate_x=array([6.50857052, 0.97399832]), index=0, x=array([6.51875819, 0.97409119]), fval=501.1275163802119, rho=-0.5136015222161269, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.51875819, 0.97409119]), radius=0.00509277983888924, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 18, 19]), model=ScalarModel(intercept=501.12751638021234, linear_terms=array([-0.01322774, -0.46656276]), square_terms=array([[3.63124415e-04, 2.67088333e-02], + [2.67088333e-02, 1.99542800e+00]]), scale=0.00509277983888924, shift=array([6.51875819, 0.97409119])), vector_model=VectorModel(intercepts=array([ 1.4283287 , 3.07288925, 3.75060056, 4.78539882, + 5.86452219, 7.39510824, 8.03652346, -0.25397907, + -3.91381883, -6.25547607, -9.48322028, -12.43249019]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6518758193778227, shift=array([6.51875819, 0.97409119])), candidate_index=20, candidate_x=array([6.5238664 , 0.97520967]), index=0, x=array([6.51875819, 0.97409119]), fval=501.1275163802119, rho=-1.3949488538254706, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.51875819, 0.97409119]), radius=0.00254638991944462, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 19, 20]), model=ScalarModel(intercept=501.1275163802118, linear_terms=array([-0.0097909 , 0.11523558]), square_terms=array([[9.00461363e-05, 6.68532362e-03], + [6.68532362e-03, 5.05921002e-01]]), scale=0.00254638991944462, shift=array([6.51875819, 0.97409119])), vector_model=VectorModel(intercepts=array([ 1.4283287 , 3.07288925, 3.75060056, 4.78539882, + 5.86452219, 7.39510824, 8.03652346, -0.25397907, + -3.91381883, -6.25547607, -9.48322028, -12.43249019]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6518758193778227, shift=array([6.51875819, 0.97409119])), candidate_index=21, candidate_x=array([6.52129688, 0.97349105]), index=0, x=array([6.51875819, 0.97409119]), fval=501.1275163802119, rho=-2.583486438433234, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.51875819, 0.97409119]), radius=0.00127319495972231, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 20, 21]), model=ScalarModel(intercept=501.1275163802118, linear_terms=array([ 0.01608665, -0.03820876]), square_terms=array([[2.29986891e-05, 1.45911019e-03], + [1.45911019e-03, 1.28408539e-01]]), scale=0.00127319495972231, shift=array([6.51875819, 0.97409119])), vector_model=VectorModel(intercepts=array([ 1.4283287 , 3.07288925, 3.75060056, 4.78539882, + 5.86452219, 7.39510824, 8.03652346, -0.25397907, + -3.91381883, -6.25547607, -9.48322028, -12.43249019]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6518758193778227, shift=array([6.51875819, 0.97409119])), candidate_index=22, candidate_x=array([6.51748888, 0.97443964]), index=0, x=array([6.51875819, 0.97409119]), fval=501.1275163802119, rho=-1.4615204029137043, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.51875819, 0.97409119]), radius=0.000636597479861155, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 21, 22]), model=ScalarModel(intercept=501.1275163802121, linear_terms=array([0.1777905 , 0.69896302]), square_terms=array([[0.00042397, 0.00052726], + [0.00052726, 0.0259929 ]]), scale=0.000636597479861155, shift=array([6.51875819, 0.97409119])), vector_model=VectorModel(intercepts=array([ 1.4283287 , 3.07288925, 3.75060056, 4.78539882, + 5.86452219, 7.39510824, 8.03652346, -0.25397907, + -3.91381883, -6.25547607, -9.48322028, -12.43249019]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6518758193778227, shift=array([6.51875819, 0.97409119])), candidate_index=23, candidate_x=array([6.51861333, 0.97347129]), index=0, x=array([6.51875819, 0.97409119]), fval=501.1275163802119, rho=-0.07425018112711534, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.51875819, 0.97409119]), radius=0.0003182987399305775, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 22, 23]), model=ScalarModel(intercept=501.1275163802119, linear_terms=array([-0.01151491, -0.01627895]), square_terms=array([[6.22359131e-06, 1.80801264e-04], + [1.80801264e-04, 8.17893261e-03]]), scale=0.0003182987399305775, shift=array([6.51875819, 0.97409119])), vector_model=VectorModel(intercepts=array([ 1.4283287 , 3.07288925, 3.75060056, 4.78539882, + 5.86452219, 7.39510824, 8.03652346, -0.25397907, + -3.91381883, -6.25547607, -9.48322028, -12.43249019]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6518758193778227, shift=array([6.51875819, 0.97409119])), candidate_index=24, candidate_x=array([6.51899387, 0.97430934]), index=0, x=array([6.51875819, 0.97409119]), fval=501.1275163802119, rho=-0.5965326811513899, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.51875819, 0.97409119]), radius=0.00015914936996528874, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 23, 24]), model=ScalarModel(intercept=501.12751638021234, linear_terms=array([ 0.01867919, -0.01384997]), square_terms=array([[ 8.14329918e-06, -5.42280863e-05], + [-5.42280863e-05, 2.09084340e-03]]), scale=0.00015914936996528874, shift=array([6.51875819, 0.97409119])), vector_model=VectorModel(intercepts=array([ 1.4283287 , 3.07288925, 3.75060056, 4.78539882, + 5.86452219, 7.39510824, 8.03652346, -0.25397907, + -3.91381883, -6.25547607, -9.48322028, -12.43249019]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6518758193778227, shift=array([6.51875819, 0.97409119])), candidate_index=25, candidate_x=array([6.51862458, 0.97418161]), index=0, x=array([6.51875819, 0.97409119]), fval=501.1275163802119, rho=-1.1519829895109042, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.51875819, 0.97409119]), radius=7.957468498264437e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 24, 25]), model=ScalarModel(intercept=501.1275163802119, linear_terms=array([-0.0078519 , 0.01164785]), square_terms=array([[8.32463502e-07, 9.75186147e-06], + [9.75186147e-06, 4.73751842e-04]]), scale=7.957468498264437e-05, shift=array([6.51875819, 0.97409119])), vector_model=VectorModel(intercepts=array([ 1.4283287 , 3.07288925, 3.75060056, 4.78539882, + 5.86452219, 7.39510824, 8.03652346, -0.25397907, + -3.91381883, -6.25547607, -9.48322028, -12.43249019]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6518758193778227, shift=array([6.51875819, 0.97409119])), candidate_index=26, candidate_x=array([6.51880379, 0.97402597]), index=0, x=array([6.51875819, 0.97409119]), fval=501.1275163802119, rho=-1.0667311728635465, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.51875819, 0.97409119]), radius=3.9787342491322185e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 25, 26]), model=ScalarModel(intercept=501.1275163802119, linear_terms=array([-0.02639207, -0.02737106]), square_terms=array([[1.67186304e-05, 4.10668348e-05], + [4.10668348e-05, 1.96545342e-04]]), scale=3.9787342491322185e-05, shift=array([6.51875819, 0.97409119])), vector_model=VectorModel(intercepts=array([ 1.4283287 , 3.07288925, 3.75060056, 4.78539882, + 5.86452219, 7.39510824, 8.03652346, -0.25397907, + -3.91381883, -6.25547607, -9.48322028, -12.43249019]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6518758193778227, shift=array([6.51875819, 0.97409119])), candidate_index=27, candidate_x=array([6.51878459, 0.97412096]), index=0, x=array([6.51875819, 0.97409119]), fval=501.1275163802119, rho=-0.4643372983952584, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.51875819, 0.97409119]), radius=1.9893671245661093e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 26, 27]), model=ScalarModel(intercept=501.12751638021166, linear_terms=array([0.0102157 , 0.00268268]), square_terms=array([[ 1.17344869e-06, -2.13935530e-06], + [-2.13935530e-06, 3.32584990e-05]]), scale=1.9893671245661093e-05, shift=array([6.51875819, 0.97409119])), vector_model=VectorModel(intercepts=array([ 1.4283287 , 3.07288925, 3.75060056, 4.78539882, + 5.86452219, 7.39510824, 8.03652346, -0.25397907, + -3.91381883, -6.25547607, -9.48322028, -12.43249019]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6518758193778227, shift=array([6.51875819, 0.97409119])), candidate_index=28, candidate_x=array([6.51873892, 0.97408614]), index=28, x=array([6.51873892, 0.97408614]), fval=501.1269763500226, rho=0.051066490577917706, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=1.9920173882682364e-05, relative_step_length=1.001332214486406, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 29 entries., 'multistart_info': {'start_parameters': [array([6.51875819, 0.97409119]), array([6.83451767, 0.96153972])], 'local_optima': [{'solution_x': array([6.51873892, 0.97408614]), 'solution_criterion': 501.1269763500226, 'states': [State(trustregion=Region(center=array([6.51875819, 0.97409119]), radius=0.6518758193778227, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=501.1275163802119, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.6518758193778227, shift=array([6.51875819, 0.97409119])), vector_model=VectorModel(intercepts=array([ 1.4283287 , 3.07288925, 3.75060056, 4.78539882, + 5.86452219, 7.39510824, 8.03652346, -0.25397907, + -3.91381883, -6.25547607, -9.48322028, -12.43249019]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6518758193778227, shift=array([6.51875819, 0.97409119])), candidate_index=0, candidate_x=array([6.51875819, 0.97409119]), index=0, x=array([6.51875819, 0.97409119]), fval=501.1275163802119, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([6.51875819, 0.97409119]), radius=0.6518758193778227, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=400.52572423834147, linear_terms=array([ 11.90696049, -154.78556577]), square_terms=array([[ 1.76232264, -45.06920125], + [ -45.06920125, 1533.53696351]]), scale=array([0.5777099, 0.3 ]), shift=array([6.51875819, 0.8 ])), vector_model=VectorModel(intercepts=array([ 1.4283287 , 3.07288925, 3.75060056, 4.78539882, + 5.86452219, 7.39510824, 8.03652346, -0.25397907, + -3.91381883, -6.25547607, -9.48322028, -12.43249019]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6518758193778227, shift=array([6.51875819, 0.97409119])), candidate_index=13, candidate_x=array([5.94104829, 0.82146339]), index=0, x=array([6.51875819, 0.97409119]), fval=501.1275163802119, rho=-2.349823853233934, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.51875819, 0.97409119]), radius=0.32593790968891134, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 4, 5, 6, 8, 10, 12, 13]), model=ScalarModel(intercept=416.48440152199043, linear_terms=array([ 1.35553668, 125.78252937]), square_terms=array([[ 2.02450902, 42.46441287], + [ 42.46441287, 911.32712956]]), scale=array([0.28885495, 0.20738188]), shift=array([6.51875819, 0.89261812])), vector_model=VectorModel(intercepts=array([ 1.4283287 , 3.07288925, 3.75060056, 4.78539882, + 5.86452219, 7.39510824, 8.03652346, -0.25397907, + -3.91381883, -6.25547607, -9.48322028, -12.43249019]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6518758193778227, shift=array([6.51875819, 0.97409119])), candidate_index=14, candidate_x=array([6.80761315, 0.85433179]), index=0, x=array([6.51875819, 0.97409119]), fval=501.1275163802119, rho=-1.831049702796678, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 4, 5, 6, 8, 10, 12, 13]), old_indices_discarded=array([ 2, 3, 7, 9, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.51875819, 0.97409119]), radius=0.16296895484445567, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 4, 5, 6, 8, 10, 12, 13, 14]), model=ScalarModel(intercept=487.99821575861523, linear_terms=array([ 1.00329653, 243.69886722]), square_terms=array([[7.80065934e-03, 6.88926991e-01], + [6.88926991e-01, 4.74302269e+02]]), scale=array([0.14442748, 0.13516815]), shift=array([6.51875819, 0.96483185])), vector_model=VectorModel(intercepts=array([ 1.4283287 , 3.07288925, 3.75060056, 4.78539882, + 5.86452219, 7.39510824, 8.03652346, -0.25397907, + -3.91381883, -6.25547607, -9.48322028, -12.43249019]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6518758193778227, shift=array([6.51875819, 0.97409119])), candidate_index=15, candidate_x=array([6.37433072, 0.89557812]), index=0, x=array([6.51875819, 0.97409119]), fval=501.1275163802119, rho=-1.9197336751074499, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 4, 5, 6, 8, 10, 12, 13, 14]), old_indices_discarded=array([ 1, 2, 3, 7, 9, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.51875819, 0.97409119]), radius=0.08148447742222784, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 14, 15]), model=ScalarModel(intercept=486.0712828893975, linear_terms=array([ 4.15090305, 139.51579954]), square_terms=array([[ 0.79922532, 21.81534971], + [ 21.81534971, 596.60341244]]), scale=0.08148447742222784, shift=array([6.51875819, 0.97409119])), vector_model=VectorModel(intercepts=array([ 1.4283287 , 3.07288925, 3.75060056, 4.78539882, + 5.86452219, 7.39510824, 8.03652346, -0.25397907, + -3.91381883, -6.25547607, -9.48322028, -12.43249019]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6518758193778227, shift=array([6.51875819, 0.97409119])), candidate_index=16, candidate_x=array([6.59949367, 0.95211879]), index=0, x=array([6.51875819, 0.97409119]), fval=501.1275163802119, rho=-0.7387791904497933, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.51875819, 0.97409119]), radius=0.04074223871111392, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 15, 16]), model=ScalarModel(intercept=501.12751638021234, linear_terms=array([ 2.06914883, 12.57107207]), square_terms=array([[ 1.32670528e-02, -4.66031303e-01], + [-4.66031303e-01, 1.02473984e+02]]), scale=0.04074223871111392, shift=array([6.51875819, 0.97409119])), vector_model=VectorModel(intercepts=array([ 1.4283287 , 3.07288925, 3.75060056, 4.78539882, + 5.86452219, 7.39510824, 8.03652346, -0.25397907, + -3.91381883, -6.25547607, -9.48322028, -12.43249019]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6518758193778227, shift=array([6.51875819, 0.97409119])), candidate_index=17, candidate_x=array([6.47803866, 0.96901274]), index=0, x=array([6.51875819, 0.97409119]), fval=501.1275163802119, rho=-0.2870768838823704, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 15, 16]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.51875819, 0.97409119]), radius=0.02037111935555696, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 16, 17]), model=ScalarModel(intercept=501.1275163802114, linear_terms=array([-0.08268818, 2.18026048]), square_terms=array([[8.70976750e-03, 5.09399469e-01], + [5.09399469e-01, 3.01453577e+01]]), scale=0.02037111935555696, shift=array([6.51875819, 0.97409119])), vector_model=VectorModel(intercepts=array([ 1.4283287 , 3.07288925, 3.75060056, 4.78539882, + 5.86452219, 7.39510824, 8.03652346, -0.25397907, + -3.91381883, -6.25547607, -9.48322028, -12.43249019]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6518758193778227, shift=array([6.51875819, 0.97409119])), candidate_index=18, candidate_x=array([6.53910164, 0.97228125]), index=0, x=array([6.51875819, 0.97409119]), fval=501.1275163802119, rho=-1.029320734318488, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.51875819, 0.97409119]), radius=0.01018555967777848, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=501.12751638021166, linear_terms=array([0.07240624, 0.1780674 ]), square_terms=array([[1.42712941e-03, 1.04982487e-01], + [1.04982487e-01, 7.94326537e+00]]), scale=0.01018555967777848, shift=array([6.51875819, 0.97409119])), vector_model=VectorModel(intercepts=array([ 1.4283287 , 3.07288925, 3.75060056, 4.78539882, + 5.86452219, 7.39510824, 8.03652346, -0.25397907, + -3.91381883, -6.25547607, -9.48322028, -12.43249019]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6518758193778227, shift=array([6.51875819, 0.97409119])), candidate_index=19, candidate_x=array([6.50857052, 0.97399832]), index=0, x=array([6.51875819, 0.97409119]), fval=501.1275163802119, rho=-0.5136015222161269, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.51875819, 0.97409119]), radius=0.00509277983888924, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 18, 19]), model=ScalarModel(intercept=501.12751638021234, linear_terms=array([-0.01322774, -0.46656276]), square_terms=array([[3.63124415e-04, 2.67088333e-02], + [2.67088333e-02, 1.99542800e+00]]), scale=0.00509277983888924, shift=array([6.51875819, 0.97409119])), vector_model=VectorModel(intercepts=array([ 1.4283287 , 3.07288925, 3.75060056, 4.78539882, + 5.86452219, 7.39510824, 8.03652346, -0.25397907, + -3.91381883, -6.25547607, -9.48322028, -12.43249019]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6518758193778227, shift=array([6.51875819, 0.97409119])), candidate_index=20, candidate_x=array([6.5238664 , 0.97520967]), index=0, x=array([6.51875819, 0.97409119]), fval=501.1275163802119, rho=-1.3949488538254706, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.51875819, 0.97409119]), radius=0.00254638991944462, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 19, 20]), model=ScalarModel(intercept=501.1275163802118, linear_terms=array([-0.0097909 , 0.11523558]), square_terms=array([[9.00461363e-05, 6.68532362e-03], + [6.68532362e-03, 5.05921002e-01]]), scale=0.00254638991944462, shift=array([6.51875819, 0.97409119])), vector_model=VectorModel(intercepts=array([ 1.4283287 , 3.07288925, 3.75060056, 4.78539882, + 5.86452219, 7.39510824, 8.03652346, -0.25397907, + -3.91381883, -6.25547607, -9.48322028, -12.43249019]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6518758193778227, shift=array([6.51875819, 0.97409119])), candidate_index=21, candidate_x=array([6.52129688, 0.97349105]), index=0, x=array([6.51875819, 0.97409119]), fval=501.1275163802119, rho=-2.583486438433234, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.51875819, 0.97409119]), radius=0.00127319495972231, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 20, 21]), model=ScalarModel(intercept=501.1275163802118, linear_terms=array([ 0.01608665, -0.03820876]), square_terms=array([[2.29986891e-05, 1.45911019e-03], + [1.45911019e-03, 1.28408539e-01]]), scale=0.00127319495972231, shift=array([6.51875819, 0.97409119])), vector_model=VectorModel(intercepts=array([ 1.4283287 , 3.07288925, 3.75060056, 4.78539882, + 5.86452219, 7.39510824, 8.03652346, -0.25397907, + -3.91381883, -6.25547607, -9.48322028, -12.43249019]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6518758193778227, shift=array([6.51875819, 0.97409119])), candidate_index=22, candidate_x=array([6.51748888, 0.97443964]), index=0, x=array([6.51875819, 0.97409119]), fval=501.1275163802119, rho=-1.4615204029137043, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.51875819, 0.97409119]), radius=0.000636597479861155, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 21, 22]), model=ScalarModel(intercept=501.1275163802121, linear_terms=array([0.1777905 , 0.69896302]), square_terms=array([[0.00042397, 0.00052726], + [0.00052726, 0.0259929 ]]), scale=0.000636597479861155, shift=array([6.51875819, 0.97409119])), vector_model=VectorModel(intercepts=array([ 1.4283287 , 3.07288925, 3.75060056, 4.78539882, + 5.86452219, 7.39510824, 8.03652346, -0.25397907, + -3.91381883, -6.25547607, -9.48322028, -12.43249019]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6518758193778227, shift=array([6.51875819, 0.97409119])), candidate_index=23, candidate_x=array([6.51861333, 0.97347129]), index=0, x=array([6.51875819, 0.97409119]), fval=501.1275163802119, rho=-0.07425018112711534, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.51875819, 0.97409119]), radius=0.0003182987399305775, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 22, 23]), model=ScalarModel(intercept=501.1275163802119, linear_terms=array([-0.01151491, -0.01627895]), square_terms=array([[6.22359131e-06, 1.80801264e-04], + [1.80801264e-04, 8.17893261e-03]]), scale=0.0003182987399305775, shift=array([6.51875819, 0.97409119])), vector_model=VectorModel(intercepts=array([ 1.4283287 , 3.07288925, 3.75060056, 4.78539882, + 5.86452219, 7.39510824, 8.03652346, -0.25397907, + -3.91381883, -6.25547607, -9.48322028, -12.43249019]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6518758193778227, shift=array([6.51875819, 0.97409119])), candidate_index=24, candidate_x=array([6.51899387, 0.97430934]), index=0, x=array([6.51875819, 0.97409119]), fval=501.1275163802119, rho=-0.5965326811513899, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.51875819, 0.97409119]), radius=0.00015914936996528874, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 23, 24]), model=ScalarModel(intercept=501.12751638021234, linear_terms=array([ 0.01867919, -0.01384997]), square_terms=array([[ 8.14329918e-06, -5.42280863e-05], + [-5.42280863e-05, 2.09084340e-03]]), scale=0.00015914936996528874, shift=array([6.51875819, 0.97409119])), vector_model=VectorModel(intercepts=array([ 1.4283287 , 3.07288925, 3.75060056, 4.78539882, + 5.86452219, 7.39510824, 8.03652346, -0.25397907, + -3.91381883, -6.25547607, -9.48322028, -12.43249019]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6518758193778227, shift=array([6.51875819, 0.97409119])), candidate_index=25, candidate_x=array([6.51862458, 0.97418161]), index=0, x=array([6.51875819, 0.97409119]), fval=501.1275163802119, rho=-1.1519829895109042, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.51875819, 0.97409119]), radius=7.957468498264437e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 24, 25]), model=ScalarModel(intercept=501.1275163802119, linear_terms=array([-0.0078519 , 0.01164785]), square_terms=array([[8.32463502e-07, 9.75186147e-06], + [9.75186147e-06, 4.73751842e-04]]), scale=7.957468498264437e-05, shift=array([6.51875819, 0.97409119])), vector_model=VectorModel(intercepts=array([ 1.4283287 , 3.07288925, 3.75060056, 4.78539882, + 5.86452219, 7.39510824, 8.03652346, -0.25397907, + -3.91381883, -6.25547607, -9.48322028, -12.43249019]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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0.94604573]), index=0, x=array([6.83451767, 0.96153972]), fval=503.07717547325797, rho=-1.2035820648304838, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.83451767, 0.96153972]), radius=0.04271573543503751, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 15, 16]), model=ScalarModel(intercept=473.4426351395954, linear_terms=array([-7.76467424, 24.20017485]), square_terms=array([[ 7.81960658, 36.4391773 ], + [ 36.4391773 , 179.02782744]]), scale=0.04271573543503751, shift=array([6.83451767, 0.96153972])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=17, candidate_x=array([6.87541772, 0.94833053]), index=0, x=array([6.83451767, 0.96153972]), fval=503.07717547325797, rho=-0.7462850555910217, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 15, 16]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.83451767, 0.96153972]), radius=0.021357867717518755, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 16, 17]), model=ScalarModel(intercept=503.07717547325836, linear_terms=array([-0.03193367, -8.35020335]), square_terms=array([[7.47370683e-03, 4.61553856e-01], + [4.61553856e-01, 2.90280752e+01]]), scale=0.021357867717518755, shift=array([6.83451767, 0.96153972])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=18, candidate_x=array([6.81325981, 0.96799912]), index=18, x=array([6.81325981, 0.96799912]), fval=501.3312531210726, rho=1.3416861895574783, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.022217565698712326, relative_step_length=1.0402520510270041, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.81325981, 0.96799912]), radius=0.04271573543503751, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 16, 17, 18]), model=ScalarModel(intercept=495.7209962643657, linear_terms=array([ 1.34551752, -10.04757394]), square_terms=array([[7.71926809e-03, 2.94803138e-01], + [2.94803138e-01, 1.80459140e+02]]), scale=0.04271573543503751, shift=array([6.81325981, 0.96799912])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=19, candidate_x=array([6.770548 , 0.97042897]), index=19, x=array([6.770548 , 0.97042897]), fval=501.32293639999193, rho=0.005077718107259092, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 16, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.042780877979297874, relative_step_length=1.0015250245277747, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.770548 , 0.97042897]), radius=0.021357867717518755, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 16, 18, 19]), model=ScalarModel(intercept=501.25072904738215, linear_terms=array([0.19645285, 2.53503583]), square_terms=array([[4.52533224e-03, 3.61357893e-01], + [3.61357893e-01, 3.03038886e+01]]), scale=0.021357867717518755, shift=array([6.770548 , 0.97042897])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=20, candidate_x=array([6.74917044, 0.96890556]), index=20, x=array([6.74917044, 0.96890556]), fval=501.24660583858616, rho=0.28031880647812146, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.021431763444109308, relative_step_length=1.0034598831478827, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.74917044, 0.96890556]), radius=0.04271573543503751, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 16, 17, 18, 19, 20]), model=ScalarModel(intercept=497.70357158348145, linear_terms=array([ -0.5208041 , -15.51687346]), square_terms=array([[6.94479053e-02, 3.57966427e+00], + [3.57966427e+00, 1.84953284e+02]]), scale=0.04271573543503751, shift=array([6.74917044, 0.96890556])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=21, candidate_x=array([6.79194745, 0.97165805]), index=20, x=array([6.74917044, 0.96890556]), fval=501.24660583858616, rho=-0.17667678260606623, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 16, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.74917044, 0.96890556]), radius=0.021357867717518755, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 16, 18, 19, 20, 21]), model=ScalarModel(intercept=501.0418468452605, linear_terms=array([ 0.12539853, -0.02256087]), square_terms=array([[4.94613572e-03, 3.80808575e-01], + [3.80808575e-01, 3.03057373e+01]]), scale=0.021357867717518755, shift=array([6.74917044, 0.96890556])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=22, candidate_x=array([6.72781445, 0.96918864]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=0.15280707534799082, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 18, 19, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.021357872793329805, relative_step_length=1.0000002376553276, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=0.04271573543503751, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 16, 17, 18, 19, 20, 21, 22]), model=ScalarModel(intercept=499.4402789183877, linear_terms=array([ -1.02052835, -19.09880474]), square_terms=array([[9.93137121e-02, 4.28436157e+00], + [4.28436157e+00, 1.86094212e+02]]), scale=0.04271573543503751, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=23, candidate_x=array([6.7706195 , 0.97257651]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-0.23892563437548955, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 16, 17, 18, 19, 20, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=0.021357867717518755, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 18, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=501.1654021820905, linear_terms=array([0.00141307, 0.0739436 ]), square_terms=array([[6.12116035e-03, 4.27791823e-01], + [4.27791823e-01, 3.02363284e+01]]), scale=0.021357867717518755, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=24, candidate_x=array([6.70645798, 0.96943857]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-4.713847073443613, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([16, 18, 19, 20, 21, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=0.010678933858759378, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 19, 20, 22, 23, 24]), model=ScalarModel(intercept=501.21585915665423, linear_terms=array([-0.01975345, 0.03910402]), square_terms=array([[1.80090281e-03, 1.15983380e-01], + [1.15983380e-01, 7.55096648e+00]]), scale=0.010678933858759378, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=25, candidate_x=array([6.73849128, 0.96896993]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-1.8944073412337845, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([16, 19, 20, 22, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=0.005339466929379689, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([20, 22, 24, 25]), model=ScalarModel(intercept=501.23004259245243, linear_terms=array([-0.02304652, -2.01666164]), square_terms=array([[4.15899121e-04, 3.00032341e-02], + [3.00032341e-02, 2.18603050e+00]]), scale=0.005339466929379689, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=26, candidate_x=array([6.72466986, 0.97414015]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-0.6243869841590974, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([20, 22, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=0.0026697334646898444, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 25, 26]), model=ScalarModel(intercept=501.2274134470572, linear_terms=array([ 0.00645166, -0.15519734]), square_terms=array([[9.51177458e-05, 7.00242898e-03], + [7.00242898e-03, 5.19312476e-01]]), scale=0.0026697334646898444, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=27, candidate_x=array([6.72515553, 0.97000886]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-1.7124434167385763, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=0.0013348667323449222, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 26, 27]), model=ScalarModel(intercept=501.2274134470572, linear_terms=array([-0.0511454 , -0.11212862]), square_terms=array([[1.10301742e-04, 3.05502249e-03], + [3.05502249e-03, 1.31450033e-01]]), scale=0.0013348667323449222, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=28, candidate_x=array([6.72891867, 0.96995443]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-0.591573475356113, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=0.0006674333661724611, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 27, 28]), model=ScalarModel(intercept=501.22741344705673, linear_terms=array([-0.00047063, 0.02530991]), square_terms=array([[5.90718768e-06, 4.24198051e-04], + [4.24198051e-04, 3.08887054e-02]]), scale=0.0006674333661724611, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=29, candidate_x=array([6.72826759, 0.96865598]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-8.425657234437928, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 27, 28]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=0.00033371668308623055, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 28, 29]), model=ScalarModel(intercept=501.2274134470576, linear_terms=array([ 0.02772457, -0.02766163]), square_terms=array([[4.66536843e-06, 1.60175503e-05], + [1.60175503e-05, 7.97518871e-03]]), scale=0.00033371668308623055, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=30, candidate_x=array([6.72754969, 0.96940377]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-1.8297324592636042, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=0.00016685834154311528, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 29, 30]), model=ScalarModel(intercept=501.2274134470567, linear_terms=array([-0.20692064, -0.20165341]), square_terms=array([[0.0001218 , 0.00033401], + [0.00033401, 0.00246797]]), scale=0.00016685834154311528, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=31, candidate_x=array([6.72793871, 0.9693 ]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-0.1732370302450059, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 29, 30]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=8.342917077155764e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 30, 31]), model=ScalarModel(intercept=501.22741344705656, linear_terms=array([0.00448848, 0.03202386]), square_terms=array([[6.79766078e-07, 1.06395699e-05], + [1.06395699e-05, 4.82739934e-04]]), scale=8.342917077155764e-05, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=32, candidate_x=array([6.72780354, 0.96910593]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-1.6246965293022224, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=4.171458538577882e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 31, 32]), model=ScalarModel(intercept=501.2274134470572, linear_terms=array([ 0.04540299, -0.03215216]), square_terms=array([[ 4.78307591e-06, -8.79441540e-06], + [-8.79441540e-06, 1.40505229e-04]]), scale=4.171458538577882e-05, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=33, candidate_x=array([6.72778037, 0.96921271]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-0.19905068438600984, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 31, 32]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=2.085729269288941e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 32, 33]), model=ScalarModel(intercept=501.22741344705696, linear_terms=array([-0.01463857, -0.01115043]), square_terms=array([[1.18032938e-06, 1.64511500e-06], + [1.64511500e-06, 3.41125000e-05]]), scale=2.085729269288941e-05, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=34, candidate_x=array([6.72783105, 0.96920127]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-0.22252229696055864, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 32, 33]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=1.0428646346444705e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 33, 34]), model=ScalarModel(intercept=501.22741344705673, linear_terms=array([-0.00051674, 0.00405539]), square_terms=array([[1.38664159e-08, 2.50676044e-07], + [2.50676044e-07, 8.63702623e-06]]), scale=1.0428646346444705e-05, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=35, candidate_x=array([6.72781578, 0.9691783 ]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-1.963280298177103, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 33, 34]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=5.214323173222352e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 34, 35]), model=ScalarModel(intercept=501.2274134470567, linear_terms=array([ 0.00396691, -0.00352958]), square_terms=array([[ 8.84977255e-08, -8.02763637e-08], + [-8.02763637e-08, 2.38818005e-06]]), scale=5.214323173222352e-06, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=36, candidate_x=array([6.72781055, 0.96919211]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-0.023801639447033406, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 34, 35]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=2.607161586611176e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 35, 36]), model=ScalarModel(intercept=501.22741344705673, linear_terms=array([-0.00212338, -0.00229278]), square_terms=array([[1.90160359e-08, 5.69811889e-08], + [5.69811889e-08, 6.16908307e-07]]), scale=2.607161586611176e-06, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=37, candidate_x=array([6.72781622, 0.96919056]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-0.026219671802032652, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 35, 36]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=1.303580793305588e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 36, 37]), model=ScalarModel(intercept=501.22741344705713, linear_terms=array([4.06519893e-06, 5.19387199e-05]), square_terms=array([[2.89936600e-11, 9.20593272e-10], + [9.20593272e-10, 1.30236266e-07]]), scale=1.303580793305588e-06, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=38, candidate_x=array([6.72781435, 0.96918734]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-9.564486075375958, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 36, 37]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 37, 38]), model=ScalarModel(intercept=501.22741344705696, linear_terms=array([ 0.00050204, -0.00042149]), square_terms=array([[ 2.97289942e-09, -6.39499610e-09], + [-6.39499610e-09, 8.69411716e-08]]), scale=1e-06, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=39, candidate_x=array([6.72781368, 0.96918929]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-0.03524631676538639, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 37, 38]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 37, 38, 39]), model=ScalarModel(intercept=501.22759431747124, linear_terms=array([ 0.00013189, -0.00019106]), square_terms=array([[ 1.93807540e-10, -5.26330437e-10], + [-5.26330437e-10, 8.07113297e-08]]), scale=1e-06, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=40, candidate_x=array([6.72781388, 0.96918947]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-0.13479910265165615, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 37, 38, 39]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 37, 38, 39, 40]), model=ScalarModel(intercept=501.22760577987816, linear_terms=array([ 0.00010942, -0.0001753 ]), square_terms=array([[ 1.33121551e-10, -2.35081804e-10], + [-2.35081804e-10, 8.03325740e-08]]), scale=1e-06, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=41, candidate_x=array([6.72781392, 0.96918949]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-0.15707565148239513, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 37, 38, 39, 40]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 37, 38, 39, 40, 41]), model=ScalarModel(intercept=501.22761025938087, linear_terms=array([ 0.00010088, -0.00016908]), square_terms=array([[ 1.13315328e-10, -1.26487044e-10], + [-1.26487044e-10, 8.01849165e-08]]), scale=1e-06, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=42, candidate_x=array([6.72781394, 0.9691895 ]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-0.1672978954959007, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 37, 38, 39, 40, 41]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 37, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=501.2276126857747, linear_terms=array([ 9.63467529e-05, -1.65702026e-04]), square_terms=array([[ 1.03526913e-10, -6.92871011e-11], + [-6.92871011e-11, 8.01050563e-08]]), scale=1e-06, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=43, candidate_x=array([6.72781395, 0.96918951]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-0.17321991079148824, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 37, 38, 39, 40, 41, 42]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 37, 38, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=501.22761421797827, linear_terms=array([ 9.35296917e-05, -1.63564492e-04]), square_terms=array([[ 9.76954192e-11, -3.38798435e-11], + [-3.38798435e-11, 8.00546807e-08]]), scale=1e-06, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=44, candidate_x=array([6.72781395, 0.96918951]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-0.17709796371999056, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 37, 38, 39, 40, 41, 42, 43]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 37, 38, 39, 40, 41, 42, 43, 44]), model=ScalarModel(intercept=501.2276152773825, linear_terms=array([ 9.16066701e-05, -1.62084960e-04]), square_terms=array([[ 9.38262729e-11, -9.77407261e-12], + [-9.77407261e-12, 8.00198778e-08]]), scale=1e-06, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=45, candidate_x=array([6.72781396, 0.96918951]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-0.17983972616620006, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 37, 38, 39, 40, 41, 42, 43, 44]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 38, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=501.22750540733114, linear_terms=array([-0.00026071, -0.00025561]), square_terms=array([[9.13381429e-10, 4.44129408e-09], + [4.44129408e-09, 8.23253357e-08]]), scale=1e-06, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=46, candidate_x=array([6.72781516, 0.96918934]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-0.07364815380731068, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 38, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([37]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 39, 40, 41, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=501.22741310102083, linear_terms=array([6.11971997e-07, 3.88455230e-05]), square_terms=array([[1.42843074e-11, 1.03541877e-09], + [1.03541877e-09, 7.57915244e-08]]), scale=1e-06, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=47, candidate_x=array([6.72781443, 0.96918764]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-9.857062002123858, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 39, 40, 41, 42, 43, 44, 45, 46]), old_indices_discarded=array([37, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 40, 41, 42, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=501.22755969669424, linear_terms=array([-4.80059039e-05, -1.70805300e-04]), square_terms=array([[5.49126329e-11, 1.63716710e-09], + [1.63716710e-09, 8.02257831e-08]]), scale=1e-06, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=48, candidate_x=array([6.72781472, 0.96918961]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-0.21557648426876538, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 40, 41, 42, 43, 44, 45, 46, 47]), old_indices_discarded=array([37, 38, 39]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 40, 41, 43, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=501.22756442641673, linear_terms=array([-2.32195187e-05, -1.59032170e-04]), square_terms=array([[2.69656250e-11, 1.33837609e-09], + [1.33837609e-09, 7.99482457e-08]]), scale=1e-06, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=49, candidate_x=array([6.72781459, 0.96918963]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-0.2447255640790162, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 40, 41, 43, 44, 45, 46, 47, 48]), old_indices_discarded=array([37, 38, 39, 42]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 40, 41, 44, 45, 46, 47, 48, 49]), model=ScalarModel(intercept=501.22756659666624, linear_terms=array([-1.22237542e-05, -1.52682563e-04]), square_terms=array([[1.93861263e-11, 1.20780236e-09], + [1.20780236e-09, 7.97999713e-08]]), scale=1e-06, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=50, candidate_x=array([6.72781453, 0.96918964]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-0.2585372755970867, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 40, 41, 44, 45, 46, 47, 48, 49]), old_indices_discarded=array([37, 38, 39, 42, 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 40, 44, 45, 46, 47, 48, 49, 50]), model=ScalarModel(intercept=501.22756824156363, linear_terms=array([-7.64989431e-06, -1.48061504e-04]), square_terms=array([[1.71054063e-11, 1.15355291e-09], + [1.15355291e-09, 7.96926826e-08]]), scale=1e-06, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], 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State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 40, 44, 45, 46, 47, 48, 50, 51]), model=ScalarModel(intercept=501.2275681838988, linear_terms=array([-9.72571253e-06, -1.47957510e-04]), square_terms=array([[1.80769873e-11, 1.17756610e-09], + [1.17756610e-09, 7.96902744e-08]]), scale=1e-06, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=52, candidate_x=array([6.72781451, 0.96918964]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-0.26729975889890145, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 40, 44, 45, 46, 47, 48, 50, 51]), old_indices_discarded=array([37, 38, 39, 41, 42, 43, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 40, 44, 45, 46, 47, 48, 50, 51]), model=ScalarModel(intercept=501.2275681838988, linear_terms=array([-9.72571253e-06, -1.47957510e-04]), square_terms=array([[1.80769873e-11, 1.17756610e-09], + [1.17756610e-09, 7.96902744e-08]]), scale=1e-06, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=53, candidate_x=array([6.72781451, 0.96918964]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-0.26729975889890145, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 40, 44, 45, 46, 47, 48, 50, 51]), old_indices_discarded=array([37, 38, 39, 41, 42, 43, 49, 52]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 40, 44, 45, 46, 47, 48, 50, 51]), model=ScalarModel(intercept=501.2275681838988, linear_terms=array([-9.72571253e-06, -1.47957510e-04]), square_terms=array([[1.80769873e-11, 1.17756610e-09], + [1.17756610e-09, 7.96902744e-08]]), scale=1e-06, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 40, 44, 45, 46, 47, 48, 50, 51]), model=ScalarModel(intercept=501.2275681838988, linear_terms=array([-9.72571253e-06, -1.47957510e-04]), square_terms=array([[1.80769873e-11, 1.17756610e-09], + [1.17756610e-09, 7.96902744e-08]]), scale=1e-06, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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linear_terms=array([-9.72571253e-06, -1.47957510e-04]), square_terms=array([[1.80769873e-11, 1.17756610e-09], + [1.17756610e-09, 7.96902744e-08]]), scale=1e-06, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=56, candidate_x=array([6.72781451, 0.96918964]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-0.26729975889890145, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 40, 44, 45, 46, 47, 48, 50, 51]), old_indices_discarded=array([37, 38, 39, 41, 42, 43, 49, 52, 53, 54, 55]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 40, 44, 45, 46, 47, 48, 50, 51]), model=ScalarModel(intercept=501.2275681838988, linear_terms=array([-9.72571253e-06, -1.47957510e-04]), square_terms=array([[1.80769873e-11, 1.17756610e-09], + [1.17756610e-09, 7.96902744e-08]]), scale=1e-06, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=57, candidate_x=array([6.72781451, 0.96918964]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-0.26729975889890145, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 40, 44, 45, 46, 47, 48, 50, 51]), old_indices_discarded=array([37, 38, 39, 41, 42, 43, 49, 52, 53, 54, 55, 56]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 40, 44, 45, 46, 47, 48, 50, 51]), model=ScalarModel(intercept=501.2275681838988, linear_terms=array([-9.72571253e-06, -1.47957510e-04]), square_terms=array([[1.80769873e-11, 1.17756610e-09], + [1.17756610e-09, 7.96902744e-08]]), scale=1e-06, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + 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50, 51]), model=ScalarModel(intercept=501.2275681838988, linear_terms=array([-9.72571253e-06, -1.47957510e-04]), square_terms=array([[1.80769873e-11, 1.17756610e-09], + [1.17756610e-09, 7.96902744e-08]]), scale=1e-06, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), 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vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.6834517669606002, shift=array([6.83451767, 0.96153972])), candidate_index=60, candidate_x=array([6.72781451, 0.96918964]), index=22, x=array([6.72781445, 0.96918864]), fval=501.22741344705696, rho=-0.26729975889890145, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 40, 44, 45, 46, 47, 48, 50, 51]), old_indices_discarded=array([37, 38, 39, 41, 42, 43, 49, 52, 53, 54, 55, 56, 57, 58, 59]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72781445, 0.96918864]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 40, 44, 45, 46, 47, 48, 50, 51]), model=ScalarModel(intercept=501.2275681838988, linear_terms=array([-9.72571253e-06, -1.47957510e-04]), square_terms=array([[1.80769873e-11, 1.17756610e-09], + [1.17756610e-09, 7.96902744e-08]]), scale=1e-06, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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0.96918864]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 40, 44, 45, 46, 47, 48, 50, 51]), model=ScalarModel(intercept=501.2275681838988, linear_terms=array([-9.72571253e-06, -1.47957510e-04]), square_terms=array([[1.80769873e-11, 1.17756610e-09], + [1.17756610e-09, 7.96902744e-08]]), scale=1e-06, shift=array([6.72781445, 0.96918864])), vector_model=VectorModel(intercepts=array([ 1.41100158, 3.00525944, 3.61548053, 4.56961125, + 5.5414129 , 6.93581401, 7.42471695, -1.11598091, + -4.60429004, -6.78060042, -9.80861909, -12.63059673]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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0.9691896401329931}, {'CRRA': 6.727814513068946, 'DiscFac': 0.9691896401329931}, {'CRRA': 6.727814513068946, 'DiscFac': 0.9691896401329931}, {'CRRA': 6.727814513068946, 'DiscFac': 0.9691896401329931}, {'CRRA': 6.727814513068946, 'DiscFac': 0.9691896401329931}, {'CRRA': 6.727814513068946, 'DiscFac': 0.9691896401329931}, {'CRRA': 6.727814513068946, 'DiscFac': 0.9691896401329931}, {'CRRA': 6.727814513068946, 'DiscFac': 0.9691896401329931}], 'criterion': [503.07717547325797, 1183.396120481936, 1135.241738848109, 1114.6788511875075, 1156.4805942455716, 1370.2995606967131, 1281.4007654935178, 1135.241738848109, 641.9040338115028, 548.1597618494977, 1381.3178685506343, 1174.3481812592236, 1326.968853164346, 828.6116665175828, 817.6725897550722, 790.9535501991393, 518.3018295957619, 513.1991117496027, 501.33125312107256, 501.32293639999193, 501.24660583858616, 501.40060082751353, 501.227413447057, 501.6005548841788, 501.2294072381694, 501.26613824146216, 501.80991855578475, 501.28168631848837, 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8.555633861999922, 9.850647897000044, 11.154472786000042, 12.467322176999915, 13.7927576269999, 15.129388597999878, 16.46352905399999, 17.791390877999902, 19.100807482999926, 20.424202722000018, 21.728083906999927, 23.03281336200007, 24.346482759000082, 25.639335947000063, 26.938558005999994, 28.249169775999917, 29.567452292000098, 30.89406715699988, 32.212094205000085, 33.529800943000055, 34.844091301999924, 36.16356657000006, 37.46958931900008, 38.765836247999914, 40.06039555999996, 41.366433961999974, 42.666288186999964, 43.95999424299998, 45.26542451299997, 46.613004456, 47.92532619200006, 49.25367465799991, 50.57502731799991, 51.888762842999995, 53.19933486700006, 54.510649564999994, 55.91155609499992, 57.281255367000085, 58.806048205000025, 60.160338701, 61.492727951000006, 62.865169901999934, 64.25322807899988, 65.75111744700007, 67.2279566499999, 68.57297918800009], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51]}}], 'exploration_sample': array([[ 6.51875819, 0.97409119], + [ 7.596875 , 0.93125 ], + [ 5.825 , 0.95 ], + [16.45625 , 0.9125 ], + [ 9.36875 , 0.8375 ], + [17.046875 , 0.63125 ], + [10.55 , 0.8 ], + [11.73125 , 0.7625 ], + [15.275 , 0.65 ], + [18.81875 , 0.5375 ], + [14.09375 , 0.9875 ], + [12.9125 , 0.575 ], + [17.6375 , 1.025 ], + [ 8.1875 , 0.725 ], + [12.321875 , 1.08125 ], + [ 3.4625 , 0.875 ], + [ 7.00625 , 0.6125 ], + [ 4.64375 , 0.6875 ], + [ 2.871875 , 0.78125 ], + [ 2.28125 , 1.0625 ]]), 'exploration_results': array([ 501.12751638, 519.11435204, 538.30979579, 597.0207673 , + 611.52511798, 615.89547778, 622.99770198, 631.34502173, + 647.27786212, 656.04258415, 694.37527482, 830.0300439 , + 914.55694912, 917.36098753, 1057.62479346, 1104.333468 , + 1108.21046316, 1193.90258834, 1244.39797507, 2082.87264995])}}" diff --git a/content/tables/min/PortfolioSub(Labor)Market_estimate_results.csv b/content/tables/min/PortfolioSub(Labor)Market_estimate_results.csv new file mode 100644 index 0000000..686cf98 --- /dev/null +++ b/content/tables/min/PortfolioSub(Labor)Market_estimate_results.csv @@ -0,0 +1,8209 @@ +CRRA,14.011832813202263 +DiscFac,1.0891599957368594 +time_to_estimate,168.5236520767212 +params,"{'CRRA': 14.011832813202263, 'DiscFac': 1.0891599957368594}" +criterion,0.7184205561063591 +start_criterion,1.3213975205794042 +start_params,"{'CRRA': 12.279830990813757, 'DiscFac': 1.0776706629061286}" +algorithm,multistart_tranquilo_ls +direction,minimize +n_free,2 +message, +success, +n_criterion_evaluations, +n_derivative_evaluations, +n_iterations, +history,"{'params': [{'CRRA': 13.066309348529067, 'DiscFac': 1.0471576581804647}, {'CRRA': 12.280456956731095, 'DiscFac': 0.5}, {'CRRA': 13.8146011388025, 'DiscFac': 0.5}, {'CRRA': 11.908337832432913, 'DiscFac': 0.5}, {'CRRA': 14.224280864625221, 'DiscFac': 1.1}, {'CRRA': 14.224280864625221, 'DiscFac': 0.5}, {'CRRA': 14.224280864625221, 'DiscFac': 0.5}, {'CRRA': 11.908337832432913, 'DiscFac': 0.8984815709189882}, {'CRRA': 14.224280864625221, 'DiscFac': 0.7709984071009821}, {'CRRA': 14.224280864625221, 'DiscFac': 1.1}, {'CRRA': 11.908337832432913, 'DiscFac': 0.97048629342172}, {'CRRA': 11.908337832432913, 'DiscFac': 0.5298026178234173}, {'CRRA': 11.908337832432913, 'DiscFac': 1.0810817543290407}, {'CRRA': 13.970035730789688, 'DiscFac': 1.1}, {'CRRA': 16.285978762981998, 'DiscFac': 0.5}, {'CRRA': 14.767415996519718, 'DiscFac': 1.1}, {'CRRA': 13.525261720208501, 'DiscFac': 1.1}, {'CRRA': 13.70108525460877, 'DiscFac': 1.1}, {'CRRA': 14.004502508260845, 'DiscFac': 1.1}, {'CRRA': 14.014669911267017, 'DiscFac': 1.0846283754324875}, {'CRRA': 14.159416350779036, 'DiscFac': 1.1}, {'CRRA': 13.942296691511007, 'DiscFac': 1.0550276666138225}, {'CRRA': 13.978483301389012, 'DiscFac': 1.0948775831831337}, {'CRRA': 14.03276321620602, 'DiscFac': 1.1}, 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'DiscFac': 1.0891607511693926}, {'CRRA': 14.011832158043546, 'DiscFac': 1.0891607512281871}, {'CRRA': 14.011832158095237, 'DiscFac': 1.089160751272985}, {'CRRA': 14.011832158029653, 'DiscFac': 1.0891607512160808}, {'CRRA': 14.011832157970844, 'DiscFac': 1.089160751165159}, {'CRRA': 14.011832157981903, 'DiscFac': 1.0891607511747805}, {'CRRA': 14.011832158038635, 'DiscFac': 1.0891607512238868}, {'CRRA': 14.011833468300475, 'DiscFac': 1.0891592401931263}, {'CRRA': 14.011833468288398, 'DiscFac': 1.089159240182655}, {'CRRA': 14.011833468269545, 'DiscFac': 1.0891592401663046}, {'CRRA': 14.011833468287934, 'DiscFac': 1.0891592401822054}, {'CRRA': 14.011833468323973, 'DiscFac': 1.0891592402134855}, {'CRRA': 14.011833468299528, 'DiscFac': 1.089159240192304}, {'CRRA': 14.011833468327906, 'DiscFac': 1.08915924021691}, {'CRRA': 14.011833468334546, 'DiscFac': 1.0891592402226586}, {'CRRA': 14.011832157988263, 'DiscFac': 1.089160751180182}, {'CRRA': 14.011833468316171, 'DiscFac': 1.0891592402066144}, {'CRRA': 14.011833468316171, 'DiscFac': 1.0891592402066144}, {'CRRA': 14.011833468316171, 'DiscFac': 1.0891592402066144}, {'CRRA': 14.011833468316171, 'DiscFac': 1.0891592402066144}, {'CRRA': 14.011833468316171, 'DiscFac': 1.0891592402066144}, {'CRRA': 14.011833468316171, 'DiscFac': 1.0891592402066144}, {'CRRA': 14.011833468316171, 'DiscFac': 1.0891592402066144}, {'CRRA': 14.011833468316171, 'DiscFac': 1.0891592402066144}], 'criterion': [1.4986734884110868, 3.6930800498276546, 3.820662632812437, 3.671505897975399, 1.361435725731372, 3.8638417260142304, 3.8638417260142304, 2.3161149911520624, 2.7171589222312944, 1.361435725731372, 1.755774291009161, 3.5701092462200235, 1.5259328711389366, 1.3630395467044545, 4.135049913310356, 1.7177184763425375, 1.4584461999630762, 1.4570806285179234, 1.6352440012556255, 1.0410129484416906, 1.9127060614796625, 1.6638058936439872, 1.3344198235621072, 2.412229418376727, 1.5644026100530035, 0.718420556106359, 1.3583428907310002, 1.8004694015466847, 1.592846442492335, 1.657972144202744, 1.4401830262828144, 1.6785780028599055, 2.0117679446685552, 1.693975874418422, 2.6363739549773766, 2.3067084438958925, 1.6834365843388674, 1.4756093975418976, 3.003608443738103, 1.7951122669157669, 1.120097338605995, 1.5359621706119526, 2.320478525587398, 1.8102170529001989, 1.813949043295858, 1.5631216198024078, 2.322376472196528, 1.204765008146623, 1.4709947263422103, 1.5893332193734733, 0.9602897636214487, 1.5865626206073609, 1.2876935617679888, 1.393450740856762, 1.5735237105291944, 1.3700688455816392, 1.1742232022873909, 3.5492281048492407, 2.368415509703494, 1.2824516276565534, 2.671298480209609, 1.336285742222334, 1.1746069257095435, 1.0925841020024283], 'runtime': [0.0, 2.2173056839997116, 2.4335195229996316, 2.650656942999831, 2.8830891839998003, 3.1058964579997337, 3.342101177999666, 3.5510437119996823, 3.78188942099996, 4.030340804999923, 4.276771940999879, 4.516578778999701, 4.718500236999716, 6.699993328999881, 8.439053768999656, 10.134882657999697, 11.832450609999796, 13.530497703000037, 15.377237071999843, 17.173570721000033, 18.907639610999922, 20.636314630000015, 22.369553777999954, 24.07437646199969, 25.76552489000005, 27.58931298699963, 29.298179384999912, 31.00593732499965, 32.7445265399997, 34.49970570400001, 36.23148644999992, 38.01005339299991, 39.856888376999905, 41.57462375499972, 43.3560608949997, 45.12837037099962, 46.92190624099976, 48.763203235999754, 50.5171019659997, 52.310331486999985, 54.226219024999864, 55.97341468900004, 57.71188124599985, 59.43016265599999, 61.13717892199975, 62.837184452999736, 64.54695662299991, 66.40381871399995, 68.13384646799977, 69.85902101900001, 71.61205858599988, 73.42455532999975, 75.25045019799973, 76.99171744099976, 78.83135237599981, 80.5315630619998, 82.28934421199983, 84.04364812599988, 85.81372394899972, 87.57166456200002, 89.33697740099979, 91.23287148300005, 93.02459795599998, 94.73499807600001], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52]}" +convergence_report,"{'one_step': {'relative_criterion_change': 0.684756876261355, 'relative_params_change': 0.06688205106671818, 'absolute_criterion_change': 0.49194341584133594, 'absolute_params_change': 0.622157551616937}, 'five_steps': {'relative_criterion_change': 0.684756876261355, 'relative_params_change': 0.06688205106671818, 'absolute_criterion_change': 0.49194341584133594, 'absolute_params_change': 0.622157551616937}}" +multistart_info,"{'start_parameters': [{'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 13.066309348529067, 'DiscFac': 1.0471576581804647}], 'local_optima': [Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute params change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 0.02632 0.2707 +relative_params_change 0.0005553 0.0918 +absolute_criterion_change 0.03186 0.3276 +absolute_params_change 0.0006017 1.071 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 0.449 1.086 +relative_params_change 0.004166 0.07772 +absolute_criterion_change 0.3226 0.7803 +absolute_params_change 0.005346 0.9465 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 12.279830990813757, 'DiscFac': 1.0776706629061286}, {'CRRA': 14.093749999999998, 'DiscFac': 0.9875}, {'CRRA': 17.6375, 'DiscFac': 1.0250000000000001}, {'CRRA': 16.45625, 'DiscFac': 0.9125000000000001}, {'CRRA': 11.73125, 'DiscFac': 0.7625000000000001}, {'CRRA': 10.549999999999999, 'DiscFac': 0.8}, {'CRRA': 12.9125, 'DiscFac': 0.575}, {'CRRA': 15.274999999999999, 'DiscFac': 0.65}, {'CRRA': 17.046875, 'DiscFac': 0.6312500000000001}, {'CRRA': 18.81875, 'DiscFac': 0.5375}, {'CRRA': 9.368749999999999, 'DiscFac': 0.8375}, {'CRRA': 8.1875, 'DiscFac': 0.7250000000000001}, {'CRRA': 7.00625, 'DiscFac': 0.6125}, {'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 2.871875, 'DiscFac': 0.78125}, {'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 2.28125, 'DiscFac': 1.0625}], 'exploration_results': array([ 1.25226984, 1.54798499, 1.77139048, 1.79567843, 2.19243348, + 2.99563577, 3.4003551 , 3.46407238, 3.67340234, 3.72448066, + 4.28414873, 4.65250486, 5.39134448, 6.53886763, 14.94020775, + 20.34265339, 25.6087008 , 29.29813793, 75.84976584])}" +algorithm_output,"{'states': [State(trustregion=Region(center=array([13.06630935, 1.04715766]), radius=1.3066309348529068, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=1.4986734884110868, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=1.3066309348529068, shift=array([13.06630935, 1.04715766])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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shift=array([13.97003573, 1.02762678])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3066309348529068, shift=array([13.06630935, 1.04715766])), candidate_index=19, candidate_x=array([14.01466991, 1.08462838]), index=19, x=array([14.01466991, 1.08462838]), fval=1.0410129484416903, rho=114.80417610410284, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 4, 6, 8, 9, 13, 16, 17, 18]), old_indices_discarded=array([ 5, 15]), step_length=0.047206958265994726, relative_step_length=0.2890300972175224, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01466991, 1.08462838]), radius=0.16332886685661335, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 4, 8, 9, 13, 16, 17, 18, 19]), model=ScalarModel(intercept=1.226523862135151, linear_terms=array([-0.01223051, -0.12778069]), square_terms=array([[ 0.01612848, -0.01186675], + [-0.01186675, 0.10601771]]), scale=array([0.14474644, 0.08005903]), shift=array([14.01466991, 1.01994097])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3066309348529068, shift=array([13.06630935, 1.04715766])), candidate_index=20, candidate_x=array([14.15941635, 1.1 ]), index=19, x=array([14.01466991, 1.08462838]), fval=1.0410129484416903, rho=-39.326073520819115, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 4, 8, 9, 13, 16, 17, 18, 19]), old_indices_discarded=array([ 5, 6, 15]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01466991, 1.08462838]), radius=0.08166443342830668, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 4, 8, 9, 13, 17, 18, 19, 20]), model=ScalarModel(intercept=1.196985077835359, linear_terms=array([0.03516311, 0.00306745]), square_terms=array([[0.01712576, 0.00136586], + [0.00136586, 0.06787167]]), scale=array([0.07237322, 0.04387242]), shift=array([14.01466991, 1.05612758])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3066309348529068, shift=array([13.06630935, 1.04715766])), candidate_index=21, candidate_x=array([13.94229669, 1.05502767]), index=19, x=array([14.01466991, 1.08462838]), fval=1.0410129484416903, rho=-14.505215148299051, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 4, 8, 9, 13, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01466991, 1.08462838]), radius=0.04083221671415334, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 18, 19, 20, 21]), model=ScalarModel(intercept=1.43364495095228, linear_terms=array([ 0.12922127, -0.32781807]), square_terms=array([[ 0.01853158, -0.06428325], + [-0.06428325, 0.32888578]]), scale=array([0.03618661, 0.02577912]), shift=array([14.01466991, 1.07422088])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3066309348529068, shift=array([13.06630935, 1.04715766])), candidate_index=22, candidate_x=array([13.9784833 , 1.09487758]), index=19, x=array([14.01466991, 1.08462838]), fval=1.0410129484416903, rho=-2.4451313207154053, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([13, 18, 19, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01466991, 1.08462838]), radius=0.02041610835707667, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 18, 19, 21, 22]), model=ScalarModel(intercept=1.0485149188081042, linear_terms=array([ 0.05139792, -0.29978337]), square_terms=array([[ 0.13343796, -0.19971635], + [-0.19971635, 0.34743672]]), scale=array([0.0180933 , 0.01673246]), shift=array([14.01466991, 1.08326754])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3066309348529068, shift=array([13.06630935, 1.04715766])), candidate_index=23, candidate_x=array([14.03276322, 1.1 ]), index=19, x=array([14.01466991, 1.08462838]), fval=1.0410129484416903, rho=-7.434794508101175, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([13, 18, 19, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01466991, 1.08462838]), radius=0.010208054178538335, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 18, 19, 22, 23]), model=ScalarModel(intercept=0.9771052862147884, linear_terms=array([ 0.06873397, -0.19272697]), square_terms=array([[0.01343003, 0.04147703], + [0.04147703, 1.11994799]]), scale=0.010208054178538335, shift=array([14.01466991, 1.08462838])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3066309348529068, shift=array([13.06630935, 1.04715766])), candidate_index=24, candidate_x=array([14.00453977, 1.08664481]), index=19, x=array([14.01466991, 1.08462838]), fval=1.0410129484416903, rho=-6.08966504598221, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([13, 18, 19, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01466991, 1.08462838]), radius=0.005104027089269167, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([18, 19, 23, 24]), model=ScalarModel(intercept=1.2926218223787844, linear_terms=array([-0.00788451, -0.32540669]), square_terms=array([[0.00911385, 0.04354991], + [0.04354991, 0.34751248]]), scale=0.005104027089269167, shift=array([14.01466991, 1.08462838])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3066309348529068, shift=array([13.06630935, 1.04715766])), candidate_index=25, candidate_x=array([14.01183281, 1.08916 ]), index=25, x=array([14.01183281, 1.08916 ]), fval=0.7184205561063591, rho=1.9242451111551888, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([18, 19, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.005346466871872233, relative_step_length=1.0474997053038329, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01183281, 1.08916 ]), radius=0.010208054178538335, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 18, 19, 22, 23, 24, 25]), model=ScalarModel(intercept=0.9530259023191747, linear_terms=array([0.0624839 , 0.22160522]), square_terms=array([[0.01172426, 0.05678182], + [0.05678182, 1.28834316]]), scale=0.010208054178538335, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3066309348529068, shift=array([13.06630935, 1.04715766])), candidate_index=26, candidate_x=array([14.00157116, 1.08789893]), index=25, x=array([14.01183281, 1.08916 ]), fval=0.7184205561063591, rho=-9.496915537884778, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([13, 18, 19, 22, 23, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01183281, 1.08916 ]), radius=0.005104027089269167, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([18, 19, 23, 24, 25, 26]), model=ScalarModel(intercept=1.0207789028264391, linear_terms=array([0.01004909, 0.14281032]), square_terms=array([[0.00371043, 0.01053812], + [0.01053812, 0.28679693]]), scale=0.005104027089269167, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 24, 25, 26, 27]), model=ScalarModel(intercept=0.8128068799853125, linear_terms=array([-0.07390409, 0.1867271 ]), square_terms=array([[0.02933352, 0.06029508], + [0.06029508, 0.51755928]]), scale=0.0025520135446345836, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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model=ScalarModel(intercept=1.209518867736394, linear_terms=array([-0.11590533, 0.18949222]), square_terms=array([[ 0.01661532, -0.00116286], + [-0.00116286, 0.11835795]]), scale=0.0012760067723172918, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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0.15503069], + [0.15503069, 0.64746692]]), scale=0.0006380033861586459, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3066309348529068, shift=array([13.06630935, 1.04715766])), candidate_index=31, candidate_x=array([14.01204275, 1.08891981]), index=25, x=array([14.01183281, 1.08916 ]), fval=0.7184205561063591, rho=-4.3997935915404245, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 29, 30]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01183281, 1.08916 ]), radius=0.00015950084653966148, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 30, 31]), model=ScalarModel(intercept=0.7184205561063558, linear_terms=array([-12.83926554, -11.58895782]), square_terms=array([[403.25856038, 364.41817194], + [364.41817194, 329.32570956]]), scale=0.00015950084653966148, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3066309348529068, shift=array([13.06630935, 1.04715766])), candidate_index=32, candidate_x=array([14.01194254, 1.08904419]), index=25, x=array([14.01183281, 1.08916 ]), fval=0.7184205561063591, rho=-6.08668048343243, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01183281, 1.08916 ]), radius=7.975042326983074e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 31, 32]), model=ScalarModel(intercept=0.7184205561063587, linear_terms=array([-0.93030573, -0.99634712]), square_terms=array([[179.36567441, 156.70454335], + [156.70454335, 136.99646585]]), scale=7.975042326983074e-05, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 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rho=-8.451134213198396, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 31, 32]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01183281, 1.08916 ]), radius=3.987521163491537e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 32, 33]), model=ScalarModel(intercept=0.7184205561063576, linear_terms=array([4.89837172, 4.58388165]), square_terms=array([[126.56088765, 115.62747724], + [115.62747724, 105.76020212]]), scale=3.987521163491537e-05, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + 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old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01183281, 1.08916 ]), radius=1.9937605817457685e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 33, 34]), model=ScalarModel(intercept=0.7184205561063594, linear_terms=array([-166.8647099 , -144.39771315]), square_terms=array([[108814.42353203, 94175.80730374], + [ 94175.80730374, 81506.51063637]]), scale=1.9937605817457685e-05, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01183281, 1.08916 ]), radius=9.968802908728842e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 34, 35]), model=ScalarModel(intercept=0.7184205561063438, linear_terms=array([49.40160575, 42.35987568]), square_terms=array([[9579.24080639, 8238.55673379], + [8238.55673379, 7085.71316346]]), scale=9.968802908728842e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), 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vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3066309348529068, shift=array([13.06630935, 1.04715766])), candidate_index=41, candidate_x=array([14.01183216, 1.08916075]), index=25, x=array([14.01183281, 1.08916 ]), fval=0.7184205561063591, rho=-2.8604468453536884, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 37, 38, 39, 40]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01183281, 1.08916 ]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 37, 38, 39, 40, 41]), model=ScalarModel(intercept=1.2861419090291868, linear_terms=array([1090.09720002, 944.7027741 ]), square_terms=array([[3520795.13786478, 3051353.17786668], + [3051353.17786668, 2644503.83384729]]), scale=1e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 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candidate_x=array([14.01183216, 1.08916075]), index=25, x=array([14.01183281, 1.08916 ]), fval=0.7184205561063591, rho=-7.909430321019699, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 37, 38, 39, 40, 41]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01183281, 1.08916 ]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 37, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=1.4656905572771453, linear_terms=array([1243.82197504, 1077.98706406]), square_terms=array([[3085809.09127438, 2674453.15149168], + [2674453.15149168, 2317933.30821108]]), scale=1e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + 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fval=0.7184205561063591, rho=-4.056989488853848, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 37, 38, 39, 40, 41, 42]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01183281, 1.08916 ]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 37, 38, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=1.548557873891164, linear_terms=array([1203.1713828 , 1042.77950053]), square_terms=array([[2438542.31045176, 2113511.47163476], + [2113511.47163476, 1831803.66933079]]), scale=1e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 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old_indices_used=array([25, 37, 38, 39, 40, 41, 42, 43]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01183281, 1.08916 ]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 37, 38, 39, 40, 41, 42, 43, 44]), model=ScalarModel(intercept=1.5887250225761516, linear_terms=array([1030.5595738 , 893.18618335]), square_terms=array([[2190616.89147569, 1898642.09058655], + [1898642.09058655, 1645582.94584737]]), scale=1e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01183281, 1.08916 ]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 38, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=1.6019772554697844, linear_terms=array([1292.43082122, 1120.26966461]), square_terms=array([[4990602.23895189, 4326174.44936223], + [4326174.44936223, 3750205.81186814]]), scale=1e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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n_evals_acceptance=1), State(trustregion=Region(center=array([14.01183281, 1.08916 ]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 39, 40, 41, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=0.7528586726265997, linear_terms=array([322.27186607, 280.09974261]), square_terms=array([[4388187.8633596 , 3805228.12048649], + [3805228.12048649, 3299714.05118195]]), scale=1e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + 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radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 40, 41, 42, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=1.2627474977574111, linear_terms=array([2306.59533568, 2000.58111819]), square_terms=array([[19867744.70173672, 17228930.38510248], + [17228930.38510248, 14940600.96617165]]), scale=1e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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model_indices=array([25, 40, 41, 42, 43, 44, 46, 47, 48]), model=ScalarModel(intercept=1.3487396237890183, linear_terms=array([2294.52073172, 1989.98708101]), square_terms=array([[17079816.273739 , 14810957.61312829], + [14810957.61312829, 12843491.01190923]]), scale=1e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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model=ScalarModel(intercept=1.4088347925358962, linear_terms=array([4942.78314754, 4286.39180143]), square_terms=array([[45933245.47083566, 39831868.76801804], + [39831868.76801804, 34540946.42122044]]), scale=1e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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linear_terms=array([1103.44013369, 957.31158437]), square_terms=array([[32450404.08701053, 28139523.59651852], + [28139523.59651852, 24401323.08846297]]), scale=1e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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square_terms=array([[14783418.16119196, 12820781.31024842], + [12820781.31024842, 11118703.03997286]]), scale=1e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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[13512851.86370382, 11719038.1383182 ]]), scale=1e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3066309348529068, shift=array([13.06630935, 1.04715766])), candidate_index=54, candidate_x=array([14.01183347, 1.08915924]), index=25, x=array([14.01183281, 1.08916 ]), fval=0.7184205561063591, rho=-2.6309212802317243, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 43, 46, 47, 48, 49, 51, 52, 53]), old_indices_discarded=array([37, 38, 39, 40, 41, 42, 44, 45, 50]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01183281, 1.08916 ]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 43, 47, 48, 49, 51, 52, 53, 54]), model=ScalarModel(intercept=1.3929420700923119, linear_terms=array([5059.04429947, 4387.21291909]), square_terms=array([[55278423.97255142, 47937597.93307385], + [47937597.93307385, 41571613.87351739]]), scale=1e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3066309348529068, shift=array([13.06630935, 1.04715766])), candidate_index=55, candidate_x=array([14.01183216, 1.08916075]), index=25, x=array([14.01183281, 1.08916 ]), fval=0.7184205561063591, rho=-2.8633694811625787, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 43, 47, 48, 49, 51, 52, 53, 54]), old_indices_discarded=array([37, 38, 39, 40, 41, 42, 44, 45, 46, 50]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01183281, 1.08916 ]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 43, 47, 48, 49, 52, 53, 54, 55]), model=ScalarModel(intercept=1.334891981281938, linear_terms=array([5354.21261732, 4643.13436611]), square_terms=array([[81150053.8400843 , 70371614.45136172], + [70371614.45136172, 61024779.24931383]]), scale=1e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3066309348529068, shift=array([13.06630935, 1.04715766])), candidate_index=56, candidate_x=array([14.01183347, 1.08915924]), index=25, x=array([14.01183281, 1.08916 ]), fval=0.7184205561063591, rho=-1.9943606061193717, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 43, 47, 48, 49, 52, 53, 54, 55]), old_indices_discarded=array([37, 38, 39, 40, 41, 42, 44, 45, 46, 50, 51]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01183281, 1.08916 ]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 43, 47, 48, 49, 52, 53, 54, 55]), model=ScalarModel(intercept=1.334891981281938, linear_terms=array([5354.21261732, 4643.13436611]), square_terms=array([[81150053.8400843 , 70371614.45136172], + [70371614.45136172, 61024779.24931383]]), scale=1e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3066309348529068, shift=array([13.06630935, 1.04715766])), candidate_index=57, candidate_x=array([14.01183347, 1.08915924]), index=25, x=array([14.01183281, 1.08916 ]), fval=0.7184205561063591, rho=-12.386174380558238, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 43, 47, 48, 49, 52, 53, 54, 55]), old_indices_discarded=array([37, 38, 39, 40, 41, 42, 44, 45, 46, 50, 51, 56]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01183281, 1.08916 ]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 43, 47, 48, 49, 52, 53, 54, 55]), model=ScalarModel(intercept=1.334891981281938, linear_terms=array([5354.21261732, 4643.13436611]), square_terms=array([[81150053.8400843 , 70371614.45136172], + [70371614.45136172, 61024779.24931383]]), scale=1e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3066309348529068, shift=array([13.06630935, 1.04715766])), candidate_index=58, candidate_x=array([14.01183347, 1.08915924]), index=25, x=array([14.01183281, 1.08916 ]), fval=0.7184205561063591, rho=-7.219538902024979, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 43, 47, 48, 49, 52, 53, 54, 55]), old_indices_discarded=array([37, 38, 39, 40, 41, 42, 44, 45, 46, 50, 51, 56, 57]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01183281, 1.08916 ]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 43, 47, 48, 49, 52, 53, 54, 55]), model=ScalarModel(intercept=1.334891981281938, linear_terms=array([5354.21261732, 4643.13436611]), square_terms=array([[81150053.8400843 , 70371614.45136172], + [70371614.45136172, 61024779.24931383]]), scale=1e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3066309348529068, shift=array([13.06630935, 1.04715766])), candidate_index=59, candidate_x=array([14.01183347, 1.08915924]), index=25, x=array([14.01183281, 1.08916 ]), fval=0.7184205561063591, rho=-2.467913161873645, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 43, 47, 48, 49, 52, 53, 54, 55]), old_indices_discarded=array([37, 38, 39, 40, 41, 42, 44, 45, 46, 50, 51, 56, 57, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01183281, 1.08916 ]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 43, 47, 48, 49, 52, 53, 54, 55]), model=ScalarModel(intercept=1.334891981281938, linear_terms=array([5354.21261732, 4643.13436611]), square_terms=array([[81150053.8400843 , 70371614.45136172], + [70371614.45136172, 61024779.24931383]]), scale=1e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3066309348529068, shift=array([13.06630935, 1.04715766])), candidate_index=60, candidate_x=array([14.01183347, 1.08915924]), index=25, x=array([14.01183281, 1.08916 ]), fval=0.7184205561063591, rho=-8.544800766349255, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 43, 47, 48, 49, 52, 53, 54, 55]), old_indices_discarded=array([37, 38, 39, 40, 41, 42, 44, 45, 46, 50, 51, 56, 57, 58, 59]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01183281, 1.08916 ]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 43, 47, 48, 49, 52, 53, 54, 55]), model=ScalarModel(intercept=1.334891981281938, linear_terms=array([5354.21261732, 4643.13436611]), square_terms=array([[81150053.8400843 , 70371614.45136172], + [70371614.45136172, 61024779.24931383]]), scale=1e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3066309348529068, shift=array([13.06630935, 1.04715766])), candidate_index=61, candidate_x=array([14.01183347, 1.08915924]), index=25, x=array([14.01183281, 1.08916 ]), fval=0.7184205561063591, rho=-2.703463872811527, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 43, 47, 48, 49, 52, 53, 54, 55]), old_indices_discarded=array([37, 38, 39, 40, 41, 42, 44, 45, 46, 50, 51, 56, 57, 58, 59, 60]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01183281, 1.08916 ]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 43, 47, 48, 49, 52, 53, 54, 55]), model=ScalarModel(intercept=1.334891981281938, linear_terms=array([5354.21261732, 4643.13436611]), square_terms=array([[81150053.8400843 , 70371614.45136172], + [70371614.45136172, 61024779.24931383]]), scale=1e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3066309348529068, shift=array([13.06630935, 1.04715766])), candidate_index=62, candidate_x=array([14.01183347, 1.08915924]), index=25, x=array([14.01183281, 1.08916 ]), fval=0.7184205561063591, rho=-1.996039584693101, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 43, 47, 48, 49, 52, 53, 54, 55]), old_indices_discarded=array([37, 38, 39, 40, 41, 42, 44, 45, 46, 50, 51, 56, 57, 58, 59, 60, 61]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01183281, 1.08916 ]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 43, 47, 48, 49, 52, 53, 54, 55]), model=ScalarModel(intercept=1.334891981281938, linear_terms=array([5354.21261732, 4643.13436611]), square_terms=array([[81150053.8400843 , 70371614.45136172], + [70371614.45136172, 61024779.24931383]]), scale=1e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3066309348529068, shift=array([13.06630935, 1.04715766])), candidate_index=63, candidate_x=array([14.01183347, 1.08915924]), index=25, x=array([14.01183281, 1.08916 ]), fval=0.7184205561063591, rho=-1.6371494163829006, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 43, 47, 48, 49, 52, 53, 54, 55]), old_indices_discarded=array([37, 38, 39, 40, 41, 42, 44, 45, 46, 50, 51, 56, 57, 58, 59, 60, 61, + 62]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 64 entries., 'multistart_info': {'start_parameters': [array([12.321875, 1.08125 ]), array([13.06630935, 1.04715766])], 'local_optima': [{'solution_x': array([13.39207935, 1.03451876]), 'solution_criterion': 1.210363971947695, 'states': [State(trustregion=Region(center=array([12.321875, 1.08125 ]), radius=1.2321875, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=1.5379709637742367, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=1.2321875, shift=array([12.321875, 1.08125 ])), vector_model=VectorModel(intercepts=array([ 0.01938559, 0.01236126, -0.02541336, 0.06675187, 0.19101415, + 0.15552265, 0.10788637, 0.02151982, -0.00180687, -0.01933219, + -0.56991473, -0.81302909, -0.20768953, -0.13461208, -0.09577825, + 0.25047508, 0.58326817]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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vector_model=VectorModel(intercepts=array([ 0.01938559, 0.01236126, -0.02541336, 0.06675187, 0.19101415, + 0.15552265, 0.10788637, 0.02151982, -0.00180687, -0.01933219, + -0.56991473, -0.81302909, -0.20768953, -0.13461208, -0.09577825, + 0.25047508, 0.58326817]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.2321875, 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candidate_x=array([14.70121879, 1.1 ]), index=13, x=array([13.41387274, 1.0480835 ]), fval=1.3627041528656627, rho=-2.2582007264460318, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 3, 5, 6, 7, 8, 10, 11, 13]), old_indices_discarded=array([ 1, 2, 4, 9, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.41387274, 1.0480835 ]), radius=1.2321875, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 2, 4, 5, 6, 8, 9, 13, 14]), model=ScalarModel(intercept=1.8597631456640993, linear_terms=array([-0.16560094, -1.39509672]), square_terms=array([[ 0.25676534, -0.02422231], + [-0.02422231, 1.5320715 ]]), scale=array([1.09199774, 0.3 ]), shift=array([13.41387274, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.01938559, 0.01236126, -0.02541336, 0.06675187, 0.19101415, + 0.15552265, 0.10788637, 0.02151982, -0.00180687, -0.01933219, + -0.56991473, -0.81302909, -0.20768953, -0.13461208, -0.09577825, + 0.25047508, 0.58326817]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.2321875, shift=array([12.321875, 1.08125 ])), candidate_index=15, candidate_x=array([14.21315436, 1.07665016]), index=13, x=array([13.41387274, 1.0480835 ]), fval=1.3627041528656627, rho=-3.8933696718720308, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 2, 4, 5, 6, 8, 9, 13, 14]), old_indices_discarded=array([ 1, 3, 7, 10, 11, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.41387274, 1.0480835 ]), radius=0.61609375, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 2, 4, 5, 6, 8, 9, 13, 15]), model=ScalarModel(intercept=1.8893357537559765, linear_terms=array([-0.02772496, -1.3695219 ]), square_terms=array([[ 0.04325758, -0.01687608], + [-0.01687608, 1.53625408]]), scale=array([0.54599887, 0.29895768]), shift=array([13.41387274, 0.80104232])), vector_model=VectorModel(intercepts=array([ 0.01938559, 0.01236126, -0.02541336, 0.06675187, 0.19101415, + 0.15552265, 0.10788637, 0.02151982, -0.00180687, -0.01933219, + -0.56991473, -0.81302909, -0.20768953, -0.13461208, -0.09577825, + 0.25047508, 0.58326817]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.2321875, shift=array([12.321875, 1.08125 ])), candidate_index=16, candidate_x=array([13.95603404, 1.07081466]), index=13, x=array([13.41387274, 1.0480835 ]), fval=1.3627041528656627, rho=-14.984993379227156, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 2, 4, 5, 6, 8, 9, 13, 15]), old_indices_discarded=array([ 1, 3, 7, 10, 11, 12, 14]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.41387274, 1.0480835 ]), radius=0.308046875, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 4, 5, 8, 9, 13, 15, 16, 17]), model=ScalarModel(intercept=1.492059422834581, linear_terms=array([-0.11965999, -0.40798019]), square_terms=array([[0.13418385, 0.01559018], + [0.01559018, 0.50991394]]), scale=array([0.27299943, 0.16245797]), shift=array([13.41387274, 0.93754203])), vector_model=VectorModel(intercepts=array([ 0.01938559, 0.01236126, -0.02541336, 0.06675187, 0.19101415, + 0.15552265, 0.10788637, 0.02151982, -0.00180687, -0.01933219, + -0.56991473, -0.81302909, -0.20768953, -0.13461208, -0.09577825, + 0.25047508, 0.58326817]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 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6, 14]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.41387274, 1.0480835 ]), radius=0.1540234375, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 4, 5, 8, 9, 13, 16, 17, 18]), model=ScalarModel(intercept=1.3794125731198714, linear_terms=array([-0.03649967, -0.14181619]), square_terms=array([[ 0.02646541, -0.02252458], + [-0.02252458, 0.15141401]]), scale=array([0.13649972, 0.09420811]), shift=array([13.41387274, 1.00579189])), vector_model=VectorModel(intercepts=array([ 0.01938559, 0.01236126, -0.02541336, 0.06675187, 0.19101415, + 0.15552265, 0.10788637, 0.02151982, -0.00180687, -0.01933219, + -0.56991473, -0.81302909, -0.20768953, -0.13461208, -0.09577825, + 0.25047508, 0.58326817]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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State(trustregion=Region(center=array([13.41387274, 1.0480835 ]), radius=0.07701171875, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 4, 8, 9, 13, 18, 19]), model=ScalarModel(intercept=1.3783908568552679, linear_terms=array([ 0.01021357, -0.00822452]), square_terms=array([[ 0.05843199, -0.08341068], + [-0.08341068, 0.18300197]]), scale=array([0.06824986, 0.06008318]), shift=array([13.41387274, 1.03991682])), vector_model=VectorModel(intercepts=array([ 0.01938559, 0.01236126, -0.02541336, 0.06675187, 0.19101415, + 0.15552265, 0.10788637, 0.02151982, -0.00180687, -0.01933219, + -0.56991473, -0.81302909, -0.20768953, -0.13461208, -0.09577825, + 0.25047508, 0.58326817]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 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radius=0.07701171875, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 4, 8, 9, 13, 18, 19, 20]), model=ScalarModel(intercept=1.329063512541693, linear_terms=array([ 0.02464634, -0.02431736]), square_terms=array([[ 0.05297532, -0.08788172], + [-0.08788172, 0.22588201]]), scale=array([0.06824986, 0.06715269]), shift=array([13.39225885, 1.03284731])), vector_model=VectorModel(intercepts=array([ 0.01938559, 0.01236126, -0.02541336, 0.06675187, 0.19101415, + 0.15552265, 0.10788637, 0.02151982, -0.00180687, -0.01933219, + -0.56991473, -0.81302909, -0.20768953, -0.13461208, -0.09577825, + 0.25047508, 0.58326817]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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model_indices=array([ 4, 9, 13, 19, 20, 21]), model=ScalarModel(intercept=1.4558785589476007, linear_terms=array([ 0.03199582, -0.13313381]), square_terms=array([[0.00508526, 0.00270481], + [0.00270481, 0.2748186 ]]), scale=0.038505859375, shift=array([13.39225885, 1.03394448])), vector_model=VectorModel(intercepts=array([ 0.01938559, 0.01236126, -0.02541336, 0.06675187, 0.19101415, + 0.15552265, 0.10788637, 0.02151982, -0.00180687, -0.01933219, + -0.56991473, -0.81302909, -0.20768953, -0.13461208, -0.09577825, + 0.25047508, 0.58326817]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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params change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 29 entries., 'history': {'params': [{'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 11.446536902444704, 'DiscFac': 0.5}, {'CRRA': 13.413872739706319, 'DiscFac': 0.5007652721540578}, {'CRRA': 11.229877260293678, 'DiscFac': 0.59273111228441}, {'CRRA': 13.294070386710855, 'DiscFac': 1.1}, {'CRRA': 13.413872739706319, 'DiscFac': 0.5}, {'CRRA': 12.446098642928627, 'DiscFac': 0.5}, {'CRRA': 11.229877260293678, 'DiscFac': 1.060949564463903}, {'CRRA': 13.413872739706319, 'DiscFac': 0.8026495106859575}, {'CRRA': 13.413872739706319, 'DiscFac': 1.038275200676022}, {'CRRA': 11.53308651761103, 'DiscFac': 1.1}, {'CRRA': 11.354974829891772, 'DiscFac': 0.5}, {'CRRA': 11.681377339711737, 'DiscFac': 1.1}, {'CRRA': 13.413872739706319, 'DiscFac': 1.0480835037137872}, {'CRRA': 14.701218785330024, 'DiscFac': 1.1}, {'CRRA': 14.21315435802697, 'DiscFac': 1.0766501636285937}, {'CRRA': 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0.28949288]), shift=array([13.97003573, 0.81050712])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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x=array([14.01466991, 1.08462838]), fval=1.0410129484416903, rho=-39.326073520819115, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 4, 8, 9, 13, 16, 17, 18, 19]), old_indices_discarded=array([ 5, 6, 15]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01466991, 1.08462838]), radius=0.08166443342830668, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 4, 8, 9, 13, 17, 18, 19, 20]), model=ScalarModel(intercept=1.196985077835359, linear_terms=array([0.03516311, 0.00306745]), square_terms=array([[0.01712576, 0.00136586], + [0.00136586, 0.06787167]]), scale=array([0.07237322, 0.04387242]), shift=array([14.01466991, 1.05612758])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3066309348529068, shift=array([13.06630935, 1.04715766])), candidate_index=21, candidate_x=array([13.94229669, 1.05502767]), index=19, x=array([14.01466991, 1.08462838]), fval=1.0410129484416903, rho=-14.505215148299051, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 4, 8, 9, 13, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01466991, 1.08462838]), radius=0.04083221671415334, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 18, 19, 20, 21]), model=ScalarModel(intercept=1.43364495095228, linear_terms=array([ 0.12922127, -0.32781807]), square_terms=array([[ 0.01853158, -0.06428325], + [-0.06428325, 0.32888578]]), scale=array([0.03618661, 0.02577912]), shift=array([14.01466991, 1.07422088])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3066309348529068, shift=array([13.06630935, 1.04715766])), candidate_index=22, candidate_x=array([13.9784833 , 1.09487758]), index=19, x=array([14.01466991, 1.08462838]), fval=1.0410129484416903, rho=-2.4451313207154053, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([13, 18, 19, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01466991, 1.08462838]), radius=0.02041610835707667, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 18, 19, 21, 22]), model=ScalarModel(intercept=1.0485149188081042, linear_terms=array([ 0.05139792, -0.29978337]), square_terms=array([[ 0.13343796, -0.19971635], + [-0.19971635, 0.34743672]]), scale=array([0.0180933 , 0.01673246]), shift=array([14.01466991, 1.08326754])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3066309348529068, shift=array([13.06630935, 1.04715766])), candidate_index=23, candidate_x=array([14.03276322, 1.1 ]), index=19, x=array([14.01466991, 1.08462838]), fval=1.0410129484416903, rho=-7.434794508101175, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([13, 18, 19, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01466991, 1.08462838]), radius=0.010208054178538335, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 18, 19, 22, 23]), model=ScalarModel(intercept=0.9771052862147884, linear_terms=array([ 0.06873397, -0.19272697]), square_terms=array([[0.01343003, 0.04147703], + [0.04147703, 1.11994799]]), scale=0.010208054178538335, shift=array([14.01466991, 1.08462838])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3066309348529068, shift=array([13.06630935, 1.04715766])), candidate_index=24, candidate_x=array([14.00453977, 1.08664481]), index=19, x=array([14.01466991, 1.08462838]), fval=1.0410129484416903, rho=-6.08966504598221, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([13, 18, 19, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01466991, 1.08462838]), radius=0.005104027089269167, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([18, 19, 23, 24]), model=ScalarModel(intercept=1.2926218223787844, linear_terms=array([-0.00788451, -0.32540669]), square_terms=array([[0.00911385, 0.04354991], + [0.04354991, 0.34751248]]), scale=0.005104027089269167, shift=array([14.01466991, 1.08462838])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3066309348529068, shift=array([13.06630935, 1.04715766])), candidate_index=25, candidate_x=array([14.01183281, 1.08916 ]), index=25, x=array([14.01183281, 1.08916 ]), fval=0.7184205561063591, rho=1.9242451111551888, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([18, 19, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.005346466871872233, relative_step_length=1.0474997053038329, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01183281, 1.08916 ]), radius=0.010208054178538335, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 18, 19, 22, 23, 24, 25]), model=ScalarModel(intercept=0.9530259023191747, linear_terms=array([0.0624839 , 0.22160522]), square_terms=array([[0.01172426, 0.05678182], + [0.05678182, 1.28834316]]), scale=0.010208054178538335, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], 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model_indices=array([18, 19, 23, 24, 25, 26]), model=ScalarModel(intercept=1.0207789028264391, linear_terms=array([0.01004909, 0.14281032]), square_terms=array([[0.00371043, 0.01053812], + [0.01053812, 0.28679693]]), scale=0.005104027089269167, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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linear_terms=array([-0.07390409, 0.1867271 ]), square_terms=array([[0.02933352, 0.06029508], + [0.06029508, 0.51755928]]), scale=0.0025520135446345836, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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model_indices=array([25, 34, 35]), model=ScalarModel(intercept=0.7184205561063438, linear_terms=array([49.40160575, 42.35987568]), square_terms=array([[9579.24080639, 8238.55673379], + [8238.55673379, 7085.71316346]]), scale=9.968802908728842e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3066309348529068, shift=array([13.06630935, 1.04715766])), candidate_index=39, candidate_x=array([14.011832 , 1.08916094]), index=25, x=array([14.01183281, 1.08916 ]), fval=0.7184205561063591, rho=-8.468629779574808, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 37, 38]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01183281, 1.08916 ]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 37, 38, 39]), model=ScalarModel(intercept=1.3029571467125889, linear_terms=array([1271.62989932, 1102.04552777]), square_terms=array([[2496938.18044439, 2164043.06872904], + [2164043.06872904, 1875529.98728359]]), scale=1e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 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1.08916075]), index=25, x=array([14.01183281, 1.08916 ]), fval=0.7184205561063591, rho=-1.124121931196161, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 37, 38, 39]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01183281, 1.08916 ]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 37, 38, 39, 40]), model=ScalarModel(intercept=1.2604794759999813, linear_terms=array([1246.25639581, 1080.03996307]), square_terms=array([[3152891.30365823, 2732523.73682316], + [2732523.73682316, 2368202.79679982]]), scale=1e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 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dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01183281, 1.08916 ]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 37, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=1.4656905572771453, linear_terms=array([1243.82197504, 1077.98706406]), square_terms=array([[3085809.09127438, 2674453.15149168], + [2674453.15149168, 2317933.30821108]]), scale=1e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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State(trustregion=Region(center=array([14.01183281, 1.08916 ]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 37, 38, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=1.548557873891164, linear_terms=array([1203.1713828 , 1042.77950053]), square_terms=array([[2438542.31045176, 2113511.47163476], + [2113511.47163476, 1831803.66933079]]), scale=1e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 37, 38, 39, 40, 41, 42, 43, 44]), model=ScalarModel(intercept=1.5887250225761516, linear_terms=array([1030.5595738 , 893.18618335]), square_terms=array([[2190616.89147569, 1898642.09058655], + [1898642.09058655, 1645582.94584737]]), scale=1e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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model_indices=array([25, 38, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=1.6019772554697844, linear_terms=array([1292.43082122, 1120.26966461]), square_terms=array([[4990602.23895189, 4326174.44936223], + [4326174.44936223, 3750205.81186814]]), scale=1e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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model=ScalarModel(intercept=0.7528586726265997, linear_terms=array([322.27186607, 280.09974261]), square_terms=array([[4388187.8633596 , 3805228.12048649], + [3805228.12048649, 3299714.05118195]]), scale=1e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3066309348529068, shift=array([13.06630935, 1.04715766])), candidate_index=50, candidate_x=array([14.01183347, 1.08915924]), index=25, x=array([14.01183281, 1.08916 ]), fval=0.7184205561063591, rho=-0.6308570109424091, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 40, 41, 42, 43, 46, 47, 48, 49]), old_indices_discarded=array([37, 38, 39, 44, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01183281, 1.08916 ]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 41, 42, 43, 46, 47, 48, 49, 50]), model=ScalarModel(intercept=1.2465352455569267, linear_terms=array([1103.44013369, 957.31158437]), square_terms=array([[32450404.08701053, 28139523.59651852], + [28139523.59651852, 24401323.08846297]]), scale=1e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 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candidate_index=51, candidate_x=array([14.01183347, 1.08915924]), index=25, x=array([14.01183281, 1.08916 ]), fval=0.7184205561063591, rho=-2.800014341621393, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 41, 42, 43, 46, 47, 48, 49, 50]), old_indices_discarded=array([37, 38, 39, 40, 44, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([14.01183281, 1.08916 ]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 42, 43, 46, 47, 48, 49, 50, 51]), model=ScalarModel(intercept=1.4358728804192635, linear_terms=array([2094.60340738, 1816.88243966]), square_terms=array([[14783418.16119196, 12820781.31024842], + [12820781.31024842, 11118703.03997286]]), scale=1e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 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State(trustregion=Region(center=array([14.01183281, 1.08916 ]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 43, 47, 48, 49, 52, 53, 54, 55]), model=ScalarModel(intercept=1.334891981281938, linear_terms=array([5354.21261732, 4643.13436611]), square_terms=array([[81150053.8400843 , 70371614.45136172], + [70371614.45136172, 61024779.24931383]]), scale=1e-06, shift=array([14.01183281, 1.08916 ])), vector_model=VectorModel(intercepts=array([ 0.01969896, 0.01945701, -0.03259519, 0.00365763, 0.06642872, + 0.01584107, 0.01293238, -0.15622707, -0.24807933, -0.37491567, + -0.64746253, -0.7797042 , -0.15633981, -0.04042764, 0.05764117, + 0.21556169, 0.4030829 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 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66.40381871399995, 68.13384646799977, 69.85902101900001, 71.61205858599988, 73.42455532999975, 75.25045019799973, 76.99171744099976, 78.83135237599981, 80.5315630619998, 82.28934421199983, 84.04364812599988, 85.81372394899972, 87.57166456200002, 89.33697740099979, 91.23287148300005, 93.02459795599998, 94.73499807600001], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52]}, 'multistart_info': {...}}], 'exploration_sample': array([[12.321875 , 1.08125 ], + [12.27983099, 1.07767066], + [14.09375 , 0.9875 ], + [17.6375 , 1.025 ], + [16.45625 , 0.9125 ], + [11.73125 , 0.7625 ], + [10.55 , 0.8 ], + [12.9125 , 0.575 ], + [15.275 , 0.65 ], + [17.046875 , 0.63125 ], + [18.81875 , 0.5375 ], + [ 9.36875 , 0.8375 ], + [ 8.1875 , 0.725 ], + [ 7.00625 , 0.6125 ], + [ 5.825 , 0.95 ], + [ 4.64375 , 0.6875 ], + [ 2.871875 , 0.78125 ], + [ 3.4625 , 0.875 ], + [ 2.28125 , 1.0625 ]]), 'exploration_results': array([ 1.25226984, 1.54798499, 1.77139048, 1.79567843, 2.19243348, + 2.99563577, 3.4003551 , 3.46407238, 3.67340234, 3.72448066, + 4.28414873, 4.65250486, 5.39134448, 6.53886763, 14.94020775, + 20.34265339, 25.6087008 , 29.29813793, 75.84976584])}}" diff --git a/content/tables/min/PortfolioSub(Stock)(Labor)Market_estimate_results.csv b/content/tables/min/PortfolioSub(Stock)(Labor)Market_estimate_results.csv new file mode 100644 index 0000000..39ac677 --- /dev/null +++ b/content/tables/min/PortfolioSub(Stock)(Labor)Market_estimate_results.csv @@ -0,0 +1,6021 @@ +CRRA,5.573894562325964 +DiscFac,1.0637390075406437 +time_to_estimate,240.28510308265686 +params,"{'CRRA': 5.573894562325964, 'DiscFac': 1.0637390075406437}" +criterion,1.4220519178994522 +start_criterion,3.7528915666217584 +start_params,"{'CRRA': 5.0, 'DiscFac': 0.95}" +algorithm,multistart_tranquilo_ls +direction,minimize +n_free,2 +message,Absolute criterion change smaller than tolerance. +success, +n_criterion_evaluations, +n_derivative_evaluations, +n_iterations, +history,"{'params': [{'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 5.3827721337329075, 'DiscFac': 0.5}, {'CRRA': 6.341227184076231, 'DiscFac': 0.9843397863096757}, {'CRRA': 5.308772815923768, 'DiscFac': 0.9942847020759833}, {'CRRA': 6.341227184076231, 'DiscFac': 1.099329565298806}, {'CRRA': 6.341227184076231, 'DiscFac': 0.7325487756828386}, {'CRRA': 6.341227184076231, 'DiscFac': 0.661184756844424}, {'CRRA': 5.670357912186334, 'DiscFac': 1.1}, {'CRRA': 6.341227184076231, 'DiscFac': 1.0027250193492325}, {'CRRA': 6.341227184076231, 'DiscFac': 1.0927982791010287}, {'CRRA': 5.308772815923768, 'DiscFac': 1.0118564969416792}, {'CRRA': 5.957814024190954, 'DiscFac': 0.5}, {'CRRA': 6.114808484578101, 'DiscFac': 1.1}, {'CRRA': 5.793348226117784, 'DiscFac': 0.8284226490669568}, {'CRRA': 5.75003997251512, 'DiscFac': 0.8880821392041797}, 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1.0637390075406437}], 'criterion': [3.0263314834387383, 4.314184027187926, nan, 2.6142787419100317, nan, 4.730311355914472, 4.931838606867419, 1.9543423789678298, nan, nan, 2.1984626089974055, 4.789253019202322, nan, 3.940933836887815, 3.4612490178599495, 2.070351234692411, 1.7490483015806304, 1.4289096777146673, 1.9829352389581485, 1.6954301219950216, 1.4443085463640908, 1.4384944542770688, 1.4260383541426693, 1.42426971311261, 1.427741913899455, 1.4220984404550594, 1.427437660317148, 1.422217891677385, 1.4225162577343387, 1.4221438433511406, 1.4221892061726984, 1.4220519178994522], 'runtime': [0.0, 2.1092147859999386, 2.320780406999802, 2.53563283099993, 2.7502362279997215, 2.9750869179997608, 3.2098066859998653, 3.4356893949998266, 3.6836077269999805, 3.9124819979997483, 4.17926271899978, 4.423696945999836, 4.664147187999788, 6.505867149999631, 8.205766164999659, 9.91364971899975, 11.730312985999717, 13.458470247999685, 15.2081106239998, 16.93993415199975, 18.660550382999645, 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rho=1.7809591946937149, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 3, 7, 10, 12, 13, 14, 15, 16]), old_indices_discarded=array([ 1, 2, 4, 5, 6, 8, 9, 11]), step_length=0.05644057588664191, relative_step_length=0.09689369250925652, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.51947854, 1.06583909]), radius=0.5824999999999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 3, 7, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=2.3354605283597563, linear_terms=array([ 1.46424334, -2.36914478]), square_terms=array([[ 1.06094696, -1.63153519], + [-1.63153519, 2.58355658]]), scale=array([0.51622718, 0.27519405]), shift=array([5.51947854, 0.82480595])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=18, candidate_x=array([5.60087809, 1.1 ]), index=17, x=array([5.51947854, 1.06583909]), fval=1.4289096777146673, rho=-85.51150568657764, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 7, 13, 14, 15, 16, 17]), old_indices_discarded=array([ 2, 4, 5, 6, 8, 9, 10, 11, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.51947854, 1.06583909]), radius=0.29124999999999995, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 7, 10, 14, 15, 16, 17, 18]), model=ScalarModel(intercept=3.1012802436018085, linear_terms=array([-0.92259607, -5.33641639]), square_terms=array([[0.27027798, 1.20759192], + [1.20759192, 7.70294614]]), scale=array([0.25811359, 0.14613725]), shift=array([5.51947854, 0.95386275])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=19, candidate_x=array([5.77759213, 1.03219317]), index=17, x=array([5.51947854, 1.06583909]), fval=1.4289096777146673, rho=-4.019293806179108, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 3, 7, 10, 14, 15, 16, 17, 18]), old_indices_discarded=array([ 1, 2, 4, 5, 6, 8, 9, 11, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.51947854, 1.06583909]), radius=0.14562499999999998, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 14, 15, 16, 17, 18, 19]), model=ScalarModel(intercept=1.5213811565094542, linear_terms=array([-0.14355744, -1.03791084]), square_terms=array([[0.06159175, 0.28510759], + [0.28510759, 2.13210366]]), scale=array([0.1290568 , 0.08160886]), shift=array([5.51947854, 1.01839114])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=20, candidate_x=array([5.54569318, 1.05590177]), index=17, x=array([5.51947854, 1.06583909]), fval=1.4289096777146673, rho=-1.535972006541552, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 10, 14, 15, 16, 17, 18, 19]), old_indices_discarded=array([ 0, 1, 11, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.51947854, 1.06583909]), radius=0.07281249999999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 15, 16, 17, 18, 19, 20]), model=ScalarModel(intercept=1.3026835229178233, linear_terms=array([ 0.01296259, -0.29445874]), square_terms=array([[ 0.00193973, -0.00535854], + [-0.00535854, 1.93956479]]), scale=array([0.0645284 , 0.04934466]), shift=array([5.51947854, 1.05065534])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.51947854, 1.06583909]), radius=0.036406249999999994, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 15, 16, 17, 18, 20, 21]), model=ScalarModel(intercept=1.3985870458518956, linear_terms=array([-0.03050627, 0.11095194]), square_terms=array([[0.00179814, 0.01810855], + [0.01810855, 0.86143979]]), scale=array([0.0322642, 0.0322642]), shift=array([5.51947854, 1.06583909])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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State(trustregion=Region(center=array([5.55174274, 1.06100528]), radius=0.018203124999999997, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 17, 18, 20, 22]), model=ScalarModel(intercept=1.4163790473957398, linear_terms=array([-0.00983569, -0.08333704]), square_terms=array([[2.86954770e-04, 8.05036733e-04], + [8.05036733e-04, 3.32811019e-01]]), scale=0.018203124999999997, shift=array([5.55174274, 1.06100528])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + 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radius=0.009101562499999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 20, 22, 23]), model=ScalarModel(intercept=1.4255871683087975, linear_terms=array([ 0.01282382, -0.01799885]), square_terms=array([[ 0.00027616, -0.00248466], + [-0.00248466, 0.08699657]]), scale=0.009101562499999999, shift=array([5.56995508, 1.065396 ])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=24, candidate_x=array([5.56080919, 1.06681982]), index=23, x=array([5.56995508, 1.065396 ]), fval=1.42426971311261, rho=-0.24612567440050911, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([16, 20, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.56995508, 1.065396 ]), radius=0.004550781249999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 23, 24]), model=ScalarModel(intercept=1.42426971311261, linear_terms=array([-4.86697548e-05, 7.42140875e-03]), square_terms=array([[ 7.70644314e-06, -1.20696978e-04], + [-1.20696978e-04, 1.96339837e-02]]), scale=0.004550781249999999, shift=array([5.56995508, 1.065396 ])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=25, candidate_x=array([5.57417906, 1.06370262]), index=25, x=array([5.57417906, 1.06370262]), fval=1.4220984404550594, rho=1.5482217964560738, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([22, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.004550781249999896, relative_step_length=0.9999999999999774, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.57417906, 1.06370262]), radius=0.009101562499999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 18, 20, 22, 23, 24, 25]), model=ScalarModel(intercept=1.4247281558811358, linear_terms=array([ 0.00539427, -0.03219716]), square_terms=array([[ 3.96935302e-05, -1.28952506e-03], + [-1.28952506e-03, 8.65974369e-02]]), scale=0.009101562499999999, shift=array([5.57417906, 1.06370262])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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[-1.13770520e-04, 1.96031452e-02]]), scale=0.004550781249999999, shift=array([5.57417906, 1.06370262])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=27, candidate_x=array([5.57873026, 1.06364354]), index=25, x=array([5.57417906, 1.06370262]), fval=1.4220984404550594, rho=-0.7907220678531115, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 23, 24, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.57417906, 1.06370262]), radius=0.0022753906249999996, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 25, 26, 27]), model=ScalarModel(intercept=1.4221663020015802, linear_terms=array([5.73401438e-05, 7.45830401e-04]), square_terms=array([[ 1.12265319e-06, -2.59043861e-05], + [-2.59043861e-05, 4.91328724e-03]]), scale=0.0022753906249999996, shift=array([5.57417906, 1.06370262])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=28, candidate_x=array([5.57190552, 1.06334956]), index=25, x=array([5.57417906, 1.06370262]), fval=1.4220984404550594, rho=-3.5610604492495965, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 25, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.57417906, 1.06370262]), radius=0.0011376953124999998, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 25, 27, 28]), model=ScalarModel(intercept=1.422325827336663, linear_terms=array([-4.61917023e-05, 2.38441492e-04]), square_terms=array([[ 3.55694453e-07, -6.48069226e-06], + [-6.48069226e-06, 1.22037302e-03]]), scale=0.0011376953124999998, shift=array([5.57417906, 1.06370262])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=29, candidate_x=array([5.57531788, 1.063494 ]), index=25, x=array([5.57417906, 1.06370262]), fval=1.4220984404550594, rho=-0.6669519353256694, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 25, 27, 28]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.57417906, 1.06370262]), radius=0.0005688476562499999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 28, 29]), model=ScalarModel(intercept=1.422098440455059, linear_terms=array([-4.34744633e-05, -3.03707274e-04]), square_terms=array([[ 8.37764168e-08, -1.07708651e-06], + [-1.07708651e-06, 2.98889073e-04]]), scale=0.0005688476562499999, shift=array([5.57417906, 1.06370262])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=30, candidate_x=array([5.57453507, 1.06417097]), index=25, x=array([5.57417906, 1.06370262]), fval=1.4220984404550594, rho=-0.5142705306272952, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.57417906, 1.06370262]), radius=0.00028442382812499995, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 29, 30]), model=ScalarModel(intercept=1.422098440455059, linear_terms=array([ 4.17356026e-06, -1.04084673e-05]), square_terms=array([[ 1.93011592e-08, -1.59744779e-07], + [-1.59744779e-07, 7.59670263e-05]]), scale=0.00028442382812499995, shift=array([5.57417906, 1.06370262])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=31, candidate_x=array([5.57389456, 1.06373901]), index=31, x=array([5.57389456, 1.06373901]), fval=1.4220519178994522, rho=9.583354885845637, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([25, 29, 30]), old_indices_discarded=array([], dtype=int64), step_length=0.00028681890451018565, relative_step_length=1.0084208007499749, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 32 entries., 'multistart_info': {'start_parameters': [array([5.825, 0.95 ]), array([7.55033227, 1.06886786])], 'local_optima': [{'solution_x': array([5.57389456, 1.06373901]), 'solution_criterion': 1.4220519178994522, 'states': [State(trustregion=Region(center=array([5.825, 0.95 ]), radius=0.5824999999999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=3.0263314834387383, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=0, candidate_x=array([5.825, 0.95 ]), index=0, x=array([5.825, 0.95 ]), fval=3.0263314834387383, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([5.825, 0.95 ]), radius=0.5824999999999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=1.1269978152790263, linear_terms=array([-0.51127322, -0.63965657]), square_terms=array([[11.76668356, 13.01143564], + [13.01143564, 15.17207492]]), scale=array([0.51622718, 0.3 ]), shift=array([5.825, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.825, 0.95 ]), radius=0.29124999999999995, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 2, 3, 7, 8, 10, 11, 12, 13]), model=ScalarModel(intercept=1.581609097434704, linear_terms=array([2.65278709, 2.28940689]), square_terms=array([[8.20115491, 7.03588261], + [7.03588261, 6.38777128]]), scale=array([0.25811359, 0.2040568 ]), shift=array([5.825 , 0.8959432])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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State(trustregion=Region(center=array([5.825, 0.95 ]), radius=0.14562499999999998, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 2, 3, 7, 8, 11, 12, 13, 14]), model=ScalarModel(intercept=1.6064049690312896, linear_terms=array([1.823518 , 1.65703042]), square_terms=array([[3.57574989, 3.17459736], + [3.17459736, 3.01964201]]), scale=0.14562499999999998, shift=array([5.825, 0.95 ])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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radius=0.29124999999999995, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 7, 10, 11, 12, 13, 14, 15]), model=ScalarModel(intercept=1.8544790200782664, linear_terms=array([-0.67331221, -1.21592495]), square_terms=array([[1.63171239, 1.52446346], + [1.52446346, 2.03881587]]), scale=array([0.25811359, 0.17313272]), shift=array([5.69316128, 0.92686728])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 7, 10, 12, 13, 14, 15, 16]), model=ScalarModel(intercept=7.085140584300428, linear_terms=array([ -5.65921381, -14.17503365]), square_terms=array([[ 5.20815903, 7.08665734], + [ 7.08665734, 17.08804688]]), scale=array([0.51622718, 0.26201318]), shift=array([5.56938443, 0.83798682])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 3, 7, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=2.3354605283597563, linear_terms=array([ 1.46424334, -2.36914478]), square_terms=array([[ 1.06094696, -1.63153519], + [-1.63153519, 2.58355658]]), scale=array([0.51622718, 0.27519405]), shift=array([5.51947854, 0.82480595])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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17, 18]), model=ScalarModel(intercept=3.1012802436018085, linear_terms=array([-0.92259607, -5.33641639]), square_terms=array([[0.27027798, 1.20759192], + [1.20759192, 7.70294614]]), scale=array([0.25811359, 0.14613725]), shift=array([5.51947854, 0.95386275])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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linear_terms=array([-0.14355744, -1.03791084]), square_terms=array([[0.06159175, 0.28510759], + [0.28510759, 2.13210366]]), scale=array([0.1290568 , 0.08160886]), shift=array([5.51947854, 1.01839114])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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-0.00535854], + [-0.00535854, 1.93956479]]), scale=array([0.0645284 , 0.04934466]), shift=array([5.51947854, 1.05065534])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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shift=array([5.51947854, 1.06583909])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=22, candidate_x=array([5.55174274, 1.06100528]), index=22, x=array([5.55174274, 1.06100528]), fval=1.426038354142669, rho=0.07310801718365617, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 7, 15, 16, 17, 18, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.0326242888718403, relative_step_length=0.8961178059217939, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.55174274, 1.06100528]), radius=0.018203124999999997, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 17, 18, 20, 22]), model=ScalarModel(intercept=1.4163790473957398, linear_terms=array([-0.00983569, -0.08333704]), square_terms=array([[2.86954770e-04, 8.05036733e-04], + [8.05036733e-04, 3.32811019e-01]]), scale=0.018203124999999997, shift=array([5.55174274, 1.06100528])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=23, candidate_x=array([5.56995508, 1.065396 ]), index=23, x=array([5.56995508, 1.065396 ]), fval=1.42426971311261, rho=0.0887754386245564, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([16, 17, 18, 20, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.01873413361292202, relative_step_length=1.0291712886068751, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.56995508, 1.065396 ]), radius=0.009101562499999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 20, 22, 23]), model=ScalarModel(intercept=1.4255871683087975, linear_terms=array([ 0.01282382, -0.01799885]), square_terms=array([[ 0.00027616, -0.00248466], + [-0.00248466, 0.08699657]]), scale=0.009101562499999999, shift=array([5.56995508, 1.065396 ])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=24, candidate_x=array([5.56080919, 1.06681982]), index=23, x=array([5.56995508, 1.065396 ]), fval=1.42426971311261, rho=-0.24612567440050911, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([16, 20, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.56995508, 1.065396 ]), radius=0.004550781249999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 23, 24]), model=ScalarModel(intercept=1.42426971311261, linear_terms=array([-4.86697548e-05, 7.42140875e-03]), square_terms=array([[ 7.70644314e-06, -1.20696978e-04], + [-1.20696978e-04, 1.96339837e-02]]), scale=0.004550781249999999, shift=array([5.56995508, 1.065396 ])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=25, candidate_x=array([5.57417906, 1.06370262]), index=25, x=array([5.57417906, 1.06370262]), fval=1.4220984404550594, rho=1.5482217964560738, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([22, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.004550781249999896, relative_step_length=0.9999999999999774, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.57417906, 1.06370262]), radius=0.009101562499999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 18, 20, 22, 23, 24, 25]), model=ScalarModel(intercept=1.4247281558811358, linear_terms=array([ 0.00539427, -0.03219716]), square_terms=array([[ 3.96935302e-05, -1.28952506e-03], + [-1.28952506e-03, 8.65974369e-02]]), scale=0.009101562499999999, shift=array([5.57417906, 1.06370262])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=26, candidate_x=array([5.56503074, 1.06677666]), index=25, x=array([5.57417906, 1.06370262]), fval=1.4220984404550594, rho=-0.48986170098858195, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([16, 18, 20, 22, 23, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.57417906, 1.06370262]), radius=0.004550781249999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 23, 24, 25, 26]), model=ScalarModel(intercept=1.422282780554977, linear_terms=array([-0.00015294, 0.00037013]), square_terms=array([[ 7.13476990e-06, -1.13770520e-04], + [-1.13770520e-04, 1.96031452e-02]]), scale=0.004550781249999999, shift=array([5.57417906, 1.06370262])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.57417906, 1.06370262]), radius=0.0022753906249999996, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 25, 26, 27]), model=ScalarModel(intercept=1.4221663020015802, linear_terms=array([5.73401438e-05, 7.45830401e-04]), square_terms=array([[ 1.12265319e-06, -2.59043861e-05], + [-2.59043861e-05, 4.91328724e-03]]), scale=0.0022753906249999996, shift=array([5.57417906, 1.06370262])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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State(trustregion=Region(center=array([5.57417906, 1.06370262]), radius=0.0011376953124999998, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 25, 27, 28]), model=ScalarModel(intercept=1.422325827336663, linear_terms=array([-4.61917023e-05, 2.38441492e-04]), square_terms=array([[ 3.55694453e-07, -6.48069226e-06], + [-6.48069226e-06, 1.22037302e-03]]), scale=0.0011376953124999998, shift=array([5.57417906, 1.06370262])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 28, 29]), model=ScalarModel(intercept=1.422098440455059, linear_terms=array([-4.34744633e-05, -3.03707274e-04]), square_terms=array([[ 8.37764168e-08, -1.07708651e-06], + [-1.07708651e-06, 2.98889073e-04]]), scale=0.0005688476562499999, shift=array([5.57417906, 1.06370262])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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model=ScalarModel(intercept=1.422098440455059, linear_terms=array([ 4.17356026e-06, -1.04084673e-05]), square_terms=array([[ 1.93011592e-08, -1.59744779e-07], + [-1.59744779e-07, 7.59670263e-05]]), scale=0.00028442382812499995, shift=array([5.57417906, 1.06370262])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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old_indices_discarded=array([ 1, 2, 3, 5, 8, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=0.09437915341508152, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 13, 15, 16]), model=ScalarModel(intercept=inf, linear_terms=array([ inf, -inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=array([0.08364135, 0.05738674]), shift=array([7.55033227, 1.04261326])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([16]), old_indices_used=array([ 0, 13, 15]), old_indices_discarded=array([14]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=0.04718957670754076, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=inf, linear_terms=array([inf, inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=array([0.04182067, 0.03647641]), shift=array([7.55033227, 1.06352359])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([18]), old_indices_used=array([ 0, 17]), old_indices_discarded=array([16]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=0.02359478835377038, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 19, 20]), model=ScalarModel(intercept=inf, linear_terms=array([ inf, -inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=0.02359478835377038, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([20]), old_indices_used=array([ 0, 17, 19]), old_indices_discarded=array([18]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=0.01179739417688519, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 19, 20, 21, 22]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=0.01179739417688519, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([22]), old_indices_used=array([ 0, 17, 19, 20, 21]), old_indices_discarded=array([18]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=0.005898697088442595, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 19, 21, 22, 23, 24]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=0.005898697088442595, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([24]), old_indices_used=array([ 0, 17, 19, 21, 22, 23]), old_indices_discarded=array([20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=0.0029493485442212974, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 19, 21, 23, 24, 25, 26]), model=ScalarModel(intercept=inf, linear_terms=array([ inf, -inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=0.0029493485442212974, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([26]), old_indices_used=array([ 0, 17, 19, 21, 23, 24, 25]), old_indices_discarded=array([22]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=0.0014746742721106487, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 19, 21, 23, 25, 26, 27, 28]), model=ScalarModel(intercept=inf, linear_terms=array([ inf, -inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=0.0014746742721106487, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([28]), old_indices_used=array([ 0, 17, 19, 21, 23, 25, 26, 27]), old_indices_discarded=array([24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=0.0007373371360553244, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 19, 21, 23, 25, 27, 29, 30]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=0.0007373371360553244, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([30]), old_indices_used=array([ 0, 17, 19, 21, 23, 25, 27, 29]), old_indices_discarded=array([26, 28]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=0.0003686685680276622, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 19, 21, 23, 25, 27, 29, 31, 32]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=0.0003686685680276622, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([32]), old_indices_used=array([ 0, 19, 21, 23, 25, 27, 29, 31]), old_indices_discarded=array([17, 28, 30]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=0.0001843342840138311, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 21, 23, 25, 27, 29, 31, 33, 34]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=0.0001843342840138311, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([34]), old_indices_used=array([ 0, 21, 23, 25, 27, 29, 31, 33]), old_indices_discarded=array([17, 19, 30, 32]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=9.216714200691555e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 23, 25, 27, 29, 31, 33, 35, 36]), model=ScalarModel(intercept=inf, linear_terms=array([ inf, -inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=9.216714200691555e-05, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([36]), old_indices_used=array([ 0, 23, 25, 27, 29, 31, 33, 35]), old_indices_discarded=array([17, 19, 21, 32, 34]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=4.608357100345777e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 25, 27, 29, 31, 33, 35, 37, 38]), model=ScalarModel(intercept=inf, linear_terms=array([inf, inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=4.608357100345777e-05, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([38]), old_indices_used=array([ 0, 25, 27, 29, 31, 33, 35, 37]), old_indices_discarded=array([17, 19, 21, 23, 34, 36]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=2.3041785501728886e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 27, 29, 31, 33, 35, 37, 39, 40]), model=ScalarModel(intercept=inf, linear_terms=array([ inf, -inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=2.3041785501728886e-05, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([40]), old_indices_used=array([ 0, 27, 29, 31, 33, 35, 37, 39]), old_indices_discarded=array([17, 19, 21, 23, 25, 36, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1.1520892750864443e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 29, 31, 33, 35, 37, 39, 41, 42]), model=ScalarModel(intercept=inf, linear_terms=array([inf, inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1.1520892750864443e-05, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([42]), old_indices_used=array([ 0, 29, 31, 33, 35, 37, 39, 41]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 38, 40]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=5.7604463754322216e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 31, 33, 35, 37, 39, 41, 43, 44]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=5.7604463754322216e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([44]), old_indices_used=array([ 0, 31, 33, 35, 37, 39, 41, 43]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 40, 42]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=2.8802231877161108e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 33, 35, 37, 39, 41, 43, 45, 46]), model=ScalarModel(intercept=inf, linear_terms=array([inf, inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=2.8802231877161108e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([46]), old_indices_used=array([ 0, 33, 35, 37, 39, 41, 43, 45]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 42, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1.4401115938580554e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 35, 37, 39, 41, 43, 45, 47, 48]), model=ScalarModel(intercept=inf, linear_terms=array([inf, inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1.4401115938580554e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([48]), old_indices_used=array([ 0, 35, 37, 39, 41, 43, 45, 47]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 44, 46]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 37, 39, 41, 43, 45, 47, 49, 50]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([50]), old_indices_used=array([ 0, 37, 39, 41, 43, 45, 47, 49]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 46, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 39, 41, 43, 45, 47, 49, 51, 52]), model=ScalarModel(intercept=inf, linear_terms=array([ inf, -inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([52]), old_indices_used=array([ 0, 39, 41, 43, 45, 47, 49, 51]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 46, 48, 50]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 43, 45, 47, 49, 51, 52, 53, 54]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([54]), old_indices_used=array([ 0, 43, 45, 47, 49, 51, 52, 53]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 46, 48, 50]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 45, 47, 49, 51, 52, 53, 55, 56]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([56]), old_indices_used=array([ 0, 45, 47, 49, 51, 52, 53, 55]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 46, 48, 50, + 54]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 49, 51, 52, 53, 55, 56, 57, 58]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([58]), old_indices_used=array([ 0, 49, 51, 52, 53, 55, 56, 57]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 50, 54]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 51, 52, 53, 55, 56, 57, 59, 60]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([60]), old_indices_used=array([ 0, 51, 52, 53, 55, 56, 57, 59]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 54, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 53, 55, 56, 57, 59, 61, 62]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([62]), old_indices_used=array([ 0, 52, 53, 55, 56, 57, 59, 61]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 54, 58, 60]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 57, 59, 61, 62, 63, 64]), model=ScalarModel(intercept=inf, linear_terms=array([ inf, -inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([64]), old_indices_used=array([ 0, 52, 56, 57, 59, 61, 62, 63]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 58, 60]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 59, 61, 62, 63, 65, 66]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([66]), old_indices_used=array([ 0, 52, 56, 59, 61, 62, 63, 65]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 57, 58, 60, 64]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 62, 63, 65, 66, 67, 68]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([68]), old_indices_used=array([ 0, 52, 56, 62, 63, 65, 66, 67]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, 61, 64]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 62, 65, 66, 67, 69, 70]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([70]), old_indices_used=array([ 0, 52, 56, 62, 65, 66, 67, 69]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 68]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 62, 66, 69, 70, 71, 72]), model=ScalarModel(intercept=inf, linear_terms=array([inf, inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([72]), old_indices_used=array([ 0, 52, 56, 62, 66, 69, 70, 71]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 62, 66, 70, 72, 73, 74]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([74]), old_indices_used=array([ 0, 52, 56, 62, 66, 70, 72, 73]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68, + 69, 71]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 62, 66, 70, 72, 75, 76]), model=ScalarModel(intercept=inf, linear_terms=array([inf, inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([76]), old_indices_used=array([ 0, 52, 56, 62, 66, 70, 72, 75]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68, + 69, 71, 73, 74]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 62, 66, 70, 72, 76, 78]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([78]), old_indices_used=array([ 0, 52, 56, 62, 66, 70, 72, 76]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68, + 69, 71, 73, 74, 75, 77]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 62, 66, 70, 72, 78, 80]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([80]), old_indices_used=array([ 0, 52, 56, 62, 66, 70, 72, 78]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68, + 69, 71, 73, 74, 75, 76, 77, 79]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 62, 66, 70, 78, 80, 82]), model=ScalarModel(intercept=inf, linear_terms=array([ inf, -inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([82]), old_indices_used=array([ 0, 52, 56, 62, 66, 70, 78, 80]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68, + 69, 71, 72, 73, 74, 75, 76, 77, 79, 81]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 62, 66, 70, 78, 80, 84]), model=ScalarModel(intercept=inf, linear_terms=array([inf, inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([84]), old_indices_used=array([ 0, 52, 56, 62, 66, 70, 78, 80]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68, + 69, 71, 72, 73, 74, 75, 76, 77, 79, 81, 82, 83]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 62, 66, 70, 78, 80, 86]), model=ScalarModel(intercept=inf, linear_terms=array([inf, inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([86]), old_indices_used=array([ 0, 52, 56, 62, 66, 70, 78, 80]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68, + 69, 71, 72, 73, 74, 75, 76, 77, 79, 81, 82, 83, 84, 85]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 56, 62, 66, 70, 78, 80, 86, 88]), model=ScalarModel(intercept=inf, linear_terms=array([ inf, -inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([88]), old_indices_used=array([ 0, 56, 62, 66, 70, 78, 80, 86]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, + 68, 69, 71, 72, 73, 74, 75, 76, 77, 79, 81, 82, 83, 84, 85, 87]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 56, 62, 66, 70, 78, 80, 86, 90]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([90]), old_indices_used=array([ 0, 56, 62, 66, 70, 78, 80, 86]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, + 68, 69, 71, 72, 73, 74, 75, 76, 77, 79, 81, 82, 83, 84, 85, 87, 88, + 89]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 56, 62, 66, 70, 78, 80, 86, 92]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([92]), old_indices_used=array([ 0, 56, 62, 66, 70, 78, 80, 86]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, + 68, 69, 71, 72, 73, 74, 75, 76, 77, 79, 81, 82, 83, 84, 85, 87, 88, + 89, 90, 91]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 56, 62, 66, 70, 78, 80, 86, 94]), model=ScalarModel(intercept=inf, linear_terms=array([inf, inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([94]), old_indices_used=array([ 0, 56, 62, 66, 70, 78, 80, 86]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, + 68, 69, 71, 72, 73, 74, 75, 76, 77, 79, 81, 82, 83, 84, 85, 87, 88, + 89, 90, 91, 92, 93]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 56, 62, 66, 70, 78, 80, 86, 96]), model=ScalarModel(intercept=inf, linear_terms=array([ inf, -inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([96]), old_indices_used=array([ 0, 56, 62, 66, 70, 78, 80, 86]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, + 68, 69, 71, 72, 73, 74, 75, 76, 77, 79, 81, 82, 83, 84, 85, 87, 88, + 89, 90, 91, 92, 93, 94, 95]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 56, 62, 66, 70, 78, 80, 86, 98]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([98]), old_indices_used=array([ 0, 56, 62, 66, 70, 78, 80, 86]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, + 68, 69, 71, 72, 73, 74, 75, 76, 77, 79, 81, 82, 83, 84, 85, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Maximum number of criterion evaluations reached.', 'tranquilo_history': History for least_squares function with 100 entries., 'history': {'params': [{'CRRA': 7.5503322732065214, 'DiscFac': 1.0688678584866826}, {'CRRA': 6.881201497543467, 'DiscFac': 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7.5503322732065214, 'DiscFac': 1.0688678584866826}, {'CRRA': 7.550332250101332, 'DiscFac': 1.0688668587536432}, {'CRRA': 7.5503322732065214, 'DiscFac': 1.0688678584866826}, {'CRRA': 7.550332174267114, 'DiscFac': 1.0688688535801423}, {'CRRA': 7.5503322732065214, 'DiscFac': 1.0688678584866826}, {'CRRA': 7.55033237214568, 'DiscFac': 1.0688668633931981}, {'CRRA': 7.5503322732065214, 'DiscFac': 1.0688678584866826}, {'CRRA': 7.5503323721458235, 'DiscFac': 1.0688668633932124}, {'CRRA': 7.5503322732065214, 'DiscFac': 1.0688678584866826}, {'CRRA': 7.550332372145812, 'DiscFac': 1.0688668633932112}, {'CRRA': 7.5503322732065214, 'DiscFac': 1.0688678584866826}, {'CRRA': 7.550332174266841, 'DiscFac': 1.0688688535801154}, {'CRRA': 7.5503322732065214, 'DiscFac': 1.0688678584866826}, {'CRRA': 7.550332174267176, 'DiscFac': 1.0688688535801485}, {'CRRA': 7.5503322732065214, 'DiscFac': 1.0688678584866826}], 'criterion': [nan, 5.5395827111397296, 6.054158174623402, 5.5613444250597865, nan, 6.26999489704744, 6.0387433323182025, nan, nan, nan, nan, 5.571706618549453, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan], 'runtime': [0.0, 2.328494080999917, 2.5476781890001803, 2.7613755900001706, 2.9831037750000178, 3.2319878500002233, 3.451330895999945, 3.686206747000142, 3.931726117999915, 4.153793400000268, 4.406197809999867, 4.657425629000045, 4.9140708650002125, 6.904129762000139, 8.62666656600004, 10.469215058999907, 12.175716226000077, 13.898204776000057, 15.646962670999983, 17.379415701000198, 19.11758814399991, 20.84886875300026, 22.704385076000108, 24.402293366999857, 26.127271145000122, 27.857393392000176, 29.59283604400025, 31.405433552999966, 33.34631329700005, 35.294670816000234, 37.08944409000014, 38.88546167000004, 40.620699676000186, 42.32569570800024, 44.12526996899987, 45.82664452400013, 47.76171708999982, 49.54912002099991, 51.321899781999946, 53.14778549999983, 54.992761710000195, 56.88239375400008, 58.73952688700001, 60.59187545700024, 62.36865698700012, 64.15370713699986, 65.92303226400008, 67.73910037399992, 69.61133773600022, 71.49170302699986, 73.45302224199986, 75.3591821949999, 77.18019060000006, 78.9008996030002, 80.67005395200022, 82.49309870800016, 84.26152974200022, 86.17230510900026, 87.94852979500001, 89.70901951499991, 91.44370474200014, 93.1722660270002, 94.87196946899985, 96.69248874000004, 98.75914207799997, 100.71350908700015, 102.51248370900021, 104.27288464599997, 105.99656527800016, 107.74241232099985, 109.45312153199984, 111.20908445099985, 113.08005569700026, 114.87827648099983, 116.77893422199986, 118.6311070050001, 120.48852345700016, 122.23814602399989, 124.22244466700022, 125.95811692500001, 127.85704271700024, 129.64778518799994, 131.51783528299984, 133.36864930799993, 135.20897757800003, 137.17955607500016, 138.93780772699984, 140.6892026380001, 142.4551368309999, 144.1857052280002, 145.95847650199994, 147.76372151700025, 149.73393046899992, 151.55659189200014, 153.36182119000023, 155.12572341299983, 156.85440400300013, 158.59608259800007, 160.32910335399993, 162.09345126300013], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88]}}], 'exploration_sample': array([[ 5.825 , 0.95 ], + [12.321875, 1.08125 ], + [ 5. , 0.95 ], + [ 4.64375 , 0.6875 ], + [14.09375 , 0.9875 ], + [ 9.36875 , 0.8375 ], + [17.6375 , 1.025 ], + [ 8.1875 , 0.725 ], + [10.55 , 0.8 ], + [16.45625 , 0.9125 ], + [ 7.00625 , 0.6125 ], + [11.73125 , 0.7625 ], + [15.275 , 0.65 ], + [12.9125 , 0.575 ], + [17.046875, 0.63125 ], + [18.81875 , 0.5375 ], + [ 3.4625 , 0.875 ], + [ 2.28125 , 1.0625 ], + [ 2.871875, 0.78125 ]]), 'exploration_results': array([ 3.02633148, 3.30050278, 3.74724591, 3.92461378, 4.45534719, + 4.52159416, 4.77507862, 5.06340436, 5.16761845, 5.42393003, + 5.50914405, 5.69522002, 6.68407291, 6.73863805, 6.84560996, + 7.20052264, 7.72130492, 8.47185488, 10.67451262])}}" diff --git a/content/tables/min/PortfolioSub(Stock)Market_estimate_results.csv b/content/tables/min/PortfolioSub(Stock)Market_estimate_results.csv new file mode 100644 index 0000000..9dbfeaa --- /dev/null +++ b/content/tables/min/PortfolioSub(Stock)Market_estimate_results.csv @@ -0,0 +1,3518 @@ +CRRA,4.242027288057683 +DiscFac,0.9833806705623286 +time_to_estimate,74.66971564292908 +params,"{'CRRA': 4.242027288057683, 'DiscFac': 0.9833806705623286}" +criterion,1.5876279609075603 +start_criterion,1.5876292611493417 +start_params,"{'CRRA': 4.242030481097337, 'DiscFac': 0.9833811989466528}" +algorithm,multistart_tranquilo_ls +direction,minimize +n_free,2 +message,Absolute criterion change smaller than tolerance. +success, +n_criterion_evaluations, +n_derivative_evaluations, +n_iterations, +history,"{'params': [{'CRRA': 4.242030481097337, 'DiscFac': 0.9833811989466528}, {'CRRA': 3.866090318003359, 'DiscFac': 0.6090124559299177}, {'CRRA': 4.617970644191314, 'DiscFac': 0.7959483679304038}, {'CRRA': 3.866090318003359, 'DiscFac': 0.9003625912366793}, {'CRRA': 4.617861537983963, 'DiscFac': 1.1}, {'CRRA': 4.617970644191314, 'DiscFac': 0.7294749147361314}, {'CRRA': 4.601974218487584, 'DiscFac': 0.6074410358526751}, {'CRRA': 3.8881606530327915, 'DiscFac': 1.1}, {'CRRA': 4.617970644191314, 'DiscFac': 0.9756753153557713}, {'CRRA': 4.314996287442069, 'DiscFac': 1.1}, {'CRRA': 3.866090318003359, 'DiscFac': 1.0956042400456183}, {'CRRA': 4.143320811214283, 'DiscFac': 0.6074410358526751}, {'CRRA': 4.2369258172160285, 'DiscFac': 1.1}, {'CRRA': 4.133318553862013, 'DiscFac': 0.8601172642325396}, {'CRRA': 4.092965500617242, 'DiscFac': 0.9013897613548475}, {'CRRA': 4.153471314030488, 'DiscFac': 0.9250370642369384}, {'CRRA': 4.191228580657776, 'DiscFac': 0.9681871888315208}, {'CRRA': 4.215297697523913, 'DiscFac': 0.9840940747766532}, {'CRRA': 4.256459683327901, 'DiscFac': 0.9812837869229949}, {'CRRA': 4.248648245544559, 'DiscFac': 0.9829155931605158}, {'CRRA': 4.2387021886508345, 'DiscFac': 0.9826511792916172}, {'CRRA': 4.240367669492092, 'DiscFac': 0.9831161999919373}, {'CRRA': 4.2422636524294175, 'DiscFac': 0.9825861648997909}, {'CRRA': 4.242440765395577, 'DiscFac': 0.983211931990717}, {'CRRA': 4.241839441124415, 'DiscFac': 0.9832744285945232}, {'CRRA': 4.242046271580153, 'DiscFac': 0.9834835532858018}, {'CRRA': 4.2420795402716225, 'DiscFac': 0.9833633976969068}, {'CRRA': 4.2420046638999995, 'DiscFac': 0.9833775511549192}, {'CRRA': 4.242029472795148, 'DiscFac': 0.9833941052695937}, {'CRRA': 4.242032758596346, 'DiscFac': 0.9833751400309451}, {'CRRA': 4.242027288057683, 'DiscFac': 0.9833806705623286}], 'criterion': [1.5876292611493417, 4.388075846561168, 3.6057596559345013, 4.1162318084465515, 7.442613271266951, 3.737761393216249, 3.8560302459126334, 9.397193744120152, 1.6226333504067376, 8.063261814400159, 8.8224837848772, 4.09852537527331, 8.261918784924704, 3.9094160894413705, 3.4367411890710167, 2.7266546241051177, 1.673996116569148, 1.5882265039211922, 1.5883233911565737, 1.5877004742936713, 1.587649024825247, 1.5879244359915534, 1.5876776543822713, 1.5878350711177123, 1.5879235559647933, 1.5876752020095402, 1.587693121026412, 1.5876342398304937, 1.5876403856006087, 1.5876417730877617, 1.5876279609075605], 'runtime': [0.0, 1.5780707439998878, 1.7842999229997076, 2.004510731999744, 2.221870129999843, 2.4473023380000996, 2.6690129619996696, 2.90139628299994, 3.1577693979998003, 3.374397773000055, 3.6161538719998134, 3.841147362999891, 4.217604835999737, 5.718130669999937, 6.961544460999903, 8.198964767999769, 9.467069380999874, 10.732859773999735, 11.996178713000063, 13.294657668999662, 14.557964072999766, 15.818718369999715, 17.08274131899998, 18.331843930000105, 19.73133806299984, 20.974915189000058, 22.21207696400006, 23.447582182999668, 24.678527696999936, 25.93378734099997, 27.18556611699978], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]}" +convergence_report, +multistart_info,"{'start_parameters': [{'CRRA': 4.242030481097337, 'DiscFac': 0.9833811989466528}, {'CRRA': 4.705669260952328, 'DiscFac': 0.9736036985151767}], 'local_optima': [Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 8.19e-07* 8.19e-07* +relative_params_change 9.248e-07* 9.248e-07* +absolute_criterion_change 1.3e-06* 1.3e-06* +absolute_params_change 3.236e-06* 3.236e-06* + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 0.0005557 0.01853 +relative_params_change 0.006672 0.05371 +absolute_criterion_change 0.0008913 0.02972 +absolute_params_change 0.02942 0.2354 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 4.242030481097337, 'DiscFac': 0.9833811989466528}, {'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 7.596874999999999, 'DiscFac': 0.9312500000000001}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 9.368749999999999, 'DiscFac': 0.8375}, {'CRRA': 8.1875, 'DiscFac': 0.7250000000000001}, {'CRRA': 10.549999999999999, 'DiscFac': 0.8}, {'CRRA': 11.73125, 'DiscFac': 0.7625000000000001}, {'CRRA': 7.00625, 'DiscFac': 0.6125}, {'CRRA': 15.274999999999999, 'DiscFac': 0.65}, {'CRRA': 12.9125, 'DiscFac': 0.575}, {'CRRA': 17.046875, 'DiscFac': 0.6312500000000001}, {'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 16.45625, 'DiscFac': 0.9125000000000001}, {'CRRA': 14.093749999999998, 'DiscFac': 0.9875}, {'CRRA': 18.81875, 'DiscFac': 0.5375}, {'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 17.6375, 'DiscFac': 1.0250000000000001}, {'CRRA': 2.871875, 'DiscFac': 0.78125}, {'CRRA': 2.28125, 'DiscFac': 1.0625}], 'exploration_results': array([ 1.58762926, 2.04247528, 2.9717037 , 3.76994253, 3.80031518, + 4.27735638, 4.32695188, 4.78264529, 5.25346612, 5.79528191, + 5.94010747, 6.02403907, 6.09730665, 6.10855726, 6.12270544, + 6.45799199, 7.01428788, 7.3683214 , 8.72764877, 13.61935456])}" +algorithm_output,"{'states': [State(trustregion=Region(center=array([4.24203048, 0.9833812 ]), radius=0.4242030481097337, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=1.5876292611493417, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.4242030481097337, shift=array([4.24203048, 0.9833812 ])), vector_model=VectorModel(intercepts=array([ 0.07258773, 0.19040179, 0.23875405, 0.27636943, 0.35655762, + 0.38659094, 0.46008788, -0.11110168, -0.31032389, -0.33883604, + -0.43631436, -0.66907146, -0.20076546, -0.02216249, 0.01729751, + 0.01298348, 0.14734387]), linear_terms=array([[0., 0.], + [0., 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scale=0.013256345253429178, shift=array([4.24203048, 0.9833812 ])), vector_model=VectorModel(intercepts=array([ 0.07258773, 0.19040179, 0.23875405, 0.27636943, 0.35655762, + 0.38659094, 0.46008788, -0.11110168, -0.31032389, -0.33883604, + -0.43631436, -0.66907146, -0.20076546, -0.02216249, 0.01729751, + 0.01298348, 0.14734387]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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0.07258773, 0.19040179, 0.23875405, 0.27636943, 0.35655762, + 0.38659094, 0.46008788, -0.11110168, -0.31032389, -0.33883604, + -0.43631436, -0.66907146, -0.20076546, -0.02216249, 0.01729751, + 0.01298348, 0.14734387]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4242030481097337, shift=array([4.24203048, 0.9833812 ])), candidate_index=19, candidate_x=array([4.24864825, 0.98291559]), index=0, x=array([4.24203048, 0.9833812 ]), fval=1.5876292611493414, rho=-0.6241827120081535, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.24203048, 0.9833812 ]), radius=0.0033140863133572945, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 18, 19]), model=ScalarModel(intercept=1.587629261149342, linear_terms=array([0.00015398, 0.00208197]), square_terms=array([[9.59461595e-06, 1.69296559e-04], + [1.69296559e-04, 8.57310263e-03]]), scale=0.0033140863133572945, shift=array([4.24203048, 0.9833812 ])), vector_model=VectorModel(intercepts=array([ 0.07258773, 0.19040179, 0.23875405, 0.27636943, 0.35655762, + 0.38659094, 0.46008788, -0.11110168, -0.31032389, -0.33883604, + -0.43631436, -0.66907146, -0.20076546, -0.02216249, 0.01729751, + 0.01298348, 0.14734387]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4242030481097337, shift=array([4.24203048, 0.9833812 ])), candidate_index=20, candidate_x=array([4.23870219, 0.98265118]), index=0, x=array([4.24203048, 0.9833812 ]), fval=1.5876292611493414, rho=-0.054450674157168186, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.24203048, 0.9833812 ]), radius=0.0016570431566786472, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 19, 20]), model=ScalarModel(intercept=1.587629261149341, linear_terms=array([3.35543462e-05, 4.23638159e-04]), square_terms=array([[2.59245710e-06, 4.97127724e-05], + [4.97127724e-05, 2.31415706e-03]]), scale=0.0016570431566786472, shift=array([4.24203048, 0.9833812 ])), vector_model=VectorModel(intercepts=array([ 0.07258773, 0.19040179, 0.23875405, 0.27636943, 0.35655762, + 0.38659094, 0.46008788, -0.11110168, -0.31032389, -0.33883604, + -0.43631436, -0.66907146, -0.20076546, -0.02216249, 0.01729751, + 0.01298348, 0.14734387]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4242030481097337, shift=array([4.24203048, 0.9833812 ])), candidate_index=21, candidate_x=array([4.24036767, 0.9831162 ]), index=0, x=array([4.24203048, 0.9833812 ]), fval=1.5876292611493414, rho=-4.7194167560134765, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.24203048, 0.9833812 ]), radius=0.0008285215783393236, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 20, 21]), model=ScalarModel(intercept=1.5876292611493419, linear_terms=array([-0.00063361, 0.00317707]), square_terms=array([[1.20777869e-06, 1.41359263e-05], + [1.41359263e-05, 5.51334270e-04]]), scale=0.0008285215783393236, shift=array([4.24203048, 0.9833812 ])), vector_model=VectorModel(intercepts=array([ 0.07258773, 0.19040179, 0.23875405, 0.27636943, 0.35655762, + 0.38659094, 0.46008788, -0.11110168, -0.31032389, -0.33883604, + -0.43631436, -0.66907146, -0.20076546, -0.02216249, 0.01729751, + 0.01298348, 0.14734387]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.24203048, 0.9833812 ]), radius=0.0004142607891696618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 21, 22]), model=ScalarModel(intercept=1.5876292611493423, linear_terms=array([-7.76212343e-05, 8.76962771e-05]), square_terms=array([[1.80898809e-07, 3.46928165e-06], + [3.46928165e-06, 1.43411455e-04]]), scale=0.0004142607891696618, shift=array([4.24203048, 0.9833812 ])), vector_model=VectorModel(intercepts=array([ 0.07258773, 0.19040179, 0.23875405, 0.27636943, 0.35655762, + 0.38659094, 0.46008788, -0.11110168, -0.31032389, -0.33883604, + -0.43631436, -0.66907146, -0.20076546, -0.02216249, 0.01729751, + 0.01298348, 0.14734387]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4242030481097337, shift=array([4.24203048, 0.9833812 ])), candidate_index=23, candidate_x=array([4.24244077, 0.98321193]), index=0, x=array([4.24203048, 0.9833812 ]), fval=1.5876292611493414, rho=-2.0167065521906635, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.24203048, 0.9833812 ]), radius=0.0002071303945848309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 22, 23]), model=ScalarModel(intercept=1.5876292611493408, linear_terms=array([1.37691140e-04, 9.56134057e-05]), square_terms=array([[6.38229831e-08, 5.06023679e-07], + [5.06023679e-07, 3.56393383e-05]]), scale=0.0002071303945848309, shift=array([4.24203048, 0.9833812 ])), vector_model=VectorModel(intercepts=array([ 0.07258773, 0.19040179, 0.23875405, 0.27636943, 0.35655762, + 0.38659094, 0.46008788, -0.11110168, -0.31032389, -0.33883604, + -0.43631436, -0.66907146, -0.20076546, -0.02216249, 0.01729751, + 0.01298348, 0.14734387]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 23, 24]), model=ScalarModel(intercept=1.5876292611493426, linear_terms=array([-3.81689245e-05, -2.11584544e-04]), square_terms=array([[1.65957692e-08, 2.94582741e-07], + [2.94582741e-07, 9.72087338e-06]]), scale=0.00010356519729241545, shift=array([4.24203048, 0.9833812 ])), vector_model=VectorModel(intercepts=array([ 0.07258773, 0.19040179, 0.23875405, 0.27636943, 0.35655762, + 0.38659094, 0.46008788, -0.11110168, -0.31032389, -0.33883604, + -0.43631436, -0.66907146, -0.20076546, -0.02216249, 0.01729751, + 0.01298348, 0.14734387]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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model=ScalarModel(intercept=1.587629261149342, linear_terms=array([-9.85177212e-05, 3.63345201e-05]), square_terms=array([[3.27645831e-08, 1.44271153e-07], + [1.44271153e-07, 2.08587199e-06]]), scale=5.1782598646207726e-05, shift=array([4.24203048, 0.9833812 ])), vector_model=VectorModel(intercepts=array([ 0.07258773, 0.19040179, 0.23875405, 0.27636943, 0.35655762, + 0.38659094, 0.46008788, -0.11110168, -0.31032389, -0.33883604, + -0.43631436, -0.66907146, -0.20076546, -0.02216249, 0.01729751, + 0.01298348, 0.14734387]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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square_terms=array([[2.65514601e-09, 5.31575830e-09], + [5.31575830e-09, 5.31088154e-07]]), scale=2.5891299323103863e-05, shift=array([4.24203048, 0.9833812 ])), vector_model=VectorModel(intercepts=array([ 0.07258773, 0.19040179, 0.23875405, 0.27636943, 0.35655762, + 0.38659094, 0.46008788, -0.11110168, -0.31032389, -0.33883604, + -0.43631436, -0.66907146, -0.20076546, -0.02216249, 0.01729751, + 0.01298348, 0.14734387]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], 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scale=1.2945649661551932e-05, shift=array([4.24203048, 0.9833812 ])), vector_model=VectorModel(intercepts=array([ 0.07258773, 0.19040179, 0.23875405, 0.27636943, 0.35655762, + 0.38659094, 0.46008788, -0.11110168, -0.31032389, -0.33883604, + -0.43631436, -0.66907146, -0.20076546, -0.02216249, 0.01729751, + 0.01298348, 0.14734387]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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vector_model=VectorModel(intercepts=array([ 0.07258773, 0.19040179, 0.23875405, 0.27636943, 0.35655762, + 0.38659094, 0.46008788, -0.11110168, -0.31032389, -0.33883604, + -0.43631436, -0.66907146, -0.20076546, -0.02216249, 0.01729751, + 0.01298348, 0.14734387]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4242030481097337, shift=array([4.24203048, 0.9833812 ])), candidate_index=29, candidate_x=array([4.24203276, 0.98337514]), index=0, x=array([4.24203048, 0.9833812 ]), fval=1.5876292611493414, rho=-2.1807941427759236, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 27, 28]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.24203048, 0.9833812 ]), radius=3.236412415387983e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 28, 29]), model=ScalarModel(intercept=1.5876292611493419, linear_terms=array([3.17318259e-05, 5.25230008e-06]), square_terms=array([[6.52765022e-09, 1.06773354e-09], + [1.06773354e-09, 8.31455570e-09]]), scale=3.236412415387983e-06, shift=array([4.24203048, 0.9833812 ])), vector_model=VectorModel(intercepts=array([ 0.07258773, 0.19040179, 0.23875405, 0.27636943, 0.35655762, + 0.38659094, 0.46008788, -0.11110168, -0.31032389, -0.33883604, + -0.43631436, -0.66907146, -0.20076546, -0.02216249, 0.01729751, + 0.01298348, 0.14734387]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4242030481097337, shift=array([4.24203048, 0.9833812 ])), candidate_index=30, candidate_x=array([4.24202729, 0.98338067]), index=30, x=array([4.24202729, 0.98338067]), fval=1.5876279609075603, rho=0.04042963020560158, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=3.2364629190140795e-06, relative_step_length=1.0000156048178088, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 31 entries., 'multistart_info': {'start_parameters': [array([4.24203048, 0.9833812 ]), array([4.70566926, 0.9736037 ])], 'local_optima': [{'solution_x': array([4.24202729, 0.98338067]), 'solution_criterion': 1.5876279609075603, 'states': [State(trustregion=Region(center=array([4.24203048, 0.9833812 ]), radius=0.4242030481097337, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=1.5876292611493417, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.4242030481097337, shift=array([4.24203048, 0.9833812 ])), vector_model=VectorModel(intercepts=array([ 0.07258773, 0.19040179, 0.23875405, 0.27636943, 0.35655762, + 0.38659094, 0.46008788, -0.11110168, -0.31032389, -0.33883604, + -0.43631436, -0.66907146, -0.20076546, -0.02216249, 0.01729751, + 0.01298348, 0.14734387]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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0.35655762, + 0.38659094, 0.46008788, -0.11110168, -0.31032389, -0.33883604, + -0.43631436, -0.66907146, -0.20076546, -0.02216249, 0.01729751, + 0.01298348, 0.14734387]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4242030481097337, shift=array([4.24203048, 0.9833812 ])), candidate_index=13, candidate_x=array([4.13331855, 0.86011726]), index=0, x=array([4.24203048, 0.9833812 ]), fval=1.5876292611493414, rho=-2.0634711104962373, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.24203048, 0.9833812 ]), radius=0.21210152405486685, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 7, 8, 9, 10, 11, 12, 13]), model=ScalarModel(intercept=1.468574209401057, linear_terms=array([-0.04135932, 1.32189612]), square_terms=array([[ 0.08570077, -0.35947045], + [-0.35947045, 5.28398255]]), scale=array([0.18797008, 0.15229444]), shift=array([4.24203048, 0.94770556])), vector_model=VectorModel(intercepts=array([ 0.07258773, 0.19040179, 0.23875405, 0.27636943, 0.35655762, + 0.38659094, 0.46008788, -0.11110168, -0.31032389, 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dtype=int64), old_indices_used=array([ 0, 3, 7, 8, 9, 11, 12, 13, 14]), old_indices_discarded=array([ 2, 4, 5, 6, 10]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.24203048, 0.9833812 ]), radius=0.05302538101371671, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 9, 12, 13, 14, 15]), model=ScalarModel(intercept=1.4963636570991579, linear_terms=array([0.04255276, 0.55152192]), square_terms=array([[ 0.05765926, -0.01345445], + [-0.01345445, 2.00056259]]), scale=0.05302538101371671, shift=array([4.24203048, 0.9833812 ])), vector_model=VectorModel(intercepts=array([ 0.07258773, 0.19040179, 0.23875405, 0.27636943, 0.35655762, + 0.38659094, 0.46008788, -0.11110168, -0.31032389, -0.33883604, + -0.43631436, -0.66907146, -0.20076546, -0.02216249, 0.01729751, + 0.01298348, 0.14734387]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.24203048, 0.9833812 ]), radius=0.026512690506858356, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 15, 16]), model=ScalarModel(intercept=1.6198931043258744, linear_terms=array([ 0.12625895, -0.12334431]), square_terms=array([[ 0.05941064, -0.10117528], + [-0.10117528, 0.73014477]]), scale=0.026512690506858356, shift=array([4.24203048, 0.9833812 ])), vector_model=VectorModel(intercepts=array([ 0.07258773, 0.19040179, 0.23875405, 0.27636943, 0.35655762, + 0.38659094, 0.46008788, -0.11110168, -0.31032389, -0.33883604, + -0.43631436, -0.66907146, -0.20076546, -0.02216249, 0.01729751, + 0.01298348, 0.14734387]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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State(trustregion=Region(center=array([4.24203048, 0.9833812 ]), radius=0.013256345253429178, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 16, 17]), model=ScalarModel(intercept=1.5876292611493403, linear_terms=array([0.00031998, 0.01902441]), square_terms=array([[0.00018057, 0.00326465], + [0.00326465, 0.14269986]]), scale=0.013256345253429178, shift=array([4.24203048, 0.9833812 ])), vector_model=VectorModel(intercepts=array([ 0.07258773, 0.19040179, 0.23875405, 0.27636943, 0.35655762, + 0.38659094, 0.46008788, -0.11110168, -0.31032389, -0.33883604, + -0.43631436, -0.66907146, -0.20076546, -0.02216249, 0.01729751, + 0.01298348, 0.14734387]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=1.5876292611493423, linear_terms=array([-4.79150438e-05, 1.71571221e-03]), square_terms=array([[4.37165369e-05, 7.87935804e-04], + [7.87935804e-04, 3.55635800e-02]]), scale=0.006628172626714589, shift=array([4.24203048, 0.9833812 ])), vector_model=VectorModel(intercepts=array([ 0.07258773, 0.19040179, 0.23875405, 0.27636943, 0.35655762, + 0.38659094, 0.46008788, -0.11110168, -0.31032389, -0.33883604, + -0.43631436, -0.66907146, -0.20076546, -0.02216249, 0.01729751, + 0.01298348, 0.14734387]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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linear_terms=array([0.00015398, 0.00208197]), square_terms=array([[9.59461595e-06, 1.69296559e-04], + [1.69296559e-04, 8.57310263e-03]]), scale=0.0033140863133572945, shift=array([4.24203048, 0.9833812 ])), vector_model=VectorModel(intercepts=array([ 0.07258773, 0.19040179, 0.23875405, 0.27636943, 0.35655762, + 0.38659094, 0.46008788, -0.11110168, -0.31032389, -0.33883604, + -0.43631436, -0.66907146, -0.20076546, -0.02216249, 0.01729751, + 0.01298348, 0.14734387]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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[4.97127724e-05, 2.31415706e-03]]), scale=0.0016570431566786472, shift=array([4.24203048, 0.9833812 ])), vector_model=VectorModel(intercepts=array([ 0.07258773, 0.19040179, 0.23875405, 0.27636943, 0.35655762, + 0.38659094, 0.46008788, -0.11110168, -0.31032389, -0.33883604, + -0.43631436, -0.66907146, -0.20076546, -0.02216249, 0.01729751, + 0.01298348, 0.14734387]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4242030481097337, shift=array([4.24203048, 0.9833812 ])), candidate_index=21, candidate_x=array([4.24036767, 0.9831162 ]), index=0, x=array([4.24203048, 0.9833812 ]), fval=1.5876292611493414, rho=-4.7194167560134765, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.24203048, 0.9833812 ]), radius=0.0008285215783393236, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 20, 21]), model=ScalarModel(intercept=1.5876292611493419, linear_terms=array([-0.00063361, 0.00317707]), square_terms=array([[1.20777869e-06, 1.41359263e-05], + [1.41359263e-05, 5.51334270e-04]]), scale=0.0008285215783393236, shift=array([4.24203048, 0.9833812 ])), vector_model=VectorModel(intercepts=array([ 0.07258773, 0.19040179, 0.23875405, 0.27636943, 0.35655762, + 0.38659094, 0.46008788, -0.11110168, -0.31032389, -0.33883604, + -0.43631436, -0.66907146, -0.20076546, -0.02216249, 0.01729751, + 0.01298348, 0.14734387]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4242030481097337, shift=array([4.24203048, 0.9833812 ])), candidate_index=22, candidate_x=array([4.24226365, 0.98258616]), index=0, x=array([4.24203048, 0.9833812 ]), fval=1.5876292611493414, rho=-0.01625618639906515, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.24203048, 0.9833812 ]), radius=0.0004142607891696618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 21, 22]), model=ScalarModel(intercept=1.5876292611493423, linear_terms=array([-7.76212343e-05, 8.76962771e-05]), square_terms=array([[1.80898809e-07, 3.46928165e-06], + [3.46928165e-06, 1.43411455e-04]]), scale=0.0004142607891696618, shift=array([4.24203048, 0.9833812 ])), vector_model=VectorModel(intercepts=array([ 0.07258773, 0.19040179, 0.23875405, 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candidate_x=array([4.24244077, 0.98321193]), index=0, x=array([4.24203048, 0.9833812 ]), fval=1.5876292611493414, rho=-2.0167065521906635, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.24203048, 0.9833812 ]), radius=0.0002071303945848309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 22, 23]), model=ScalarModel(intercept=1.5876292611493408, linear_terms=array([1.37691140e-04, 9.56134057e-05]), square_terms=array([[6.38229831e-08, 5.06023679e-07], + [5.06023679e-07, 3.56393383e-05]]), scale=0.0002071303945848309, shift=array([4.24203048, 0.9833812 ])), vector_model=VectorModel(intercepts=array([ 0.07258773, 0.19040179, 0.23875405, 0.27636943, 0.35655762, + 0.38659094, 0.46008788, -0.11110168, -0.31032389, -0.33883604, + 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State(trustregion=Region(center=array([4.24203048, 0.9833812 ]), radius=1.2945649661551932e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 26, 27]), model=ScalarModel(intercept=1.5876292611493426, linear_terms=array([ 2.91939423e-06, -3.83055954e-05]), square_terms=array([[1.69238935e-10, 2.10204016e-09], + [2.10204016e-09, 1.40789466e-07]]), scale=1.2945649661551932e-05, shift=array([4.24203048, 0.9833812 ])), vector_model=VectorModel(intercepts=array([ 0.07258773, 0.19040179, 0.23875405, 0.27636943, 0.35655762, + 0.38659094, 0.46008788, -0.11110168, -0.31032389, -0.33883604, + -0.43631436, -0.66907146, -0.20076546, -0.02216249, 0.01729751, + 0.01298348, 0.14734387]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 27, 28]), model=ScalarModel(intercept=1.587629261149341, linear_terms=array([-2.00798138e-06, 5.38965059e-06]), square_terms=array([[6.66454752e-11, 6.05615580e-10], + [6.05615580e-10, 3.28444776e-08]]), scale=6.472824830775966e-06, shift=array([4.24203048, 0.9833812 ])), vector_model=VectorModel(intercepts=array([ 0.07258773, 0.19040179, 0.23875405, 0.27636943, 0.35655762, + 0.38659094, 0.46008788, -0.11110168, -0.31032389, -0.33883604, + -0.43631436, -0.66907146, -0.20076546, -0.02216249, 0.01729751, + 0.01298348, 0.14734387]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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model=ScalarModel(intercept=1.5876292611493419, linear_terms=array([3.17318259e-05, 5.25230008e-06]), square_terms=array([[6.52765022e-09, 1.06773354e-09], + [1.06773354e-09, 8.31455570e-09]]), scale=3.236412415387983e-06, shift=array([4.24203048, 0.9833812 ])), vector_model=VectorModel(intercepts=array([ 0.07258773, 0.19040179, 0.23875405, 0.27636943, 0.35655762, + 0.38659094, 0.46008788, -0.11110168, -0.31032389, -0.33883604, + -0.43631436, -0.66907146, -0.20076546, -0.02216249, 0.01729751, + 0.01298348, 0.14734387]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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index=23, x=array([4.41166975, 0.97289059]), fval=1.6040821847466233, rho=0.24917368370649054, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 19, 20, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.02941513105367178, relative_step_length=1.0001597451060564, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 24 entries., 'history': {'params': [{'CRRA': 4.705669260952328, 'DiscFac': 0.9736036985151767}, {'CRRA': 4.348419805736947, 'DiscFac': 0.5565746183820434}, {'CRRA': 5.122698341085461, 'DiscFac': 0.9952455070622018}, {'CRRA': 4.288640180819194, 'DiscFac': 1.0042527064713087}, {'CRRA': 5.122698341085461, 'DiscFac': 1.0993927812777562}, {'CRRA': 5.122698341085461, 'DiscFac': 0.7671961303324353}, {'CRRA': 5.122698341085461, 'DiscFac': 0.7025610983806745}, {'CRRA': 4.580743163257176, 'DiscFac': 1.1}, {'CRRA': 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1.9845234911763256, 1.7766160584712591, 6.747910783998845, 3.613475921409838, 3.7797436371608626, 7.507212460562142, 2.2911072534309, 6.271759430670934, 2.1645488859958806, 3.9152079634746326, 6.985956689331683, 3.6035404962226445, 2.579039548739623, 2.657856939703968, 1.6338056353224137, 1.6569672368755408, 1.6278046306164462, 1.6239964564991458, 1.6067575995254133, 1.6218698633503479, 1.6049735108741838, 1.6040821847466233], 'runtime': [0.0, 1.586177479000071, 1.7946122309999737, 2.004966568999862, 2.247134769999775, 2.469910119999895, 2.7596060769997166, 2.9623845329997494, 3.1731714059997103, 3.55554515599988, 3.812486024000009, 4.016020335000121, 4.243500022999797, 5.687079881000045, 6.939442543000041, 8.20021526399978, 9.451716502999716, 10.713262261999716, 11.968579557999874, 13.200601013999858, 14.450635835999947, 15.687370332999762, 16.921538503999727, 18.303166583999882], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]}}], 'exploration_sample': array([[ 4.24203048, 0.9833812 ], + [ 5.825 , 0.95 ], + [ 7.596875 , 0.93125 ], + [ 4.64375 , 0.6875 ], + [ 9.36875 , 0.8375 ], + [ 8.1875 , 0.725 ], + [10.55 , 0.8 ], + [11.73125 , 0.7625 ], + [ 7.00625 , 0.6125 ], + [15.275 , 0.65 ], + [12.9125 , 0.575 ], + [17.046875 , 0.63125 ], + [ 3.4625 , 0.875 ], + [16.45625 , 0.9125 ], + [14.09375 , 0.9875 ], + [18.81875 , 0.5375 ], + [12.321875 , 1.08125 ], + [17.6375 , 1.025 ], + [ 2.871875 , 0.78125 ], + [ 2.28125 , 1.0625 ]]), 'exploration_results': array([ 1.58762926, 2.04247528, 2.9717037 , 3.76994253, 3.80031518, + 4.27735638, 4.32695188, 4.78264529, 5.25346612, 5.79528191, + 5.94010747, 6.02403907, 6.09730665, 6.10855726, 6.12270544, + 6.45799199, 7.01428788, 7.3683214 , 8.72764877, 13.61935456])}}" diff --git a/content/tables/min/Portfolio_estimate_results.csv b/content/tables/min/Portfolio_estimate_results.csv new file mode 100644 index 0000000..33b0825 --- /dev/null +++ b/content/tables/min/Portfolio_estimate_results.csv @@ -0,0 +1,8433 @@ +CRRA,10.932879562536495 +DiscFac,0.7902625251506934 +time_to_estimate,124.8826515674591 +params,"{'CRRA': 10.932879562536495, 'DiscFac': 0.7902625251506934}" +criterion,1.313120069801016 +start_criterion,1.8080316873478892 +start_params,"{'CRRA': 10.865980160003074, 'DiscFac': 0.8065707082845225}" +algorithm,multistart_tranquilo_ls +direction,minimize +n_free,2 +message, +success, +n_criterion_evaluations, +n_derivative_evaluations, +n_iterations, +history,"{'params': [{'CRRA': 10.93181738096547, 'DiscFac': 0.7904012123192823}, {'CRRA': 9.98513569945267, 'DiscFac': 0.5}, {'CRRA': 11.900624471679874, 'DiscFac': 0.6950450323019065}, {'CRRA': 9.963010290251065, 'DiscFac': 0.7678374491842943}, {'CRRA': 11.801333799766656, 'DiscFac': 1.1}, {'CRRA': 11.567418784043562, 'DiscFac': 0.5}, {'CRRA': 11.900624471679874, 'DiscFac': 0.5149587583222188}, {'CRRA': 9.963010290251065, 'DiscFac': 0.9023157095700898}, {'CRRA': 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47, 48, 49, 50, 51]}" +convergence_report,"{'one_step': {'relative_criterion_change': 0.05485542570812735, 'relative_params_change': 0.015167018205836458, 'absolute_criterion_change': 0.07203176043482062, 'absolute_params_change': 0.15713861513387023}, 'five_steps': {'relative_criterion_change': 0.05485542570812735, 'relative_params_change': 0.015167018205836458, 'absolute_criterion_change': 0.07203176043482062, 'absolute_params_change': 0.15713861513387023}}" +multistart_info,"{'start_parameters': [{'CRRA': 10.865980160003074, 'DiscFac': 0.8065707082845226}, {'CRRA': 10.93181738096547, 'DiscFac': 0.7904012123192823}], 'local_optima': [Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute params change smaller than tolerance., Minimize with 2 free parameters terminated. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps 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Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 10.865980160003074, 'DiscFac': 0.8065707082845225}, {'CRRA': 10.549999999999999, 'DiscFac': 0.8}, {'CRRA': 11.73125, 'DiscFac': 0.7625000000000001}, {'CRRA': 9.368749999999999, 'DiscFac': 0.8375}, {'CRRA': 7.596874999999999, 'DiscFac': 0.9312500000000001}, {'CRRA': 12.9125, 'DiscFac': 0.575}, {'CRRA': 15.274999999999999, 'DiscFac': 0.65}, {'CRRA': 17.046875, 'DiscFac': 0.6312500000000001}, {'CRRA': 16.45625, 'DiscFac': 0.9125000000000001}, {'CRRA': 18.81875, 'DiscFac': 0.5375}, {'CRRA': 8.1875, 'DiscFac': 0.7250000000000001}, {'CRRA': 14.093749999999998, 'DiscFac': 0.9875}, {'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 17.6375, 'DiscFac': 1.0250000000000001}, {'CRRA': 7.00625, 'DiscFac': 0.6125}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 2.871875, 'DiscFac': 0.78125}, {'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 2.28125, 'DiscFac': 1.0625}], 'exploration_results': array([ 1.66924823, 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model=ScalarModel(intercept=1.4837537011748891, linear_terms=array([-0.05367988, -1.03198654]), square_terms=array([[0.0114258 , 0.04908533], + [0.04908533, 3.6679078 ]]), scale=0.13664771726206837, shift=array([10.93181738, 0.79040121])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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0.03971665, -0.15809735], + [-0.15809735, 1.68670716]]), scale=0.06832385863103418, shift=array([10.93181738, 0.79040121])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=17, candidate_x=array([10.99584193, 0.76334533]), index=0, x=array([10.93181738, 0.79040121]), fval=1.3458889917053984, rho=-2.549330157587601, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 15, 16]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93181738, 0.79040121]), radius=0.03416192931551709, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 16, 17]), model=ScalarModel(intercept=1.3458889917053982, linear_terms=array([ 0.0608538 , -0.02245367]), square_terms=array([[ 0.02809653, -0.11087423], + [-0.11087423, 0.66491919]]), scale=0.03416192931551709, shift=array([10.93181738, 0.79040121])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=18, candidate_x=array([10.89788477, 0.78619352]), index=0, x=array([10.93181738, 0.79040121]), fval=1.3458889917053984, rho=-9.632103264169317, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93181738, 0.79040121]), radius=0.017080964657758546, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=1.3458889917053993, linear_terms=array([-0.13137314, -0.39410754]), square_terms=array([[0.02757962, 0.13607199], + [0.13607199, 0.82630592]]), scale=0.017080964657758546, shift=array([10.93181738, 0.79040121])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=19, candidate_x=array([10.94989538, 0.7952195 ]), index=0, x=array([10.93181738, 0.79040121]), fval=1.3458889917053984, rho=-3.462957753472434, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93181738, 0.79040121]), radius=0.008540482328879273, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 18, 19]), model=ScalarModel(intercept=1.3458889917053996, linear_terms=array([-0.35754501, 2.15661366]), square_terms=array([[ 0.16785065, -0.92396784], + [-0.92396784, 5.1900445 ]]), scale=0.008540482328879273, shift=array([10.93181738, 0.79040121])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=20, candidate_x=array([10.92404773, 0.785491 ]), index=0, x=array([10.93181738, 0.79040121]), fval=1.3458889917053984, rho=-0.46526208667632196, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93181738, 0.79040121]), radius=0.0042702411644396365, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 19, 20]), model=ScalarModel(intercept=1.3458889917053993, linear_terms=array([ 0.25557349, -0.55134151]), square_terms=array([[ 0.08776588, -0.21583066], + [-0.21583066, 0.53896107]]), scale=0.0042702411644396365, shift=array([10.93181738, 0.79040121])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93181738, 0.79040121]), radius=0.0021351205822198183, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 20, 21]), model=ScalarModel(intercept=1.3458889917053984, linear_terms=array([-0.17370195, 0.20138857]), square_terms=array([[ 0.0275573 , -0.025045 ], + [-0.025045 , 0.02917958]]), scale=0.0021351205822198183, shift=array([10.93181738, 0.79040121])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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State(trustregion=Region(center=array([10.93181738, 0.79040121]), radius=0.0010675602911099091, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 21, 22]), model=ScalarModel(intercept=1.345888991705398, linear_terms=array([-0.34398925, -0.28030349]), square_terms=array([[0.28335504, 0.35903218], + [0.35903218, 0.48284435]]), scale=0.0010675602911099091, shift=array([10.93181738, 0.79040121])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 20, 21, 22, 23]), model=ScalarModel(intercept=1.5210962294884438, linear_terms=array([-0.04387406, 0.04604552]), square_terms=array([[ 0.02819276, -0.03391789], + [-0.03391789, 0.04355014]]), scale=0.0021351205822198183, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=24, candidate_x=array([10.93505837, 0.78978275]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-12.434023643552575, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 20, 21, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=0.0010675602911099091, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 22, 23, 24]), model=ScalarModel(intercept=1.369833584189443, linear_terms=array([ 0.0744195, -0.2394611]), square_terms=array([[ 0.00535681, -0.01555128], + [-0.01555128, 0.19848642]]), scale=0.0010675602911099091, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=25, candidate_x=array([10.9320527 , 0.79093779]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-2.505424534030739, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 22, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=0.0005337801455549546, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 22, 23, 24, 25]), model=ScalarModel(intercept=1.5161584482121029, linear_terms=array([ 0.01624856, -0.07192904]), square_terms=array([[0.00064543, 0.00043128], + [0.00043128, 0.03011791]]), scale=0.0005337801455549546, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=26, candidate_x=array([10.93265976, 0.79074895]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-6.7136422267632225, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 22, 23, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=0.0002668900727774773, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 23, 25, 26]), model=ScalarModel(intercept=1.3394981413224147, linear_terms=array([0.04628427, 0.16322606]), square_terms=array([[ 0.01007514, -0.00071297], + [-0.00071297, 0.02561863]]), scale=0.0002668900727774773, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=27, candidate_x=array([10.93279457, 0.79000953]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-2.6718277158994708, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 23, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=0.00013344503638873864, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 26, 27]), model=ScalarModel(intercept=1.3131200698010168, linear_terms=array([-0.3180517 , -0.05793159]), square_terms=array([[0.19286012, 0.04111232], + [0.04111232, 0.01117805]]), scale=0.00013344503638873864, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=28, candidate_x=array([10.93300996, 0.79023418]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-0.8909721967707089, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=6.672251819436932e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 27, 28]), model=ScalarModel(intercept=1.3131200698010157, linear_terms=array([ 0.03703733, -0.09483085]), square_terms=array([[ 0.02890229, -0.02508008], + [-0.02508008, 0.02873033]]), scale=6.672251819436932e-05, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=29, candidate_x=array([10.93287414, 0.79032903]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-5.007489862080094, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 27, 28]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=3.336125909718466e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 28, 29]), model=ScalarModel(intercept=1.3131200698010168, linear_terms=array([0.06225276, 0.15377724]), square_terms=array([[0.02179103, 0.03302971], + [0.03302971, 0.06051494]]), scale=3.336125909718466e-05, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=30, candidate_x=array([10.93287979, 0.79022916]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-6.194465468462012, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1.668062954859233e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 29, 30]), model=ScalarModel(intercept=1.3131200698010133, linear_terms=array([-4.48468239, -0.29100783]), square_terms=array([[28.82982817, 1.99741271], + [ 1.99741271, 0.14792491]]), scale=1.668062954859233e-05, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=31, candidate_x=array([10.93288329, 0.79024616]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-0.9717036530476211, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 29, 30]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=8.340314774296165e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 30, 31]), model=ScalarModel(intercept=1.3131200698010161, linear_terms=array([-0.06790314, -0.13054157]), square_terms=array([[1.40499078, 0.08394391], + [0.08394391, 0.03153909]]), scale=8.340314774296165e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=32, candidate_x=array([10.93287947, 0.79027087]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-7.821781123666908, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=4.1701573871480825e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 31, 32]), model=ScalarModel(intercept=1.3131200698010155, linear_terms=array([1.56386222, 0.29859846]), square_terms=array([[5.65675375, 1.02132498], + [1.02132498, 0.1883508 ]]), scale=4.1701573871480825e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=33, candidate_x=array([10.93287918, 0.79025824]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-1.15419640927803, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 31, 32]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=2.0850786935740413e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 32, 33]), model=ScalarModel(intercept=1.313120069801016, linear_terms=array([-1.50409802, 0.12482309]), square_terms=array([[ 2.64362560e+01, -6.41713874e-01], + [-6.41713874e-01, 2.50102322e-02]]), scale=2.0850786935740413e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=34, candidate_x=array([10.93287963, 0.79026044]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-2.3407718972837506, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 32, 33]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1.0425393467870206e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 33, 34]), model=ScalarModel(intercept=1.3131200698010164, linear_terms=array([ 0.76177466, -0.07146838]), square_terms=array([[ 1.584454 , -0.13548613], + [-0.13548613, 0.04113995]]), scale=1.0425393467870206e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=35, candidate_x=array([10.93287916, 0.79026349]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-2.475776174577993, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 33, 34]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 33, 34, 35]), model=ScalarModel(intercept=1.4857371898102218, linear_terms=array([-0.24807612, 0.03429466]), square_terms=array([[0.47720114, 0.04133078], + [0.04133078, 0.01274017]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=36, candidate_x=array([10.93288011, 0.79026158]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-2.788154753912562, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 33, 34, 35]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 33, 34, 35, 36]), model=ScalarModel(intercept=1.517236446481693, linear_terms=array([-0.15164444, 0.01637071]), square_terms=array([[ 0.19505026, -0.03220236], + [-0.03220236, 0.0128953 ]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=37, candidate_x=array([10.93288042, 0.79026317]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-8.702070657512285, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 33, 34, 35, 36]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 33, 34, 35, 36, 37]), model=ScalarModel(intercept=1.57422740541263, linear_terms=array([0.06573596, 0.03343249]), square_terms=array([[ 0.11485781, -0.02093627], + [-0.02093627, 0.01453458]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=38, candidate_x=array([10.932879 , 0.79026163]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-8.800341902078221, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 33, 34, 35, 36, 37]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 33, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=1.6019203557997321, linear_terms=array([-0.01844312, 0.04730531]), square_terms=array([[ 0.22516243, -0.0435608 ], + [-0.0435608 , 0.01787496]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 33, 34, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=1.6259036041677064, linear_terms=array([-0.05981542, 0.05301504]), square_terms=array([[ 0.25444391, -0.04684312], + [-0.04684312, 0.01813331]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=40, candidate_x=array([10.9328796, 0.7902615]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-4.528414958794048, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 33, 34, 35, 36, 37, 38, 39]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 33, 34, 35, 36, 37, 38, 39, 40]), model=ScalarModel(intercept=1.6057501860150625, linear_terms=array([-0.0532196, 0.0487717]), square_terms=array([[ 0.25999131, -0.04872255], + [-0.04872255, 0.01868625]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 34, 35, 36, 37, 38, 39, 40, 41]), model=ScalarModel(intercept=1.6018240193184852, linear_terms=array([-0.02378513, 0.05708336]), square_terms=array([[ 0.29444838, -0.00233053], + [-0.00233053, 0.01939056]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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42]), model=ScalarModel(intercept=1.6243485111120757, linear_terms=array([-0.02810649, 0.05971906]), square_terms=array([[ 0.29737503, -0.00988441], + [-0.00988441, 0.02529909]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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square_terms=array([[0.14442284, 0.02259497], + [0.02259497, 0.02476783]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=44, candidate_x=array([10.93287921, 0.79026237]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-49.21728929801738, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 35, 37, 38, 39, 40, 41, 42, 43]), old_indices_discarded=array([33, 34, 36]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 35, 38, 39, 40, 41, 42, 43, 44]), model=ScalarModel(intercept=1.5565507357227995, linear_terms=array([-0.26618018, -0.05220279]), square_terms=array([[0.33866815, 0.07958103], + [0.07958103, 0.03872501]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=45, candidate_x=array([10.93288047, 0.79026201]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-0.5717057116430284, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 35, 38, 39, 40, 41, 42, 43, 44]), old_indices_discarded=array([33, 34, 36, 37]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 35, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=1.559456244590351, linear_terms=array([-0.36358674, -0.08893948]), square_terms=array([[0.11050714, 0.01112043], + [0.01112043, 0.02096709]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=46, candidate_x=array([10.93288056, 0.79026259]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-2.01962069824313, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 35, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([33, 34, 36, 37, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 35, 39, 40, 41, 42, 43, 44, 46]), model=ScalarModel(intercept=1.6660179525819816, linear_terms=array([0.13065068, 0.03372412]), square_terms=array([[0.085621 , 0.04261103], + [0.04261103, 0.03472074]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=47, candidate_x=array([10.93287857, 0.79026268]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-7.317897504266242, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 35, 39, 40, 41, 42, 43, 44, 46]), old_indices_discarded=array([33, 34, 36, 37, 38, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 39, 40, 41, 42, 43, 44, 46, 47]), model=ScalarModel(intercept=1.6922585308061082, linear_terms=array([0.03333883, 0.07393528]), square_terms=array([[0.10317645, 0.02845643], + [0.02845643, 0.09306488]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=48, candidate_x=array([10.93287967, 0.79026153]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-4.438606139713224, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 39, 40, 41, 42, 43, 44, 46, 47]), old_indices_discarded=array([33, 34, 35, 36, 37, 38, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 39, 41, 42, 43, 44, 46, 47, 48]), model=ScalarModel(intercept=1.6901067692656, linear_terms=array([0.02528777, 0.0593735 ]), square_terms=array([[0.09961485, 0.04514218], + [0.04514218, 0.0590593 ]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=49, candidate_x=array([10.93287974, 0.79026154]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-13.302580193796775, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 39, 41, 42, 43, 44, 46, 47, 48]), old_indices_discarded=array([33, 34, 35, 36, 37, 38, 40, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 39, 42, 43, 44, 46, 47, 48, 49]), model=ScalarModel(intercept=1.6904849068902246, linear_terms=array([0.02256157, 0.0044972 ]), square_terms=array([[0.10240727, 0.06368978], + [0.06368978, 0.06617034]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=50, candidate_x=array([10.93287912, 0.79026288]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-161.65896670611443, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 39, 42, 43, 44, 46, 47, 48, 49]), old_indices_discarded=array([33, 34, 35, 36, 37, 38, 40, 41, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 39, 42, 43, 44, 46, 47, 48, 50]), model=ScalarModel(intercept=1.7393390871878376, linear_terms=array([-0.0178234, 0.0603962]), square_terms=array([[0.11769997, 0.04725408], + [0.04725408, 0.05129806]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=51, candidate_x=array([10.93287996, 0.79026161]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-11.031371910072169, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 39, 42, 43, 44, 46, 47, 48, 50]), old_indices_discarded=array([33, 34, 35, 36, 37, 38, 40, 41, 45, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 42, 43, 44, 46, 47, 48, 50, 51]), model=ScalarModel(intercept=1.7466683376581398, linear_terms=array([0.01459866, 0.06240381]), square_terms=array([[0.11643567, 0.02907633], + [0.02907633, 0.0415455 ]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=52, candidate_x=array([10.93287967, 0.79026152]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-6.278012112549348, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 42, 43, 44, 46, 47, 48, 50, 51]), old_indices_discarded=array([33, 34, 35, 36, 37, 38, 39, 40, 41, 45, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 42, 43, 44, 46, 47, 48, 50, 51]), model=ScalarModel(intercept=1.7466683376581398, linear_terms=array([0.01459866, 0.06240381]), square_terms=array([[0.11643567, 0.02907633], + [0.02907633, 0.0415455 ]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=53, candidate_x=array([10.93287967, 0.79026152]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-13.986509527807948, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 42, 43, 44, 46, 47, 48, 50, 51]), old_indices_discarded=array([33, 34, 35, 36, 37, 38, 39, 40, 41, 45, 49, 52]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 42, 43, 44, 46, 47, 48, 50, 51]), model=ScalarModel(intercept=1.7466683376581398, linear_terms=array([0.01459866, 0.06240381]), square_terms=array([[0.11643567, 0.02907633], + [0.02907633, 0.0415455 ]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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linear_terms=array([0.01459866, 0.06240381]), square_terms=array([[0.11643567, 0.02907633], + [0.02907633, 0.0415455 ]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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0.79026253]), fval=1.313120069801016, rho=-5.386626230349772, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 42, 43, 44, 46, 47, 48, 50, 51]), old_indices_discarded=array([33, 34, 35, 36, 37, 38, 39, 40, 41, 45, 49, 52, 53, 54, 55, 56, 57, + 58, 59, 60, 61]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 63 entries., 'multistart_info': {'start_parameters': [array([10.86598016, 0.80657071]), array([10.93181738, 0.79040121])], 'local_optima': [{'solution_x': array([11.08997132, 0.78642526]), 'solution_criterion': 1.3851518302358365, 'states': [State(trustregion=Region(center=array([10.86598016, 0.80657071]), radius=1.0865980160003075, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=1.60570154023694, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=1.0865980160003075, shift=array([10.86598016, 0.80657071])), vector_model=VectorModel(intercepts=array([ 9.14475173e-02, 1.43712580e-01, 1.28348065e-01, 2.03001159e-01, + 2.98228345e-01, 2.77177265e-01, 2.14543405e-01, -2.49514916e-01, + -3.73541087e-01, -4.12281730e-01, -6.11962785e-01, -7.28360979e-01, + -1.28618964e-01, -1.70198940e-04, 3.95789503e-02, 1.02427570e-01, + 4.27882970e-02]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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vector_model=VectorModel(intercepts=array([ 9.14475173e-02, 1.43712580e-01, 1.28348065e-01, 2.03001159e-01, + 2.98228345e-01, 2.77177265e-01, 2.14543405e-01, -2.49514916e-01, + -3.73541087e-01, -4.12281730e-01, -6.11962785e-01, -7.28360979e-01, + -1.28618964e-01, -1.70198940e-04, 3.95789503e-02, 1.02427570e-01, + 4.27882970e-02]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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vector_model=VectorModel(intercepts=array([ 9.14475173e-02, 1.43712580e-01, 1.28348065e-01, 2.03001159e-01, + 2.98228345e-01, 2.77177265e-01, 2.14543405e-01, -2.49514916e-01, + -3.73541087e-01, -4.12281730e-01, -6.11962785e-01, -7.28360979e-01, + -1.28618964e-01, -1.70198940e-04, 3.95789503e-02, 1.02427570e-01, + 4.27882970e-02]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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shift=array([10.61675167, 0.8 ])), vector_model=VectorModel(intercepts=array([ 9.14475173e-02, 1.43712580e-01, 1.28348065e-01, 2.03001159e-01, + 2.98228345e-01, 2.77177265e-01, 2.14543405e-01, -2.49514916e-01, + -3.73541087e-01, -4.12281730e-01, -6.11962785e-01, -7.28360979e-01, + -1.28618964e-01, -1.70198940e-04, 3.95789503e-02, 1.02427570e-01, + 4.27882970e-02]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.0865980160003075, shift=array([10.86598016, 0.80657071])), candidate_index=15, candidate_x=array([11.09823788, 0.78823881]), index=15, x=array([11.09823788, 0.78823881]), fval=1.5036097957557228, rho=0.7993173221785217, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 7, 10, 11, 12, 13, 14]), old_indices_discarded=array([2, 4, 5, 6, 8, 9]), step_length=0.48391576236244993, relative_step_length=0.8906987777204132, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([11.09823788, 0.78823881]), radius=1.0865980160003075, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 4, 5, 6, 8, 9, 11, 12, 14, 15]), model=ScalarModel(intercept=1.7439210915386285, linear_terms=array([-2.89573159, 5.42120858]), square_terms=array([[ 12.98144217, -12.32728654], + [-12.32728654, 17.37030385]]), scale=array([0.96297242, 0.3 ]), shift=array([11.09823788, 0.8 ])), vector_model=VectorModel(intercepts=array([ 9.14475173e-02, 1.43712580e-01, 1.28348065e-01, 2.03001159e-01, + 2.98228345e-01, 2.77177265e-01, 2.14543405e-01, -2.49514916e-01, + -3.73541087e-01, -4.12281730e-01, -6.11962785e-01, -7.28360979e-01, + -1.28618964e-01, -1.70198940e-04, 3.95789503e-02, 1.02427570e-01, + 4.27882970e-02]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.0865980160003075, shift=array([10.86598016, 0.80657071])), candidate_index=16, candidate_x=array([10.88176692, 0.65851171]), index=15, x=array([11.09823788, 0.78823881]), fval=1.5036097957557228, rho=-1.016878706995413, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 4, 5, 6, 8, 9, 11, 12, 14, 15]), old_indices_discarded=array([ 0, 1, 2, 3, 7, 10, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([11.09823788, 0.78823881]), radius=0.5432990080001537, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 2, 4, 5, 9, 13, 14, 15, 16]), model=ScalarModel(intercept=1.557306110651818, linear_terms=array([0.17038603, 0.24819622]), square_terms=array([[0.24274846, 0.95604815], + [0.95604815, 6.61926631]]), scale=array([0.48148621, 0.3 ]), shift=array([11.09823788, 0.8 ])), vector_model=VectorModel(intercepts=array([ 9.14475173e-02, 1.43712580e-01, 1.28348065e-01, 2.03001159e-01, + 2.98228345e-01, 2.77177265e-01, 2.14543405e-01, -2.49514916e-01, + -3.73541087e-01, -4.12281730e-01, -6.11962785e-01, -7.28360979e-01, + -1.28618964e-01, -1.70198940e-04, 3.95789503e-02, 1.02427570e-01, + 4.27882970e-02]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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scale=0.27164950400007687, shift=array([11.09823788, 0.78823881])), vector_model=VectorModel(intercepts=array([ 9.14475173e-02, 1.43712580e-01, 1.28348065e-01, 2.03001159e-01, + 2.98228345e-01, 2.77177265e-01, 2.14543405e-01, -2.49514916e-01, + -3.73541087e-01, -4.12281730e-01, -6.11962785e-01, -7.28360979e-01, + -1.28618964e-01, -1.70198940e-04, 3.95789503e-02, 1.02427570e-01, + 4.27882970e-02]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.0865980160003075, shift=array([10.86598016, 0.80657071])), candidate_index=18, candidate_x=array([10.9566258 , 0.79876377]), index=15, x=array([11.09823788, 0.78823881]), fval=1.5036097957557228, rho=-41.50556163366547, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 4, 5, 9, 13, 14, 15, 16, 17]), old_indices_discarded=array([ 1, 2, 3, 6, 7, 8, 10, 11, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([11.09823788, 0.78823881]), radius=0.13582475200003843, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 5, 9, 13, 14, 15, 16, 17, 18]), model=ScalarModel(intercept=1.5730858395002656, linear_terms=array([0.0197939 , 0.02070833]), square_terms=array([[0.05420778, 0.19048857], + [0.19048857, 1.14327233]]), scale=0.13582475200003843, shift=array([11.09823788, 0.78823881])), vector_model=VectorModel(intercepts=array([ 9.14475173e-02, 1.43712580e-01, 1.28348065e-01, 2.03001159e-01, + 2.98228345e-01, 2.77177265e-01, 2.14543405e-01, -2.49514916e-01, + -3.73541087e-01, -4.12281730e-01, -6.11962785e-01, -7.28360979e-01, + -1.28618964e-01, -1.70198940e-04, 3.95789503e-02, 1.02427570e-01, + 4.27882970e-02]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.0865980160003075, shift=array([10.86598016, 0.80657071])), candidate_index=19, candidate_x=array([10.96397038, 0.80874741]), index=15, x=array([11.09823788, 0.78823881]), fval=1.5036097957557228, rho=-91.28624833781697, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 5, 9, 13, 14, 15, 16, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([11.09823788, 0.78823881]), radius=0.06791237600001922, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 15, 16, 18, 19]), model=ScalarModel(intercept=1.6166183365461033, linear_terms=array([-0.03312745, 0.02079503]), square_terms=array([[0.03282489, 0.03631815], + [0.03631815, 0.10700277]]), scale=0.06791237600001922, shift=array([11.09823788, 0.78823881])), vector_model=VectorModel(intercepts=array([ 9.14475173e-02, 1.43712580e-01, 1.28348065e-01, 2.03001159e-01, + 2.98228345e-01, 2.77177265e-01, 2.14543405e-01, -2.49514916e-01, + -3.73541087e-01, -4.12281730e-01, -6.11962785e-01, -7.28360979e-01, + -1.28618964e-01, -1.70198940e-04, 3.95789503e-02, 1.02427570e-01, + 4.27882970e-02]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.0865980160003075, shift=array([10.86598016, 0.80657071])), candidate_index=20, candidate_x=array([11.15986206, 0.75962052]), index=15, x=array([11.09823788, 0.78823881]), fval=1.5036097957557228, rho=-5.751836137267117, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 15, 16, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([11.09823788, 0.78823881]), radius=0.03395618800000961, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([15, 18, 19, 20]), model=ScalarModel(intercept=1.5922975311727696, linear_terms=array([-0.00443071, 0.18517255]), square_terms=array([[0.12342037, 0.51867886], + [0.51867886, 2.24348617]]), scale=0.03395618800000961, shift=array([11.09823788, 0.78823881])), vector_model=VectorModel(intercepts=array([ 9.14475173e-02, 1.43712580e-01, 1.28348065e-01, 2.03001159e-01, + 2.98228345e-01, 2.77177265e-01, 2.14543405e-01, -2.49514916e-01, + -3.73541087e-01, -4.12281730e-01, -6.11962785e-01, -7.28360979e-01, + -1.28618964e-01, -1.70198940e-04, 3.95789503e-02, 1.02427570e-01, + 4.27882970e-02]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.0865980160003075, shift=array([10.86598016, 0.80657071])), candidate_index=21, candidate_x=array([11.13074646, 0.77811337]), index=15, x=array([11.09823788, 0.78823881]), fval=1.5036097957557228, rho=-12.703857404141978, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([15, 18, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([11.09823788, 0.78823881]), radius=0.016978094000004804, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([15, 20, 21]), model=ScalarModel(intercept=1.503609795755723, linear_terms=array([0.57816192, 1.23965064]), square_terms=array([[0.8933243 , 1.69700782], + [1.69700782, 3.28017824]]), scale=0.016978094000004804, shift=array([11.09823788, 0.78823881])), vector_model=VectorModel(intercepts=array([ 9.14475173e-02, 1.43712580e-01, 1.28348065e-01, 2.03001159e-01, + 2.98228345e-01, 2.77177265e-01, 2.14543405e-01, -2.49514916e-01, + -3.73541087e-01, -4.12281730e-01, -6.11962785e-01, -7.28360979e-01, + -1.28618964e-01, -1.70198940e-04, 3.95789503e-02, 1.02427570e-01, + 4.27882970e-02]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.0865980160003075, shift=array([10.86598016, 0.80657071])), candidate_index=22, candidate_x=array([11.11067398, 0.77556785]), index=15, x=array([11.09823788, 0.78823881]), fval=1.5036097957557228, rho=-0.40259012696691143, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([15, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([11.09823788, 0.78823881]), radius=0.008489047000002402, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([15, 21, 22]), model=ScalarModel(intercept=1.5036097957557222, linear_terms=array([0.11727312, 0.06822205]), square_terms=array([[0.04630344, 0.01610138], + [0.01610138, 0.02401498]]), scale=0.008489047000002402, shift=array([11.09823788, 0.78823881])), vector_model=VectorModel(intercepts=array([ 9.14475173e-02, 1.43712580e-01, 1.28348065e-01, 2.03001159e-01, + 2.98228345e-01, 2.77177265e-01, 2.14543405e-01, -2.49514916e-01, + -3.73541087e-01, -4.12281730e-01, -6.11962785e-01, -7.28360979e-01, + -1.28618964e-01, -1.70198940e-04, 3.95789503e-02, 1.02427570e-01, + 4.27882970e-02]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.0865980160003075, shift=array([10.86598016, 0.80657071])), candidate_index=23, candidate_x=array([11.08988975, 0.78669845]), index=23, x=array([11.08988975, 0.78669845]), fval=1.4683267814443777, rho=0.3457509136648504, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([15, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.008489047000001648, relative_step_length=0.9999999999999111, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([11.08988975, 0.78669845]), radius=0.016978094000004804, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([15, 20, 21, 22, 23]), model=ScalarModel(intercept=1.4796252720029304, linear_terms=array([0.21929803, 0.44001239]), square_terms=array([[0.0846051 , 0.14062643], + [0.14062643, 0.26814978]]), scale=0.016978094000004804, shift=array([11.08988975, 0.78669845])), vector_model=VectorModel(intercepts=array([ 9.14475173e-02, 1.43712580e-01, 1.28348065e-01, 2.03001159e-01, + 2.98228345e-01, 2.77177265e-01, 2.14543405e-01, -2.49514916e-01, + -3.73541087e-01, -4.12281730e-01, -6.11962785e-01, -7.28360979e-01, + -1.28618964e-01, -1.70198940e-04, 3.95789503e-02, 1.02427570e-01, + 4.27882970e-02]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.0865980160003075, shift=array([10.86598016, 0.80657071])), candidate_index=24, candidate_x=array([11.09095832, 0.76975402]), index=23, x=array([11.08988975, 0.78669845]), fval=1.4683267814443777, rho=-0.48410314085940204, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([15, 20, 21, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([11.08988975, 0.78669845]), radius=0.008489047000002402, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([15, 21, 22, 23, 24]), model=ScalarModel(intercept=1.4104653304175414, linear_terms=array([0.09962719, 0.00024473]), square_terms=array([[ 0.00885492, -0.00306677], + [-0.00306677, 0.0598029 ]]), scale=0.008489047000002402, shift=array([11.08988975, 0.78669845])), vector_model=VectorModel(intercepts=array([ 9.14475173e-02, 1.43712580e-01, 1.28348065e-01, 2.03001159e-01, + 2.98228345e-01, 2.77177265e-01, 2.14543405e-01, -2.49514916e-01, + -3.73541087e-01, -4.12281730e-01, -6.11962785e-01, -7.28360979e-01, + -1.28618964e-01, -1.70198940e-04, 3.95789503e-02, 1.02427570e-01, + 4.27882970e-02]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.0865980160003075, shift=array([10.86598016, 0.80657071])), candidate_index=25, candidate_x=array([11.08140247, 0.7865119 ]), index=23, x=array([11.08988975, 0.78669845]), fval=1.4683267814443777, rho=-0.2728962840636467, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([15, 21, 22, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([11.08988975, 0.78669845]), radius=0.004244523500001201, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([15, 23, 24, 25]), model=ScalarModel(intercept=1.4670240737640365, linear_terms=array([-0.00102832, 0.01251112]), square_terms=array([[ 0.00464744, -0.00344607], + [-0.00344607, 0.02466967]]), scale=0.004244523500001201, shift=array([11.08988975, 0.78669845])), vector_model=VectorModel(intercepts=array([ 9.14475173e-02, 1.43712580e-01, 1.28348065e-01, 2.03001159e-01, + 2.98228345e-01, 2.77177265e-01, 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x=array([11.08988975, 0.78669845]), fval=1.4683267814443777, rho=-1.1204426683076343, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([11.08988975, 0.78669845]), radius=0.0005305654375001501, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 26, 27, 28]), model=ScalarModel(intercept=1.6024888264541408, linear_terms=array([-0.79209944, 0.25560868]), square_terms=array([[ 3.92123701, -1.10832244], + [-1.10832244, 0.31522467]]), scale=0.0005305654375001501, shift=array([11.08988975, 0.78669845])), vector_model=VectorModel(intercepts=array([ 9.14475173e-02, 1.43712580e-01, 1.28348065e-01, 2.03001159e-01, + 2.98228345e-01, 2.77177265e-01, 2.14543405e-01, -2.49514916e-01, + -3.73541087e-01, -4.12281730e-01, -6.11962785e-01, -7.28360979e-01, 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rho=0.9206794038257172, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([23, 27, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=0.00028510644603139203, relative_step_length=1.0747267947747212, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute params change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 31 entries., 'history': {'params': [{'CRRA': 10.865980160003074, 'DiscFac': 0.8065707082845226}, {'CRRA': 9.924999899294773, 'DiscFac': 0.5}, {'CRRA': 11.828952578926094, 'DiscFac': 0.6950450323019065}, {'CRRA': 9.903007741080055, 'DiscFac': 0.7678374491842943}, {'CRRA': 11.73025988827056, 'DiscFac': 1.1}, {'CRRA': 11.49775363323564, 'DiscFac': 0.5}, {'CRRA': 11.828952578926094, 'DiscFac': 0.5149587583222188}, {'CRRA': 9.903007741080055, 'DiscFac': 0.9023157095700898}, {'CRRA': 11.828952578926094, 'DiscFac': 0.9249107259700196}, {'CRRA': 11.535492727039749, 'DiscFac': 1.1}, {'CRRA': 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shift=array([10.93181738, 0.79040121])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=13, candidate_x=array([10.31376659, 0.75716979]), index=0, x=array([10.93181738, 0.79040121]), fval=1.3458889917053984, rho=-6.26331478829806, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93181738, 0.79040121]), radius=0.5465908690482735, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 2, 3, 4, 5, 9, 11, 12, 13]), model=ScalarModel(intercept=1.4934384174300563, linear_terms=array([0.09110483, 0.97391029]), square_terms=array([[0.07683632, 0.49828606], + [0.49828606, 8.05555662]]), scale=array([0.48440355, 0.3 ]), shift=array([10.93181738, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=14, candidate_x=array([10.60692057, 0.77617661]), index=0, x=array([10.93181738, 0.79040121]), fval=1.3458889917053984, rho=-15.95841495890369, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 2, 3, 4, 5, 9, 11, 12, 13]), old_indices_discarded=array([ 1, 6, 7, 8, 10]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93181738, 0.79040121]), radius=0.27329543452413674, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 4, 5, 9, 11, 12, 13, 14]), model=ScalarModel(intercept=1.4377009351183347, linear_terms=array([0.05563635, 0.60920727]), square_terms=array([[0.05625026, 0.42747588], + [0.42747588, 6.26064239]]), scale=0.27329543452413674, shift=array([10.93181738, 0.79040121])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=15, candidate_x=array([10.65862237, 0.78299298]), index=0, x=array([10.93181738, 0.79040121]), fval=1.3458889917053984, rho=-22.291786361941437, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 3, 4, 5, 9, 11, 12, 13, 14]), old_indices_discarded=array([ 1, 2, 6, 7, 8, 10]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93181738, 0.79040121]), radius=0.13664771726206837, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 13, 14, 15]), model=ScalarModel(intercept=1.4837537011748891, linear_terms=array([-0.05367988, -1.03198654]), square_terms=array([[0.0114258 , 0.04908533], + [0.04908533, 3.6679078 ]]), scale=0.13664771726206837, shift=array([10.93181738, 0.79040121])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=16, candidate_x=array([11.0679988 , 0.82673473]), index=0, x=array([10.93181738, 0.79040121]), fval=1.3458889917053984, rho=-1.937765683248982, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 13, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93181738, 0.79040121]), radius=0.06832385863103418, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 14, 15, 16]), model=ScalarModel(intercept=1.3439913923068907, linear_terms=array([-0.10803621, 0.81954671]), square_terms=array([[ 0.03971665, -0.15809735], + [-0.15809735, 1.68670716]]), scale=0.06832385863103418, shift=array([10.93181738, 0.79040121])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=17, candidate_x=array([10.99584193, 0.76334533]), index=0, x=array([10.93181738, 0.79040121]), fval=1.3458889917053984, rho=-2.549330157587601, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 15, 16]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93181738, 0.79040121]), radius=0.03416192931551709, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 16, 17]), model=ScalarModel(intercept=1.3458889917053982, linear_terms=array([ 0.0608538 , -0.02245367]), square_terms=array([[ 0.02809653, -0.11087423], + [-0.11087423, 0.66491919]]), scale=0.03416192931551709, shift=array([10.93181738, 0.79040121])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=18, candidate_x=array([10.89788477, 0.78619352]), index=0, x=array([10.93181738, 0.79040121]), fval=1.3458889917053984, rho=-9.632103264169317, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93181738, 0.79040121]), radius=0.017080964657758546, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=1.3458889917053993, linear_terms=array([-0.13137314, -0.39410754]), square_terms=array([[0.02757962, 0.13607199], + [0.13607199, 0.82630592]]), scale=0.017080964657758546, shift=array([10.93181738, 0.79040121])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=19, candidate_x=array([10.94989538, 0.7952195 ]), index=0, x=array([10.93181738, 0.79040121]), fval=1.3458889917053984, rho=-3.462957753472434, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93181738, 0.79040121]), radius=0.008540482328879273, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 18, 19]), model=ScalarModel(intercept=1.3458889917053996, linear_terms=array([-0.35754501, 2.15661366]), square_terms=array([[ 0.16785065, -0.92396784], + [-0.92396784, 5.1900445 ]]), scale=0.008540482328879273, shift=array([10.93181738, 0.79040121])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=20, candidate_x=array([10.92404773, 0.785491 ]), index=0, x=array([10.93181738, 0.79040121]), fval=1.3458889917053984, rho=-0.46526208667632196, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93181738, 0.79040121]), radius=0.0042702411644396365, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 19, 20]), model=ScalarModel(intercept=1.3458889917053993, linear_terms=array([ 0.25557349, -0.55134151]), square_terms=array([[ 0.08776588, -0.21583066], + [-0.21583066, 0.53896107]]), scale=0.0042702411644396365, shift=array([10.93181738, 0.79040121])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=21, candidate_x=array([10.92798406, 0.79298835]), index=0, x=array([10.93181738, 0.79040121]), fval=1.3458889917053984, rho=-2.1687265198246086, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93181738, 0.79040121]), radius=0.0021351205822198183, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 20, 21]), model=ScalarModel(intercept=1.3458889917053984, linear_terms=array([-0.17370195, 0.20138857]), square_terms=array([[ 0.0275573 , -0.025045 ], + [-0.025045 , 0.02917958]]), scale=0.0021351205822198183, shift=array([10.93181738, 0.79040121])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=22, candidate_x=array([10.93258248, 0.78840788]), index=0, x=array([10.93181738, 0.79040121]), fval=1.3458889917053984, rho=-3.1260969646257797, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93181738, 0.79040121]), radius=0.0010675602911099091, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 21, 22]), model=ScalarModel(intercept=1.345888991705398, linear_terms=array([-0.34398925, -0.28030349]), square_terms=array([[0.28335504, 0.35903218], + [0.35903218, 0.48284435]]), scale=0.0010675602911099091, shift=array([10.93181738, 0.79040121])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=23, candidate_x=array([10.93287956, 0.79026253]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=0.15760282629103436, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0010711973770310625, relative_step_length=1.003406913830948, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=0.0021351205822198183, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 20, 21, 22, 23]), model=ScalarModel(intercept=1.5210962294884438, linear_terms=array([-0.04387406, 0.04604552]), square_terms=array([[ 0.02819276, -0.03391789], + [-0.03391789, 0.04355014]]), scale=0.0021351205822198183, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=24, candidate_x=array([10.93505837, 0.78978275]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-12.434023643552575, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 20, 21, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=0.0010675602911099091, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 22, 23, 24]), model=ScalarModel(intercept=1.369833584189443, linear_terms=array([ 0.0744195, -0.2394611]), square_terms=array([[ 0.00535681, -0.01555128], + [-0.01555128, 0.19848642]]), scale=0.0010675602911099091, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=25, candidate_x=array([10.9320527 , 0.79093779]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-2.505424534030739, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 22, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=0.0005337801455549546, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 22, 23, 24, 25]), model=ScalarModel(intercept=1.5161584482121029, linear_terms=array([ 0.01624856, -0.07192904]), square_terms=array([[0.00064543, 0.00043128], + [0.00043128, 0.03011791]]), scale=0.0005337801455549546, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=26, candidate_x=array([10.93265976, 0.79074895]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-6.7136422267632225, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 22, 23, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=0.0002668900727774773, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 23, 25, 26]), model=ScalarModel(intercept=1.3394981413224147, linear_terms=array([0.04628427, 0.16322606]), square_terms=array([[ 0.01007514, -0.00071297], + [-0.00071297, 0.02561863]]), scale=0.0002668900727774773, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=27, candidate_x=array([10.93279457, 0.79000953]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-2.6718277158994708, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 23, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=0.00013344503638873864, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 26, 27]), model=ScalarModel(intercept=1.3131200698010168, linear_terms=array([-0.3180517 , -0.05793159]), square_terms=array([[0.19286012, 0.04111232], + [0.04111232, 0.01117805]]), scale=0.00013344503638873864, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=28, candidate_x=array([10.93300996, 0.79023418]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-0.8909721967707089, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=6.672251819436932e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 27, 28]), model=ScalarModel(intercept=1.3131200698010157, linear_terms=array([ 0.03703733, -0.09483085]), square_terms=array([[ 0.02890229, -0.02508008], + [-0.02508008, 0.02873033]]), scale=6.672251819436932e-05, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=29, candidate_x=array([10.93287414, 0.79032903]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-5.007489862080094, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 27, 28]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=3.336125909718466e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 28, 29]), model=ScalarModel(intercept=1.3131200698010168, linear_terms=array([0.06225276, 0.15377724]), square_terms=array([[0.02179103, 0.03302971], + [0.03302971, 0.06051494]]), scale=3.336125909718466e-05, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=30, candidate_x=array([10.93287979, 0.79022916]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-6.194465468462012, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1.668062954859233e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 29, 30]), model=ScalarModel(intercept=1.3131200698010133, linear_terms=array([-4.48468239, -0.29100783]), square_terms=array([[28.82982817, 1.99741271], + [ 1.99741271, 0.14792491]]), scale=1.668062954859233e-05, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=31, candidate_x=array([10.93288329, 0.79024616]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-0.9717036530476211, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 29, 30]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=8.340314774296165e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 30, 31]), model=ScalarModel(intercept=1.3131200698010161, linear_terms=array([-0.06790314, -0.13054157]), square_terms=array([[1.40499078, 0.08394391], + [0.08394391, 0.03153909]]), scale=8.340314774296165e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=32, candidate_x=array([10.93287947, 0.79027087]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-7.821781123666908, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=4.1701573871480825e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 31, 32]), model=ScalarModel(intercept=1.3131200698010155, linear_terms=array([1.56386222, 0.29859846]), square_terms=array([[5.65675375, 1.02132498], + [1.02132498, 0.1883508 ]]), scale=4.1701573871480825e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=33, candidate_x=array([10.93287918, 0.79025824]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-1.15419640927803, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 31, 32]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=2.0850786935740413e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 32, 33]), model=ScalarModel(intercept=1.313120069801016, linear_terms=array([-1.50409802, 0.12482309]), square_terms=array([[ 2.64362560e+01, -6.41713874e-01], + [-6.41713874e-01, 2.50102322e-02]]), scale=2.0850786935740413e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=34, candidate_x=array([10.93287963, 0.79026044]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-2.3407718972837506, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 32, 33]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1.0425393467870206e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 33, 34]), model=ScalarModel(intercept=1.3131200698010164, linear_terms=array([ 0.76177466, -0.07146838]), square_terms=array([[ 1.584454 , -0.13548613], + [-0.13548613, 0.04113995]]), scale=1.0425393467870206e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=35, candidate_x=array([10.93287916, 0.79026349]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-2.475776174577993, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 33, 34]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 33, 34, 35]), model=ScalarModel(intercept=1.4857371898102218, linear_terms=array([-0.24807612, 0.03429466]), square_terms=array([[0.47720114, 0.04133078], + [0.04133078, 0.01274017]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=36, candidate_x=array([10.93288011, 0.79026158]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-2.788154753912562, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 33, 34, 35]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 33, 34, 35, 36]), model=ScalarModel(intercept=1.517236446481693, linear_terms=array([-0.15164444, 0.01637071]), square_terms=array([[ 0.19505026, -0.03220236], + [-0.03220236, 0.0128953 ]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=37, candidate_x=array([10.93288042, 0.79026317]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-8.702070657512285, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 33, 34, 35, 36]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 33, 34, 35, 36, 37]), model=ScalarModel(intercept=1.57422740541263, linear_terms=array([0.06573596, 0.03343249]), square_terms=array([[ 0.11485781, -0.02093627], + [-0.02093627, 0.01453458]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=38, candidate_x=array([10.932879 , 0.79026163]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-8.800341902078221, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 33, 34, 35, 36, 37]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 33, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=1.6019203557997321, linear_terms=array([-0.01844312, 0.04730531]), square_terms=array([[ 0.22516243, -0.0435608 ], + [-0.0435608 , 0.01787496]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=39, candidate_x=array([10.93287947, 0.79026153]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-13.219230410118247, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 33, 34, 35, 36, 37, 38]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 33, 34, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=1.6259036041677064, linear_terms=array([-0.05981542, 0.05301504]), square_terms=array([[ 0.25444391, -0.04684312], + [-0.04684312, 0.01813331]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=40, candidate_x=array([10.9328796, 0.7902615]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-4.528414958794048, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 33, 34, 35, 36, 37, 38, 39]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 33, 34, 35, 36, 37, 38, 39, 40]), model=ScalarModel(intercept=1.6057501860150625, linear_terms=array([-0.0532196, 0.0487717]), square_terms=array([[ 0.25999131, -0.04872255], + [-0.04872255, 0.01868625]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=41, candidate_x=array([10.93287958, 0.79026151]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-5.702154494524176, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 33, 34, 35, 36, 37, 38, 39, 40]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 34, 35, 36, 37, 38, 39, 40, 41]), model=ScalarModel(intercept=1.6018240193184852, linear_terms=array([-0.02378513, 0.05708336]), square_terms=array([[ 0.29444838, -0.00233053], + [-0.00233053, 0.01939056]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=42, candidate_x=array([10.93287963, 0.79026153]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-11.487815465989993, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 34, 35, 36, 37, 38, 39, 40, 41]), old_indices_discarded=array([33]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 35, 36, 37, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=1.6243485111120757, linear_terms=array([-0.02810649, 0.05971906]), square_terms=array([[ 0.29737503, -0.00988441], + [-0.00988441, 0.02529909]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=43, candidate_x=array([10.93287962, 0.79026152]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-11.410424660145699, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 35, 36, 37, 38, 39, 40, 41, 42]), old_indices_discarded=array([33, 34]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 35, 37, 38, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=1.6443204278158488, linear_terms=array([0.05385465, 0.0117093 ]), square_terms=array([[0.14442284, 0.02259497], + [0.02259497, 0.02476783]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=44, candidate_x=array([10.93287921, 0.79026237]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-49.21728929801738, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 35, 37, 38, 39, 40, 41, 42, 43]), old_indices_discarded=array([33, 34, 36]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 35, 38, 39, 40, 41, 42, 43, 44]), model=ScalarModel(intercept=1.5565507357227995, linear_terms=array([-0.26618018, -0.05220279]), square_terms=array([[0.33866815, 0.07958103], + [0.07958103, 0.03872501]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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model=ScalarModel(intercept=1.559456244590351, linear_terms=array([-0.36358674, -0.08893948]), square_terms=array([[0.11050714, 0.01112043], + [0.01112043, 0.02096709]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=46, candidate_x=array([10.93288056, 0.79026259]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-2.01962069824313, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 35, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([33, 34, 36, 37, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 35, 39, 40, 41, 42, 43, 44, 46]), model=ScalarModel(intercept=1.6660179525819816, linear_terms=array([0.13065068, 0.03372412]), square_terms=array([[0.085621 , 0.04261103], + [0.04261103, 0.03472074]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=47, candidate_x=array([10.93287857, 0.79026268]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-7.317897504266242, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 35, 39, 40, 41, 42, 43, 44, 46]), old_indices_discarded=array([33, 34, 36, 37, 38, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 39, 40, 41, 42, 43, 44, 46, 47]), model=ScalarModel(intercept=1.6922585308061082, linear_terms=array([0.03333883, 0.07393528]), square_terms=array([[0.10317645, 0.02845643], + [0.02845643, 0.09306488]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=48, candidate_x=array([10.93287967, 0.79026153]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-4.438606139713224, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 39, 40, 41, 42, 43, 44, 46, 47]), old_indices_discarded=array([33, 34, 35, 36, 37, 38, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 39, 41, 42, 43, 44, 46, 47, 48]), model=ScalarModel(intercept=1.6901067692656, linear_terms=array([0.02528777, 0.0593735 ]), square_terms=array([[0.09961485, 0.04514218], + [0.04514218, 0.0590593 ]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=49, candidate_x=array([10.93287974, 0.79026154]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-13.302580193796775, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 39, 41, 42, 43, 44, 46, 47, 48]), old_indices_discarded=array([33, 34, 35, 36, 37, 38, 40, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 39, 42, 43, 44, 46, 47, 48, 49]), model=ScalarModel(intercept=1.6904849068902246, linear_terms=array([0.02256157, 0.0044972 ]), square_terms=array([[0.10240727, 0.06368978], + [0.06368978, 0.06617034]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=50, candidate_x=array([10.93287912, 0.79026288]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-161.65896670611443, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 39, 42, 43, 44, 46, 47, 48, 49]), old_indices_discarded=array([33, 34, 35, 36, 37, 38, 40, 41, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 39, 42, 43, 44, 46, 47, 48, 50]), model=ScalarModel(intercept=1.7393390871878376, linear_terms=array([-0.0178234, 0.0603962]), square_terms=array([[0.11769997, 0.04725408], + [0.04725408, 0.05129806]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.093181738096547, shift=array([10.93181738, 0.79040121])), candidate_index=51, candidate_x=array([10.93287996, 0.79026161]), index=23, x=array([10.93287956, 0.79026253]), fval=1.313120069801016, rho=-11.031371910072169, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 39, 42, 43, 44, 46, 47, 48, 50]), old_indices_discarded=array([33, 34, 35, 36, 37, 38, 40, 41, 45, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 42, 43, 44, 46, 47, 48, 50, 51]), model=ScalarModel(intercept=1.7466683376581398, linear_terms=array([0.01459866, 0.06240381]), square_terms=array([[0.11643567, 0.02907633], + [0.02907633, 0.0415455 ]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + 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44, 46, 47, 48, 50, 51]), old_indices_discarded=array([33, 34, 35, 36, 37, 38, 39, 40, 41, 45, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 42, 43, 44, 46, 47, 48, 50, 51]), model=ScalarModel(intercept=1.7466683376581398, linear_terms=array([0.01459866, 0.06240381]), square_terms=array([[0.11643567, 0.02907633], + [0.02907633, 0.0415455 ]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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40, 41, 45, 49, 52]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 42, 43, 44, 46, 47, 48, 50, 51]), model=ScalarModel(intercept=1.7466683376581398, linear_terms=array([0.01459866, 0.06240381]), square_terms=array([[0.11643567, 0.02907633], + [0.02907633, 0.0415455 ]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 42, 43, 44, 46, 47, 48, 50, 51]), model=ScalarModel(intercept=1.7466683376581398, linear_terms=array([0.01459866, 0.06240381]), square_terms=array([[0.11643567, 0.02907633], + [0.02907633, 0.0415455 ]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 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State(trustregion=Region(center=array([10.93287956, 0.79026253]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 42, 43, 44, 46, 47, 48, 50, 51]), model=ScalarModel(intercept=1.7466683376581398, linear_terms=array([0.01459866, 0.06240381]), square_terms=array([[0.11643567, 0.02907633], + [0.02907633, 0.0415455 ]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 42, 43, 44, 46, 47, 48, 50, 51]), model=ScalarModel(intercept=1.7466683376581398, linear_terms=array([0.01459866, 0.06240381]), square_terms=array([[0.11643567, 0.02907633], + [0.02907633, 0.0415455 ]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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upper=array([20. , 1.1]))), model_indices=array([23, 42, 43, 44, 46, 47, 48, 50, 51]), model=ScalarModel(intercept=1.7466683376581398, linear_terms=array([0.01459866, 0.06240381]), square_terms=array([[0.11643567, 0.02907633], + [0.02907633, 0.0415455 ]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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44, 46, 47, 48, 50, 51]), model=ScalarModel(intercept=1.7466683376581398, linear_terms=array([0.01459866, 0.06240381]), square_terms=array([[0.11643567, 0.02907633], + [0.02907633, 0.0415455 ]]), scale=1e-06, shift=array([10.93287956, 0.79026253])), vector_model=VectorModel(intercepts=array([ 0.07662483, 0.15716087, 0.13244484, 0.17250486, 0.20958337, + 0.24388919, 0.24229599, -0.09490357, -0.3115525 , -0.38173414, + -0.53749867, -0.71114441, -0.23462165, -0.05952639, -0.00218429, + 0.01974005, 0.02225134]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51]}, 'multistart_info': {...}}], 'exploration_sample': array([[10.86598016, 0.80657071], + [10.55 , 0.8 ], + [11.73125 , 0.7625 ], + [ 9.36875 , 0.8375 ], + [ 7.596875 , 0.93125 ], + [12.9125 , 0.575 ], + [15.275 , 0.65 ], + [17.046875 , 0.63125 ], + [16.45625 , 0.9125 ], + [18.81875 , 0.5375 ], + [ 8.1875 , 0.725 ], + [14.09375 , 0.9875 ], + [12.321875 , 1.08125 ], + [17.6375 , 1.025 ], + [ 7.00625 , 0.6125 ], + [ 4.64375 , 0.6875 ], + [ 2.871875 , 0.78125 ], + [ 3.4625 , 0.875 ], + [ 2.28125 , 1.0625 ]]), 'exploration_results': array([ 1.66924823, 1.74531296, 2.10648864, 2.17581064, + 2.66803924, 2.70500778, 2.87740953, 3.0406658 , + 3.37839016, 3.53882151, 3.99992851, 4.37946561, + 4.47403011, 4.90296965, 6.27675034, 21.17347996, + 25.72759101, 28.93912422, 124.53209372])}}" diff --git a/content/tables/min/WarmGlowPortfolioSub(Labor)Market_estimate_results.csv b/content/tables/min/WarmGlowPortfolioSub(Labor)Market_estimate_results.csv new file mode 100644 index 0000000..edb999f --- /dev/null +++ b/content/tables/min/WarmGlowPortfolioSub(Labor)Market_estimate_results.csv @@ -0,0 +1,8207 @@ +CRRA,13.76522943668619 +DiscFac,1.0738842444835321 +time_to_estimate,166.45062899589539 +params,"{'CRRA': 13.76522943668619, 'DiscFac': 1.0738842444835321}" +criterion,0.6882579284467486 +start_criterion,28.714580565529207 +start_params,"{'CRRA': 5.0, 'DiscFac': 0.95}" +algorithm,multistart_tranquilo_ls +direction,minimize +n_free,2 +message, +success, +n_criterion_evaluations, +n_derivative_evaluations, +n_iterations, +history,"{'params': [{'CRRA': 13.597593925796534, 'DiscFac': 1.020747282479346}, {'CRRA': 12.392538539955158, 'DiscFac': 0.6174149114063475}, {'CRRA': 14.80264931163791, 'DiscFac': 0.8326677874318185}, {'CRRA': 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'DiscFac': 1.0738850599644876}, {'CRRA': 13.76523021512763, 'DiscFac': 1.073883616766271}, {'CRRA': 13.765230214507426, 'DiscFac': 1.073883615997389}, {'CRRA': 13.765230214739557, 'DiscFac': 1.0738836162850556}, {'CRRA': 13.765230214887971, 'DiscFac': 1.0738836164690735}, {'CRRA': 13.765228657845949, 'DiscFac': 1.0738848717058849}, {'CRRA': 13.765230214640988, 'DiscFac': 1.0738836161630856}, {'CRRA': 13.765228657644837, 'DiscFac': 1.0738848714563134}, {'CRRA': 13.765230214734727, 'DiscFac': 1.0738836162788903}, {'CRRA': 13.765228658030406, 'DiscFac': 1.0738848719349061}, {'CRRA': 13.765228658584434, 'DiscFac': 1.0738848726220682}, {'CRRA': 13.765228658553019, 'DiscFac': 1.0738848725830974}, {'CRRA': 13.765230216618574, 'DiscFac': 1.0738836186197498}, {'CRRA': 13.765230216717889, 'DiscFac': 1.073883618741515}, {'CRRA': 13.765230216717889, 'DiscFac': 1.073883618741515}, {'CRRA': 13.765230216717889, 'DiscFac': 1.073883618741515}, {'CRRA': 13.765230216717889, 'DiscFac': 1.073883618741515}, {'CRRA': 13.765230216717889, 'DiscFac': 1.073883618741515}, {'CRRA': 13.765230216717889, 'DiscFac': 1.073883618741515}, {'CRRA': 13.765230216717889, 'DiscFac': 1.073883618741515}, {'CRRA': 13.765230216717889, 'DiscFac': 1.073883618741515}, {'CRRA': 13.765230216717889, 'DiscFac': 1.073883618741515}, {'CRRA': 13.765230216717889, 'DiscFac': 1.073883618741515}, {'CRRA': 13.765230216717889, 'DiscFac': 1.073883618741515}, {'CRRA': 13.765230216717889, 'DiscFac': 1.073883618741515}], 'criterion': [1.5919418620501866, 3.256642791598931, 2.4245720174482632, 2.6770326909650377, 1.375271059626373, 3.932115023884988, 3.8900565091396633, 1.3788040584356367, 2.67342734680254, 1.3747875134503535, 1.7273269565534193, 3.784579404744082, 1.3717696200767946, 1.3671976679432574, 1.5932413313061198, 1.7940144542579126, 1.4714362912230357, 1.4586163616235037, 1.4116247061572644, 1.1237511246855783, 1.817413667189244, 1.7640558941311795, 1.2590902699405484, 2.1087642405633775, 1.6806322555078967, 0.6882579284467486, 1.3619794529851408, 1.7708804039433663, 1.487817536704842, 1.6073083741913023, 1.3515606737064714, 1.5988332872617663, 2.1071882633058205, 1.6046549090592486, 2.4392140191496465, 2.0969547685400998, 1.619697166814026, 1.4177027742030972, 2.7375198870821396, 1.648837292228864, 1.2017087559283488, 1.6123653333088765, 2.1559864840618888, 1.6813226057480248, 1.7555339640362284, 1.5641420916511097, 2.121351535271443, 1.195628783451865, 1.366634904655717, 1.7240618907150989, 0.9835036694297299, 1.5576561839970382, 1.389378348605993, 1.323322900002188, 1.4964292964688457, 1.432272428511995, 1.2033755926739262, 3.1657769462342085, 2.197985075310209, 1.2162154879079494, 2.342003085876411, 1.4622856861257032, 1.2041984771527132], 'runtime': [0.0, 2.0825537890000305, 2.304332205999799, 2.5202568370000336, 2.752349718000005, 2.9856617369996457, 3.2192967379996844, 3.4485792460000084, 3.701930288999847, 4.015171013999861, 4.454668695999771, 4.669011841999691, 4.894209664999835, 6.820035805999851, 8.57855164600005, 10.310429240999838, 12.091082415000074, 13.79618391699978, 15.48075700299978, 17.167798326999673, 18.990243108999948, 20.715586998999697, 22.467751674999818, 24.235677248999764, 25.957298829999672, 27.703975633000027, 29.413045380999847, 31.228969932999917, 32.923921876999884, 34.6790341789997, 36.43617003500003, 38.32504238499996, 40.16391709299978, 41.907183702999646, 43.74042012699965, 45.45150464900007, 47.17200287099968, 48.90636308400008, 50.674419703999774, 52.51235168999983, 54.256131906000064, 56.12240377699982, 57.842474824999954, 59.62035617699985, 61.38132491599981, 63.246293440000045, 65.0222065969997, 66.75320940899974, 68.49351131899994, 70.48024981899971, 72.27372107699966, 74.07178742799988, 75.84724168000002, 77.53998098800002, 79.26190303999965, 80.98321577199977, 82.81359508299965, 84.52387710899984, 86.25460904200008, 88.0250253449999, 89.76157718399963, 91.52082204299995, 93.24137594800004], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51]}" +convergence_report,"{'one_step': {'relative_criterion_change': 0.7585906706214464, 'relative_params_change': 0.04559161987110576, 'absolute_criterion_change': 0.5221060435009465, 'absolute_params_change': 0.37522077252389147}, 'five_steps': {'relative_criterion_change': 0.7585906706214464, 'relative_params_change': 0.04559161987110576, 'absolute_criterion_change': 0.5221060435009465, 'absolute_params_change': 0.37522077252389147}}" +multistart_info,"{'start_parameters': [{'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 13.597593925796534, 'DiscFac': 1.020747282479346}], 'local_optima': [Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute params change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 0.02632 0.2707 +relative_params_change 0.0005553 0.0918 +absolute_criterion_change 0.03186 0.3276 +absolute_params_change 0.0006017 1.071 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 0.6327 0.9982 +relative_params_change 0.0007548 0.07704 +absolute_criterion_change 0.4355 0.687 +absolute_params_change 0.002656 1.007 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 14.093749999999998, 'DiscFac': 0.9875}, {'CRRA': 17.6375, 'DiscFac': 1.0250000000000001}, {'CRRA': 16.45625, 'DiscFac': 0.9125000000000001}, {'CRRA': 11.73125, 'DiscFac': 0.7625000000000001}, {'CRRA': 10.549999999999999, 'DiscFac': 0.8}, {'CRRA': 12.9125, 'DiscFac': 0.575}, {'CRRA': 15.274999999999999, 'DiscFac': 0.65}, {'CRRA': 17.046875, 'DiscFac': 0.6312500000000001}, {'CRRA': 18.81875, 'DiscFac': 0.5375}, {'CRRA': 9.368749999999999, 'DiscFac': 0.8375}, {'CRRA': 8.1875, 'DiscFac': 0.7250000000000001}, {'CRRA': 7.00625, 'DiscFac': 0.6125}, {'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 2.871875, 'DiscFac': 0.78125}, {'CRRA': 5.0, 'DiscFac': 0.95}, {'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 2.28125, 'DiscFac': 1.0625}], 'exploration_results': array([ 1.25226984, 1.77139048, 1.79567843, 2.19243348, 2.99563577, + 3.4003551 , 3.46407238, 3.67340234, 3.72448066, 4.28414873, + 4.65250486, 5.39134448, 6.53886763, 14.94020775, 20.34265339, + 25.6087008 , 28.43667398, 29.29813793, 75.84976584])}" +algorithm_output,"{'states': [State(trustregion=Region(center=array([13.59759393, 1.02074728]), radius=1.3597593925796536, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=1.5919418620501866, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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State(trustregion=Region(center=array([13.59759393, 1.02074728]), radius=1.3597593925796536, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=2.034809583725369, linear_terms=array([ 0.00692503, -1.51067031]), square_terms=array([[ 0.14904974, -0.03200334], + [-0.03200334, 1.29590588]]), scale=array([1.20505539, 0.3 ]), shift=array([13.59759393, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=13, candidate_x=array([13.80035011, 1.1 ]), index=13, x=array([13.80035011, 1.1 ]), fval=1.3671976679432574, rho=2.1596481898710547, accepted=True, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.2176948880908889, relative_step_length=0.16009809476505346, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.80035011, 1.1 ]), radius=1.3597593925796536, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 2, 4, 5, 6, 8, 11, 12, 13]), model=ScalarModel(intercept=1.9544299211503704, linear_terms=array([ 0.19985396, -1.35020304]), square_terms=array([[ 0.11438723, -0.28080018], + [-0.28080018, 1.18331746]]), scale=array([1.20505539, 0.3 ]), shift=array([13.80035011, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=14, candidate_x=array([14.65310858, 1.1 ]), index=13, x=array([13.80035011, 1.1 ]), fval=1.3671976679432574, rho=-7.892356930659339, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 2, 4, 5, 6, 8, 11, 12, 13]), old_indices_discarded=array([ 1, 3, 7, 9, 10]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.80035011, 1.1 ]), radius=0.6798796962898268, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 2, 4, 6, 9, 11, 12, 13, 14]), model=ScalarModel(intercept=1.9524600277342483, linear_terms=array([ 0.03980169, -1.41316029]), square_terms=array([[ 0.0307801 , -0.03640509], + [-0.03640509, 1.39773428]]), scale=array([0.60252769, 0.3 ]), shift=array([13.80035011, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=15, candidate_x=array([13.73386081, 1.1 ]), index=13, x=array([13.80035011, 1.1 ]), fval=1.3671976679432574, rho=-2277.466238878963, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 2, 4, 6, 9, 11, 12, 13, 14]), old_indices_discarded=array([ 1, 3, 5, 7, 8, 10]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.80035011, 1.1 ]), radius=0.3399398481449134, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 4, 6, 9, 11, 12, 13, 14, 15]), model=ScalarModel(intercept=1.4423114067995801, linear_terms=array([ 0.00970986, -0.30971613]), square_terms=array([[0.01737096, 0.00617437], + [0.00617437, 0.34251207]]), scale=array([0.30126385, 0.15063192]), shift=array([13.80035011, 0.94936808])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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-0.07753849]), square_terms=array([[0.53597227, 0.0007038 ], + [0.0007038 , 0.05909585]]), scale=array([0.15063192, 0.07531596]), shift=array([13.80035011, 1.02468404])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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scale=array([0.07531596, 0.03765798]), shift=array([13.80035011, 1.06234202])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=19, candidate_x=array([13.76269213, 1.07467026]), index=19, x=array([13.76269213, 1.07467026]), fval=1.123751124685578, rho=1.7655548058481516, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([12, 13, 15, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.0453841295972527, relative_step_length=1.0680508294610014, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76269213, 1.07467026]), radius=0.08498496203622835, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 13, 15, 16, 17, 18, 19]), model=ScalarModel(intercept=1.15503149521224, linear_terms=array([-0.08309304, -0.2101502 ]), square_terms=array([[0.02472817, 0.0671273 ], + [0.0671273 , 0.60172359]]), scale=array([0.07531596, 0.05032285]), shift=array([13.76269213, 1.04967715])), vector_model=VectorModel(intercepts=array([ 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1.02074728])), candidate_index=20, candidate_x=array([13.83800809, 1.06163832]), index=19, x=array([13.76269213, 1.07467026]), fval=1.123751124685578, rho=-12.049711039797472, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 13, 15, 16, 17, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76269213, 1.07467026]), radius=0.042492481018114175, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 13, 15, 17, 18, 19, 20]), model=ScalarModel(intercept=1.3037825390286737, linear_terms=array([ 0.05958201, -0.15298978]), square_terms=array([[ 0.00838945, -0.02709215], + [-0.02709215, 0.33469544]]), scale=array([0.03765798, 0.03149386]), shift=array([13.76269213, 1.06850614])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + 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1.08035274]), index=19, x=array([13.76269213, 1.07467026]), fval=1.123751124685578, rho=-11.530216603469125, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 13, 15, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76269213, 1.07467026]), radius=0.021246240509057088, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([12, 13, 15, 18, 19, 20, 21]), model=ScalarModel(intercept=1.339765491859563, linear_terms=array([ 0.01289748, -0.05831618]), square_terms=array([[ 0.00337072, -0.00852184], + [-0.00852184, 0.15088885]]), scale=0.021246240509057088, shift=array([13.76269213, 1.07467026])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=22, candidate_x=array([13.74137005, 1.08137517]), index=19, x=array([13.76269213, 1.07467026]), fval=1.123751124685578, rho=-6.962948052081567, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([12, 13, 15, 18, 19, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76269213, 1.07467026]), radius=0.010623120254528544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 15, 18, 19, 21, 22]), model=ScalarModel(intercept=1.0959366501510284, linear_terms=array([-0.07603389, 0.0592279 ]), square_terms=array([[ 0.01482588, -0.01887136], + [-0.01887136, 0.05507578]]), scale=0.010623120254528544, shift=array([13.76269213, 1.07467026])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=23, candidate_x=array([13.77278794, 1.07078824]), index=19, x=array([13.76269213, 1.07467026]), fval=1.123751124685578, rho=-12.796215190602183, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([13, 15, 18, 19, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76269213, 1.07467026]), radius=0.005311560127264272, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 22, 23]), model=ScalarModel(intercept=1.123751124685578, linear_terms=array([2.35075615, 6.72344032]), square_terms=array([[ 34.95552465, 104.4822096 ], + [104.4822096 , 312.38317194]]), scale=0.005311560127264272, shift=array([13.76269213, 1.07467026])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76269213, 1.07467026]), radius=0.002655780063632136, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 23, 24]), model=ScalarModel(intercept=1.1237511246855785, linear_terms=array([-0.5780775 , -1.19841878]), square_terms=array([[ 5.94858558, 17.60275441], + [17.60275441, 52.66397965]]), scale=0.002655780063632136, shift=array([13.76269213, 1.07467026])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=25, candidate_x=array([13.76522944, 1.07388424]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=2.835499899865532, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([19, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.002656264319473295, relative_step_length=1.0001823403405237, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=0.005311560127264272, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 22, 23, 24, 25]), model=ScalarModel(intercept=0.9444375568015315, linear_terms=array([0.44532753, 1.19395003]), square_terms=array([[ 20.99849631, 63.51413818], + [ 63.51413818, 192.14709976]]), scale=0.005311560127264272, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 23, 24, 25, 26]), model=ScalarModel(intercept=0.7549074447561732, linear_terms=array([-1.08491772, -3.21566214]), square_terms=array([[ 4.95581197, 13.40310954], + [13.40310954, 36.80550289]]), scale=0.002655780063632136, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=27, candidate_x=array([13.76281591, 1.07499468]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-5.765434623109165, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([19, 23, 24, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=0.001327890031816068, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 25, 26, 27]), model=ScalarModel(intercept=0.6663131578299749, linear_terms=array([0.31579992, 1.29406005]), square_terms=array([[ 0.97652328, 3.33987753], + [ 3.33987753, 11.68276301]]), scale=0.001327890031816068, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=28, candidate_x=array([13.76646532, 1.07338524]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-7.097581043735553, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([19, 25, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=0.000663945015908034, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 25, 27, 28]), model=ScalarModel(intercept=1.0748283427553293, linear_terms=array([0.74775752, 1.86090971]), square_terms=array([[0.94615569, 2.1520877 ], + [2.1520877 , 5.00035967]]), scale=0.000663945015908034, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=29, candidate_x=array([13.76574184, 1.07341989]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-2.41030856971559, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([19, 25, 27, 28]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=0.000331972507954017, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 28, 29]), model=ScalarModel(intercept=0.688257928446749, linear_terms=array([-0.05009521, -0.4261496 ]), square_terms=array([[0.05474491, 0.13563449], + [0.13563449, 0.64186031]]), scale=0.000331972507954017, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=30, candidate_x=array([13.76498653, 1.07414649]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-4.057932281360053, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=0.0001659862539770085, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 29, 30]), model=ScalarModel(intercept=0.6882579284467485, linear_terms=array([2.51110852, 2.58551584]), square_terms=array([[17.31961069, 17.86076585], + [17.86076585, 18.4307605 ]]), scale=0.0001659862539770085, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=31, candidate_x=array([13.76509921, 1.07398717]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-4.996089828139207, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 29, 30]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=8.299312698850425e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 30, 31]), model=ScalarModel(intercept=0.6882579284467492, linear_terms=array([-0.83335016, -0.64209979]), square_terms=array([[2.48376422, 2.0304305 ], + [2.0304305 , 1.6811628 ]]), scale=8.299312698850425e-05, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=32, candidate_x=array([13.76529773, 1.07383408]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-8.895409172081964, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=4.1496563494252124e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 31, 32]), model=ScalarModel(intercept=0.6882579284467484, linear_terms=array([5.4261937 , 7.07144682]), square_terms=array([[196.90079522, 251.86367885], + [251.86367885, 322.26054074]]), scale=4.1496563494252124e-05, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 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old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=2.0748281747126062e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 32, 33]), model=ScalarModel(intercept=0.6882579284467489, linear_terms=array([-1.67471137, -2.43734694]), square_terms=array([[44.36150417, 58.4429871 ], + [58.4429871 , 77.26404403]]), scale=2.0748281747126062e-05, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1.0374140873563031e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 33, 34]), model=ScalarModel(intercept=0.6882579284467507, linear_terms=array([-32.53614671, -40.36884666]), square_terms=array([[5704.80115467, 7072.67560803], + [7072.67560803, 8768.54976842]]), scale=1.0374140873563031e-05, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], 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radius=5.1870704367815156e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 34, 35]), model=ScalarModel(intercept=0.6882579284467505, linear_terms=array([ 9.69356396, 12.41688657]), square_terms=array([[531.75055055, 673.71090102], + [673.71090102, 853.87114041]]), scale=5.1870704367815156e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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model_indices=array([25, 35, 36]), model=ScalarModel(intercept=0.688257928446749, linear_terms=array([ 96.92131584, 119.52724588]), square_terms=array([[23233.92339938, 28666.34702734], + [28666.34702734, 35368.97768226]]), scale=2.5935352183907578e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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linear_terms=array([-228.72939942, -283.40527822]), square_terms=array([[149814.22087996, 185594.86902726], + [185594.86902726, 229921.14598203]]), scale=1.2967676091953789e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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[253921.58304407, 315109.6517833 ]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=40, candidate_x=array([13.76523021, 1.07388362]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-2.1629857204623297, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 37, 38, 39]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 37, 38, 39, 40]), model=ScalarModel(intercept=1.1405666279545543, linear_terms=array([205.50981035, 254.96369968]), square_terms=array([[196975.02816638, 244290.03299938], + [244290.03299938, 302970.52114327]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=41, candidate_x=array([13.76523021, 1.07388362]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-5.914600339115848, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 37, 38, 39, 40]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 37, 38, 39, 40, 41]), model=ScalarModel(intercept=1.1856833521849348, linear_terms=array([158.29089716, 196.3772247 ]), square_terms=array([[214418.42009996, 265947.86330119], + [265947.86330119, 329860.98150686]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=42, candidate_x=array([13.76523021, 1.07388362]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-17.707333949313302, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 37, 38, 39, 40, 41]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 37, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=1.3049678277178531, linear_terms=array([220.22289196, 273.08372696]), square_terms=array([[208999.40253 , 259169.5243306 ], + [259169.5243306 , 321382.95662051]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + 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old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 37, 38, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=1.3567974599896127, linear_terms=array([202.98250055, 251.79397558]), square_terms=array([[144095.09085198, 178734.35986993], + [178734.35986993, 221700.63899107]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 37, 38, 39, 40, 41, 42, 43, 44]), model=ScalarModel(intercept=1.4152002732237223, linear_terms=array([188.23186675, 233.45474242]), square_terms=array([[136071.15256904, 168774.1945395 ], + [168774.1945395 , 209337.0286982 ]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=45, candidate_x=array([13.76522866, 1.07388487]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-6.369117061581065, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 37, 38, 39, 40, 41, 42, 43, 44]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 38, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=1.4322930669889091, linear_terms=array([165.85163954, 205.76495158]), square_terms=array([[132937.5754333 , 164910.82906923], + [164910.82906923, 204574.07883395]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=46, candidate_x=array([13.76523021, 1.07388362]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-12.178849532771347, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 38, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([37]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 39, 40, 41, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=1.3093308313412153, linear_terms=array([123.65989376, 153.3047285 ]), square_terms=array([[323967.17174465, 401840.36181195], + [401840.36181195, 498432.23105826]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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model_indices=array([25, 39, 41, 42, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=1.0590382139587964, linear_terms=array([-280.9809403 , -348.70796313]), square_terms=array([[266181.47955628, 330177.67116074], + [330177.67116074, 409560.10325178]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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model=ScalarModel(intercept=1.220859614346961, linear_terms=array([-138.3649051 , -171.73746763]), square_terms=array([[158556.44632179, 196651.07147605], + [196651.07147605, 243898.29439121]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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-295.51259223]), square_terms=array([[ 74869.56239741, 92919.65993007], + [ 92919.65993007, 115321.54114857]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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86896.16232258, 107945.82494064]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=51, candidate_x=array([13.76523022, 1.07388362]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-2.6081564997863604, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 39, 42, 43, 44, 45, 47, 49, 50]), old_indices_discarded=array([37, 38, 40, 41, 46, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 39, 42, 43, 44, 45, 47, 49, 50]), model=ScalarModel(intercept=1.2399950339941943, linear_terms=array([-189.67214848, -235.48113278]), square_terms=array([[ 69951.25959547, 86896.16232258], + [ 86896.16232258, 107945.82494064]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=52, candidate_x=array([13.76523022, 1.07388362]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-2.1033304004202726, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 39, 42, 43, 44, 45, 47, 49, 50]), old_indices_discarded=array([37, 38, 40, 41, 46, 48, 51]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 39, 42, 43, 44, 45, 47, 49, 50]), model=ScalarModel(intercept=1.2399950339941943, linear_terms=array([-189.67214848, -235.48113278]), square_terms=array([[ 69951.25959547, 86896.16232258], + [ 86896.16232258, 107945.82494064]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=53, candidate_x=array([13.76523022, 1.07388362]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-1.9051669620622436, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 39, 42, 43, 44, 45, 47, 49, 50]), old_indices_discarded=array([37, 38, 40, 41, 46, 48, 51, 52]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 39, 42, 43, 44, 45, 47, 49, 50]), model=ScalarModel(intercept=1.2399950339941943, linear_terms=array([-189.67214848, -235.48113278]), square_terms=array([[ 69951.25959547, 86896.16232258], + [ 86896.16232258, 107945.82494064]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + 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57, 58, 59, 60, 61]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 63 entries., 'multistart_info': {'start_parameters': [array([12.321875, 1.08125 ]), array([13.59759393, 1.02074728])], 'local_optima': [{'solution_x': array([13.39207935, 1.03451876]), 'solution_criterion': 1.210363971947695, 'states': [State(trustregion=Region(center=array([12.321875, 1.08125 ]), radius=1.2321875, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=1.5379709637742367, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=1.2321875, shift=array([12.321875, 1.08125 ])), vector_model=VectorModel(intercepts=array([ 0.01938559, 0.01236126, -0.02541336, 0.06675187, 0.19101415, + 0.15552265, 0.10788637, 0.02151982, -0.00180687, -0.01933219, + -0.56991473, -0.81302909, -0.20768953, -0.13461208, -0.09577825, + 0.25047508, 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linear_terms=array([-0.11965999, -0.40798019]), square_terms=array([[0.13418385, 0.01559018], + [0.01559018, 0.50991394]]), scale=array([0.27299943, 0.16245797]), shift=array([13.41387274, 0.93754203])), vector_model=VectorModel(intercepts=array([ 0.01938559, 0.01236126, -0.02541336, 0.06675187, 0.19101415, + 0.15552265, 0.10788637, 0.02151982, -0.00180687, -0.01933219, + -0.56991473, -0.81302909, -0.20768953, -0.13461208, -0.09577825, + 0.25047508, 0.58326817]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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0.15141401]]), scale=array([0.13649972, 0.09420811]), shift=array([13.41387274, 1.00579189])), vector_model=VectorModel(intercepts=array([ 0.01938559, 0.01236126, -0.02541336, 0.06675187, 0.19101415, + 0.15552265, 0.10788637, 0.02151982, -0.00180687, -0.01933219, + -0.56991473, -0.81302909, -0.20768953, -0.13461208, -0.09577825, + 0.25047508, 0.58326817]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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vector_model=VectorModel(intercepts=array([ 0.01938559, 0.01236126, -0.02541336, 0.06675187, 0.19101415, + 0.15552265, 0.10788637, 0.02151982, -0.00180687, -0.01933219, + -0.56991473, -0.81302909, -0.20768953, -0.13461208, -0.09577825, + 0.25047508, 0.58326817]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.2321875, shift=array([12.321875, 1.08125 ])), candidate_index=20, candidate_x=array([13.39225885, 1.03394448]), index=20, x=array([13.39225885, 1.03394448]), fval=1.2422204164694501, rho=67.64661076310064, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 4, 8, 9, 13, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.025827737562700674, relative_step_length=0.33537412204166234, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.39225885, 1.03394448]), radius=0.07701171875, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 4, 8, 9, 13, 18, 19, 20]), model=ScalarModel(intercept=1.329063512541693, linear_terms=array([ 0.02464634, -0.02431736]), square_terms=array([[ 0.05297532, -0.08788172], + [-0.08788172, 0.22588201]]), scale=array([0.06824986, 0.06715269]), shift=array([13.39225885, 1.03284731])), vector_model=VectorModel(intercepts=array([ 0.01938559, 0.01236126, -0.02541336, 0.06675187, 0.19101415, + 0.15552265, 0.10788637, 0.02151982, -0.00180687, -0.01933219, + -0.56991473, -0.81302909, -0.20768953, -0.13461208, -0.09577825, + 0.25047508, 0.58326817]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.2321875, shift=array([12.321875, 1.08125 ])), candidate_index=21, candidate_x=array([13.33708417, 1.01895545]), index=20, x=array([13.39225885, 1.03394448]), fval=1.2422204164694501, rho=-106.24217111440103, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 4, 8, 9, 13, 18, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.39225885, 1.03394448]), radius=0.038505859375, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 4, 9, 13, 19, 20, 21]), model=ScalarModel(intercept=1.4558785589476007, linear_terms=array([ 0.03199582, -0.13313381]), square_terms=array([[0.00508526, 0.00270481], + [0.00270481, 0.2748186 ]]), scale=0.038505859375, shift=array([13.39225885, 1.03394448])), vector_model=VectorModel(intercepts=array([ 0.01938559, 0.01236126, -0.02541336, 0.06675187, 0.19101415, + 0.15552265, 0.10788637, 0.02151982, -0.00180687, -0.01933219, + -0.56991473, -0.81302909, -0.20768953, -0.13461208, -0.09577825, + 0.25047508, 0.58326817]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.2321875, shift=array([12.321875, 1.08125 ])), candidate_index=22, candidate_x=array([13.3541983 , 1.05118446]), index=20, x=array([13.39225885, 1.03394448]), fval=1.2422204164694501, rho=-6.452870384435016, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 4, 9, 13, 19, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.39225885, 1.03394448]), radius=0.0192529296875, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 9, 13, 20, 21, 22]), model=ScalarModel(intercept=1.4157454760310837, linear_terms=array([-0.03025277, -0.32371426]), square_terms=array([[ 0.03408775, -0.09379796], + [-0.09379796, 0.50863551]]), scale=0.0192529296875, shift=array([13.39225885, 1.03394448])), vector_model=VectorModel(intercepts=array([ 0.01938559, 0.01236126, -0.02541336, 0.06675187, 0.19101415, + 0.15552265, 0.10788637, 0.02151982, -0.00180687, -0.01933219, + -0.56991473, -0.81302909, -0.20768953, -0.13461208, -0.09577825, + 0.25047508, 0.58326817]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.2321875, shift=array([12.321875, 1.08125 ])), candidate_index=23, candidate_x=array([13.4067861, 1.0466706]), index=20, x=array([13.39225885, 1.03394448]), fval=1.2422204164694501, rho=-1.3455197239916337, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 9, 13, 20, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.39225885, 1.03394448]), radius=0.00962646484375, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 9, 13, 20, 22, 23]), model=ScalarModel(intercept=1.2461213821670756, linear_terms=array([-0.01139901, 0.03479926]), square_terms=array([[ 0.01697025, -0.01299824], + [-0.01299824, 0.05399418]]), scale=0.00962646484375, shift=array([13.39225885, 1.03394448])), vector_model=VectorModel(intercepts=array([ 0.01938559, 0.01236126, -0.02541336, 0.06675187, 0.19101415, + 0.15552265, 0.10788637, 0.02151982, -0.00180687, -0.01933219, + -0.56991473, -0.81302909, -0.20768953, -0.13461208, -0.09577825, + 0.25047508, 0.58326817]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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State(trustregion=Region(center=array([13.39225885, 1.03394448]), radius=0.004813232421875, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 9, 20, 23, 24]), model=ScalarModel(intercept=1.2552760540332801, linear_terms=array([-0.06136149, 0.10404948]), square_terms=array([[ 0.04088732, -0.04708259], + [-0.04708259, 0.0835845 ]]), scale=0.004813232421875, shift=array([13.39225885, 1.03394448])), vector_model=VectorModel(intercepts=array([ 0.01938559, 0.01236126, -0.02541336, 0.06675187, 0.19101415, + 0.15552265, 0.10788637, 0.02151982, -0.00180687, -0.01933219, + -0.56991473, -0.81302909, -0.20768953, -0.13461208, -0.09577825, + 0.25047508, 0.58326817]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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upper=array([20. , 1.1]))), model_indices=array([20, 24, 25]), model=ScalarModel(intercept=1.242220416469452, linear_terms=array([-0.49254682, -0.10435951]), square_terms=array([[3.37854 , 0.8095457 ], + [0.8095457 , 0.20786122]]), scale=0.0024066162109375, shift=array([13.39225885, 1.03394448])), vector_model=VectorModel(intercepts=array([ 0.01938559, 0.01236126, -0.02541336, 0.06675187, 0.19101415, + 0.15552265, 0.10788637, 0.02151982, -0.00180687, -0.01933219, + -0.56991473, -0.81302909, -0.20768953, -0.13461208, -0.09577825, + 0.25047508, 0.58326817]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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-0.03745233]), square_terms=array([[2.87010276, 0.40232869], + [0.40232869, 0.06805038]]), scale=0.00120330810546875, shift=array([13.39225885, 1.03394448])), vector_model=VectorModel(intercepts=array([ 0.01938559, 0.01236126, -0.02541336, 0.06675187, 0.19101415, + 0.15552265, 0.10788637, 0.02151982, -0.00180687, -0.01933219, + -0.56991473, -0.81302909, -0.20768953, -0.13461208, -0.09577825, + 0.25047508, 0.58326817]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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1.03394448])), vector_model=VectorModel(intercepts=array([ 0.01938559, 0.01236126, -0.02541336, 0.06675187, 0.19101415, + 0.15552265, 0.10788637, 0.02151982, -0.00180687, -0.01933219, + -0.56991473, -0.81302909, -0.20768953, -0.13461208, -0.09577825, + 0.25047508, 0.58326817]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.2321875, shift=array([12.321875, 1.08125 ])), candidate_index=28, candidate_x=array([13.39207935, 1.03451876]), index=28, x=array([13.39207935, 1.03451876]), fval=1.210363971947695, rho=1.3634463837141155, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([20, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0006016745527352553, relative_step_length=1.0000340727379582, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute params change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 29 entries., 'history': {'params': [{'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 11.446536902444704, 'DiscFac': 0.5}, {'CRRA': 13.413872739706319, 'DiscFac': 0.5007652721540578}, {'CRRA': 11.229877260293678, 'DiscFac': 0.59273111228441}, {'CRRA': 13.294070386710855, 'DiscFac': 1.1}, {'CRRA': 13.413872739706319, 'DiscFac': 0.5}, {'CRRA': 12.446098642928627, 'DiscFac': 0.5}, {'CRRA': 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State(trustregion=Region(center=array([13.80035011, 1.1 ]), radius=0.08498496203622835, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 13, 15, 16, 17]), model=ScalarModel(intercept=1.2630637427538014, linear_terms=array([0.0673167 , 0.12201243]), square_terms=array([[0.06493105, 0.0942397 ], + [0.0942397 , 0.27245921]]), scale=array([0.07531596, 0.03765798]), shift=array([13.80035011, 1.06234202])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([12, 13, 15, 17, 18]), model=ScalarModel(intercept=1.494530967111558, linear_terms=array([0.102457 , 0.02803027]), square_terms=array([[0.03812849, 0.01170315], + [0.01170315, 0.0472904 ]]), scale=array([0.03765798, 0.01882899]), shift=array([13.80035011, 1.08117101])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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model_indices=array([ 0, 12, 13, 15, 16, 17, 18, 19]), model=ScalarModel(intercept=1.15503149521224, linear_terms=array([-0.08309304, -0.2101502 ]), square_terms=array([[0.02472817, 0.0671273 ], + [0.0671273 , 0.60172359]]), scale=array([0.07531596, 0.05032285]), shift=array([13.76269213, 1.04967715])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=20, candidate_x=array([13.83800809, 1.06163832]), index=19, x=array([13.76269213, 1.07467026]), fval=1.123751124685578, rho=-12.049711039797472, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 13, 15, 16, 17, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76269213, 1.07467026]), radius=0.042492481018114175, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 13, 15, 17, 18, 19, 20]), model=ScalarModel(intercept=1.3037825390286737, linear_terms=array([ 0.05958201, -0.15298978]), square_terms=array([[ 0.00838945, -0.02709215], + [-0.02709215, 0.33469544]]), scale=array([0.03765798, 0.03149386]), shift=array([13.76269213, 1.06850614])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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0.01289748, -0.05831618]), square_terms=array([[ 0.00337072, -0.00852184], + [-0.00852184, 0.15088885]]), scale=0.021246240509057088, shift=array([13.76269213, 1.07467026])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=22, candidate_x=array([13.74137005, 1.08137517]), index=19, x=array([13.76269213, 1.07467026]), fval=1.123751124685578, rho=-6.962948052081567, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([12, 13, 15, 18, 19, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76269213, 1.07467026]), radius=0.010623120254528544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 15, 18, 19, 21, 22]), model=ScalarModel(intercept=1.0959366501510284, linear_terms=array([-0.07603389, 0.0592279 ]), square_terms=array([[ 0.01482588, -0.01887136], + [-0.01887136, 0.05507578]]), scale=0.010623120254528544, shift=array([13.76269213, 1.07467026])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=23, candidate_x=array([13.77278794, 1.07078824]), index=19, x=array([13.76269213, 1.07467026]), fval=1.123751124685578, rho=-12.796215190602183, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([13, 15, 18, 19, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76269213, 1.07467026]), radius=0.005311560127264272, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 22, 23]), model=ScalarModel(intercept=1.123751124685578, linear_terms=array([2.35075615, 6.72344032]), square_terms=array([[ 34.95552465, 104.4822096 ], + [104.4822096 , 312.38317194]]), scale=0.005311560127264272, shift=array([13.76269213, 1.07467026])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=24, candidate_x=array([13.75762036, 1.07625184]), index=19, x=array([13.76269213, 1.07467026]), fval=1.123751124685578, rho=-3.3674374603538753, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([19, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76269213, 1.07467026]), radius=0.002655780063632136, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 23, 24]), model=ScalarModel(intercept=1.1237511246855785, linear_terms=array([-0.5780775 , -1.19841878]), square_terms=array([[ 5.94858558, 17.60275441], + [17.60275441, 52.66397965]]), scale=0.002655780063632136, shift=array([13.76269213, 1.07467026])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=25, candidate_x=array([13.76522944, 1.07388424]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=2.835499899865532, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([19, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.002656264319473295, relative_step_length=1.0001823403405237, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=0.005311560127264272, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 22, 23, 24, 25]), model=ScalarModel(intercept=0.9444375568015315, linear_terms=array([0.44532753, 1.19395003]), square_terms=array([[ 20.99849631, 63.51413818], + [ 63.51413818, 192.14709976]]), scale=0.005311560127264272, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=26, candidate_x=array([13.76017631, 1.07552117]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-13.438176044734822, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([19, 22, 23, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=0.002655780063632136, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 23, 24, 25, 26]), model=ScalarModel(intercept=0.7549074447561732, linear_terms=array([-1.08491772, -3.21566214]), square_terms=array([[ 4.95581197, 13.40310954], + [13.40310954, 36.80550289]]), scale=0.002655780063632136, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 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23, 24, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=0.001327890031816068, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 25, 26, 27]), model=ScalarModel(intercept=0.6663131578299749, linear_terms=array([0.31579992, 1.29406005]), square_terms=array([[ 0.97652328, 3.33987753], + [ 3.33987753, 11.68276301]]), scale=0.001327890031816068, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=0.000663945015908034, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 25, 27, 28]), model=ScalarModel(intercept=1.0748283427553293, linear_terms=array([0.74775752, 1.86090971]), square_terms=array([[0.94615569, 2.1520877 ], + [2.1520877 , 5.00035967]]), scale=0.000663945015908034, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 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radius=0.000331972507954017, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 28, 29]), model=ScalarModel(intercept=0.688257928446749, linear_terms=array([-0.05009521, -0.4261496 ]), square_terms=array([[0.05474491, 0.13563449], + [0.13563449, 0.64186031]]), scale=0.000331972507954017, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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30]), model=ScalarModel(intercept=0.6882579284467485, linear_terms=array([2.51110852, 2.58551584]), square_terms=array([[17.31961069, 17.86076585], + [17.86076585, 18.4307605 ]]), scale=0.0001659862539770085, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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square_terms=array([[2.48376422, 2.0304305 ], + [2.0304305 , 1.6811628 ]]), scale=8.299312698850425e-05, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=32, candidate_x=array([13.76529773, 1.07383408]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-8.895409172081964, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=4.1496563494252124e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 31, 32]), model=ScalarModel(intercept=0.6882579284467484, linear_terms=array([5.4261937 , 7.07144682]), square_terms=array([[196.90079522, 251.86367885], + [251.86367885, 322.26054074]]), scale=4.1496563494252124e-05, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=33, candidate_x=array([13.76526169, 1.07385813]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-6.602379228031406, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 31, 32]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=2.0748281747126062e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 32, 33]), model=ScalarModel(intercept=0.6882579284467489, linear_terms=array([-1.67471137, -2.43734694]), square_terms=array([[44.36150417, 58.4429871 ], + [58.4429871 , 77.26404403]]), scale=2.0748281747126062e-05, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , 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candidate_index=34, candidate_x=array([13.76521321, 1.07389717]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-14.213590995613345, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 32, 33]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1.0374140873563031e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 33, 34]), model=ScalarModel(intercept=0.6882579284467507, linear_terms=array([-32.53614671, -40.36884666]), square_terms=array([[5704.80115467, 7072.67560803], + [7072.67560803, 8768.54976842]]), scale=1.0374140873563031e-05, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=35, candidate_x=array([13.76522139, 1.07389079]), index=25, x=array([13.76522944, 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0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=36, candidate_x=array([13.76523347, 1.07388099]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-8.167753639507541, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 34, 35]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=2.5935352183907578e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 35, 36]), model=ScalarModel(intercept=0.688257928446749, linear_terms=array([ 96.92131584, 119.52724588]), square_terms=array([[23233.92339938, 28666.34702734], + [28666.34702734, 35368.97768226]]), scale=2.5935352183907578e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1.2967676091953789e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 36, 37]), model=ScalarModel(intercept=0.6882579284467522, linear_terms=array([-228.72939942, -283.40527822]), square_terms=array([[149814.22087996, 185594.86902726], + [185594.86902726, 229921.14598203]]), scale=1.2967676091953789e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 37, 38]), model=ScalarModel(intercept=0.6882579284479667, linear_terms=array([110.01063583, 136.7138673 ]), square_terms=array([[204615.15154828, 253921.58304407], + [253921.58304407, 315109.6517833 ]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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upper=array([20. , 1.1]))), model_indices=array([25, 37, 38, 39]), model=ScalarModel(intercept=1.1577741715871368, linear_terms=array([242.70494131, 301.06849961]), square_terms=array([[155662.83189264, 193036.2230012 ], + [193036.2230012 , 239382.71308459]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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linear_terms=array([205.50981035, 254.96369968]), square_terms=array([[196975.02816638, 244290.03299938], + [244290.03299938, 302970.52114327]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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[265947.86330119, 329860.98150686]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=43, candidate_x=array([13.76522866, 1.07388487]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-8.647410683350707, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 37, 38, 39, 40, 41, 42]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 37, 38, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=1.3567974599896127, linear_terms=array([202.98250055, 251.79397558]), square_terms=array([[144095.09085198, 178734.35986993], + [178734.35986993, 221700.63899107]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=44, candidate_x=array([13.76523021, 1.07388362]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-7.117835688525804, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 37, 38, 39, 40, 41, 42, 43]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 37, 38, 39, 40, 41, 42, 43, 44]), model=ScalarModel(intercept=1.4152002732237223, linear_terms=array([188.23186675, 233.45474242]), square_terms=array([[136071.15256904, 168774.1945395 ], + [168774.1945395 , 209337.0286982 ]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=45, candidate_x=array([13.76522866, 1.07388487]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-6.369117061581065, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 37, 38, 39, 40, 41, 42, 43, 44]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 38, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=1.4322930669889091, linear_terms=array([165.85163954, 205.76495158]), square_terms=array([[132937.5754333 , 164910.82906923], + [164910.82906923, 204574.07883395]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=46, candidate_x=array([13.76523021, 1.07388362]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-12.178849532771347, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 38, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([37]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 39, 40, 41, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=1.3093308313412153, linear_terms=array([123.65989376, 153.3047285 ]), square_terms=array([[323967.17174465, 401840.36181195], + [401840.36181195, 498432.23105826]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + 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43, 44, 45, 46]), old_indices_discarded=array([37, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 39, 41, 42, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=1.0590382139587964, linear_terms=array([-280.9809403 , -348.70796313]), square_terms=array([[266181.47955628, 330177.67116074], + [330177.67116074, 409560.10325178]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 39, 41, 42, 43, 44, 45, 47, 48]), model=ScalarModel(intercept=1.220859614346961, linear_terms=array([-138.3649051 , -171.73746763]), square_terms=array([[158556.44632179, 196651.07147605], + [196651.07147605, 243898.29439121]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 39, 42, 43, 44, 45, 47, 48, 49]), model=ScalarModel(intercept=1.2282682420444786, linear_terms=array([-238.11010807, -295.51259223]), square_terms=array([[ 74869.56239741, 92919.65993007], + [ 92919.65993007, 115321.54114857]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 39, 42, 43, 44, 45, 47, 49, 50]), model=ScalarModel(intercept=1.2399950339941943, linear_terms=array([-189.67214848, -235.48113278]), square_terms=array([[ 69951.25959547, 86896.16232258], + [ 86896.16232258, 107945.82494064]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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model_indices=array([25, 39, 42, 43, 44, 45, 47, 49, 50]), model=ScalarModel(intercept=1.2399950339941943, linear_terms=array([-189.67214848, -235.48113278]), square_terms=array([[ 69951.25959547, 86896.16232258], + [ 86896.16232258, 107945.82494064]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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model=ScalarModel(intercept=1.2399950339941943, linear_terms=array([-189.67214848, -235.48113278]), square_terms=array([[ 69951.25959547, 86896.16232258], + [ 86896.16232258, 107945.82494064]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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linear_terms=array([-189.67214848, -235.48113278]), square_terms=array([[ 69951.25959547, 86896.16232258], + [ 86896.16232258, 107945.82494064]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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square_terms=array([[ 69951.25959547, 86896.16232258], + [ 86896.16232258, 107945.82494064]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), 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1.6046549090592486, 2.4392140191496465, 2.0969547685400998, 1.619697166814026, 1.4177027742030972, 2.7375198870821396, 1.648837292228864, 1.2017087559283488, 1.6123653333088765, 2.1559864840618888, 1.6813226057480248, 1.7555339640362284, 1.5641420916511097, 2.121351535271443, 1.195628783451865, 1.366634904655717, 1.7240618907150989, 0.9835036694297299, 1.5576561839970382, 1.389378348605993, 1.323322900002188, 1.4964292964688457, 1.432272428511995, 1.2033755926739262, 3.1657769462342085, 2.197985075310209, 1.2162154879079494, 2.342003085876411, 1.4622856861257032, 1.2041984771527132], 'runtime': [0.0, 2.0825537890000305, 2.304332205999799, 2.5202568370000336, 2.752349718000005, 2.9856617369996457, 3.2192967379996844, 3.4485792460000084, 3.701930288999847, 4.015171013999861, 4.454668695999771, 4.669011841999691, 4.894209664999835, 6.820035805999851, 8.57855164600005, 10.310429240999838, 12.091082415000074, 13.79618391699978, 15.48075700299978, 17.167798326999673, 18.990243108999948, 20.715586998999697, 22.467751674999818, 24.235677248999764, 25.957298829999672, 27.703975633000027, 29.413045380999847, 31.228969932999917, 32.923921876999884, 34.6790341789997, 36.43617003500003, 38.32504238499996, 40.16391709299978, 41.907183702999646, 43.74042012699965, 45.45150464900007, 47.17200287099968, 48.90636308400008, 50.674419703999774, 52.51235168999983, 54.256131906000064, 56.12240377699982, 57.842474824999954, 59.62035617699985, 61.38132491599981, 63.246293440000045, 65.0222065969997, 66.75320940899974, 68.49351131899994, 70.48024981899971, 72.27372107699966, 74.07178742799988, 75.84724168000002, 77.53998098800002, 79.26190303999965, 80.98321577199977, 82.81359508299965, 84.52387710899984, 86.25460904200008, 88.0250253449999, 89.76157718399963, 91.52082204299995, 93.24137594800004], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51]}, 'multistart_info': {...}}], 'exploration_sample': array([[12.321875, 1.08125 ], + [14.09375 , 0.9875 ], + [17.6375 , 1.025 ], + [16.45625 , 0.9125 ], + [11.73125 , 0.7625 ], + [10.55 , 0.8 ], + [12.9125 , 0.575 ], + [15.275 , 0.65 ], + [17.046875, 0.63125 ], + [18.81875 , 0.5375 ], + [ 9.36875 , 0.8375 ], + [ 8.1875 , 0.725 ], + [ 7.00625 , 0.6125 ], + [ 5.825 , 0.95 ], + [ 4.64375 , 0.6875 ], + [ 2.871875, 0.78125 ], + [ 5. , 0.95 ], + [ 3.4625 , 0.875 ], + [ 2.28125 , 1.0625 ]]), 'exploration_results': array([ 1.25226984, 1.77139048, 1.79567843, 2.19243348, 2.99563577, + 3.4003551 , 3.46407238, 3.67340234, 3.72448066, 4.28414873, + 4.65250486, 5.39134448, 6.53886763, 14.94020775, 20.34265339, + 25.6087008 , 28.43667398, 29.29813793, 75.84976584])}}" diff --git a/content/tables/min/WarmGlowPortfolioSub(Stock)(Labor)Market_estimate_results.csv b/content/tables/min/WarmGlowPortfolioSub(Stock)(Labor)Market_estimate_results.csv new file mode 100644 index 0000000..1a501be --- /dev/null +++ b/content/tables/min/WarmGlowPortfolioSub(Stock)(Labor)Market_estimate_results.csv @@ -0,0 +1,6021 @@ +CRRA,5.573894562325964 +DiscFac,1.0637390075406437 +time_to_estimate,236.7087128162384 +params,"{'CRRA': 5.573894562325964, 'DiscFac': 1.0637390075406437}" +criterion,1.4220519178994522 +start_criterion,3.7528915666217584 +start_params,"{'CRRA': 5.0, 'DiscFac': 0.95}" +algorithm,multistart_tranquilo_ls +direction,minimize +n_free,2 +message,Absolute criterion change smaller than tolerance. +success, +n_criterion_evaluations, +n_derivative_evaluations, +n_iterations, +history,"{'params': [{'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 5.3827721337329075, 'DiscFac': 0.5}, {'CRRA': 6.341227184076231, 'DiscFac': 0.9843397863096757}, {'CRRA': 5.308772815923768, 'DiscFac': 0.9942847020759833}, {'CRRA': 6.341227184076231, 'DiscFac': 1.099329565298806}, {'CRRA': 6.341227184076231, 'DiscFac': 0.7325487756828386}, {'CRRA': 6.341227184076231, 'DiscFac': 0.661184756844424}, {'CRRA': 5.670357912186334, 'DiscFac': 1.1}, {'CRRA': 6.341227184076231, 'DiscFac': 1.0027250193492325}, {'CRRA': 6.341227184076231, 'DiscFac': 1.0927982791010287}, {'CRRA': 5.308772815923768, 'DiscFac': 1.0118564969416792}, {'CRRA': 5.957814024190954, 'DiscFac': 0.5}, {'CRRA': 6.114808484578101, 'DiscFac': 1.1}, {'CRRA': 5.793348226117784, 'DiscFac': 0.8284226490669568}, {'CRRA': 5.75003997251512, 'DiscFac': 0.8880821392041797}, {'CRRA': 5.693161284486315, 'DiscFac': 1.0118481504711487}, {'CRRA': 5.569384429577121, 'DiscFac': 1.0922008146955873}, {'CRRA': 5.5194785395513035, 'DiscFac': 1.0658390858610285}, {'CRRA': 5.600878088644884, 'DiscFac': 1.1}, {'CRRA': 5.77759213158942, 'DiscFac': 1.0321931747072446}, {'CRRA': 5.545693184498602, 'DiscFac': 1.055901773046547}, {'CRRA': 5.454950141541775, 'DiscFac': 1.0580103701909507}, {'CRRA': 5.551742738556068, 'DiscFac': 1.0610052791857207}, {'CRRA': 5.569955078014674, 'DiscFac': 1.0653960027788554}, {'CRRA': 5.560809192249429, 'DiscFac': 1.0668198165422604}, {'CRRA': 5.574179063160669, 'DiscFac': 1.0637026157965719}, {'CRRA': 5.565030737518938, 'DiscFac': 1.0667766565553518}, {'CRRA': 5.578730259698591, 'DiscFac': 1.0636435394860717}, {'CRRA': 5.571905522822675, 'DiscFac': 1.0633495597956906}, {'CRRA': 5.575317879327959, 'DiscFac': 1.063494000480499}, {'CRRA': 5.574535066002697, 'DiscFac': 1.0641709732701494}, {'CRRA': 5.573894562325964, 'DiscFac': 1.0637390075406437}], 'criterion': [3.0263314834387383, 4.314184027187926, nan, 2.6142787419100317, nan, 4.730311355914472, 4.931838606867419, 1.9543423789678298, nan, nan, 2.1984626089974055, 4.789253019202322, nan, 3.940933836887815, 3.4612490178599495, 2.070351234692411, 1.7490483015806304, 1.4289096777146673, 1.9829352389581485, 1.6954301219950216, 1.4443085463640908, 1.4384944542770688, 1.4260383541426693, 1.42426971311261, 1.427741913899455, 1.4220984404550594, 1.427437660317148, 1.422217891677385, 1.4225162577343387, 1.4221438433511406, 1.4221892061726984, 1.4220519178994522], 'runtime': [0.0, 2.3060040300001674, 2.5296067510003013, 2.746056181000313, 2.955891735000023, 3.1852807540003596, 3.418634059999931, 3.646312295000371, 3.8771810240000377, 4.1162852550000935, 4.332836274000329, 4.594906272000117, 4.8272969570002715, 6.81588306499998, 8.537752602000182, 10.276266272000157, 12.019839181999942, 13.738447911000094, 15.57650557900024, 17.296406226000272, 19.017319258000043, 20.712462229000266, 22.41346721400032, 24.151581480999994, 25.881935801000054, 27.747960174000127, 29.504578251000112, 31.23275424700023, 32.95328207200009, 34.65527811200036, 36.36891135099995, 38.069648728000175], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]}" +convergence_report, +multistart_info,"{'start_parameters': [{'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 7.5503322732065214, 'DiscFac': 1.0688678584866826}], 'local_optima': [Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance., Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Maximum number of criterion evaluations reached.], 'exploration_sample': [{'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 5.0, 'DiscFac': 0.95}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 14.093749999999998, 'DiscFac': 0.9875}, {'CRRA': 9.368749999999999, 'DiscFac': 0.8375}, {'CRRA': 17.6375, 'DiscFac': 1.0250000000000001}, {'CRRA': 8.1875, 'DiscFac': 0.7250000000000001}, {'CRRA': 10.549999999999999, 'DiscFac': 0.8}, {'CRRA': 16.45625, 'DiscFac': 0.9125000000000001}, {'CRRA': 7.00625, 'DiscFac': 0.6125}, {'CRRA': 11.73125, 'DiscFac': 0.7625000000000001}, {'CRRA': 15.274999999999999, 'DiscFac': 0.65}, {'CRRA': 12.9125, 'DiscFac': 0.575}, {'CRRA': 17.046875, 'DiscFac': 0.6312500000000001}, {'CRRA': 18.81875, 'DiscFac': 0.5375}, {'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 2.28125, 'DiscFac': 1.0625}, {'CRRA': 2.871875, 'DiscFac': 0.78125}], 'exploration_results': array([ 3.02633148, 3.30050278, 3.74724591, 3.92461378, 4.45534719, + 4.52159416, 4.77507862, 5.06340436, 5.16761845, 5.42393003, + 5.50914405, 5.69522002, 6.68407291, 6.73863805, 6.84560996, + 7.20052264, 7.72130492, 8.47185488, 10.67451262])}" +algorithm_output,"{'states': [State(trustregion=Region(center=array([5.825, 0.95 ]), radius=0.5824999999999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=3.0263314834387383, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, 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rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([5.825, 0.95 ]), radius=0.5824999999999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=1.1269978152790263, linear_terms=array([-0.51127322, -0.63965657]), square_terms=array([[11.76668356, 13.01143564], + [13.01143564, 15.17207492]]), scale=array([0.51622718, 0.3 ]), shift=array([5.825, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 7, 10, 11, 12, 13, 14, 15]), model=ScalarModel(intercept=1.8544790200782664, linear_terms=array([-0.67331221, -1.21592495]), square_terms=array([[1.63171239, 1.52446346], + [1.52446346, 2.03881587]]), scale=array([0.25811359, 0.17313272]), shift=array([5.69316128, 0.92686728])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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3, 7, 10, 12, 13, 14, 15, 16]), model=ScalarModel(intercept=7.085140584300428, linear_terms=array([ -5.65921381, -14.17503365]), square_terms=array([[ 5.20815903, 7.08665734], + [ 7.08665734, 17.08804688]]), scale=array([0.51622718, 0.26201318]), shift=array([5.56938443, 0.83798682])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=17, candidate_x=array([5.51947854, 1.06583909]), index=17, x=array([5.51947854, 1.06583909]), fval=1.4289096777146673, rho=1.7809591946937149, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 3, 7, 10, 12, 13, 14, 15, 16]), old_indices_discarded=array([ 1, 2, 4, 5, 6, 8, 9, 11]), step_length=0.05644057588664191, relative_step_length=0.09689369250925652, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.51947854, 1.06583909]), radius=0.5824999999999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 3, 7, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=2.3354605283597563, linear_terms=array([ 1.46424334, -2.36914478]), square_terms=array([[ 1.06094696, -1.63153519], + [-1.63153519, 2.58355658]]), scale=array([0.51622718, 0.27519405]), shift=array([5.51947854, 0.82480595])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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linear_terms=array([-0.92259607, -5.33641639]), square_terms=array([[0.27027798, 1.20759192], + [1.20759192, 7.70294614]]), scale=array([0.25811359, 0.14613725]), shift=array([5.51947854, 0.95386275])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=19, candidate_x=array([5.77759213, 1.03219317]), index=17, x=array([5.51947854, 1.06583909]), fval=1.4289096777146673, rho=-4.019293806179108, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 3, 7, 10, 14, 15, 16, 17, 18]), old_indices_discarded=array([ 1, 2, 4, 5, 6, 8, 9, 11, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.51947854, 1.06583909]), radius=0.14562499999999998, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 14, 15, 16, 17, 18, 19]), model=ScalarModel(intercept=1.5213811565094542, linear_terms=array([-0.14355744, -1.03791084]), square_terms=array([[0.06159175, 0.28510759], + [0.28510759, 2.13210366]]), scale=array([0.1290568 , 0.08160886]), shift=array([5.51947854, 1.01839114])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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scale=array([0.0645284 , 0.04934466]), shift=array([5.51947854, 1.05065534])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=21, candidate_x=array([5.45495014, 1.05801037]), index=17, x=array([5.51947854, 1.06583909]), fval=1.4289096777146673, rho=-0.27578700766971787, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 10, 15, 16, 17, 18, 19, 20]), old_indices_discarded=array([ 0, 13, 14]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.51947854, 1.06583909]), radius=0.036406249999999994, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 15, 16, 17, 18, 20, 21]), model=ScalarModel(intercept=1.3985870458518956, linear_terms=array([-0.03050627, 0.11095194]), square_terms=array([[0.00179814, 0.01810855], + [0.01810855, 0.86143979]]), scale=array([0.0322642, 0.0322642]), shift=array([5.51947854, 1.06583909])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=22, candidate_x=array([5.55174274, 1.06100528]), index=22, x=array([5.55174274, 1.06100528]), fval=1.426038354142669, rho=0.07310801718365617, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 7, 15, 16, 17, 18, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.0326242888718403, relative_step_length=0.8961178059217939, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.55174274, 1.06100528]), radius=0.018203124999999997, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 17, 18, 20, 22]), model=ScalarModel(intercept=1.4163790473957398, linear_terms=array([-0.00983569, -0.08333704]), square_terms=array([[2.86954770e-04, 8.05036733e-04], + [8.05036733e-04, 3.32811019e-01]]), scale=0.018203124999999997, shift=array([5.55174274, 1.06100528])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=23, candidate_x=array([5.56995508, 1.065396 ]), index=23, x=array([5.56995508, 1.065396 ]), fval=1.42426971311261, rho=0.0887754386245564, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([16, 17, 18, 20, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.01873413361292202, relative_step_length=1.0291712886068751, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.56995508, 1.065396 ]), radius=0.009101562499999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 20, 22, 23]), model=ScalarModel(intercept=1.4255871683087975, linear_terms=array([ 0.01282382, -0.01799885]), square_terms=array([[ 0.00027616, -0.00248466], + [-0.00248466, 0.08699657]]), scale=0.009101562499999999, shift=array([5.56995508, 1.065396 ])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=24, candidate_x=array([5.56080919, 1.06681982]), index=23, x=array([5.56995508, 1.065396 ]), fval=1.42426971311261, rho=-0.24612567440050911, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([16, 20, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.56995508, 1.065396 ]), radius=0.004550781249999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 23, 24]), model=ScalarModel(intercept=1.42426971311261, linear_terms=array([-4.86697548e-05, 7.42140875e-03]), square_terms=array([[ 7.70644314e-06, -1.20696978e-04], + [-1.20696978e-04, 1.96339837e-02]]), scale=0.004550781249999999, shift=array([5.56995508, 1.065396 ])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=25, candidate_x=array([5.57417906, 1.06370262]), index=25, x=array([5.57417906, 1.06370262]), fval=1.4220984404550594, rho=1.5482217964560738, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([22, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.004550781249999896, relative_step_length=0.9999999999999774, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.57417906, 1.06370262]), radius=0.009101562499999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 18, 20, 22, 23, 24, 25]), model=ScalarModel(intercept=1.4247281558811358, linear_terms=array([ 0.00539427, -0.03219716]), square_terms=array([[ 3.96935302e-05, -1.28952506e-03], + [-1.28952506e-03, 8.65974369e-02]]), scale=0.009101562499999999, shift=array([5.57417906, 1.06370262])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=26, candidate_x=array([5.56503074, 1.06677666]), index=25, x=array([5.57417906, 1.06370262]), fval=1.4220984404550594, rho=-0.48986170098858195, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([16, 18, 20, 22, 23, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.57417906, 1.06370262]), radius=0.004550781249999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 23, 24, 25, 26]), model=ScalarModel(intercept=1.422282780554977, linear_terms=array([-0.00015294, 0.00037013]), square_terms=array([[ 7.13476990e-06, -1.13770520e-04], + [-1.13770520e-04, 1.96031452e-02]]), scale=0.004550781249999999, shift=array([5.57417906, 1.06370262])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=27, candidate_x=array([5.57873026, 1.06364354]), index=25, x=array([5.57417906, 1.06370262]), fval=1.4220984404550594, rho=-0.7907220678531115, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 23, 24, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.57417906, 1.06370262]), radius=0.0022753906249999996, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 25, 26, 27]), model=ScalarModel(intercept=1.4221663020015802, linear_terms=array([5.73401438e-05, 7.45830401e-04]), square_terms=array([[ 1.12265319e-06, -2.59043861e-05], + [-2.59043861e-05, 4.91328724e-03]]), scale=0.0022753906249999996, shift=array([5.57417906, 1.06370262])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=28, candidate_x=array([5.57190552, 1.06334956]), index=25, x=array([5.57417906, 1.06370262]), fval=1.4220984404550594, rho=-3.5610604492495965, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 25, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.57417906, 1.06370262]), radius=0.0011376953124999998, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 25, 27, 28]), model=ScalarModel(intercept=1.422325827336663, linear_terms=array([-4.61917023e-05, 2.38441492e-04]), square_terms=array([[ 3.55694453e-07, -6.48069226e-06], + [-6.48069226e-06, 1.22037302e-03]]), scale=0.0011376953124999998, shift=array([5.57417906, 1.06370262])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=29, candidate_x=array([5.57531788, 1.063494 ]), index=25, x=array([5.57417906, 1.06370262]), fval=1.4220984404550594, rho=-0.6669519353256694, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 25, 27, 28]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.57417906, 1.06370262]), radius=0.0005688476562499999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 28, 29]), model=ScalarModel(intercept=1.422098440455059, linear_terms=array([-4.34744633e-05, -3.03707274e-04]), square_terms=array([[ 8.37764168e-08, -1.07708651e-06], + [-1.07708651e-06, 2.98889073e-04]]), scale=0.0005688476562499999, shift=array([5.57417906, 1.06370262])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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model=ScalarModel(intercept=1.422098440455059, linear_terms=array([ 4.17356026e-06, -1.04084673e-05]), square_terms=array([[ 1.93011592e-08, -1.59744779e-07], + [-1.59744779e-07, 7.59670263e-05]]), scale=0.00028442382812499995, shift=array([5.57417906, 1.06370262])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=31, candidate_x=array([5.57389456, 1.06373901]), index=31, x=array([5.57389456, 1.06373901]), fval=1.4220519178994522, rho=9.583354885845637, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([25, 29, 30]), old_indices_discarded=array([], dtype=int64), step_length=0.00028681890451018565, relative_step_length=1.0084208007499749, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 32 entries., 'multistart_info': {'start_parameters': [array([5.825, 0.95 ]), array([7.55033227, 1.06886786])], 'local_optima': [{'solution_x': array([5.57389456, 1.06373901]), 'solution_criterion': 1.4220519178994522, 'states': [State(trustregion=Region(center=array([5.825, 0.95 ]), radius=0.5824999999999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=3.0263314834387383, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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candidate_index=15, candidate_x=array([5.69316128, 1.01184815]), index=15, x=array([5.69316128, 1.01184815]), fval=2.070351234692411, rho=2.2229628748013988, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 2, 3, 7, 8, 11, 12, 13, 14]), old_indices_discarded=array([ 1, 4, 5, 6, 9, 10]), step_length=0.1456249999999998, relative_step_length=0.9999999999999989, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.69316128, 1.01184815]), radius=0.29124999999999995, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 7, 10, 11, 12, 13, 14, 15]), model=ScalarModel(intercept=1.8544790200782664, linear_terms=array([-0.67331221, -1.21592495]), square_terms=array([[1.63171239, 1.52446346], + [1.52446346, 2.03881587]]), scale=array([0.25811359, 0.17313272]), shift=array([5.69316128, 0.92686728])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, 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x=array([5.51947854, 1.06583909]), fval=1.4289096777146673, rho=1.7809591946937149, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 3, 7, 10, 12, 13, 14, 15, 16]), old_indices_discarded=array([ 1, 2, 4, 5, 6, 8, 9, 11]), step_length=0.05644057588664191, relative_step_length=0.09689369250925652, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.51947854, 1.06583909]), radius=0.5824999999999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 3, 7, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=2.3354605283597563, linear_terms=array([ 1.46424334, -2.36914478]), square_terms=array([[ 1.06094696, -1.63153519], + [-1.63153519, 2.58355658]]), scale=array([0.51622718, 0.27519405]), shift=array([5.51947854, 0.82480595])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, 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fval=1.4289096777146673, rho=-85.51150568657764, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 7, 13, 14, 15, 16, 17]), old_indices_discarded=array([ 2, 4, 5, 6, 8, 9, 10, 11, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.51947854, 1.06583909]), radius=0.29124999999999995, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 7, 10, 14, 15, 16, 17, 18]), model=ScalarModel(intercept=3.1012802436018085, linear_terms=array([-0.92259607, -5.33641639]), square_terms=array([[0.27027798, 1.20759192], + [1.20759192, 7.70294614]]), scale=array([0.25811359, 0.14613725]), shift=array([5.51947854, 0.95386275])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=19, candidate_x=array([5.77759213, 1.03219317]), index=17, x=array([5.51947854, 1.06583909]), fval=1.4289096777146673, rho=-4.019293806179108, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 3, 7, 10, 14, 15, 16, 17, 18]), old_indices_discarded=array([ 1, 2, 4, 5, 6, 8, 9, 11, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.51947854, 1.06583909]), radius=0.14562499999999998, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 14, 15, 16, 17, 18, 19]), model=ScalarModel(intercept=1.5213811565094542, linear_terms=array([-0.14355744, -1.03791084]), square_terms=array([[0.06159175, 0.28510759], + [0.28510759, 2.13210366]]), scale=array([0.1290568 , 0.08160886]), shift=array([5.51947854, 1.01839114])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=20, candidate_x=array([5.54569318, 1.05590177]), index=17, x=array([5.51947854, 1.06583909]), fval=1.4289096777146673, rho=-1.535972006541552, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 10, 14, 15, 16, 17, 18, 19]), old_indices_discarded=array([ 0, 1, 11, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.51947854, 1.06583909]), radius=0.07281249999999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 15, 16, 17, 18, 19, 20]), model=ScalarModel(intercept=1.3026835229178233, linear_terms=array([ 0.01296259, -0.29445874]), square_terms=array([[ 0.00193973, -0.00535854], + [-0.00535854, 1.93956479]]), scale=array([0.0645284 , 0.04934466]), shift=array([5.51947854, 1.05065534])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 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old_indices_discarded=array([ 0, 13, 14]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.51947854, 1.06583909]), radius=0.036406249999999994, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 15, 16, 17, 18, 20, 21]), model=ScalarModel(intercept=1.3985870458518956, linear_terms=array([-0.03050627, 0.11095194]), square_terms=array([[0.00179814, 0.01810855], + [0.01810855, 0.86143979]]), scale=array([0.0322642, 0.0322642]), shift=array([5.51947854, 1.06583909])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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relative_step_length=0.8961178059217939, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.55174274, 1.06100528]), radius=0.018203124999999997, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 17, 18, 20, 22]), model=ScalarModel(intercept=1.4163790473957398, linear_terms=array([-0.00983569, -0.08333704]), square_terms=array([[2.86954770e-04, 8.05036733e-04], + [8.05036733e-04, 3.32811019e-01]]), scale=0.018203124999999997, shift=array([5.55174274, 1.06100528])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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n_evals_acceptance=1), State(trustregion=Region(center=array([5.56995508, 1.065396 ]), radius=0.009101562499999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 20, 22, 23]), model=ScalarModel(intercept=1.4255871683087975, linear_terms=array([ 0.01282382, -0.01799885]), square_terms=array([[ 0.00027616, -0.00248466], + [-0.00248466, 0.08699657]]), scale=0.009101562499999999, shift=array([5.56995508, 1.065396 ])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 23, 24]), model=ScalarModel(intercept=1.42426971311261, linear_terms=array([-4.86697548e-05, 7.42140875e-03]), square_terms=array([[ 7.70644314e-06, -1.20696978e-04], + [-1.20696978e-04, 1.96339837e-02]]), scale=0.004550781249999999, shift=array([5.56995508, 1.065396 ])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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model_indices=array([16, 18, 20, 22, 23, 24, 25]), model=ScalarModel(intercept=1.4247281558811358, linear_terms=array([ 0.00539427, -0.03219716]), square_terms=array([[ 3.96935302e-05, -1.28952506e-03], + [-1.28952506e-03, 8.65974369e-02]]), scale=0.009101562499999999, shift=array([5.57417906, 1.06370262])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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square_terms=array([[ 1.12265319e-06, -2.59043861e-05], + [-2.59043861e-05, 4.91328724e-03]]), scale=0.0022753906249999996, shift=array([5.57417906, 1.06370262])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), 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State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=0.18875830683016304, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 4, 6, 7, 9, 10, 12, 13, 14]), model=ScalarModel(intercept=19.22375838635329, linear_terms=array([1.32892629, 5.63610224]), square_terms=array([[0.15092368, 0.22405344], + [0.22405344, 0.99645599]]), scale=array([0.16728269, 0.09920742]), shift=array([7.55033227, 1.00079258])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], 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model=ScalarModel(intercept=inf, linear_terms=array([ inf, -inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=array([0.08364135, 0.05738674]), shift=array([7.55033227, 1.04261326])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([18]), old_indices_used=array([ 0, 17]), old_indices_discarded=array([16]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=0.02359478835377038, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 19, 20]), model=ScalarModel(intercept=inf, linear_terms=array([ inf, -inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=0.02359478835377038, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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model=ScalarModel(intercept=inf, linear_terms=array([-inf, inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=0.01179739417688519, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([24]), old_indices_used=array([ 0, 17, 19, 21, 22, 23]), old_indices_discarded=array([20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=0.0029493485442212974, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 19, 21, 23, 24, 25, 26]), model=ScalarModel(intercept=inf, linear_terms=array([ inf, -inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=0.0029493485442212974, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([26]), old_indices_used=array([ 0, 17, 19, 21, 23, 24, 25]), old_indices_discarded=array([22]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=0.0014746742721106487, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 19, 21, 23, 25, 26, 27, 28]), model=ScalarModel(intercept=inf, linear_terms=array([ inf, -inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=0.0014746742721106487, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([28]), old_indices_used=array([ 0, 17, 19, 21, 23, 25, 26, 27]), old_indices_discarded=array([24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=0.0007373371360553244, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 19, 21, 23, 25, 27, 29, 30]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=0.0007373371360553244, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([30]), old_indices_used=array([ 0, 17, 19, 21, 23, 25, 27, 29]), old_indices_discarded=array([26, 28]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=0.0003686685680276622, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 19, 21, 23, 25, 27, 29, 31, 32]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=0.0003686685680276622, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([32]), old_indices_used=array([ 0, 19, 21, 23, 25, 27, 29, 31]), old_indices_discarded=array([17, 28, 30]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=0.0001843342840138311, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 21, 23, 25, 27, 29, 31, 33, 34]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=0.0001843342840138311, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([34]), old_indices_used=array([ 0, 21, 23, 25, 27, 29, 31, 33]), old_indices_discarded=array([17, 19, 30, 32]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=9.216714200691555e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 23, 25, 27, 29, 31, 33, 35, 36]), model=ScalarModel(intercept=inf, linear_terms=array([ inf, -inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=9.216714200691555e-05, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([36]), old_indices_used=array([ 0, 23, 25, 27, 29, 31, 33, 35]), old_indices_discarded=array([17, 19, 21, 32, 34]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=4.608357100345777e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 25, 27, 29, 31, 33, 35, 37, 38]), model=ScalarModel(intercept=inf, linear_terms=array([inf, inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=4.608357100345777e-05, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([38]), old_indices_used=array([ 0, 25, 27, 29, 31, 33, 35, 37]), old_indices_discarded=array([17, 19, 21, 23, 34, 36]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=2.3041785501728886e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 27, 29, 31, 33, 35, 37, 39, 40]), model=ScalarModel(intercept=inf, linear_terms=array([ inf, -inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=2.3041785501728886e-05, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([40]), old_indices_used=array([ 0, 27, 29, 31, 33, 35, 37, 39]), old_indices_discarded=array([17, 19, 21, 23, 25, 36, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1.1520892750864443e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 29, 31, 33, 35, 37, 39, 41, 42]), model=ScalarModel(intercept=inf, linear_terms=array([inf, inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1.1520892750864443e-05, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([42]), old_indices_used=array([ 0, 29, 31, 33, 35, 37, 39, 41]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 38, 40]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=5.7604463754322216e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 31, 33, 35, 37, 39, 41, 43, 44]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=5.7604463754322216e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([44]), old_indices_used=array([ 0, 31, 33, 35, 37, 39, 41, 43]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 40, 42]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=2.8802231877161108e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 33, 35, 37, 39, 41, 43, 45, 46]), model=ScalarModel(intercept=inf, linear_terms=array([inf, inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=2.8802231877161108e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([46]), old_indices_used=array([ 0, 33, 35, 37, 39, 41, 43, 45]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 42, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1.4401115938580554e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 35, 37, 39, 41, 43, 45, 47, 48]), model=ScalarModel(intercept=inf, linear_terms=array([inf, inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1.4401115938580554e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([48]), old_indices_used=array([ 0, 35, 37, 39, 41, 43, 45, 47]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 44, 46]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 37, 39, 41, 43, 45, 47, 49, 50]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([50]), old_indices_used=array([ 0, 37, 39, 41, 43, 45, 47, 49]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 46, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 39, 41, 43, 45, 47, 49, 51, 52]), model=ScalarModel(intercept=inf, linear_terms=array([ inf, -inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([52]), old_indices_used=array([ 0, 39, 41, 43, 45, 47, 49, 51]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 46, 48, 50]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 43, 45, 47, 49, 51, 52, 53, 54]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([54]), old_indices_used=array([ 0, 43, 45, 47, 49, 51, 52, 53]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 46, 48, 50]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 45, 47, 49, 51, 52, 53, 55, 56]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([56]), old_indices_used=array([ 0, 45, 47, 49, 51, 52, 53, 55]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 46, 48, 50, + 54]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 49, 51, 52, 53, 55, 56, 57, 58]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([58]), old_indices_used=array([ 0, 49, 51, 52, 53, 55, 56, 57]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 50, 54]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 51, 52, 53, 55, 56, 57, 59, 60]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([60]), old_indices_used=array([ 0, 51, 52, 53, 55, 56, 57, 59]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 54, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 53, 55, 56, 57, 59, 61, 62]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([62]), old_indices_used=array([ 0, 52, 53, 55, 56, 57, 59, 61]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 54, 58, 60]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 57, 59, 61, 62, 63, 64]), model=ScalarModel(intercept=inf, linear_terms=array([ inf, -inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([64]), old_indices_used=array([ 0, 52, 56, 57, 59, 61, 62, 63]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 58, 60]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 59, 61, 62, 63, 65, 66]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([66]), old_indices_used=array([ 0, 52, 56, 59, 61, 62, 63, 65]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 57, 58, 60, 64]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 62, 63, 65, 66, 67, 68]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([68]), old_indices_used=array([ 0, 52, 56, 62, 63, 65, 66, 67]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, 61, 64]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 62, 65, 66, 67, 69, 70]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([70]), old_indices_used=array([ 0, 52, 56, 62, 65, 66, 67, 69]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 68]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 62, 66, 69, 70, 71, 72]), model=ScalarModel(intercept=inf, linear_terms=array([inf, inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([72]), old_indices_used=array([ 0, 52, 56, 62, 66, 69, 70, 71]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 62, 66, 70, 72, 73, 74]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([74]), old_indices_used=array([ 0, 52, 56, 62, 66, 70, 72, 73]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68, + 69, 71]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 62, 66, 70, 72, 75, 76]), model=ScalarModel(intercept=inf, linear_terms=array([inf, inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([76]), old_indices_used=array([ 0, 52, 56, 62, 66, 70, 72, 75]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68, + 69, 71, 73, 74]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 62, 66, 70, 72, 76, 78]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([78]), old_indices_used=array([ 0, 52, 56, 62, 66, 70, 72, 76]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68, + 69, 71, 73, 74, 75, 77]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 62, 66, 70, 72, 78, 80]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([80]), old_indices_used=array([ 0, 52, 56, 62, 66, 70, 72, 78]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68, + 69, 71, 73, 74, 75, 76, 77, 79]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 62, 66, 70, 78, 80, 82]), model=ScalarModel(intercept=inf, linear_terms=array([ inf, -inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([82]), old_indices_used=array([ 0, 52, 56, 62, 66, 70, 78, 80]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68, + 69, 71, 72, 73, 74, 75, 76, 77, 79, 81]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 62, 66, 70, 78, 80, 84]), model=ScalarModel(intercept=inf, linear_terms=array([inf, inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([84]), old_indices_used=array([ 0, 52, 56, 62, 66, 70, 78, 80]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68, + 69, 71, 72, 73, 74, 75, 76, 77, 79, 81, 82, 83]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 62, 66, 70, 78, 80, 86]), model=ScalarModel(intercept=inf, linear_terms=array([inf, inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([86]), old_indices_used=array([ 0, 52, 56, 62, 66, 70, 78, 80]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68, + 69, 71, 72, 73, 74, 75, 76, 77, 79, 81, 82, 83, 84, 85]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 56, 62, 66, 70, 78, 80, 86, 88]), model=ScalarModel(intercept=inf, linear_terms=array([ inf, -inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([88]), old_indices_used=array([ 0, 56, 62, 66, 70, 78, 80, 86]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, + 68, 69, 71, 72, 73, 74, 75, 76, 77, 79, 81, 82, 83, 84, 85, 87]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 56, 62, 66, 70, 78, 80, 86, 90]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([90]), old_indices_used=array([ 0, 56, 62, 66, 70, 78, 80, 86]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, + 68, 69, 71, 72, 73, 74, 75, 76, 77, 79, 81, 82, 83, 84, 85, 87, 88, + 89]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 56, 62, 66, 70, 78, 80, 86, 92]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([92]), old_indices_used=array([ 0, 56, 62, 66, 70, 78, 80, 86]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, + 68, 69, 71, 72, 73, 74, 75, 76, 77, 79, 81, 82, 83, 84, 85, 87, 88, + 89, 90, 91]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 56, 62, 66, 70, 78, 80, 86, 94]), model=ScalarModel(intercept=inf, linear_terms=array([inf, inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, 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5.50914405, 5.69522002, 6.68407291, 6.73863805, 6.84560996, + 7.20052264, 7.72130492, 8.47185488, 10.67451262])}}" diff --git a/content/tables/min/WarmGlowPortfolioSub(Stock)Market_estimate_results.csv b/content/tables/min/WarmGlowPortfolioSub(Stock)Market_estimate_results.csv new file mode 100644 index 0000000..b87147c --- /dev/null +++ b/content/tables/min/WarmGlowPortfolioSub(Stock)Market_estimate_results.csv @@ -0,0 +1,5941 @@ +CRRA,4.272642056859294 +DiscFac,0.9814607088251204 +time_to_estimate,106.73483872413635 +params,"{'CRRA': 4.272642056859294, 'DiscFac': 0.9814607088251204}" +criterion,1.5881921698252235 +start_criterion,1.7417506643900147 +start_params,"{'CRRA': 5.0, 'DiscFac': 0.95}" +algorithm,multistart_tranquilo_ls +direction,minimize +n_free,2 +message,Absolute criterion change smaller than tolerance. +success, +n_criterion_evaluations, +n_derivative_evaluations, +n_iterations, +history,"{'params': [{'CRRA': 4.707203308184448, 'DiscFac': 0.9723293237231975}, {'CRRA': 4.290038276655113, 'DiscFac': 0.5612839565173267}, {'CRRA': 5.124368339713784, 'DiscFac': 0.7815145778668051}, {'CRRA': 4.290038276655113, 'DiscFac': 0.8852911049439405}, {'CRRA': 5.124368339713784, 'DiscFac': 1.0998082225394454}, {'CRRA': 5.124368339713784, 'DiscFac': 0.6528702672894295}, {'CRRA': 5.088882093668508, 'DiscFac': 0.5551642921938619}, {'CRRA': 4.290038276655113, 'DiscFac': 1.0958487440640778}, {'CRRA': 5.124368339713784, 'DiscFac': 0.9644452739308207}, {'CRRA': 4.815289190462201, 'DiscFac': 1.1}, {'CRRA': 4.290038276655113, 'DiscFac': 1.0869940700172442}, {'CRRA': 4.610395613673098, 'DiscFac': 0.5551642921938619}, {'CRRA': 4.712667209754667, 'DiscFac': 1.1}, {'CRRA': 4.290038276655113, 'DiscFac': 0.8302806857872134}, {'CRRA': 4.49862079241978, 'DiscFac': 0.8632291852888655}, {'CRRA': 4.618262130250856, 'DiscFac': 0.892211907379606}, {'CRRA': 4.649360364309195, 'DiscFac': 0.9610417287955715}, {'CRRA': 4.677179125402864, 'DiscFac': 0.9726952599678501}, {'CRRA': 4.662420411739433, 'DiscFac': 0.9709527874372768}, {'CRRA': 4.633143948980592, 'DiscFac': 0.9673433127210762}, {'CRRA': 4.647708424364658, 'DiscFac': 0.9710769920318861}, {'CRRA': 4.618217430273746, 'DiscFac': 0.9715282984145067}, {'CRRA': 4.559385583543956, 'DiscFac': 0.9740437573449842}, {'CRRA': 4.442362348737134, 'DiscFac': 0.9615159200987019}, {'CRRA': 4.6177820933812335, 'DiscFac': 0.966832786178363}, {'CRRA': 4.530168028631268, 'DiscFac': 0.9705981657398585}, {'CRRA': 4.54468016749636, 'DiscFac': 0.9744118507601772}, {'CRRA': 4.515251965828596, 'DiscFac': 0.974531215771109}, {'CRRA': 4.456469672419852, 'DiscFac': 0.9719249832509226}, {'CRRA': 4.54388800665835, 'DiscFac': 0.9677847513502521}, {'CRRA': 4.5005611019825995, 'DiscFac': 0.9754513255437164}, {'CRRA': 4.529485918905441, 'DiscFac': 0.9678147143724265}, {'CRRA': 4.485839431860874, 'DiscFac': 0.9752072544842302}, {'CRRA': 4.456429538458829, 'DiscFac': 0.9759793251278149}, {'CRRA': 4.397608962906071, 'DiscFac': 0.9774938921710231}, {'CRRA': 4.279965862398321, 'DiscFac': 0.9804443756996974}, {'CRRA': 4.122203844999166, 'DiscFac': 0.937104673702021}, {'CRRA': 4.163432700992639, 'DiscFac': 0.9640546179784433}, {'CRRA': 4.336906613655518, 'DiscFac': 0.9656153058500048}, {'CRRA': 4.249020769576481, 'DiscFac': 0.9761381800429344}, {'CRRA': 4.294680071351096, 'DiscFac': 0.9804072400803372}, {'CRRA': 4.272642056859294, 'DiscFac': 0.9814607088251204}], 'criterion': [1.6380478126416131, 4.03723195367052, 3.5440740950812013, 3.2759678353900803, 6.781083894625252, 3.8713442516298198, 3.996890584141768, 7.627891908022631, 1.7551707114922444, 7.144112642232848, 6.6404717126627215, 3.935564366818633, 7.291412101525699, 3.862333195265191, 3.197857033577577, 2.6761443032128733, 1.6565559654451956, 1.631941462430788, 1.6292672840092992, 1.6313284776792012, 1.6269015883981968, 1.6215771152258682, 1.6122243121836672, 1.6696000135319509, 1.6306703543399446, 1.614669208950377, 1.6100467129384297, 1.6064550393910892, 1.6076695381563089, 1.6233520305796163, 1.6045259070805813, 1.6226940047077765, 1.6029216364692263, 1.599716096343115, 1.5939391812466719, 1.5886714368056305, 2.4007693475753995, 1.7378906646063588, 1.6562535862817716, 1.601747168932624, 1.5887715770723967, 1.5881921698252237], 'runtime': [0.0, 1.6821844799997052, 1.9150985659998696, 2.1253850919997603, 2.3511059309998927, 2.5650789659998736, 2.9549039649996303, 3.1735108909997507, 3.420102272999884, 3.6554864049999196, 3.8900463229997513, 4.125565374999951, 4.354761785999926, 5.818293315999654, 7.186155066999618, 8.53306442999974, 9.823283617000016, 11.099486161999721, 12.398446895999768, 13.630369614999836, 14.879300103999867, 16.134582642999703, 17.520538064999982, 18.790717263999795, 20.063523801999963, 21.335516795999865, 22.614525473999947, 23.88811478999969, 25.139806550999765, 26.40284090499972, 27.666455542999756, 28.90823847599995, 30.15419865199965, 31.539459458999772, 32.79186168299975, 34.119294054999955, 35.38719801199977, 36.70442176400002, 38.07354152799962, 39.34327665000001, 40.64497849999998, 41.9631218479999], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]}" +convergence_report,"{'one_step': {'relative_criterion_change': 0.00015933602220390196, 'relative_params_change': 0.0066586234867468804, 'absolute_criterion_change': 0.000253056222835335, 'absolute_params_change': 0.02844554200489083}, 'five_steps': {'relative_criterion_change': 0.00015933602220390196, 'relative_params_change': 0.0066586234867468804, 'absolute_criterion_change': 0.000253056222835335, 'absolute_params_change': 0.02844554200489083}}" +multistart_info,"{'start_parameters': [{'CRRA': 5.0, 'DiscFac': 0.95}, {'CRRA': 4.707203308184448, 'DiscFac': 0.9723293237231975}], 'local_optima': [Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 5.482e-05 0.01069 +relative_params_change 0.008558 0.06089 +absolute_criterion_change 8.708e-05 0.01698 +absolute_params_change 0.0362 0.2564 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 0.0003018 0.01028 +relative_params_change 0.002003 0.05369 +absolute_criterion_change 0.0004793 0.01633 +absolute_params_change 0.007394 0.228 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 5.0, 'DiscFac': 0.95}, {'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 7.596874999999999, 'DiscFac': 0.9312500000000001}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 9.368749999999999, 'DiscFac': 0.8375}, {'CRRA': 8.1875, 'DiscFac': 0.7250000000000001}, {'CRRA': 10.549999999999999, 'DiscFac': 0.8}, {'CRRA': 11.73125, 'DiscFac': 0.7625000000000001}, {'CRRA': 7.00625, 'DiscFac': 0.6125}, {'CRRA': 15.274999999999999, 'DiscFac': 0.65}, {'CRRA': 12.9125, 'DiscFac': 0.575}, {'CRRA': 17.046875, 'DiscFac': 0.6312500000000001}, {'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 16.45625, 'DiscFac': 0.9125000000000001}, {'CRRA': 14.093749999999998, 'DiscFac': 0.9875}, {'CRRA': 18.81875, 'DiscFac': 0.5375}, {'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 17.6375, 'DiscFac': 1.0250000000000001}, {'CRRA': 2.871875, 'DiscFac': 0.78125}, {'CRRA': 2.28125, 'DiscFac': 1.0625}], 'exploration_results': array([ 1.74175066, 2.04247528, 2.9717037 , 3.76994253, 3.80031518, + 4.27735638, 4.32695188, 4.78264529, 5.25346612, 5.79528191, + 5.94010747, 6.02403907, 6.09730665, 6.10855726, 6.12270544, + 6.45799199, 7.01428788, 7.3683214 , 8.72764877, 13.61935456])}" +algorithm_output,"{'states': [State(trustregion=Region(center=array([4.70720331, 0.97232932]), radius=0.4707203308184449, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=1.6380478126416131, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + 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radius=0.11768008270461122, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 7, 8, 9, 10, 11, 12, 14]), model=ScalarModel(intercept=1.854395217885178, linear_terms=array([0.030548 , 1.44658812]), square_terms=array([[ 0.01441336, -0.05269915], + [-0.05269915, 2.1098409 ]]), scale=0.11768008270461122, shift=array([4.70720331, 0.97232932])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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1.1]))), model_indices=array([ 0, 9, 12, 14, 15]), model=ScalarModel(intercept=1.4384011150252112, linear_terms=array([0.06693427, 0.16511788]), square_terms=array([[ 0.06028812, -0.33441041], + [-0.33441041, 2.50134611]]), scale=0.05884004135230561, shift=array([4.70720331, 0.97232932])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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linear_terms=array([ 0.1685441 , -0.13872507]), square_terms=array([[ 0.05020446, -0.12669357], + [-0.12669357, 0.64471625]]), scale=0.029420020676152805, shift=array([4.70720331, 0.97232932])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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[0.00326021, 0.12782284]]), scale=0.014710010338076403, shift=array([4.67717913, 0.97269526])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=18, candidate_x=array([4.66242041, 0.97095279]), index=18, x=array([4.66242041, 0.97095279]), fval=1.6292672840092994, rho=0.6756001269705089, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.014861219314677702, relative_step_length=1.010279324971642, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.66242041, 0.97095279]), radius=0.029420020676152805, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 15, 16, 17, 18]), model=ScalarModel(intercept=1.4810088090029692, linear_terms=array([ 0.11958572, -0.03732605]), square_terms=array([[ 0.05065772, -0.12829538], + [-0.12829538, 0.65103621]]), scale=0.029420020676152805, shift=array([4.66242041, 0.97095279])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=19, candidate_x=array([4.63314395, 0.96734331]), index=18, x=array([4.66242041, 0.97095279]), fval=1.6292672840092994, rho=-0.020590492456450688, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 15, 16, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.66242041, 0.97095279]), radius=0.014710010338076403, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 16, 17, 18, 19]), model=ScalarModel(intercept=1.6292156586595015, linear_terms=array([0.00188502, 0.00233158]), square_terms=array([[0.00018003, 0.0034169 ], + [0.0034169 , 0.12685944]]), scale=0.014710010338076403, shift=array([4.66242041, 0.97095279])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=20, candidate_x=array([4.64770842, 0.97107699]), index=20, x=array([4.64770842, 0.97107699]), fval=1.6269015883981965, rho=1.3143613594368355, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.014712511658342373, relative_step_length=1.0001700420467752, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.64770842, 0.97107699]), radius=0.029420020676152805, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 15, 16, 17, 18, 19, 20]), model=ScalarModel(intercept=1.5063160859816729, linear_terms=array([ 0.05041148, -0.04272255]), square_terms=array([[ 0.0075054 , -0.03307161], + [-0.03307161, 0.58165163]]), scale=0.029420020676152805, shift=array([4.64770842, 0.97107699])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, 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x=array([4.61821743, 0.9715283 ]), fval=1.6215771152258682, rho=0.11367207027105729, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 15, 16, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.029494447103161476, relative_step_length=1.002529788399129, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.61821743, 0.9715283 ]), radius=0.05884004135230561, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 15, 16, 17, 18, 19, 20, 21]), model=ScalarModel(intercept=1.517081598562027, linear_terms=array([ 0.03194748, -0.0915037 ]), square_terms=array([[0.00517109, 0.00423992], + [0.00423992, 2.21259895]]), scale=0.05884004135230561, shift=array([4.61821743, 0.9715283 ])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=22, candidate_x=array([4.55938558, 0.97404376]), index=22, x=array([4.55938558, 0.97404376]), fval=1.6122243121836672, rho=0.2975814223142011, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 15, 16, 17, 18, 19, 20, 21]), old_indices_discarded=array([ 9, 14]), step_length=0.058885598606691694, relative_step_length=1.000774255988593, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.55938558, 0.97404376]), radius=0.11768008270461122, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 15, 16, 17, 18, 19, 20, 21, 22]), model=ScalarModel(intercept=1.6244940972117774, linear_terms=array([0.00768524, 0.3968281 ]), square_terms=array([[ 0.01794061, -0.11175552], + [-0.11175552, 4.76976273]]), scale=0.11768008270461122, shift=array([4.55938558, 0.97404376])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=23, candidate_x=array([4.44236235, 0.96151592]), index=22, x=array([4.55938558, 0.97404376]), fval=1.6122243121836672, rho=-2.222167750707154, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 15, 16, 17, 18, 19, 20, 21, 22]), old_indices_discarded=array([ 0, 1, 3, 4, 7, 8, 9, 10, 11, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.55938558, 0.97404376]), radius=0.05884004135230561, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([15, 16, 17, 18, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=1.6256896704319683, linear_terms=array([0.0043886 , 0.10549552]), square_terms=array([[0.00419989, 0.0564693 ], + [0.0564693 , 1.33147903]]), scale=0.05884004135230561, shift=array([4.55938558, 0.97404376])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=24, candidate_x=array([4.61778209, 0.96683279]), index=22, x=array([4.55938558, 0.97404376]), fval=1.6122243121836672, rho=-5.466651138741908, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([15, 16, 17, 18, 19, 20, 21, 22, 23]), old_indices_discarded=array([ 0, 3, 9, 10, 12, 14]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.55938558, 0.97404376]), radius=0.029420020676152805, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([15, 16, 18, 19, 20, 21, 22, 23, 24]), model=ScalarModel(intercept=1.6269177150442447, linear_terms=array([0.00261458, 0.05266846]), square_terms=array([[0.00103242, 0.01385775], + [0.01385775, 0.33190979]]), scale=0.029420020676152805, shift=array([4.55938558, 0.97404376])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=25, candidate_x=array([4.53016803, 0.97059817]), index=22, x=array([4.55938558, 0.97404376]), fval=1.6122243121836672, rho=-0.5597649420514624, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([15, 16, 18, 19, 20, 21, 22, 23, 24]), old_indices_discarded=array([14, 17]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.55938558, 0.97404376]), radius=0.014710010338076403, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([21, 22, 24, 25]), model=ScalarModel(intercept=1.6125186187197176, linear_terms=array([0.00192027, 0.00012471]), square_terms=array([[0.00019263, 0.00355747], + [0.00355747, 0.13532065]]), scale=0.014710010338076403, shift=array([4.55938558, 0.97404376])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=26, candidate_x=array([4.54468017, 0.97441185]), index=26, x=array([4.54468017, 0.97441185]), fval=1.6100467129384297, rho=1.1664111683806726, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([21, 22, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.01471002222619643, relative_step_length=1.0000008081653073, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.54468017, 0.97441185]), radius=0.029420020676152805, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 19, 20, 21, 22, 23, 24, 25, 26]), model=ScalarModel(intercept=1.610617175628848, linear_terms=array([0.00467212, 0.01317766]), square_terms=array([[0.00085989, 0.01539591], + [0.01539591, 0.54391644]]), scale=0.029420020676152805, shift=array([4.54468017, 0.97441185])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=27, candidate_x=array([4.51525197, 0.97453122]), index=27, x=array([4.51525197, 0.97453122]), fval=1.6064550393910895, rho=0.8455422416631787, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([16, 19, 20, 21, 22, 23, 24, 25, 26]), old_indices_discarded=array([14, 15, 17, 18]), step_length=0.029428443747579514, relative_step_length=1.000286304062102, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.51525197, 0.97453122]), radius=0.05884004135230561, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 19, 21, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=1.6171036489097632, linear_terms=array([0.00432157, 0.08549957]), square_terms=array([[0.00404165, 0.04083728], + [0.04083728, 0.9748026 ]]), scale=0.05884004135230561, shift=array([4.51525197, 0.97453122])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=28, candidate_x=array([4.45646967, 0.97192498]), index=27, x=array([4.51525197, 0.97453122]), fval=1.6064550393910895, rho=-0.3653445002297127, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 19, 21, 22, 23, 24, 25, 26, 27]), old_indices_discarded=array([ 0, 3, 7, 10, 12, 13, 15, 16, 17, 18, 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.51525197, 0.97453122]), radius=0.029420020676152805, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 21, 22, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=1.6158549160636237, linear_terms=array([0.0016874, 0.045421 ]), square_terms=array([[0.00106973, 0.01073937], + [0.01073937, 0.24481613]]), scale=0.029420020676152805, shift=array([4.51525197, 0.97453122])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=29, candidate_x=array([4.54388801, 0.96778475]), index=27, x=array([4.51525197, 0.97453122]), fval=1.6064550393910895, rho=-3.9976405851055894, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 21, 22, 23, 24, 25, 26, 27, 28]), old_indices_discarded=array([15, 16, 19, 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.51525197, 0.97453122]), radius=0.014710010338076403, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=1.6066876339598914, linear_terms=array([ 0.00156986, -0.00517551]), square_terms=array([[0.00019907, 0.00364165], + [0.00364165, 0.13928521]]), scale=0.014710010338076403, shift=array([4.51525197, 0.97453122])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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model=ScalarModel(intercept=1.6151275211840224, linear_terms=array([-0.00091945, 0.04982336]), square_terms=array([[0.0018448 , 0.01496444], + [0.01496444, 0.24558318]]), scale=0.029420020676152805, shift=array([4.5005611 , 0.97545133])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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0.00619617]), square_terms=array([[0.0002099 , 0.00380383], + [0.00380383, 0.14225083]]), scale=0.014710010338076403, shift=array([4.5005611 , 0.97545133])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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0.01532586], + [0.01532586, 0.57103593]]), scale=0.029420020676152805, shift=array([4.48583943, 0.97520725])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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scale=0.05884004135230561, shift=array([4.45642954, 0.97597933])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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scale=0.11768008270461122, shift=array([4.39760896, 0.97749389])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=36, candidate_x=array([4.12220384, 0.93710467]), index=35, x=array([4.27996586, 0.98044438]), fval=1.5886714368056303, rho=-3.916267884563185, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 13, 23, 28, 30, 32, 34, 35]), old_indices_discarded=array([ 0, 1, 2, 4, 5, 6, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, + 20, 21, 22, 24, 25, 26, 27, 29, 31, 33]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.27996586, 0.98044438]), radius=0.11768008270461122, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 13, 23, 33, 34, 35, 36]), model=ScalarModel(intercept=1.4622568288955424, linear_terms=array([0.02225047, 0.92573591]), square_terms=array([[ 0.05426403, -0.13596618], 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[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=37, candidate_x=array([4.1634327 , 0.96405462]), index=35, x=array([4.27996586, 0.98044438]), fval=1.5886714368056303, rho=-2.181712710002738, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 10, 13, 23, 33, 34, 35, 36]), old_indices_discarded=array([ 0, 1, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, 26, + 27, 28, 29, 30, 31, 32]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.27996586, 0.98044438]), radius=0.05884004135230561, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 13, 23, 34, 35, 36, 37]), model=ScalarModel(intercept=1.4660466698117092, linear_terms=array([0.01297109, 0.40170174]), square_terms=array([[0.01156038, 0.07971153], + 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shift=array([4.27996586, 0.98044438])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=39, candidate_x=array([4.24902077, 0.97613818]), index=35, x=array([4.27996586, 0.98044438]), fval=1.5886714368056303, rho=-1.3905549963491703, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 10, 34, 35, 37, 38]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.27996586, 0.98044438]), radius=0.014710010338076403, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([35, 38, 39]), model=ScalarModel(intercept=1.588671436805631, linear_terms=array([-0.00081796, -0.00402187]), square_terms=array([[0.00023222, 0.00443072], + [0.00443072, 0.1618569 ]]), scale=0.014710010338076403, shift=array([4.27996586, 0.98044438])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=40, candidate_x=array([4.29468007, 0.98040724]), index=35, x=array([4.27996586, 0.98044438]), fval=1.5886714368056303, rho=-0.14254194492005098, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 38, 39]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.27996586, 0.98044438]), radius=0.007355005169038201, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([35, 39, 40]), model=ScalarModel(intercept=1.5886714368056296, linear_terms=array([-1.01760419e-05, -4.87679307e-03]), square_terms=array([[5.26567689e-05, 9.93842843e-04], + [9.93842843e-04, 4.23791006e-02]]), scale=0.007355005169038201, shift=array([4.27996586, 0.98044438])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=41, candidate_x=array([4.27264206, 0.98146071]), index=41, x=array([4.27264206, 0.98146071]), fval=1.5881921698252235, rho=1.2960293054666532, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([35, 39, 40]), old_indices_discarded=array([], dtype=int64), step_length=0.007393988138705295, relative_step_length=1.005300196093838, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 42 entries., 'multistart_info': {'start_parameters': [array([5. , 0.95]), array([4.70720331, 0.97232932])], 'local_optima': [{'solution_x': array([4.24419676, 0.98157843]), 'solution_criterion': 1.5884452260480588, 'states': [State(trustregion=Region(center=array([5. , 0.95]), radius=0.5, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=1.7417506643900147, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.5, shift=array([5. , 0.95])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5, shift=array([5. , 0.95])), candidate_index=0, candidate_x=array([5. , 0.95]), index=0, x=array([5. , 0.95]), fval=1.7417506643900145, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([5. , 0.95]), radius=0.5, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=1.525682901381052, linear_terms=array([ 0.35147642, -0.70797287]), square_terms=array([[ 0.17228309, -0.70481542], + [-0.70481542, 7.78269567]]), scale=array([0.44311346, 0.29655673]), shift=array([5. , 0.80344327])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 7, 8, 9, 10, 12, 13, 14]), model=ScalarModel(intercept=1.5012663356887093, linear_terms=array([ 0.08935364, -0.18990169]), square_terms=array([[ 0.02022595, -0.24678211], + [-0.24678211, 7.22788319]]), scale=0.125, shift=array([5. , 0.95])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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model=ScalarModel(intercept=1.7089526132207769, linear_terms=array([ 0.02648415, -0.90309879]), square_terms=array([[ 2.01607285e-03, -3.09418837e-02], + [-3.09418837e-02, 2.59137456e+00]]), scale=0.0625, shift=array([4.87496272, 0.94902461])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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square_terms=array([[ 0.02293557, -0.32021801], + [-0.32021801, 9.48068192]]), scale=0.125, shift=array([4.81232469, 0.96994443])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5, shift=array([5. , 0.95])), candidate_index=17, candidate_x=array([4.6875834 , 0.96047652]), index=17, x=array([4.6875834 , 0.96047652]), fval=1.6605566084700965, rho=0.019087760659877613, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 3, 7, 9, 10, 12, 14, 15, 16]), old_indices_discarded=array([ 1, 11, 13]), step_length=0.12510008504138495, relative_step_length=1.0008006803310796, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.6875834 , 0.96047652]), radius=0.0625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 12, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=1.4081104203383767, linear_terms=array([0.02058664, 0.27473715]), square_terms=array([[ 0.02001168, -0.16820066], + [-0.16820066, 1.89038505]]), scale=0.0625, shift=array([4.6875834 , 0.96047652])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5, shift=array([5. , 0.95])), candidate_index=18, candidate_x=array([4.62612848, 0.94622607]), index=17, x=array([4.6875834 , 0.96047652]), fval=1.6605566084700965, rho=-2.0577598152094736, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 10, 12, 13, 14, 15, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.6875834 , 0.96047652]), radius=0.03125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 16, 17, 18]), model=ScalarModel(intercept=1.6564086025715405, linear_terms=array([-0.00040164, -0.13821286]), square_terms=array([[0.0010511 , 0.01749125], + [0.01749125, 0.49318606]]), scale=0.03125, shift=array([4.6875834 , 0.96047652])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5, shift=array([5. , 0.95])), candidate_index=19, candidate_x=array([4.65669195, 0.970249 ]), index=19, x=array([4.65669195, 0.970249 ]), fval=1.6291014270431563, rho=1.332667694520173, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 16, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.03240034924580121, relative_step_length=1.0368111758656386, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65669195, 0.970249 ]), radius=0.0625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 13, 14, 16, 17, 18, 19]), model=ScalarModel(intercept=1.4667362991629962, linear_terms=array([0.03429873, 0.35050851]), square_terms=array([[ 0.07672055, -0.32331271], + [-0.32331271, 1.74181997]]), scale=0.0625, shift=array([4.65669195, 0.970249 ])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5, shift=array([5. , 0.95])), candidate_index=20, candidate_x=array([4.59740369, 0.94773026]), index=19, x=array([4.65669195, 0.970249 ]), fval=1.6291014270431563, rho=-1.221321739629065, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 10, 13, 14, 16, 17, 18, 19]), old_indices_discarded=array([15]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65669195, 0.970249 ]), radius=0.03125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 14, 16, 17, 18, 19, 20]), model=ScalarModel(intercept=1.476547966942911, linear_terms=array([0.03370192, 0.03763453]), square_terms=array([[ 0.00506063, -0.03670318], + [-0.03670318, 0.56722334]]), scale=0.03125, shift=array([4.65669195, 0.970249 ])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5, shift=array([5. , 0.95])), candidate_index=21, candidate_x=array([4.62562764, 0.9663926 ]), index=19, x=array([4.65669195, 0.970249 ]), fval=1.6291014270431563, rho=-0.08905240689766346, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 14, 16, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65669195, 0.970249 ]), radius=0.015625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([17, 18, 19, 20, 21]), model=ScalarModel(intercept=1.6322981269191386, linear_terms=array([0.00381601, 0.00635055]), square_terms=array([[0.00024779, 0.00408403], + [0.00408403, 0.13797549]]), scale=0.015625, shift=array([4.65669195, 0.970249 ])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5, shift=array([5. , 0.95])), candidate_index=22, candidate_x=array([4.64105275, 0.96999908]), index=22, x=array([4.64105275, 0.96999908]), fval=1.6271336788414648, rho=0.5298332001777031, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([17, 18, 19, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.015641195495581482, relative_step_length=1.0010365117172149, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.64105275, 0.96999908]), radius=0.03125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 14, 17, 18, 19, 20, 21, 22]), model=ScalarModel(intercept=1.4934936330221924, linear_terms=array([0.03884984, 0.00088909]), square_terms=array([[ 0.00687594, -0.04632293], + [-0.04632293, 0.56349202]]), scale=0.03125, shift=array([4.64105275, 0.96999908])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5, shift=array([5. , 0.95])), candidate_index=23, candidate_x=array([4.60989908, 0.96754444]), index=22, x=array([4.64105275, 0.96999908]), fval=1.6271336788414648, rho=-0.036286714935296296, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 14, 17, 18, 19, 20, 21, 22]), old_indices_discarded=array([10]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.64105275, 0.96999908]), radius=0.015625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([17, 18, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=1.629077033113828, linear_terms=array([0.00316852, 0.0009159 ]), square_terms=array([[0.00023897, 0.00406143], + [0.00406143, 0.13846122]]), scale=0.015625, shift=array([4.64105275, 0.96999908])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5, shift=array([5. , 0.95])), candidate_index=24, candidate_x=array([4.62543122, 0.97034637]), index=24, x=array([4.62543122, 0.97034637]), fval=1.6244654953135362, rho=0.8651468419534107, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([17, 18, 19, 20, 21, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.015625394514317673, relative_step_length=1.000025248916331, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.62543122, 0.97034637]), radius=0.03125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 17, 18, 19, 20, 21, 22, 23, 24]), model=ScalarModel(intercept=1.637881414293269, linear_terms=array([0.00458585, 0.02180017]), square_terms=array([[0.00098328, 0.01001956], + [0.01001956, 0.27420015]]), scale=0.03125, shift=array([4.62543122, 0.97034637])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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2.22309204]]), scale=0.0625, shift=array([4.59411882, 0.96902136])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5, shift=array([5. , 0.95])), candidate_index=26, candidate_x=array([4.53171043, 0.97299769]), index=26, x=array([4.53171043, 0.97299769]), fval=1.6090597971175158, rho=1.2503670663900857, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([17, 18, 19, 20, 21, 22, 23, 24, 25]), old_indices_discarded=array([ 3, 7, 10, 13, 14, 15, 16]), step_length=0.06253494026175699, relative_step_length=1.0005590441881118, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.53171043, 0.97299769]), radius=0.125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 18, 20, 21, 22, 23, 24, 25, 26]), model=ScalarModel(intercept=1.6102616360135478, linear_terms=array([0.03712606, 0.18206296]), square_terms=array([[ 0.01015217, -0.01895962], + [-0.01895962, 4.60594093]]), scale=0.125, shift=array([4.53171043, 0.97299769])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5, shift=array([5. , 0.95])), candidate_index=27, candidate_x=array([4.40674873, 0.96757506]), index=26, x=array([4.53171043, 0.97299769]), fval=1.6090597971175158, rho=-0.5639375590226261, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 18, 20, 21, 22, 23, 24, 25, 26]), old_indices_discarded=array([ 0, 1, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.53171043, 0.97299769]), radius=0.0625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 18, 20, 21, 22, 23, 24, 25, 26]), model=ScalarModel(intercept=1.6102616360135507, linear_terms=array([0.01856303, 0.09103148]), square_terms=array([[ 0.00253804, -0.00473991], + [-0.00473991, 1.15148523]]), scale=0.0625, shift=array([4.53171043, 0.97299769])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5, shift=array([5. , 0.95])), candidate_index=28, candidate_x=array([4.46924045, 0.96787265]), index=26, x=array([4.53171043, 0.97299769]), fval=1.6090597971175158, rho=-0.6660251871790788, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 18, 20, 21, 22, 23, 24, 25, 26]), old_indices_discarded=array([ 7, 10, 13, 14, 16, 17, 19, 27]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.53171043, 0.97299769]), radius=0.03125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([18, 20, 21, 22, 23, 24, 25, 26, 28]), model=ScalarModel(intercept=1.6130550857976087, linear_terms=array([0.00349422, 0.00036862]), square_terms=array([[0.00092013, 0.01618579], + [0.01618579, 0.55845701]]), scale=0.03125, shift=array([4.53171043, 0.97299769])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5, shift=array([5. , 0.95])), candidate_index=29, candidate_x=array([4.50047281, 0.97387764]), index=29, x=array([4.50047281, 0.97387764]), fval=1.605426494943335, rho=1.1155576394171702, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([18, 20, 21, 22, 23, 24, 25, 26, 28]), old_indices_discarded=array([ 3, 7, 10, 19, 27]), step_length=0.03125001041288541, relative_step_length=1.000000333212333, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.50047281, 0.97387764]), radius=0.0625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 20, 23, 24, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=1.6183556456638188, linear_terms=array([0.00684669, 0.05973239]), square_terms=array([[0.00278798, 0.03172634], + [0.03172634, 1.10937006]]), scale=0.0625, shift=array([4.50047281, 0.97387764])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5, shift=array([5. , 0.95])), candidate_index=30, candidate_x=array([4.43792976, 0.97230568]), index=29, x=array([4.50047281, 0.97387764]), fval=1.605426494943335, rho=-0.07150461942939365, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 20, 23, 24, 25, 26, 27, 28, 29]), old_indices_discarded=array([ 7, 10, 13, 14, 16, 17, 18, 19, 21, 22]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.50047281, 0.97387764]), radius=0.03125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 20, 23, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=1.6184027642815897, linear_terms=array([0.0045845 , 0.03080401]), square_terms=array([[0.00058656, 0.00613405], + [0.00613405, 0.27515297]]), scale=0.03125, shift=array([4.50047281, 0.97387764])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5, shift=array([5. , 0.95])), candidate_index=31, candidate_x=array([4.46916051, 0.97111184]), index=29, x=array([4.50047281, 0.97387764]), fval=1.605426494943335, rho=-0.9123426176285044, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 20, 23, 25, 26, 27, 28, 29, 30]), old_indices_discarded=array([10, 18, 21, 22, 24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.50047281, 0.97387764]), radius=0.015625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([26, 28, 29, 30, 31]), model=ScalarModel(intercept=1.605617847169665, linear_terms=array([ 0.00128156, -0.01188114]), square_terms=array([[0.0002254 , 0.0042768 ], + [0.0042768 , 0.16410452]]), scale=0.015625, shift=array([4.50047281, 0.97387764])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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model=ScalarModel(intercept=1.600137490679293, linear_terms=array([ 0.00511285, -0.03923899]), square_terms=array([[0.00379447, 0.07073241], + [0.07073241, 2.64617384]]), scale=0.0625, shift=array([4.45362123, 0.97543964])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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square_terms=array([[ 0.04649406, 0.64815023], + [ 0.64815023, 20.13386188]]), scale=array([0.22155673, 0.17033768]), shift=array([4.28039139, 0.92966232])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], 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candidate_x=array([4.29003828, 0.83028069]), index=0, x=array([4.70720331, 0.97232932]), fval=1.6380478126416131, rho=-2.200880078069248, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.70720331, 0.97232932]), radius=0.23536016540922244, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 4, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=1.652854750173936, linear_terms=array([0.06969601, 1.57909406]), square_terms=array([[ 0.03163761, -0.06566736], + [-0.06566736, 4.02842576]]), scale=array([0.20858252, 0.1681266 ]), shift=array([4.70720331, 0.9318734 ])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=14, candidate_x=array([4.49862079, 0.86322919]), index=0, x=array([4.70720331, 0.97232932]), fval=1.6380478126416131, rho=-1.7600178518411977, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 3, 4, 7, 8, 9, 10, 11, 12]), old_indices_discarded=array([ 1, 2, 5, 6, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.70720331, 0.97232932]), radius=0.11768008270461122, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 7, 8, 9, 10, 11, 12, 14]), model=ScalarModel(intercept=1.854395217885178, linear_terms=array([0.030548 , 1.44658812]), square_terms=array([[ 0.01441336, -0.05269915], + [-0.05269915, 2.1098409 ]]), scale=0.11768008270461122, shift=array([4.70720331, 0.97232932])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=15, candidate_x=array([4.61826213, 0.89221191]), index=0, x=array([4.70720331, 0.97232932]), fval=1.6380478126416131, rho=-1.915374410311117, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 3, 7, 8, 9, 10, 11, 12, 14]), old_indices_discarded=array([ 1, 2, 4, 5, 6, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.70720331, 0.97232932]), radius=0.05884004135230561, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 9, 12, 14, 15]), model=ScalarModel(intercept=1.4384011150252112, linear_terms=array([0.06693427, 0.16511788]), square_terms=array([[ 0.06028812, -0.33441041], + [-0.33441041, 2.50134611]]), scale=0.05884004135230561, shift=array([4.70720331, 0.97232932])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=16, candidate_x=array([4.64936036, 0.96104173]), index=0, x=array([4.70720331, 0.97232932]), fval=1.6380478126416131, rho=-0.2167656601879471, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 9, 12, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.70720331, 0.97232932]), radius=0.029420020676152805, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 15, 16]), model=ScalarModel(intercept=1.6372268956710594, linear_terms=array([ 0.1685441 , -0.13872507]), square_terms=array([[ 0.05020446, -0.12669357], + [-0.12669357, 0.64471625]]), scale=0.029420020676152805, shift=array([4.70720331, 0.97232932])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=17, candidate_x=array([4.67717913, 0.97269526]), index=17, x=array([4.67717913, 0.97269526]), fval=1.631941462430788, rho=0.04184469522185483, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 15, 16]), old_indices_discarded=array([], dtype=int64), step_length=0.03002641272341987, relative_step_length=1.0206115438850998, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.67717913, 0.97269526]), radius=0.014710010338076403, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 16, 17]), model=ScalarModel(intercept=1.6319414624307902, linear_terms=array([0.00310345, 0.01871238]), square_terms=array([[0.0001745 , 0.00326021], + [0.00326021, 0.12782284]]), scale=0.014710010338076403, shift=array([4.67717913, 0.97269526])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=18, candidate_x=array([4.66242041, 0.97095279]), index=18, x=array([4.66242041, 0.97095279]), fval=1.6292672840092994, rho=0.6756001269705089, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.014861219314677702, relative_step_length=1.010279324971642, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.66242041, 0.97095279]), radius=0.029420020676152805, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 15, 16, 17, 18]), model=ScalarModel(intercept=1.4810088090029692, linear_terms=array([ 0.11958572, -0.03732605]), square_terms=array([[ 0.05065772, -0.12829538], + [-0.12829538, 0.65103621]]), scale=0.029420020676152805, shift=array([4.66242041, 0.97095279])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=19, candidate_x=array([4.63314395, 0.96734331]), index=18, x=array([4.66242041, 0.97095279]), fval=1.6292672840092994, rho=-0.020590492456450688, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 15, 16, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.66242041, 0.97095279]), radius=0.014710010338076403, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 16, 17, 18, 19]), model=ScalarModel(intercept=1.6292156586595015, linear_terms=array([0.00188502, 0.00233158]), square_terms=array([[0.00018003, 0.0034169 ], + [0.0034169 , 0.12685944]]), scale=0.014710010338076403, shift=array([4.66242041, 0.97095279])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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model=ScalarModel(intercept=1.5063160859816729, linear_terms=array([ 0.05041148, -0.04272255]), square_terms=array([[ 0.0075054 , -0.03307161], + [-0.03307161, 0.58165163]]), scale=0.029420020676152805, shift=array([4.64770842, 0.97107699])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=21, candidate_x=array([4.61821743, 0.9715283 ]), index=21, x=array([4.61821743, 0.9715283 ]), fval=1.6215771152258682, rho=0.11367207027105729, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 15, 16, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.029494447103161476, relative_step_length=1.002529788399129, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.61821743, 0.9715283 ]), radius=0.05884004135230561, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 15, 16, 17, 18, 19, 20, 21]), model=ScalarModel(intercept=1.517081598562027, linear_terms=array([ 0.03194748, -0.0915037 ]), square_terms=array([[0.00517109, 0.00423992], + [0.00423992, 2.21259895]]), scale=0.05884004135230561, shift=array([4.61821743, 0.9715283 ])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=22, candidate_x=array([4.55938558, 0.97404376]), index=22, x=array([4.55938558, 0.97404376]), fval=1.6122243121836672, rho=0.2975814223142011, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 15, 16, 17, 18, 19, 20, 21]), old_indices_discarded=array([ 9, 14]), step_length=0.058885598606691694, relative_step_length=1.000774255988593, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.55938558, 0.97404376]), radius=0.11768008270461122, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 15, 16, 17, 18, 19, 20, 21, 22]), model=ScalarModel(intercept=1.6244940972117774, linear_terms=array([0.00768524, 0.3968281 ]), square_terms=array([[ 0.01794061, -0.11175552], + [-0.11175552, 4.76976273]]), scale=0.11768008270461122, shift=array([4.55938558, 0.97404376])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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1.33147903]]), scale=0.05884004135230561, shift=array([4.55938558, 0.97404376])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=24, candidate_x=array([4.61778209, 0.96683279]), index=22, x=array([4.55938558, 0.97404376]), fval=1.6122243121836672, rho=-5.466651138741908, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([15, 16, 17, 18, 19, 20, 21, 22, 23]), old_indices_discarded=array([ 0, 3, 9, 10, 12, 14]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.55938558, 0.97404376]), radius=0.029420020676152805, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([15, 16, 18, 19, 20, 21, 22, 23, 24]), model=ScalarModel(intercept=1.6269177150442447, linear_terms=array([0.00261458, 0.05266846]), square_terms=array([[0.00103242, 0.01385775], + [0.01385775, 0.33190979]]), scale=0.029420020676152805, shift=array([4.55938558, 0.97404376])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=25, candidate_x=array([4.53016803, 0.97059817]), index=22, x=array([4.55938558, 0.97404376]), fval=1.6122243121836672, rho=-0.5597649420514624, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([15, 16, 18, 19, 20, 21, 22, 23, 24]), old_indices_discarded=array([14, 17]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.55938558, 0.97404376]), radius=0.014710010338076403, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([21, 22, 24, 25]), model=ScalarModel(intercept=1.6125186187197176, linear_terms=array([0.00192027, 0.00012471]), square_terms=array([[0.00019263, 0.00355747], + [0.00355747, 0.13532065]]), scale=0.014710010338076403, shift=array([4.55938558, 0.97404376])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=26, candidate_x=array([4.54468017, 0.97441185]), index=26, x=array([4.54468017, 0.97441185]), fval=1.6100467129384297, rho=1.1664111683806726, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([21, 22, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.01471002222619643, relative_step_length=1.0000008081653073, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.54468017, 0.97441185]), radius=0.029420020676152805, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 19, 20, 21, 22, 23, 24, 25, 26]), model=ScalarModel(intercept=1.610617175628848, linear_terms=array([0.00467212, 0.01317766]), square_terms=array([[0.00085989, 0.01539591], + [0.01539591, 0.54391644]]), scale=0.029420020676152805, shift=array([4.54468017, 0.97441185])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=27, candidate_x=array([4.51525197, 0.97453122]), index=27, x=array([4.51525197, 0.97453122]), fval=1.6064550393910895, rho=0.8455422416631787, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([16, 19, 20, 21, 22, 23, 24, 25, 26]), old_indices_discarded=array([14, 15, 17, 18]), step_length=0.029428443747579514, relative_step_length=1.000286304062102, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.51525197, 0.97453122]), radius=0.05884004135230561, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 19, 21, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=1.6171036489097632, linear_terms=array([0.00432157, 0.08549957]), square_terms=array([[0.00404165, 0.04083728], + [0.04083728, 0.9748026 ]]), scale=0.05884004135230561, shift=array([4.51525197, 0.97453122])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=28, candidate_x=array([4.45646967, 0.97192498]), index=27, x=array([4.51525197, 0.97453122]), fval=1.6064550393910895, rho=-0.3653445002297127, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 19, 21, 22, 23, 24, 25, 26, 27]), old_indices_discarded=array([ 0, 3, 7, 10, 12, 13, 15, 16, 17, 18, 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.51525197, 0.97453122]), radius=0.029420020676152805, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 21, 22, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=1.6158549160636237, linear_terms=array([0.0016874, 0.045421 ]), square_terms=array([[0.00106973, 0.01073937], + [0.01073937, 0.24481613]]), scale=0.029420020676152805, shift=array([4.51525197, 0.97453122])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=29, candidate_x=array([4.54388801, 0.96778475]), index=27, x=array([4.51525197, 0.97453122]), fval=1.6064550393910895, rho=-3.9976405851055894, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 21, 22, 23, 24, 25, 26, 27, 28]), old_indices_discarded=array([15, 16, 19, 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.51525197, 0.97453122]), radius=0.014710010338076403, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=1.6066876339598914, linear_terms=array([ 0.00156986, -0.00517551]), square_terms=array([[0.00019907, 0.00364165], + [0.00364165, 0.13928521]]), scale=0.014710010338076403, shift=array([4.51525197, 0.97453122])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=30, candidate_x=array([4.5005611 , 0.97545133]), index=30, x=array([4.5005611 , 0.97545133]), fval=1.6045259070805813, rho=1.104079170574446, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([22, 25, 26, 27, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=0.014719649538465622, relative_step_length=1.0006552816869387, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.5005611 , 0.97545133]), radius=0.029420020676152805, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 22, 23, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=1.6151275211840224, linear_terms=array([-0.00091945, 0.04982336]), square_terms=array([[0.0018448 , 0.01496444], + [0.01496444, 0.24558318]]), scale=0.029420020676152805, shift=array([4.5005611 , 0.97545133])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=31, candidate_x=array([4.52948592, 0.96781471]), index=30, x=array([4.5005611 , 0.97545133]), fval=1.6045259070805813, rho=-2.139770334550352, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 22, 23, 25, 26, 27, 28, 29, 30]), old_indices_discarded=array([15, 19, 21, 24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.5005611 , 0.97545133]), radius=0.014710010338076403, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 23, 25, 26, 27, 28, 29, 30, 31]), model=ScalarModel(intercept=1.6052861266606393, linear_terms=array([0.00202734, 0.00619617]), square_terms=array([[0.0002099 , 0.00380383], + [0.00380383, 0.14225083]]), scale=0.014710010338076403, shift=array([4.5005611 , 0.97545133])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=32, candidate_x=array([4.48583943, 0.97520725]), index=32, x=array([4.48583943, 0.97520725]), fval=1.6029216364692263, rho=0.8252881334307689, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([22, 23, 25, 26, 27, 28, 29, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.014723693213830385, relative_step_length=1.0009301744485226, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.48583943, 0.97520725]), radius=0.029420020676152805, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 25, 26, 27, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=1.603281247362616, linear_terms=array([0.00373974, 0.00024847]), square_terms=array([[0.00085108, 0.01532586], + [0.01532586, 0.57103593]]), scale=0.029420020676152805, shift=array([4.48583943, 0.97520725])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=33, candidate_x=array([4.45642954, 0.97597933]), index=33, x=array([4.45642954, 0.97597933]), fval=1.599716096343115, rho=0.9127111762327604, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([23, 25, 26, 27, 28, 29, 30, 31, 32]), old_indices_discarded=array([14, 21, 22, 24]), step_length=0.0294200258837129, relative_step_length=1.0000001770073568, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.45642954, 0.97597933]), radius=0.05884004135230561, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 25, 27, 28, 29, 30, 31, 32, 33]), model=ScalarModel(intercept=1.5997771172632202, linear_terms=array([0.00705164, 0.0023645 ]), square_terms=array([[0.00344304, 0.06165063], + [0.06165063, 2.29724063]]), scale=0.05884004135230561, shift=array([4.45642954, 0.97597933])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=34, candidate_x=array([4.39760896, 0.97749389]), index=34, x=array([4.39760896, 0.97749389]), fval=1.5939391812466719, rho=0.9480590071850733, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([23, 25, 27, 28, 29, 30, 31, 32, 33]), old_indices_discarded=array([ 0, 3, 7, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, 26]), step_length=0.05884007156424952, relative_step_length=1.000000513458917, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.39760896, 0.97749389]), radius=0.11768008270461122, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 25, 27, 28, 30, 31, 32, 33, 34]), model=ScalarModel(intercept=1.593889235787229, linear_terms=array([0.01040724, 0.00815608]), square_terms=array([[0.01329919, 0.24024205], + [0.24024205, 9.25060511]]), scale=0.11768008270461122, shift=array([4.39760896, 0.97749389])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=35, candidate_x=array([4.27996586, 0.98044438]), index=35, x=array([4.27996586, 0.98044438]), fval=1.5886714368056303, rho=0.7899999841070257, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([23, 25, 27, 28, 30, 31, 32, 33, 34]), old_indices_discarded=array([ 0, 1, 3, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, + 22, 24, 26, 29]), step_length=0.11768009368678081, relative_step_length=1.0000000933222457, n_evals_per_point=1, 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, 8.72764877, 13.61935456])}}" diff --git a/content/tables/min/WarmGlowPortfolio_estimate_results.csv b/content/tables/min/WarmGlowPortfolio_estimate_results.csv new file mode 100644 index 0000000..b21ab12 --- /dev/null +++ b/content/tables/min/WarmGlowPortfolio_estimate_results.csv @@ -0,0 +1,36082 @@ +CRRA,7.1639015143593845 +BeqFac,20.000633171274284 +BeqShift,19.79517609692503 +time_to_estimate,602.1032071113586 +params,"{'CRRA': 7.1639015143593845, 'BeqFac': 20.000633171274284, 'BeqShift': 19.79517609692503}" +criterion,2.1803868818934804 +start_criterion,2.1256418965625494 +start_params,"{'CRRA': 9.370461268457287, 'BeqFac': 67.92926162554892, 'BeqShift': 52.186320909731975}" +algorithm,multistart_tranquilo_ls +direction,minimize +n_free,3 +message, +success, +n_criterion_evaluations, +n_derivative_evaluations, +n_iterations, +history,"{'params': [{'CRRA': 15.274999999999999, 'BeqFac': 32.5, 'BeqShift': 17.5}, {'CRRA': 16.182801876545053, 'BeqFac': 34.12985962684907, 'BeqShift': 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263.0288178349874, 264.117916512987, 265.2085039419908, 266.2923197999917, 267.37747748299444, 268.4661660409911, 269.7096656089998, 270.85161694498674, 271.97389261498756, 273.07656889899226, 274.1595409739966, 275.26785371798906, 276.3656064319948, 277.4870640129957, 278.5980592269916, 279.722612913989, 280.81513271199947, 282.03581598299206], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 13, 14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 16, 17, 18, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 20, 21, 22, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 24, 25, 26, 27, 28, 29, 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 32, 33, 34, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 36, 37, 38, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109]}" +convergence_report, +multistart_info,"{'start_parameters': [{'CRRA': 15.274999999999999, 'BeqFac': 32.5, 'BeqShift': 17.5}, {'CRRA': 5.59950516069428, 'BeqFac': 33.20816964942985, 'BeqShift': 22.523305681988035}, {'CRRA': 9.19422435335419, 'BeqFac': 23.72793018208616, 'BeqShift': 26.198036832516518}], 'local_optima': [Minimize with 3 free parameters terminated. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 0.4439 1.468 +relative_params_change 0.0003056 0.0003056 +absolute_criterion_change 0.9678 3.2 +absolute_params_change 0.005363 0.005363 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 1.405 17.75 +relative_params_change 0.003117 0.003984 +absolute_criterion_change 4.239 53.57 +absolute_params_change 0.05189 0.06976 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 0.02464 2.477 +relative_params_change 3.439e-07* 2.186 +absolute_criterion_change 0.2897 29.12 +absolute_params_change 1.353e-06* 8.505 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 15.274999999999999, 'BeqFac': 32.5, 'BeqShift': 17.5}, {'CRRA': 3.4625, 'BeqFac': 51.25, 'BeqShift': 26.25}, {'CRRA': 18.228125, 'BeqFac': 40.3125, 'BeqShift': 54.6875}, {'CRRA': 4.64375, 'BeqFac': 35.625, 'BeqShift': 65.625}, {'CRRA': 2.871875, 'BeqFac': 43.4375, 'BeqShift': 32.8125}, {'CRRA': 17.6375, 'BeqFac': 63.75, 'BeqShift': 8.75}, {'CRRA': 15.865624999999998, 'BeqFac': 59.0625, 'BeqShift': 45.9375}, {'CRRA': 12.321874999999999, 'BeqFac': 68.4375, 'BeqShift': 67.8125}, {'CRRA': 2.28125, 'BeqFac': 66.875, 'BeqShift': 39.375}, {'CRRA': 9.959375, 'BeqFac': 24.6875, 'BeqShift': 59.0625}, {'CRRA': 8.778125, 'BeqFac': 65.3125, 'BeqShift': 19.6875}, {'CRRA': 9.368749999999999, 'BeqFac': 48.125, 'BeqShift': 13.125}, {'CRRA': 9.370461268457287, 'BeqFac': 67.92926162554892, 'BeqShift': 52.186320909731975}, {'CRRA': 8.1875, 'BeqFac': 38.75, 'BeqShift': 43.75}, {'CRRA': 18.81875, 'BeqFac': 23.125, 'BeqShift': 48.125}, {'CRRA': 13.503124999999999, 'BeqFac': 52.8125, 'BeqShift': 2.1875}, {'CRRA': 14.093749999999998, 'BeqFac': 60.625, 'BeqShift': 30.625}, {'CRRA': 4.053125, 'BeqFac': 27.8125, 'BeqShift': 37.1875}, {'CRRA': 6.415625, 'BeqFac': 34.0625, 'BeqShift': 10.9375}, {'CRRA': 16.45625, 'BeqFac': 54.375, 'BeqShift': 56.875}, {'CRRA': 11.73125, 'BeqFac': 41.875, 'BeqShift': 4.375}, {'CRRA': 17.046875, 'BeqFac': 30.9375, 'BeqShift': 15.3125}, {'CRRA': 19.409375, 'BeqFac': 49.6875, 'BeqShift': 24.0625}, {'CRRA': 10.549999999999999, 'BeqFac': 45.0, 'BeqShift': 35.0}, {'CRRA': 5.824999999999999, 'BeqFac': 57.5, 'BeqShift': 52.5}, {'CRRA': 5.234375, 'BeqFac': 62.1875, 'BeqShift': 6.5625}, {'CRRA': 12.9125, 'BeqFac': 26.25, 'BeqShift': 61.25}, {'CRRA': 7.596874999999999, 'BeqFac': 55.9375, 'BeqShift': 50.3125}, {'CRRA': 14.684375, 'BeqFac': 37.1875, 'BeqShift': 41.5625}, {'CRRA': 7.00625, 'BeqFac': 29.375, 'BeqShift': 21.875}], 'exploration_results': array([2.67005783e+00, 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36]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 7.1624354 , 20. , 19.79773503]), radius=0.1015625, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([17, 37, 38, 39, 40, 42, 44, 45, 46, 47, 48, 49]), model=ScalarModel(intercept=597.5035131448108, linear_terms=array([12.29112574, 59.97734958, 28.54381456]), square_terms=array([[0.13605883, 0.62126555, 0.29568688], + [0.62126555, 3.03130454, 1.44263481], + [0.29568688, 1.44263481, 0.68656758]]), scale=array([0.08185897, 0.04092948, 0.08185897]), shift=array([ 7.1624354 , 20.04092948, 19.79773503])), vector_model=VectorModel(intercepts=array([ 0.13032265, 0.31691491, 0.69793062, 1.21943985, + 2.25579301, 2.26833552, 3.48589063, 19.75228859, + 25.30885482, 42.90746683, 45.21097246, 39.93712747, + -2.17828031, -3.51987617, -17.51962784, -35.38825166, + 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accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([17, 37, 38, 39, 40, 42, 45, 46, 47, 48, 49, 50]), old_indices_discarded=array([41, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 7.1624354 , 20. , 19.79773503]), radius=0.025390625, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([17, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]), model=ScalarModel(intercept=88.21987111533616, linear_terms=array([12.00544324, 17.50539215, 8.28407754]), square_terms=array([[0.95090805, 1.22060239, 0.69466443], + [1.22060239, 1.79866532, 0.83906052], + [0.69466443, 0.83906052, 0.51944581]]), scale=array([0.02046474, 0.01023237, 0.02046474]), shift=array([ 7.1624354 , 20.01023237, 19.79773503])), vector_model=VectorModel(intercepts=array([ 0.13032265, 0.31691491, 0.69793062, 1.21943985, + 2.25579301, 2.26833552, 3.48589063, 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model_indices=array([17, 52, 53, 54, 55, 56, 57, 60, 61, 62, 63, 64]), model=ScalarModel(intercept=84.58012860198028, linear_terms=array([-0.54227116, 6.91935277, -1.7243281 ]), square_terms=array([[ 0.00194074, -0.02395723, 0.00597025], + [-0.02395723, 0.30042715, -0.07486759], + [ 0.00597025, -0.07486759, 0.01865729]]), scale=array([0.01023237, 0.00511619, 0.01023237]), shift=array([ 7.1624354 , 20.00511619, 19.79773503])), vector_model=VectorModel(intercepts=array([ 0.13032265, 0.31691491, 0.69793062, 1.21943985, + 2.25579301, 2.26833552, 3.48589063, 19.75228859, + 25.30885482, 42.90746683, 45.21097246, 39.93712747, + -2.17828031, -3.51987617, -17.51962784, -35.38825166, + -39.95691643]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), 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State(trustregion=Region(center=array([ 7.16390218, 20.00063328, 19.79517694]), radius=3.0994415283203125e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 79, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, + 126, 127]), model=ScalarModel(intercept=362.49920227111835, linear_terms=array([ 151.37500415, -277.59157668, 9.34465185]), square_terms=array([[ 69.56882924, -92.22502774, 18.23571457], + [-92.22502774, 141.32749917, -17.24723873], + [ 18.23571457, -17.24723873, 7.367279 ]]), scale=3.0994415283203125e-06, shift=array([ 7.16390218, 20.00063328, 19.79517694])), vector_model=VectorModel(intercepts=array([ 0.13032265, 0.31691491, 0.69793062, 1.21943985, + 2.25579301, 2.26833552, 3.48589063, 19.75228859, + 25.30885482, 42.90746683, 45.21097246, 39.93712747, + -2.17828031, -3.51987617, -17.51962784, -35.38825166, + -39.95691643]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 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129, 130, 131]), old_indices_discarded=array([119, 122, 125, 126, 127]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 7.16390218, 20.00063328, 19.79517694]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 79, 116, 117, 118, 120, 121, 123, 128, 129, 130, 131, 132]), model=ScalarModel(intercept=4089081.040416187, linear_terms=array([ 5155924.19991846, -5538729.21748143, 2845115.72965128]), square_terms=array([[ 3250814.50419527, -3492176.22121422, 1793836.91746412], + [-3492176.22121422, 3751458.28539241, -1927022.96505448], + [ 1793836.91746412, -1927022.96505448, 989860.35341853]]), scale=1e-06, shift=array([ 7.16390218, 20.00063328, 19.79517694])), vector_model=VectorModel(intercepts=array([ 0.13032265, 0.31691491, 0.69793062, 1.21943985, + 2.25579301, 2.26833552, 3.48589063, 19.75228859, + 25.30885482, 42.90746683, 45.21097246, 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model_indices=array([ 79, 116, 117, 118, 120, 123, 128, 129, 130, 131, 132, 133]), model=ScalarModel(intercept=5195246.093561055, linear_terms=array([ 5301214.50006145, -7521834.23709252, 3733642.74525079]), square_terms=array([[ 2704884.7786333 , -3837934.84322383, 1905034.08672897], + [-3837934.84322383, 5445608.88243351, -2703034.34274292], + [ 1905034.08672897, -2703034.34274292, 1341705.2836381 ]]), scale=1e-06, shift=array([ 7.16390218, 20.00063328, 19.79517694])), vector_model=VectorModel(intercepts=array([ 0.13032265, 0.31691491, 0.69793062, 1.21943985, + 2.25579301, 2.26833552, 3.48589063, 19.75228859, + 25.30885482, 42.90746683, 45.21097246, 39.93712747, + -2.17828031, -3.51987617, -17.51962784, -35.38825166, + -39.95691643]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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134, 135, 136, 137, 138, 139, 141, 142, + 143, 145, 146, 147, 148, 154, 159, 160, 161, 162, 163, 164, 165, + 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, + 179, 180, 181]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 7.16390151, 20.00063317, 19.7951761 ]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([132, 140, 144, 149, 150, 151, 152, 153, 155, 156, 157, 158]), model=ScalarModel(intercept=5.1951101154837486e+22, linear_terms=array([-2.78335315e+24, -1.85893328e+23, 2.76329723e+24]), square_terms=array([[ 7.45610256e+25, 4.97974796e+24, -7.40237636e+25], + [ 4.97974796e+24, 3.32585148e+23, -4.94386555e+24], + [-7.40237636e+25, -4.94386555e+24, 7.34903730e+25]]), scale=1e-06, shift=array([ 7.16390151, 20.00063317, 19.7951761 ])), vector_model=VectorModel(intercepts=array([ 0.13032265, 0.31691491, 0.69793062, 1.21943985, 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169, 170, 171, 172, 173, 174, 175, 176, 177, 178, + 179, 180, 181, 182]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 7.16390151, 20.00063317, 19.7951761 ]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([132, 140, 144, 149, 150, 151, 152, 153, 155, 156, 157, 158]), model=ScalarModel(intercept=5.1951101154837486e+22, linear_terms=array([-2.78335315e+24, -1.85893328e+23, 2.76329723e+24]), square_terms=array([[ 7.45610256e+25, 4.97974796e+24, -7.40237636e+25], + [ 4.97974796e+24, 3.32585148e+23, -4.94386555e+24], + [-7.40237636e+25, -4.94386555e+24, 7.34903730e+25]]), scale=1e-06, shift=array([ 7.16390151, 20.00063317, 19.7951761 ])), vector_model=VectorModel(intercepts=array([ 0.13032265, 0.31691491, 0.69793062, 1.21943985, + 2.25579301, 2.26833552, 3.48589063, 19.75228859, + 25.30885482, 42.90746683, 45.21097246, 39.93712747, + 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State(trustregion=Region(center=array([ 7.16390151, 20.00063317, 19.7951761 ]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([132, 140, 144, 149, 150, 151, 152, 153, 155, 156, 157, 158]), model=ScalarModel(intercept=5.1951101154837486e+22, linear_terms=array([-2.78335315e+24, -1.85893328e+23, 2.76329723e+24]), square_terms=array([[ 7.45610256e+25, 4.97974796e+24, -7.40237636e+25], + [ 4.97974796e+24, 3.32585148e+23, -4.94386555e+24], + [-7.40237636e+25, -4.94386555e+24, 7.34903730e+25]]), scale=1e-06, shift=array([ 7.16390151, 20.00063317, 19.7951761 ])), vector_model=VectorModel(intercepts=array([ 0.13032265, 0.31691491, 0.69793062, 1.21943985, + 2.25579301, 2.26833552, 3.48589063, 19.75228859, + 25.30885482, 42.90746683, 45.21097246, 39.93712747, + -2.17828031, -3.51987617, -17.51962784, -35.38825166, + -39.95691643]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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scale=3.25, shift=array([15.275, 32.5 , 17.5 ])), candidate_index=187, candidate_x=array([ 7.16390209, 20.00063375, 19.79517668]), index=140, x=array([ 7.16390151, 20.00063317, 19.7951761 ]), fval=2.1803868818934804, rho=-2.746239466200576e-19, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([132, 140, 144, 149, 150, 151, 152, 153, 155, 156, 157, 158]), old_indices_discarded=array([ 79, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, + 128, 129, 130, 131, 133, 134, 135, 136, 137, 138, 139, 141, 142, + 143, 145, 146, 147, 148, 154, 159, 160, 161, 162, 163, 164, 165, + 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, + 179, 180, 181, 182, 183, 184, 185, 186]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 7.16390151, 20.00063317, 19.7951761 ]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), 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candidate_x=array([ 7.16390209, 20.00063375, 19.79517668]), index=140, x=array([ 7.16390151, 20.00063317, 19.7951761 ]), fval=2.1803868818934804, rho=-7.878241592341733e-21, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([132, 140, 144, 149, 150, 151, 152, 153, 155, 156, 157, 158]), old_indices_discarded=array([ 79, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, + 128, 129, 130, 131, 133, 134, 135, 136, 137, 138, 139, 141, 142, + 143, 145, 146, 147, 148, 154, 159, 160, 161, 162, 163, 164, 165, + 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, + 179, 180, 181, 182, 183, 184, 185, 186, 187]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 7.16390151, 20.00063317, 19.7951761 ]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([132, 140, 144, 149, 150, 151, 152, 153, 155, 156, 157, 158]), 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7.16390151, 20.00063317, 19.7951761 ]), fval=2.1803868818934804, rho=-2.2678684808145598e-20, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([132, 140, 144, 149, 150, 151, 152, 153, 155, 156, 157, 158]), old_indices_discarded=array([ 79, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, + 128, 129, 130, 131, 133, 134, 135, 136, 137, 138, 139, 141, 142, + 143, 145, 146, 147, 148, 154, 159, 160, 161, 162, 163, 164, 165, + 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, + 179, 180, 181, 182, 183, 184, 185, 186, 187, 188]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 7.16390151, 20.00063317, 19.7951761 ]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([132, 140, 144, 149, 150, 151, 152, 153, 155, 156, 157, 158]), model=ScalarModel(intercept=5.1951101154837486e+22, 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fval=2.1803868818934804, rho=-7.527754483029696e-21, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([132, 140, 144, 149, 150, 151, 152, 153, 155, 156, 157, 158]), old_indices_discarded=array([ 79, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, + 128, 129, 130, 131, 133, 134, 135, 136, 137, 138, 139, 141, 142, + 143, 145, 146, 147, 148, 154, 159, 160, 161, 162, 163, 164, 165, + 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, + 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 7.16390151, 20.00063317, 19.7951761 ]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([132, 140, 144, 149, 150, 151, 152, 153, 155, 156, 157, 158]), model=ScalarModel(intercept=5.1951101154837486e+22, linear_terms=array([-2.78335315e+24, -1.85893328e+23, 2.76329723e+24]), square_terms=array([[ 7.45610256e+25, 4.97974796e+24, -7.40237636e+25], + [ 4.97974796e+24, 3.32585148e+23, -4.94386555e+24], + [-7.40237636e+25, -4.94386555e+24, 7.34903730e+25]]), scale=1e-06, shift=array([ 7.16390151, 20.00063317, 19.7951761 ])), vector_model=VectorModel(intercepts=array([ 0.13032265, 0.31691491, 0.69793062, 1.21943985, + 2.25579301, 2.26833552, 3.48589063, 19.75228859, + 25.30885482, 42.90746683, 45.21097246, 39.93712747, + -2.17828031, -3.51987617, -17.51962784, -35.38825166, + -39.95691643]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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149, 150, 151, 152, 153, 155, 156, 157, 158]), old_indices_discarded=array([ 79, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, + 128, 129, 130, 131, 133, 134, 135, 136, 137, 138, 139, 141, 142, + 143, 145, 146, 147, 148, 154, 159, 160, 161, 162, 163, 164, 165, + 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, + 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 7.16390151, 20.00063317, 19.7951761 ]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([132, 140, 144, 149, 150, 151, 152, 153, 155, 156, 157, 158]), model=ScalarModel(intercept=5.1951101154837486e+22, linear_terms=array([-2.78335315e+24, -1.85893328e+23, 2.76329723e+24]), square_terms=array([[ 7.45610256e+25, 4.97974796e+24, -7.40237636e+25], + [ 4.97974796e+24, 3.32585148e+23, 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vector_model=VectorModel(intercepts=array([ 0.13032265, 0.31691491, 0.69793062, 1.21943985, + 2.25579301, 2.26833552, 3.48589063, 19.75228859, + 25.30885482, 42.90746683, 45.21097246, 39.93712747, + -2.17828031, -3.51987617, -17.51962784, -35.38825166, + -39.95691643]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + 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159, 160, 161, 162, 163, 164, 165, + 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, + 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, + 192, 193, 194, 195, 196]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 198 entries., 'multistart_info': {'start_parameters': [array([15.275, 32.5 , 17.5 ]), array([ 5.59950516, 33.20816965, 22.52330568]), array([ 9.19422435, 23.72793018, 26.19803683])], 'local_optima': [{'solution_x': array([ 7.16390151, 20.00063317, 19.7951761 ]), 'solution_criterion': 2.1803868818934804, 'states': [State(trustregion=Region(center=array([15.275, 32.5 , 17.5 ]), radius=3.25, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=[0], model=ScalarModel(intercept=9708.18018836299, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), scale=3.25, 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square_terms=array([[ 8.85216808, 4.31919452, -1.78336972], + [ 4.31919452, 3.36394846, 3.61224857], + [-1.78336972, 3.61224857, 16.59909329]]), scale=array([0.65487173, 0.32743587, 0.65487173]), shift=array([ 7.1624354 , 20.32743587, 19.79773503])), vector_model=VectorModel(intercepts=array([ 0.13032265, 0.31691491, 0.69793062, 1.21943985, + 2.25579301, 2.26833552, 3.48589063, 19.75228859, + 25.30885482, 42.90746683, 45.21097246, 39.93712747, + -2.17828031, -3.51987617, -17.51962784, -35.38825166, + -39.95691643]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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old_indices_used=array([17, 18, 22, 25, 27, 28, 29, 30, 31, 32, 33, 34]), old_indices_discarded=array([21, 23, 24, 26]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 7.1624354 , 20. , 19.79773503]), radius=0.40625, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([17, 18, 22, 25, 28, 29, 30, 31, 32, 33, 35]), model=ScalarModel(intercept=493.1940324674429, linear_terms=array([ 62.03610694, 17.85268999, -53.06971353]), square_terms=array([[ 4.41565547, 2.17810792, -1.9079943 ], + [ 2.17810792, 2.67166924, 2.29193019], + [-1.9079943 , 2.29193019, 7.39791551]]), scale=array([0.32743587, 0.16371793, 0.32743587]), shift=array([ 7.1624354 , 20.16371793, 19.79773503])), vector_model=VectorModel(intercepts=array([ 0.13032265, 0.31691491, 0.69793062, 1.21943985, + 2.25579301, 2.26833552, 3.48589063, 19.75228859, + 25.30885482, 42.90746683, 45.21097246, 39.93712747, + 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model=ScalarModel(intercept=643.5563297058364, linear_terms=array([125.26646815, 153.49933731, -62.23619275]), square_terms=array([[12.35350864, 15.16303218, -6.12910168], + [15.16303218, 18.75118478, -7.53831895], + [-6.12910168, -7.53831895, 3.05715327]]), scale=array([0.16371793, 0.08185897, 0.16371793]), shift=array([ 7.1624354 , 20.08185897, 19.79773503])), vector_model=VectorModel(intercepts=array([ 0.13032265, 0.31691491, 0.69793062, 1.21943985, + 2.25579301, 2.26833552, 3.48589063, 19.75228859, + 25.30885482, 42.90746683, 45.21097246, 39.93712747, + -2.17828031, -3.51987617, -17.51962784, -35.38825166, + -39.95691643]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + 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19.79773503]), fval=5.380788575651525, rho=-4.776847999912698, accepted=False, new_indices=array([37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48]), old_indices_used=array([17, 35, 36]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 7.1624354 , 20. , 19.79773503]), radius=0.1015625, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([17, 37, 38, 39, 40, 42, 44, 45, 46, 47, 48, 49]), model=ScalarModel(intercept=597.5035131448108, linear_terms=array([12.29112574, 59.97734958, 28.54381456]), square_terms=array([[0.13605883, 0.62126555, 0.29568688], + [0.62126555, 3.03130454, 1.44263481], + [0.29568688, 1.44263481, 0.68656758]]), scale=array([0.08185897, 0.04092948, 0.08185897]), shift=array([ 7.1624354 , 20.04092948, 19.79773503])), vector_model=VectorModel(intercepts=array([ 0.13032265, 0.31691491, 0.69793062, 1.21943985, + 2.25579301, 2.26833552, 3.48589063, 19.75228859, + 25.30885482, 42.90746683, 45.21097246, 39.93712747, + -2.17828031, -3.51987617, -17.51962784, -35.38825166, + -39.95691643]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 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0. ]), upper=array([20., 70., 70.]))), model_indices=array([17, 37, 38, 39, 40, 42, 45, 46, 47, 48, 49, 50]), model=ScalarModel(intercept=467.07942650043293, linear_terms=array([15.4700105 , 45.12352048, 30.63632835]), square_terms=array([[0.2594729 , 0.75325932, 0.51172081], + [0.75325932, 2.20095821, 1.49553934], + [0.51172081, 1.49553934, 1.01662207]]), scale=array([0.04092948, 0.02046474, 0.04092948]), shift=array([ 7.1624354 , 20.02046474, 19.79773503])), vector_model=VectorModel(intercepts=array([ 0.13032265, 0.31691491, 0.69793062, 1.21943985, + 2.25579301, 2.26833552, 3.48589063, 19.75228859, + 25.30885482, 42.90746683, 45.21097246, 39.93712747, + -2.17828031, -3.51987617, -17.51962784, -35.38825166, + -39.95691643]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 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candidate_x=array([ 7.12150591, 20. , 19.75680555]), index=17, x=array([ 7.1624354 , 20. , 19.79773503]), fval=5.380788575651525, rho=-1.010900029487394, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([17, 37, 38, 39, 40, 42, 45, 46, 47, 48, 49, 50]), old_indices_discarded=array([41, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 7.1624354 , 20. , 19.79773503]), radius=0.025390625, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([17, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]), model=ScalarModel(intercept=88.21987111533616, linear_terms=array([12.00544324, 17.50539215, 8.28407754]), square_terms=array([[0.95090805, 1.22060239, 0.69466443], + [1.22060239, 1.79866532, 0.83906052], + [0.69466443, 0.83906052, 0.51944581]]), scale=array([0.02046474, 0.01023237, 0.02046474]), shift=array([ 7.1624354 , 20.01023237, 19.79773503])), vector_model=VectorModel(intercepts=array([ 0.13032265, 0.31691491, 0.69793062, 1.21943985, + 2.25579301, 2.26833552, 3.48589063, 19.75228859, + 25.30885482, 42.90746683, 45.21097246, 39.93712747, + -2.17828031, -3.51987617, -17.51962784, -35.38825166, + -39.95691643]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 7.1624354 , 20. , 19.79773503]), radius=0.003173828125, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([17, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78]), model=ScalarModel(intercept=9.037703419347565, linear_terms=array([12.28255378, 11.89146076, 12.99534457]), square_terms=array([[14.3956872 , 10.37286243, 15.29985513], + [10.37286243, 13.76919232, 10.88737584], + [15.29985513, 10.88737584, 16.26429879]]), scale=array([0.00255809, 0.00127905, 0.00255809]), shift=array([ 7.1624354 , 20.00127905, 19.79773503])), vector_model=VectorModel(intercepts=array([ 0.13032265, 0.31691491, 0.69793062, 1.21943985, + 2.25579301, 2.26833552, 3.48589063, 19.75228859, + 25.30885482, 42.90746683, 45.21097246, 39.93712747, + -2.17828031, -3.51987617, -17.51962784, -35.38825166, + -39.95691643]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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model=ScalarModel(intercept=3.2875090605907724, linear_terms=array([-0.05930827, 0.54612936, -0.12072921]), square_terms=array([[ 0.0064547 , -0.06411034, 0.01417297], + [-0.06411034, 0.63732354, -0.14089414], + [ 0.01417297, -0.14089414, 0.03114769]]), scale=array([0.00511619, 0.00287473, 0.00511619]), shift=array([ 7.16390218, 20.00287473, 19.79517694])), vector_model=VectorModel(intercepts=array([ 0.13032265, 0.31691491, 0.69793062, 1.21943985, + 2.25579301, 2.26833552, 3.48589063, 19.75228859, + 25.30885482, 42.90746683, 45.21097246, 39.93712747, + -2.17828031, -3.51987617, -17.51962784, -35.38825166, + -39.95691643]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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vector_model=VectorModel(intercepts=array([ 0.13032265, 0.31691491, 0.69793062, 1.21943985, + 2.25579301, 2.26833552, 3.48589063, 19.75228859, + 25.30885482, 42.90746683, 45.21097246, 39.93712747, + -2.17828031, -3.51987617, -17.51962784, -35.38825166, + -39.95691643]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + 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fval=3.149270474181912, rho=-25.252549825662328, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([79, 86, 87, 88, 89, 90, 91, 92, 93, 96, 98, 99]), old_indices_discarded=array([94, 95, 97]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 7.16390218, 20.00063328, 19.79517694]), radius=2.47955322265625e-05, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 79, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, + 111, 112]), model=ScalarModel(intercept=1702.133424642421, linear_terms=array([-581.38715069, -577.8645001 , 171.4450452 ]), square_terms=array([[100.49756574, 99.53991592, -28.24461132], + [ 99.53991592, 98.78458484, -28.47993931], + [-28.24461132, -28.47993931, 9.80035071]]), scale=2.47955322265625e-05, shift=array([ 7.16390218, 20.00063328, 19.79517694])), vector_model=VectorModel(intercepts=array([ 0.13032265, 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scale=3.25, shift=array([15.275, 32.5 , 17.5 ])), candidate_index=114, candidate_x=array([ 7.16390921, 20.0006233 , 19.79517477]), index=79, x=array([ 7.16390218, 20.00063328, 19.79517694]), fval=3.149270474181912, rho=-21.88734107298974, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 79, 101, 102, 103, 104, 105, 106, 107, 110, 111, 112, 113]), old_indices_discarded=array([100, 108, 109]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 7.16390218, 20.00063328, 19.79517694]), radius=6.198883056640625e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 79, 101, 102, 103, 105, 106, 107, 110, 111, 112, 113, 114]), model=ScalarModel(intercept=1896.0020661747296, linear_terms=array([-10.38360719, -3.44394878, -4.06363502]), square_terms=array([[ 0.84034395, -1.42158815, 0.07373786], + [-1.42158815, 2.52549627, -0.1066395 ], + [ 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108, 109]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 7.16390218, 20.00063328, 19.79517694]), radius=3.0994415283203125e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 79, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, + 126, 127]), model=ScalarModel(intercept=362.49920227111835, linear_terms=array([ 151.37500415, -277.59157668, 9.34465185]), square_terms=array([[ 69.56882924, -92.22502774, 18.23571457], + [-92.22502774, 141.32749917, -17.24723873], + [ 18.23571457, -17.24723873, 7.367279 ]]), scale=3.0994415283203125e-06, shift=array([ 7.16390218, 20.00063328, 19.79517694])), vector_model=VectorModel(intercepts=array([ 0.13032265, 0.31691491, 0.69793062, 1.21943985, + 2.25579301, 2.26833552, 3.48589063, 19.75228859, + 25.30885482, 42.90746683, 45.21097246, 39.93712747, + -2.17828031, -3.51987617, -17.51962784, -35.38825166, + 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model=ScalarModel(intercept=312.7957959520835, linear_terms=array([-20.85653595, 22.81191816, -16.16361746]), square_terms=array([[ 0.70240123, -0.76825411, 0.54435429], + [-0.76825411, 0.84028095, -0.59538964], + [ 0.54435429, -0.59538964, 0.42186941]]), scale=1.5497207641601562e-06, shift=array([ 7.16390218, 20.00063328, 19.79517694])), vector_model=VectorModel(intercepts=array([ 0.13032265, 0.31691491, 0.69793062, 1.21943985, + 2.25579301, 2.26833552, 3.48589063, 19.75228859, + 25.30885482, 42.90746683, 45.21097246, 39.93712747, + -2.17828031, -3.51987617, -17.51962784, -35.38825166, + -39.95691643]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 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candidate_x=array([ 7.16390209, 20.00063375, 19.79517668]), index=140, x=array([ 7.16390151, 20.00063317, 19.7951761 ]), fval=2.1803868818934804, rho=-1.4910772199424336e-20, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([132, 140, 144, 149, 150, 151, 152, 153, 155, 156, 157, 158]), old_indices_discarded=array([ 79, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, + 128, 129, 130, 131, 133, 134, 135, 136, 137, 138, 139, 141, 142, + 143, 145, 146, 147, 148, 154, 159, 160, 161, 162, 163, 164, 165, + 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 7.16390151, 20.00063317, 19.7951761 ]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([132, 140, 144, 149, 150, 151, 152, 153, 155, 156, 157, 158]), 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7.16390151, 20.00063317, 19.7951761 ]), fval=2.1803868818934804, rho=-1.9274057153706326e-21, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([132, 140, 144, 149, 150, 151, 152, 153, 155, 156, 157, 158]), old_indices_discarded=array([ 79, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, + 128, 129, 130, 131, 133, 134, 135, 136, 137, 138, 139, 141, 142, + 143, 145, 146, 147, 148, 154, 159, 160, 161, 162, 163, 164, 165, + 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, + 179]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 7.16390151, 20.00063317, 19.7951761 ]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([132, 140, 144, 149, 150, 151, 152, 153, 155, 156, 157, 158]), model=ScalarModel(intercept=5.1951101154837486e+22, linear_terms=array([-2.78335315e+24, -1.85893328e+23, 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candidate_x=array([ 7.16390209, 20.00063375, 19.79517668]), index=140, x=array([ 7.16390151, 20.00063317, 19.7951761 ]), fval=2.1803868818934804, rho=-2.2678684808145598e-20, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([132, 140, 144, 149, 150, 151, 152, 153, 155, 156, 157, 158]), old_indices_discarded=array([ 79, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, + 128, 129, 130, 131, 133, 134, 135, 136, 137, 138, 139, 141, 142, + 143, 145, 146, 147, 148, 154, 159, 160, 161, 162, 163, 164, 165, + 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, + 179, 180, 181, 182, 183, 184, 185, 186, 187, 188]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 7.16390151, 20.00063317, 19.7951761 ]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([132, 140, 144, 149, 150, 151, 152, 153, 155, 156, 157, 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accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([ 5.59950516, 33.20816965, 22.52330568]), radius=3.320816964942985, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=1738.9322902974552, linear_terms=array([ 637.73598295, -514.32586267, 171.96775116]), square_terms=array([[2536.20577132, 848.0882782 , -184.60913629], + [ 848.0882782 , 474.1118212 , -123.58587265], + [-184.60913629, -123.58587265, 34.40592978]]), scale=3.320816964942985, shift=array([ 5.59950516, 33.20816965, 22.52330568])), vector_model=VectorModel(intercepts=array([ 0.09164835, 0.3053691 , 0.36172947, 0.71108937, 0.65589061, + 0.95241018, 1.10472729, 2.053939 , 1.42482451, 1.60528395, + 4.59503367, 5.46554822, -0.18944004, 0.06641896, 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model=ScalarModel(intercept=1879.7068510646307, linear_terms=array([ 201.13656089, -559.46196149, 98.65540935]), square_terms=array([[663.11387035, 377.50323065, -46.81601658], + [377.50323065, 367.40566774, -53.48166838], + [-46.81601658, -53.48166838, 8.10657806]]), scale=1.6604084824714924, shift=array([ 5.59950516, 33.20816965, 22.52330568])), vector_model=VectorModel(intercepts=array([ 0.09164835, 0.3053691 , 0.36172947, 0.71108937, 0.65589061, + 0.95241018, 1.10472729, 2.053939 , 1.42482451, 1.60528395, + 4.59503367, 5.46554822, -0.18944004, 0.06641896, 0.1105076 , + -0.19039482, -0.23647059]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 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34.65058229, 22.26650085]), fval=56.5879301809914, rho=0.012464587547102888, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_discarded=array([ 4, 13]), step_length=1.6618014164407677, relative_step_length=1.0008389104151059, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.81523416, 34.65058229, 22.26650085]), radius=0.8302042412357462, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 1, 2, 3, 6, 7, 9, 10, 12, 13, 14]), model=ScalarModel(intercept=1137.6335628187985, linear_terms=array([ 70.32499401, -287.06450292, 174.45432527]), square_terms=array([[ 123.98449647, 153.13258918, -68.74283119], + [ 153.13258918, 273.74879705, -136.67164545], + [ -68.74283119, -136.67164545, 70.44799316]]), scale=0.8302042412357462, shift=array([ 4.81523416, 34.65058229, 22.26650085])), vector_model=VectorModel(intercepts=array([ 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shift=array([ 5.59950516, 33.20816965, 22.52330568])), candidate_index=16, candidate_x=array([ 4.96915675, 34.27091571, 22.36133272]), index=14, x=array([ 4.81523416, 34.65058229, 22.26650085]), fval=56.5879301809914, rho=-16.92691969499092, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 13, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.81523416, 34.65058229, 22.26650085]), radius=0.20755106030893655, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=102.55665361166439, linear_terms=array([ 0.24456618, -30.69942108, -7.28937776]), square_terms=array([[3.0914653 , 2.69353108, 2.90275506], + [2.69353108, 8.32364032, 3.83059325], + [2.90275506, 3.83059325, 3.03106463]]), 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+ [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]]), scale=3.320816964942985, shift=array([ 5.59950516, 33.20816965, 22.52330568])), candidate_index=151, candidate_x=array([ 4.8305858 , 34.63919046, 22.31960876]), index=45, x=array([ 4.83058632, 34.63918997, 22.31960799]), fval=3.0183214872941457, rho=-13.712630596768875, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), old_indices_discarded=array([109, 110, 111, 122, 123, 125, 126, 127, 128, 129, 130, 131, 132, + 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, + 146, 147, 148, 149, 150]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.83058632, 34.63918997, 22.31960799]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), model=ScalarModel(intercept=17.132527360265524, linear_terms=array([ 0.42668321, 0.12293269, -0.94523972]), square_terms=array([[ 0.05448517, 0.20990801, -0.23761158], + [ 0.20990801, 0.89488618, -0.96730617], + [-0.23761158, -0.96730617, 1.06746858]]), scale=1e-06, shift=array([ 4.83058632, 34.63918997, 22.31960799])), vector_model=VectorModel(intercepts=array([ 0.09164835, 0.3053691 , 0.36172947, 0.71108937, 0.65589061, + 0.95241018, 1.10472729, 2.053939 , 1.42482451, 1.60528395, + 4.59503367, 5.46554822, -0.18944004, 0.06641896, 0.1105076 , + -0.19039482, -0.23647059]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + 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fval=3.0183214872941457, rho=-455.69076193809946, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), old_indices_discarded=array([109, 110, 111, 122, 123, 125, 126, 127, 128, 129, 130, 131, 132, + 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, + 146, 147, 148, 149, 150, 151]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.83058632, 34.63918997, 22.31960799]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), model=ScalarModel(intercept=17.132527360265524, linear_terms=array([ 0.42668321, 0.12293269, -0.94523972]), square_terms=array([[ 0.05448517, 0.20990801, -0.23761158], + [ 0.20990801, 0.89488618, -0.96730617], + [-0.23761158, -0.96730617, 1.06746858]]), scale=1e-06, 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131, 132, + 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, + 146, 147, 148, 149, 150, 151, 152]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.83058632, 34.63918997, 22.31960799]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), model=ScalarModel(intercept=17.132527360265524, linear_terms=array([ 0.42668321, 0.12293269, -0.94523972]), square_terms=array([[ 0.05448517, 0.20990801, -0.23761158], + [ 0.20990801, 0.89488618, -0.96730617], + [-0.23761158, -0.96730617, 1.06746858]]), scale=1e-06, shift=array([ 4.83058632, 34.63918997, 22.31960799])), vector_model=VectorModel(intercepts=array([ 0.09164835, 0.3053691 , 0.36172947, 0.71108937, 0.65589061, + 0.95241018, 1.10472729, 2.053939 , 1.42482451, 1.60528395, + 4.59503367, 5.46554822, -0.18944004, 0.06641896, 0.1105076 , + -0.19039482, -0.23647059]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 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34.63918997, 22.31960799]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), model=ScalarModel(intercept=17.132527360265524, linear_terms=array([ 0.42668321, 0.12293269, -0.94523972]), square_terms=array([[ 0.05448517, 0.20990801, -0.23761158], + [ 0.20990801, 0.89488618, -0.96730617], + [-0.23761158, -0.96730617, 1.06746858]]), scale=1e-06, shift=array([ 4.83058632, 34.63918997, 22.31960799])), vector_model=VectorModel(intercepts=array([ 0.09164835, 0.3053691 , 0.36172947, 0.71108937, 0.65589061, + 0.95241018, 1.10472729, 2.053939 , 1.42482451, 1.60528395, + 4.59503367, 5.46554822, -0.18944004, 0.06641896, 0.1105076 , + -0.19039482, -0.23647059]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 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scale=3.320816964942985, shift=array([ 5.59950516, 33.20816965, 22.52330568])), candidate_index=155, candidate_x=array([ 4.8305858 , 34.63919046, 22.31960876]), index=45, x=array([ 4.83058632, 34.63918997, 22.31960799]), fval=3.0183214872941457, rho=-22.262142573665894, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), old_indices_discarded=array([109, 110, 111, 122, 123, 125, 126, 127, 128, 129, 130, 131, 132, + 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, + 146, 147, 148, 149, 150, 151, 152, 153, 154]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.83058632, 34.63918997, 22.31960799]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), model=ScalarModel(intercept=17.132527360265524, linear_terms=array([ 0.42668321, 0.12293269, -0.94523972]), square_terms=array([[ 0.05448517, 0.20990801, -0.23761158], + [ 0.20990801, 0.89488618, -0.96730617], + [-0.23761158, -0.96730617, 1.06746858]]), scale=1e-06, shift=array([ 4.83058632, 34.63918997, 22.31960799])), vector_model=VectorModel(intercepts=array([ 0.09164835, 0.3053691 , 0.36172947, 0.71108937, 0.65589061, + 0.95241018, 1.10472729, 2.053939 , 1.42482451, 1.60528395, + 4.59503367, 5.46554822, -0.18944004, 0.06641896, 0.1105076 , + -0.19039482, -0.23647059]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + 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fval=3.0183214872941457, rho=-93833.06018937167, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), old_indices_discarded=array([109, 110, 111, 122, 123, 125, 126, 127, 128, 129, 130, 131, 132, + 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, + 146, 147, 148, 149, 150, 151, 152, 153, 154, 155]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.83058632, 34.63918997, 22.31960799]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), model=ScalarModel(intercept=17.132527360265524, linear_terms=array([ 0.42668321, 0.12293269, -0.94523972]), square_terms=array([[ 0.05448517, 0.20990801, -0.23761158], + [ 0.20990801, 0.89488618, -0.96730617], + [-0.23761158, -0.96730617, 1.06746858]]), 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159, 160, 161]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.83058632, 34.63918997, 22.31960799]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), model=ScalarModel(intercept=17.132527360265524, linear_terms=array([ 0.42668321, 0.12293269, -0.94523972]), square_terms=array([[ 0.05448517, 0.20990801, -0.23761158], + [ 0.20990801, 0.89488618, -0.96730617], + [-0.23761158, -0.96730617, 1.06746858]]), scale=1e-06, shift=array([ 4.83058632, 34.63918997, 22.31960799])), vector_model=VectorModel(intercepts=array([ 0.09164835, 0.3053691 , 0.36172947, 0.71108937, 0.65589061, + 0.95241018, 1.10472729, 2.053939 , 1.42482451, 1.60528395, + 4.59503367, 5.46554822, -0.18944004, 0.06641896, 0.1105076 , + -0.19039482, -0.23647059]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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linear_terms=array([ 0.42668321, 0.12293269, -0.94523972]), square_terms=array([[ 0.05448517, 0.20990801, -0.23761158], + [ 0.20990801, 0.89488618, -0.96730617], + [-0.23761158, -0.96730617, 1.06746858]]), scale=1e-06, shift=array([ 4.83058632, 34.63918997, 22.31960799])), vector_model=VectorModel(intercepts=array([ 0.09164835, 0.3053691 , 0.36172947, 0.71108937, 0.65589061, + 0.95241018, 1.10472729, 2.053939 , 1.42482451, 1.60528395, + 4.59503367, 5.46554822, -0.18944004, 0.06641896, 0.1105076 , + -0.19039482, -0.23647059]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + 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bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), model=ScalarModel(intercept=17.132527360265524, linear_terms=array([ 0.42668321, 0.12293269, -0.94523972]), square_terms=array([[ 0.05448517, 0.20990801, -0.23761158], + [ 0.20990801, 0.89488618, -0.96730617], + [-0.23761158, -0.96730617, 1.06746858]]), scale=1e-06, shift=array([ 4.83058632, 34.63918997, 22.31960799])), vector_model=VectorModel(intercepts=array([ 0.09164835, 0.3053691 , 0.36172947, 0.71108937, 0.65589061, + 0.95241018, 1.10472729, 2.053939 , 1.42482451, 1.60528395, + 4.59503367, 5.46554822, -0.18944004, 0.06641896, 0.1105076 , + -0.19039482, -0.23647059]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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22.52330568])), candidate_index=169, candidate_x=array([ 4.8305858 , 34.63919046, 22.31960876]), index=45, x=array([ 4.83058632, 34.63918997, 22.31960799]), fval=3.0183214872941457, rho=-229.42999024466653, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), old_indices_discarded=array([109, 110, 111, 122, 123, 125, 126, 127, 128, 129, 130, 131, 132, + 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, + 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, + 159, 160, 161, 162, 163, 164, 165, 166, 167, 168]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.83058632, 34.63918997, 22.31960799]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), 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150, 151, 152, 153, 154, 155, 156, 157, 158, + 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.83058632, 34.63918997, 22.31960799]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), model=ScalarModel(intercept=17.132527360265524, linear_terms=array([ 0.42668321, 0.12293269, -0.94523972]), square_terms=array([[ 0.05448517, 0.20990801, -0.23761158], + [ 0.20990801, 0.89488618, -0.96730617], + [-0.23761158, -0.96730617, 1.06746858]]), scale=1e-06, shift=array([ 4.83058632, 34.63918997, 22.31960799])), vector_model=VectorModel(intercepts=array([ 0.09164835, 0.3053691 , 0.36172947, 0.71108937, 0.65589061, + 0.95241018, 1.10472729, 2.053939 , 1.42482451, 1.60528395, + 4.59503367, 5.46554822, -0.18944004, 0.06641896, 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.83058632, 34.63918997, 22.31960799]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), model=ScalarModel(intercept=17.132527360265524, linear_terms=array([ 0.42668321, 0.12293269, -0.94523972]), square_terms=array([[ 0.05448517, 0.20990801, -0.23761158], + [ 0.20990801, 0.89488618, -0.96730617], + [-0.23761158, -0.96730617, 1.06746858]]), scale=1e-06, shift=array([ 4.83058632, 34.63918997, 22.31960799])), vector_model=VectorModel(intercepts=array([ 0.09164835, 0.3053691 , 0.36172947, 0.71108937, 0.65589061, + 0.95241018, 1.10472729, 2.053939 , 1.42482451, 1.60528395, + 4.59503367, 5.46554822, -0.18944004, 0.06641896, 0.1105076 , + -0.19039482, -0.23647059]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 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bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), model=ScalarModel(intercept=17.132527360265524, linear_terms=array([ 0.42668321, 0.12293269, -0.94523972]), square_terms=array([[ 0.05448517, 0.20990801, -0.23761158], + [ 0.20990801, 0.89488618, -0.96730617], + [-0.23761158, -0.96730617, 1.06746858]]), scale=1e-06, shift=array([ 4.83058632, 34.63918997, 22.31960799])), vector_model=VectorModel(intercepts=array([ 0.09164835, 0.3053691 , 0.36172947, 0.71108937, 0.65589061, + 0.95241018, 1.10472729, 2.053939 , 1.42482451, 1.60528395, + 4.59503367, 5.46554822, -0.18944004, 0.06641896, 0.1105076 , + -0.19039482, -0.23647059]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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22.52330568])), candidate_index=175, candidate_x=array([ 4.8305858 , 34.63919046, 22.31960876]), index=45, x=array([ 4.83058632, 34.63918997, 22.31960799]), fval=3.0183214872941457, rho=-325.1959436198726, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), old_indices_discarded=array([109, 110, 111, 122, 123, 125, 126, 127, 128, 129, 130, 131, 132, + 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, + 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, + 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, + 172, 173, 174]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.83058632, 34.63918997, 22.31960799]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), model=ScalarModel(intercept=17.132527360265524, linear_terms=array([ 0.42668321, 0.12293269, -0.94523972]), square_terms=array([[ 0.05448517, 0.20990801, -0.23761158], + [ 0.20990801, 0.89488618, -0.96730617], + [-0.23761158, -0.96730617, 1.06746858]]), scale=1e-06, shift=array([ 4.83058632, 34.63918997, 22.31960799])), vector_model=VectorModel(intercepts=array([ 0.09164835, 0.3053691 , 0.36172947, 0.71108937, 0.65589061, + 0.95241018, 1.10472729, 2.053939 , 1.42482451, 1.60528395, + 4.59503367, 5.46554822, -0.18944004, 0.06641896, 0.1105076 , + -0.19039482, -0.23647059]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + 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fval=3.0183214872941457, rho=-13.846445685609284, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), old_indices_discarded=array([109, 110, 111, 122, 123, 125, 126, 127, 128, 129, 130, 131, 132, + 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, + 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, + 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, + 172, 173, 174, 175]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.83058632, 34.63918997, 22.31960799]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), model=ScalarModel(intercept=17.132527360265524, linear_terms=array([ 0.42668321, 0.12293269, -0.94523972]), square_terms=array([[ 0.05448517, 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115, 116, 117, 118, 119, 120, 121, 124]), old_indices_discarded=array([109, 110, 111, 122, 123, 125, 126, 127, 128, 129, 130, 131, 132, + 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, + 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, + 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, + 172, 173, 174, 175, 176]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.83058632, 34.63918997, 22.31960799]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), model=ScalarModel(intercept=17.132527360265524, linear_terms=array([ 0.42668321, 0.12293269, -0.94523972]), square_terms=array([[ 0.05448517, 0.20990801, -0.23761158], + [ 0.20990801, 0.89488618, -0.96730617], + [-0.23761158, -0.96730617, 1.06746858]]), scale=1e-06, shift=array([ 4.83058632, 34.63918997, 22.31960799])), vector_model=VectorModel(intercepts=array([ 0.09164835, 0.3053691 , 0.36172947, 0.71108937, 0.65589061, + 0.95241018, 1.10472729, 2.053939 , 1.42482451, 1.60528395, + 4.59503367, 5.46554822, -0.18944004, 0.06641896, 0.1105076 , + -0.19039482, -0.23647059]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 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136, 137, 138, 139, 140, 141, 142, 143, 144, 145, + 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, + 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, + 172, 173, 174, 175, 176, 177]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.83058632, 34.63918997, 22.31960799]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), model=ScalarModel(intercept=17.132527360265524, linear_terms=array([ 0.42668321, 0.12293269, -0.94523972]), square_terms=array([[ 0.05448517, 0.20990801, -0.23761158], + [ 0.20990801, 0.89488618, -0.96730617], + [-0.23761158, -0.96730617, 1.06746858]]), scale=1e-06, shift=array([ 4.83058632, 34.63918997, 22.31960799])), vector_model=VectorModel(intercepts=array([ 0.09164835, 0.3053691 , 0.36172947, 0.71108937, 0.65589061, + 0.95241018, 1.10472729, 2.053939 , 1.42482451, 1.60528395, + 4.59503367, 5.46554822, -0.18944004, 0.06641896, 0.1105076 , + -0.19039482, -0.23647059]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 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164, 165, 166, 167, 168, 169, 170, 171, + 172, 173, 174, 175, 176, 177, 178]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.83058632, 34.63918997, 22.31960799]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), model=ScalarModel(intercept=17.132527360265524, linear_terms=array([ 0.42668321, 0.12293269, -0.94523972]), square_terms=array([[ 0.05448517, 0.20990801, -0.23761158], + [ 0.20990801, 0.89488618, -0.96730617], + [-0.23761158, -0.96730617, 1.06746858]]), scale=1e-06, shift=array([ 4.83058632, 34.63918997, 22.31960799])), vector_model=VectorModel(intercepts=array([ 0.09164835, 0.3053691 , 0.36172947, 0.71108937, 0.65589061, + 0.95241018, 1.10472729, 2.053939 , 1.42482451, 1.60528395, + 4.59503367, 5.46554822, -0.18944004, 0.06641896, 0.1105076 , + -0.19039482, -0.23647059]), 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.83058632, 34.63918997, 22.31960799]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), model=ScalarModel(intercept=17.132527360265524, linear_terms=array([ 0.42668321, 0.12293269, -0.94523972]), square_terms=array([[ 0.05448517, 0.20990801, -0.23761158], + [ 0.20990801, 0.89488618, -0.96730617], + [-0.23761158, -0.96730617, 1.06746858]]), scale=1e-06, shift=array([ 4.83058632, 34.63918997, 22.31960799])), vector_model=VectorModel(intercepts=array([ 0.09164835, 0.3053691 , 0.36172947, 0.71108937, 0.65589061, + 0.95241018, 1.10472729, 2.053939 , 1.42482451, 1.60528395, + 4.59503367, 5.46554822, -0.18944004, 0.06641896, 0.1105076 , + -0.19039482, -0.23647059]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 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radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), model=ScalarModel(intercept=17.132527360265524, linear_terms=array([ 0.42668321, 0.12293269, -0.94523972]), square_terms=array([[ 0.05448517, 0.20990801, -0.23761158], + [ 0.20990801, 0.89488618, -0.96730617], + [-0.23761158, -0.96730617, 1.06746858]]), scale=1e-06, shift=array([ 4.83058632, 34.63918997, 22.31960799])), vector_model=VectorModel(intercepts=array([ 0.09164835, 0.3053691 , 0.36172947, 0.71108937, 0.65589061, + 0.95241018, 1.10472729, 2.053939 , 1.42482451, 1.60528395, + 4.59503367, 5.46554822, -0.18944004, 0.06641896, 0.1105076 , + -0.19039482, -0.23647059]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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33.20816965, 22.52330568])), candidate_index=182, candidate_x=array([ 4.8305858 , 34.63919046, 22.31960876]), index=45, x=array([ 4.83058632, 34.63918997, 22.31960799]), fval=3.0183214872941457, rho=-254.56942603445677, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), old_indices_discarded=array([109, 110, 111, 122, 123, 125, 126, 127, 128, 129, 130, 131, 132, + 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, + 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, + 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, + 172, 173, 174, 175, 176, 177, 178, 179, 180, 181]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.83058632, 34.63918997, 22.31960799]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 45, 112, 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x=array([ 4.83058632, 34.63918997, 22.31960799]), fval=3.0183214872941457, rho=-15.328851273227288, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), old_indices_discarded=array([109, 110, 111, 122, 123, 125, 126, 127, 128, 129, 130, 131, 132, + 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, + 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, + 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, + 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.83058632, 34.63918997, 22.31960799]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), model=ScalarModel(intercept=17.132527360265524, linear_terms=array([ 0.42668321, 0.12293269, -0.94523972]), square_terms=array([[ 0.05448517, 0.20990801, -0.23761158], + [ 0.20990801, 0.89488618, -0.96730617], + [-0.23761158, -0.96730617, 1.06746858]]), scale=1e-06, shift=array([ 4.83058632, 34.63918997, 22.31960799])), vector_model=VectorModel(intercepts=array([ 0.09164835, 0.3053691 , 0.36172947, 0.71108937, 0.65589061, + 0.95241018, 1.10472729, 2.053939 , 1.42482451, 1.60528395, + 4.59503367, 5.46554822, -0.18944004, 0.06641896, 0.1105076 , + -0.19039482, -0.23647059]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + 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accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), old_indices_discarded=array([109, 110, 111, 122, 123, 125, 126, 127, 128, 129, 130, 131, 132, + 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, + 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, + 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, + 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.83058632, 34.63918997, 22.31960799]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), model=ScalarModel(intercept=17.132527360265524, linear_terms=array([ 0.42668321, 0.12293269, -0.94523972]), square_terms=array([[ 0.05448517, 0.20990801, 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117, 118, 119, 120, 121, 124]), old_indices_discarded=array([109, 110, 111, 122, 123, 125, 126, 127, 128, 129, 130, 131, 132, + 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, + 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, + 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, + 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.83058632, 34.63918997, 22.31960799]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), model=ScalarModel(intercept=17.132527360265524, linear_terms=array([ 0.42668321, 0.12293269, -0.94523972]), square_terms=array([[ 0.05448517, 0.20990801, -0.23761158], + [ 0.20990801, 0.89488618, -0.96730617], + [-0.23761158, -0.96730617, 1.06746858]]), scale=1e-06, shift=array([ 4.83058632, 34.63918997, 22.31960799])), vector_model=VectorModel(intercepts=array([ 0.09164835, 0.3053691 , 0.36172947, 0.71108937, 0.65589061, + 0.95241018, 1.10472729, 2.053939 , 1.42482451, 1.60528395, + 4.59503367, 5.46554822, -0.18944004, 0.06641896, 0.1105076 , + -0.19039482, -0.23647059]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + 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159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, + 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, + 185, 186, 187]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.83058632, 34.63918997, 22.31960799]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), model=ScalarModel(intercept=17.132527360265524, linear_terms=array([ 0.42668321, 0.12293269, -0.94523972]), square_terms=array([[ 0.05448517, 0.20990801, -0.23761158], + [ 0.20990801, 0.89488618, -0.96730617], + [-0.23761158, -0.96730617, 1.06746858]]), scale=1e-06, shift=array([ 4.83058632, 34.63918997, 22.31960799])), vector_model=VectorModel(intercepts=array([ 0.09164835, 0.3053691 , 0.36172947, 0.71108937, 0.65589061, + 0.95241018, 1.10472729, 2.053939 , 1.42482451, 1.60528395, + 4.59503367, 5.46554822, -0.18944004, 0.06641896, 0.1105076 , + -0.19039482, -0.23647059]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 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176, 177, 178, 179, 180, 181, 182, 183, 184, + 185, 186, 187, 188]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.83058632, 34.63918997, 22.31960799]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), model=ScalarModel(intercept=17.132527360265524, linear_terms=array([ 0.42668321, 0.12293269, -0.94523972]), square_terms=array([[ 0.05448517, 0.20990801, -0.23761158], + [ 0.20990801, 0.89488618, -0.96730617], + [-0.23761158, -0.96730617, 1.06746858]]), scale=1e-06, shift=array([ 4.83058632, 34.63918997, 22.31960799])), vector_model=VectorModel(intercepts=array([ 0.09164835, 0.3053691 , 0.36172947, 0.71108937, 0.65589061, + 0.95241018, 1.10472729, 2.053939 , 1.42482451, 1.60528395, + 4.59503367, 5.46554822, -0.18944004, 0.06641896, 0.1105076 , + -0.19039482, -0.23647059]), 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34.63918997, 22.31960799]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), model=ScalarModel(intercept=17.132527360265524, linear_terms=array([ 0.42668321, 0.12293269, -0.94523972]), square_terms=array([[ 0.05448517, 0.20990801, -0.23761158], + [ 0.20990801, 0.89488618, -0.96730617], + [-0.23761158, -0.96730617, 1.06746858]]), scale=1e-06, shift=array([ 4.83058632, 34.63918997, 22.31960799])), vector_model=VectorModel(intercepts=array([ 0.09164835, 0.3053691 , 0.36172947, 0.71108937, 0.65589061, + 0.95241018, 1.10472729, 2.053939 , 1.42482451, 1.60528395, + 4.59503367, 5.46554822, -0.18944004, 0.06641896, 0.1105076 , + -0.19039482, -0.23647059]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 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22.52330568])), candidate_index=194, candidate_x=array([ 4.8305858 , 34.63919046, 22.31960876]), index=45, x=array([ 4.83058632, 34.63918997, 22.31960799]), fval=3.0183214872941457, rho=-1419.778705959862, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), old_indices_discarded=array([109, 110, 111, 122, 123, 125, 126, 127, 128, 129, 130, 131, 132, + 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, + 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, + 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, + 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, + 185, 186, 187, 188, 189, 190, 191, 192, 193]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.83058632, 34.63918997, 22.31960799]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 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4.8305858 , 34.63919046, 22.31960876]), index=45, x=array([ 4.83058632, 34.63918997, 22.31960799]), fval=3.0183214872941457, rho=-258.55297426506917, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), old_indices_discarded=array([109, 110, 111, 122, 123, 125, 126, 127, 128, 129, 130, 131, 132, + 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, + 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, + 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, + 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, + 185, 186, 187, 188, 189, 190, 191, 192, 193, 194]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.83058632, 34.63918997, 22.31960799]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 45, 112, 113, 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4.83058632, 34.63918997, 22.31960799]), fval=3.0183214872941457, rho=-255.5609825834806, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 45, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 124]), old_indices_discarded=array([109, 110, 111, 122, 123, 125, 126, 127, 128, 129, 130, 131, 132, + 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, + 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, + 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, + 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, + 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'message': None, 'tranquilo_history': History for least_squares function with 197 entries., 'history': {'params': [{'CRRA': 5.59950516069428, 'BeqFac': 33.20816964942985, 'BeqShift': 22.523305681988035}, {'CRRA': 6.527087890675169, 'BeqFac': 34.87354364921939, 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model_indices=array([ 53, 54, 58, 61, 67, 111, 112, 113, 114, 115, 116, 117]), model=ScalarModel(intercept=472.2457577259905, linear_terms=array([-1771.42817561, 582.46342442, -1126.59648447]), square_terms=array([[ 3724.50782271, -1169.66846912, 2438.6264139 ], + [-1169.66846912, 380.58982332, -755.70634162], + [ 2438.6264139 , -755.70634162, 1610.13543409]]), scale=array([8.05995977e-07, 7.67575489e-07, 8.05995978e-07]), shift=array([ 2.37087515, 20.00000077, 21.73049362])), vector_model=VectorModel(intercepts=array([ 0.11142765, 0.23890431, 0.37634247, 0.94584628, 1.48540315, + 2.36666015, 3.46158235, 18.13126853, 24.97314971, 23.20240451, + 21.32454671, 16.86409763, -1.87943795, -3.41061996, -3.47037143, + -3.58779023, -2.66177004]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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23.72793018, 26.19803683])), candidate_index=118, candidate_x=array([ 2.3708758 , 20. , 21.73049281]), index=115, x=array([ 2.37087515, 20.00000073, 21.73049362]), fval=11.756137406922145, rho=-0.048592154387170204, accepted=False, new_indices=array([117]), old_indices_used=array([ 53, 54, 58, 61, 67, 111, 112, 113, 114, 115, 116]), old_indices_discarded=array([ 14, 31, 33, 35, 52, 55, 56, 57, 59, 60, 62, 63, 64, + 65, 66, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, + 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, + 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, + 105, 106, 107, 108, 109, 110]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 2.37087515, 20.00000073, 21.73049362]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 53, 54, 58, 61, 67, 111, 112, 113, 114, 115, 116, 119]), 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20.00000097, 21.73049433]), index=115, x=array([ 2.37087515, 20.00000073, 21.73049362]), fval=11.756137406922145, rho=-0.1261316666099927, accepted=False, new_indices=array([119]), old_indices_used=array([ 53, 54, 58, 61, 67, 111, 112, 113, 114, 115, 116]), old_indices_discarded=array([ 14, 31, 33, 35, 52, 55, 56, 57, 59, 60, 62, 63, 64, + 65, 66, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, + 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, + 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, + 105, 106, 107, 108, 109, 110, 117, 118]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 2.37087515, 20.00000073, 21.73049362]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 53, 54, 58, 61, 67, 111, 112, 113, 114, 115, 116, 121]), model=ScalarModel(intercept=472.2457576856781, linear_terms=array([ -934.4558521 , 582.46342408, 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fval=11.756137406922145, rho=-0.12429101752371993, accepted=False, new_indices=array([121]), old_indices_used=array([ 53, 54, 58, 61, 67, 111, 112, 113, 114, 115, 116]), old_indices_discarded=array([ 14, 31, 33, 35, 52, 55, 56, 57, 59, 60, 62, 63, 64, + 65, 66, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, + 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, + 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, + 105, 106, 107, 108, 109, 110, 117, 118, 119, 120]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 2.37087515, 20.00000073, 21.73049362]), radius=1e-06, bounds=Bounds(lower=array([ 1.1, 20. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 53, 54, 58, 61, 67, 111, 112, 113, 114, 115, 116, 123]), model=ScalarModel(intercept=472.2457577302039, linear_terms=array([-1855.88530491, 582.46342446, -1042.13935579]), square_terms=array([[ 4099.56827523, -1232.09777669, 2359.47596086], + 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1.11632625e+02, + 1.67068273e+02, 1.92242089e+02, 2.22066462e+02, 2.49895626e+02, + 2.52480098e+02, 2.54721262e+02, 4.14506851e+02, 5.22787380e+02, + 5.52488706e+02, 5.75590886e+02, 5.94674677e+02, 7.00919594e+02, + 1.05281575e+03, 1.07231128e+03, 1.16623114e+03, 1.53593807e+03, + 1.67568095e+03, 3.00665721e+03, 2.61981847e+04, 2.87556210e+04, + 2.84698965e+14, 1.93307363e+21])}}" diff --git a/content/tables/min/WarmGlowSub(Labor)Market_estimate_results.csv b/content/tables/min/WarmGlowSub(Labor)Market_estimate_results.csv new file mode 100644 index 0000000..561ccb4 --- /dev/null +++ b/content/tables/min/WarmGlowSub(Labor)Market_estimate_results.csv @@ -0,0 +1,5725 @@ +CRRA,3.936588728006248 +DiscFac,1.0540085907678047 +time_to_estimate,161.71836709976196 +params,"{'CRRA': 3.936588728006248, 'DiscFac': 1.0540085907678047}" +criterion,329.7103025044588 +start_criterion,1021.8221189664621 +start_params,"{'CRRA': 5.0, 'DiscFac': 0.95}" +algorithm,multistart_tranquilo_ls +direction,minimize +n_free,2 +message,Absolute params change smaller than tolerance. +success, +n_criterion_evaluations, +n_derivative_evaluations, +n_iterations, +history,"{'params': [{'CRRA': 2.28125, 'DiscFac': 1.0625}, {'CRRA': 2.1080599021593467, 'DiscFac': 0.8603294826310895}, {'CRRA': 2.4834205173689106, 'DiscFac': 1.0537994279097354}, {'CRRA': 2.0790794826310894, 'DiscFac': 1.057771933087904}, {'CRRA': 2.4834205173689106, 'DiscFac': 1.0997321942805047}, {'CRRA': 2.4834205173689106, 'DiscFac': 0.9532212916001106}, {'CRRA': 2.4834205173689106, 'DiscFac': 0.9247148727392314}, {'CRRA': 2.220687379772545, 'DiscFac': 1.1}, {'CRRA': 2.4834205173689106, 'DiscFac': 1.061143425100633}, {'CRRA': 2.4834205173689106, 'DiscFac': 1.097123266376995}, {'CRRA': 2.0790794826310894, 'DiscFac': 1.0647910016988402}, {'CRRA': 2.3332640759975307, 'DiscFac': 0.8603294826310895}, {'CRRA': 2.394747958016102, 'DiscFac': 1.1}, {'CRRA': 2.4834205173689106, 'DiscFac': 0.9899428036653586}, {'CRRA': 2.07907948263109, 'DiscFac': 0.9485587811919458}, {'CRRA': 2.28125, 'DiscFac': 0.96099455546875}, {'CRRA': 2.3823352586844555, 'DiscFac': 0.9607313480539168}, {'CRRA': 2.4282698628957866, 'DiscFac': 1.0081333352714825}, {'CRRA': 2.5293551215802417, 'DiscFac': 1.0142151123588048}, {'CRRA': 2.327184604211331, 'DiscFac': 1.0042800166741386}, {'CRRA': 2.6304403802646967, 'DiscFac': 1.011840268982357}, {'CRRA': 2.471991127423394, 'DiscFac': 1.009320111112641}, {'CRRA': 2.5015367392175882, 'DiscFac': 1.0216177504748765}, {'CRRA': 2.558511717535144, 'DiscFac': 1.0276161614272141}, {'CRRA': 2.457426458850689, 'DiscFac': 1.0215305331751778}, {'CRRA': 2.501336747071488, 'DiscFac': 1.032691442390799}, {'CRRA': 2.400251488387033, 'DiscFac': 1.0200750916944055}, {'CRRA': 2.4443399820357583, 'DiscFac': 1.0306581001930368}, {'CRRA': 2.529847972449796, 'DiscFac': 1.0332493498533561}, {'CRRA': 2.4728518520027922, 'DiscFac': 1.030689880241442}, {'CRRA': 2.501298804864475, 'DiscFac': 1.0339816077574975}, {'CRRA': 2.4870436609694155, 'DiscFac': 1.0336762542833158}, {'CRRA': 2.508428915804454, 'DiscFac': 1.0344991822092973}, {'CRRA': 2.522685008079987, 'DiscFac': 1.034865842524518}, {'CRRA': 2.551190128144725, 'DiscFac': 1.035758034361826}, {'CRRA': 2.6082170741394894, 'DiscFac': 1.0364618559042662}, {'CRRA': 2.7093023328239445, 'DiscFac': 1.0367805544622455}, {'CRRA': 2.911472850192855, 'DiscFac': 1.0408124327854944}, {'CRRA': 2.5071318154550344, 'DiscFac': 1.0202514750960077}, {'CRRA': 3.1136433675617656, 'DiscFac': 1.0432969594038992}, {'CRRA': 3.5179844022995868, 'DiscFac': 1.0489010582495062}, {'CRRA': 4.326666471775228, 'DiscFac': 1.0600035453861216}, {'CRRA': 3.6695953173282456, 'DiscFac': 1.0400304624642023}, {'CRRA': 3.7201549196684973, 'DiscFac': 1.0429564711070365}, {'CRRA': 3.619069660984042, 'DiscFac': 1.0505916418459438}, {'CRRA': 3.8212401783529524, 'DiscFac': 1.045418168161877}, {'CRRA': 3.720154919668497, 'DiscFac': 1.0516844930147706}, {'CRRA': 3.9223254370374074, 'DiscFac': 1.0528478154975809}, {'CRRA': 4.0234106957218625, 'DiscFac': 1.0519377270409527}, {'CRRA': 3.972868066379635, 'DiscFac': 1.0513925274659786}, {'CRRA': 3.9510227312473494, 'DiscFac': 1.044184367781091}, {'CRRA': 3.936570381499215, 'DiscFac': 1.0539560047246967}, {'CRRA': 3.9650916064968427, 'DiscFac': 1.0532271335050025}, {'CRRA': 3.950880843359089, 'DiscFac': 1.0576517158357353}, {'CRRA': 3.942717863080446, 'DiscFac': 1.0576496711573096}, {'CRRA': 3.939514938794341, 'DiscFac': 1.0559647128707559}, {'CRRA': 3.934826911150665, 'DiscFac': 1.0534504975078616}, {'CRRA': 3.9364198915079336, 'DiscFac': 1.054834318815751}, {'CRRA': 3.9364773937214226, 'DiscFac': 1.0543917500553501}, {'CRRA': 3.9367871060935027, 'DiscFac': 1.053904423427387}, {'CRRA': 3.9365307235799625, 'DiscFac': 1.0538519144060265}, {'CRRA': 3.936588728006248, 'DiscFac': 1.0540085907678047}], 'criterion': [935.1742875807975, 1216.3667380409247, 537.8019291484245, 977.1749661237271, 2745.528105187298, 1104.5340573739804, 1160.7836080321674, 3634.6960133978646, 712.1801444821974, 2548.614474702864, 1315.7509613360983, 1211.6501625971741, 3027.4173047684917, 829.2721531871346, 1130.9599480882032, 1082.1793984220099, 1079.4130249190662, 587.2414223119063, 515.4916303449894, 635.2341462339416, 549.723374235226, 573.7335598306056, 435.1912542062541, 391.26096822653744, 434.0675583086433, 369.99789296880266, 445.1353337699835, 376.6905626309715, 368.0234155462132, 376.192011530031, 367.923628769737, 368.91309740946303, 367.07558654605555, 366.07487118769455, 364.011321961592, 360.46133848486267, 356.02846339667883, 346.25080137140253, 448.3611711226967, 339.6154241917441, 332.1534899338143, nan, 355.96913835725707, 345.58998031666033, 331.264805944376, 340.25945935892037, 330.6429285017878, 329.8974806948583, nan, 331.2336481029307, 348.15397411998606, 329.71144182431374, nan, nan, nan, nan, 329.79512799746055, nan, 329.83396840591786, 329.71310846190204, 329.72030476716975, 329.71030250445887], 'runtime': [0.0, 1.9218434060003347, 1.9573878740002328, 1.9930188299999827, 2.0296304660000715, 2.067346479000207, 2.104832086999977, 2.1473167859999194, 2.1956436190002933, 2.226860226000099, 2.2686677520000558, 2.3080797679999705, 2.35938854799997, 4.7379500380002355, 6.5540031410000665, 8.301995650000208, 10.023663359000238, 11.769620493000275, 13.528613295000014, 15.398279455000193, 17.119948382000075, 18.82712327900026, 20.693633022000085, 22.432113200999993, 24.168750933999945, 25.953714025999943, 27.702613398000267, 29.59289010900011, 31.293931536999935, 33.03048518100013, 34.752097221999975, 36.46265411000013, 38.206760251000105, 39.978110974000174, 41.712195129000065, 43.46177701100032, 45.15451058100007, 46.84149160200013, 48.520592077999936, 50.23075040599997, 51.918374385000334, 53.72429173699993, 55.465167314000155, 57.19107925900016, 58.92002409399993, 60.734104887000285, 62.52712700100028, 64.3429545869999, 66.05636371500032, 67.74632934100009, 69.43303644900016, 71.1167774710002, 72.81955898000024, 74.59246622499995, 76.29678089400022, 78.05459004900013, 79.9560147420002, 81.65282278299992, 83.3562907569999, 85.056695794, 86.74315362900006, 88.47902612000007], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50]}" +convergence_report, +multistart_info,"{'start_parameters': [{'CRRA': 2.28125, 'DiscFac': 1.0625}, {'CRRA': 4.489691583904104, 'DiscFac': 1.0235451798335713}], 'local_optima': [Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute params change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 0.02247 0.09591 +relative_params_change 0.1151 0.2751 +absolute_criterion_change 7.462 31.86 +absolute_params_change 0.4044 0.9669 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute params change smaller than tolerance.], 'exploration_sample': [{'CRRA': 2.28125, 'DiscFac': 1.0625}, {'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 5.0, 'DiscFac': 0.95}, {'CRRA': 7.00625, 'DiscFac': 0.6125}, {'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 2.871875, 'DiscFac': 0.78125}], 'exploration_results': array([ 935.17428758, 989.46212984, 1021.90990852, 1165.31803535, + 1180.04944214, 1208.05004997, 1224.63960931])}" +algorithm_output,"{'states': [State(trustregion=Region(center=array([2.28125, 1.0625 ]), radius=0.22812500000000002, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=935.1742875807975, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.22812500000000002, 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upper=array([20. , 1.1]))), model_indices=array([ 2, 4, 5, 6, 8, 9, 12, 13, 16]), model=ScalarModel(intercept=439.60724813400134, linear_terms=array([ 29.06098493, -581.19536117]), square_terms=array([[ 4.7075158 , -74.8572189 ], + [ -74.8572189 , 1594.56453251]]), scale=0.057031250000000006, shift=array([2.48342052, 0.9899428 ])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=17, candidate_x=array([2.42826986, 1.00813334]), index=17, x=array([2.42826986, 1.00813334]), fval=587.2414223119063, rho=2.2603078820668574, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 4, 5, 6, 8, 9, 12, 13, 16]), old_indices_discarded=array([ 0, 7, 11, 15]), step_length=0.05807314464472628, relative_step_length=1.0182688376061593, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.42826986, 1.00813334]), radius=0.11406250000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 4, 5, 6, 8, 12, 13, 16, 17]), model=ScalarModel(intercept=362.9312431568839, linear_terms=array([ -1.43610402, -455.95002481]), square_terms=array([[ 3.41974498e+00, -3.77425452e+01], + [-3.77425452e+01, 4.45506833e+03]]), scale=array([0.10108526, 0.09647596]), shift=array([2.42826986, 1.00352404])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=18, candidate_x=array([2.52935512, 1.01421511]), index=18, x=array([2.52935512, 1.01421511]), fval=515.4916303449894, rho=6.91127251824898, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 4, 5, 6, 8, 12, 13, 16, 17]), old_indices_discarded=array([ 0, 1, 3, 7, 9, 10, 11, 14, 15]), step_length=0.10126804795118288, relative_step_length=0.8878294614898224, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.52935512, 1.01421511]), radius=0.22812500000000002, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 4, 5, 6, 8, 12, 13, 16, 17, 18]), model=ScalarModel(intercept=1359.9712425796195, linear_terms=array([ 530.39350929, -4516.74879736]), square_terms=array([[ 139.52777984, -1134.4366891 ], + [-1134.4366891 , 10091.18405894]]), scale=array([0.20217052, 0.1439777 ]), shift=array([2.52935512, 0.9560223 ])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=19, candidate_x=array([2.3271846 , 1.00428002]), index=18, x=array([2.52935512, 1.01421511]), fval=515.4916303449894, rho=-4.58101497389655, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 4, 5, 6, 8, 12, 13, 16, 17, 18]), old_indices_discarded=array([ 0, 1, 2, 3, 7, 9, 10, 11, 14, 15]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.52935512, 1.01421511]), radius=0.11406250000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 4, 5, 6, 8, 9, 13, 17, 18]), model=ScalarModel(intercept=356.13502417264516, linear_terms=array([ -21.86571545, -371.31406634]), square_terms=array([[ 5.32733963, 144.20725695], + [ 144.20725695, 4022.4388802 ]]), scale=array([0.10108526, 0.09343507]), shift=array([2.52935512, 1.00656493])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=20, candidate_x=array([2.63044038, 1.01184027]), index=18, x=array([2.52935512, 1.01421511]), fval=515.4916303449894, rho=-3.937360051298474, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 4, 5, 6, 8, 9, 13, 17, 18]), old_indices_discarded=array([ 0, 1, 3, 7, 10, 11, 12, 14, 15, 16, 19]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.52935512, 1.01421511]), radius=0.057031250000000006, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 4, 5, 6, 8, 9, 13, 18, 20]), model=ScalarModel(intercept=363.9485394385331, linear_terms=array([ 63.83466939, -205.61803243]), square_terms=array([[ 76.34919354, -329.30910554], + [-329.30910554, 1448.28366523]]), scale=0.057031250000000006, shift=array([2.52935512, 1.01421511])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=21, candidate_x=array([2.47199113, 1.00932011]), index=18, x=array([2.52935512, 1.01421511]), fval=515.4916303449894, rho=-1.876806075442851, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 4, 5, 6, 8, 9, 13, 18, 20]), old_indices_discarded=array([ 0, 11, 12, 15, 16, 17, 19]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.52935512, 1.01421511]), radius=0.028515625000000003, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 4, 5, 6, 8, 9, 13, 18, 21]), model=ScalarModel(intercept=437.8465274491715, linear_terms=array([ 100.6571281 , -233.15172692]), square_terms=array([[ 50.52435959, -135.50013132], + [-135.50013132, 372.32698017]]), scale=0.028515625000000003, shift=array([2.52935512, 1.01421511])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=22, candidate_x=array([2.50153674, 1.02161775]), index=22, x=array([2.50153674, 1.02161775]), fval=435.1912542062541, rho=0.9143899853131772, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 4, 5, 6, 8, 9, 13, 18, 21]), old_indices_discarded=array([17, 20]), step_length=0.02878648030503738, relative_step_length=1.0094984874095299, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.50153674, 1.02161775]), radius=0.057031250000000006, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 5, 8, 9, 13, 17, 18, 21, 22]), model=ScalarModel(intercept=362.50552881448345, linear_terms=array([ 0.21162612, -185.90090938]), square_terms=array([[ 2.37422818e-01, -1.88009021e+01], + [-1.88009021e+01, 1.94454231e+03]]), scale=0.057031250000000006, shift=array([2.50153674, 1.02161775])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=23, candidate_x=array([2.55851172, 1.02761616]), index=23, x=array([2.55851172, 1.02761616]), fval=391.26096822653744, rho=4.206827026062353, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 5, 8, 9, 13, 17, 18, 21, 22]), old_indices_discarded=array([ 0, 4, 6, 11, 12, 15, 16, 19, 20]), step_length=0.05728986898430706, relative_step_length=1.0045346890399045, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.55851172, 1.02761616]), radius=0.11406250000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 4, 5, 8, 13, 18, 20, 21, 22, 23]), model=ScalarModel(intercept=475.91436029922244, linear_terms=array([ 146.37643596, -1002.24191137]), square_terms=array([[ 75.34609329, -574.12412706], + [-574.12412706, 4492.7084406 ]]), scale=array([0.10108526, 0.08673455]), shift=array([2.55851172, 1.01326545])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=24, candidate_x=array([2.45742646, 1.02153053]), index=23, x=array([2.55851172, 1.02761616]), fval=391.26096822653744, rho=-1.7281556396495996, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 4, 5, 8, 13, 18, 20, 21, 22, 23]), old_indices_discarded=array([ 0, 1, 2, 3, 6, 7, 9, 10, 11, 12, 14, 15, 16, 17, 19]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.55851172, 1.02761616]), radius=0.057031250000000006, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 8, 13, 18, 20, 21, 22, 23, 24]), model=ScalarModel(intercept=376.95231493068496, linear_terms=array([ 7.67927515, -262.62815684]), square_terms=array([[ 1.43467190e+00, -5.72964126e+01], + [-5.72964126e+01, 2.30456392e+03]]), scale=0.057031250000000006, shift=array([2.55851172, 1.02761616])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=25, candidate_x=array([2.50133675, 1.03269144]), index=25, x=array([2.50133675, 1.03269144]), fval=369.99789296880266, rho=1.3196944736104015, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 8, 13, 18, 20, 21, 22, 23, 24]), old_indices_discarded=array([ 0, 4, 5, 6, 9, 11, 12, 15, 16, 17, 19]), step_length=0.05739978853949947, relative_step_length=1.0064620456241002, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.50133675, 1.03269144]), radius=0.11406250000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 4, 6, 8, 13, 17, 18, 23, 24, 25]), model=ScalarModel(intercept=376.9565890121687, linear_terms=array([ 26.48679953, -345.52089772]), square_terms=array([[ 14.62220936, -198.46876477], + [-198.46876477, 2898.25266642]]), scale=array([0.10108526, 0.08419691]), shift=array([2.50133675, 1.01580309])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=26, candidate_x=array([2.40025149, 1.02007509]), index=25, x=array([2.50133675, 1.03269144]), fval=369.99789296880266, rho=-6.312050224003466, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 4, 6, 8, 13, 17, 18, 23, 24, 25]), old_indices_discarded=array([ 0, 1, 2, 3, 5, 7, 9, 10, 11, 12, 14, 15, 16, 19, 20, 21, 22]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.50133675, 1.03269144]), radius=0.057031250000000006, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 8, 13, 18, 21, 22, 23, 24, 25]), model=ScalarModel(intercept=362.22667160322885, linear_terms=array([ 4.61005975, -18.04304404]), square_terms=array([[ 4.46594012, -100.28453856], + [-100.28453856, 2301.28686072]]), scale=0.057031250000000006, shift=array([2.50133675, 1.03269144])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=27, candidate_x=array([2.44433998, 1.0306581 ]), index=25, x=array([2.50133675, 1.03269144]), fval=369.99789296880266, rho=-1.740901072399487, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 8, 13, 18, 21, 22, 23, 24, 25]), old_indices_discarded=array([ 0, 4, 5, 6, 9, 11, 12, 15, 16, 17, 19, 20, 26]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.50133675, 1.03269144]), radius=0.028515625000000003, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 8, 13, 18, 21, 22, 24, 25, 27]), model=ScalarModel(intercept=361.65660924410525, linear_terms=array([-1.30104938, -4.97478383]), square_terms=array([[ 7.96168857e-02, -6.28732324e+00], + [-6.28732324e+00, 5.74231721e+02]]), scale=0.028515625000000003, shift=array([2.50133675, 1.03269144])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=28, candidate_x=array([2.52984797, 1.03324935]), index=28, x=array([2.52984797, 1.03324935]), fval=368.0234155462132, rho=1.4396777758036234, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 8, 13, 18, 21, 22, 24, 25, 27]), old_indices_discarded=array([ 4, 5, 6, 9, 12, 16, 17, 20, 23, 26]), step_length=0.02851668342057767, relative_step_length=1.0000371172147784, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.52984797, 1.03324935]), radius=0.057031250000000006, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 8, 13, 18, 21, 22, 23, 25, 28]), model=ScalarModel(intercept=366.55770728049407, linear_terms=array([ 14.5612219 , -63.39484606]), square_terms=array([[ 12.58220499, -165.74806918], + [-165.74806918, 2268.97440442]]), scale=0.057031250000000006, shift=array([2.52984797, 1.03324935])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=29, candidate_x=array([2.47285185, 1.03068988]), index=28, x=array([2.52984797, 1.03324935]), fval=368.0234155462132, rho=-0.7726027898783459, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 8, 13, 18, 21, 22, 23, 25, 28]), old_indices_discarded=array([ 0, 4, 5, 6, 9, 11, 12, 15, 16, 17, 19, 20, 24, 26, 27]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.52984797, 1.03324935]), radius=0.028515625000000003, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 8, 18, 21, 22, 23, 25, 28, 29]), model=ScalarModel(intercept=365.0305729249685, linear_terms=array([ 1.44330514, -37.34903706]), square_terms=array([[ 5.87745365e-01, -1.98667496e+01], + [-1.98667496e+01, 6.79541800e+02]]), scale=0.028515625000000003, shift=array([2.52984797, 1.03324935])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=30, candidate_x=array([2.5012988 , 1.03398161]), index=30, x=array([2.5012988 , 1.03398161]), fval=367.923628769737, rho=0.07258712397424556, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 8, 18, 21, 22, 23, 25, 28, 29]), old_indices_discarded=array([ 4, 5, 6, 9, 13, 17, 20, 24, 26, 27]), step_length=0.02855855688673584, relative_step_length=1.0015055565759416, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.5012988 , 1.03398161]), radius=0.014257812500000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 8, 18, 21, 22, 25, 28, 29, 30]), model=ScalarModel(intercept=364.1222320916586, linear_terms=array([ 0.32732752, -1.55141702]), square_terms=array([[ 1.61489154e-01, -5.18747063e+00], + [-5.18747063e+00, 1.69455215e+02]]), scale=0.014257812500000001, shift=array([2.5012988 , 1.03398161])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=31, candidate_x=array([2.48704366, 1.03367625]), index=30, x=array([2.5012988 , 1.03398161]), fval=367.923628769737, rho=-3.4652427297692596, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 8, 18, 21, 22, 25, 28, 29, 30]), old_indices_discarded=array([ 4, 9, 13, 23, 24, 27]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.5012988 , 1.03398161]), radius=0.007128906250000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 8, 18, 22, 25, 28, 29, 30, 31]), model=ScalarModel(intercept=365.04854475169265, linear_terms=array([-0.75068331, -3.51804325]), square_terms=array([[2.82154940e-03, 1.06048737e-01], + [1.06048737e-01, 4.62554460e+01]]), scale=0.007128906250000001, shift=array([2.5012988 , 1.03398161])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=32, candidate_x=array([2.50842892, 1.03449918]), index=32, x=array([2.50842892, 1.03449918]), fval=367.07558654605555, rho=0.9689609326985643, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 8, 18, 22, 25, 28, 29, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.007148871612329106, relative_step_length=1.0028006206883566, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.50842892, 1.03449918]), radius=0.014257812500000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 18, 22, 25, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=365.01877144878074, linear_terms=array([-1.23284448, -3.51419515]), square_terms=array([[ 9.05393247e-03, -8.87054726e-01], + [-8.87054726e-01, 1.69894400e+02]]), scale=0.014257812500000001, shift=array([2.50842892, 1.03449918])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=33, candidate_x=array([2.52268501, 1.03486584]), index=33, x=array([2.52268501, 1.03486584]), fval=366.07487118769455, rho=0.7786619814704517, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 18, 22, 25, 28, 29, 30, 31, 32]), old_indices_discarded=array([ 4, 8, 9, 13, 21, 23, 24, 27]), step_length=0.014260806665657823, relative_step_length=1.0002100017557267, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.52268501, 1.03486584]), radius=0.028515625000000003, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([18, 22, 23, 25, 28, 30, 31, 32, 33]), model=ScalarModel(intercept=366.89662617731534, linear_terms=array([-1.15714064, -8.80524388]), square_terms=array([[ 1.43053818e-01, -8.85407788e+00], + [-8.85407788e+00, 5.63019267e+02]]), scale=0.028515625000000003, shift=array([2.52268501, 1.03486584])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=34, candidate_x=array([2.55119013, 1.03575803]), index=34, x=array([2.55119013, 1.03575803]), fval=364.01132196159205, rho=1.514996115362972, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([18, 22, 23, 25, 28, 30, 31, 32, 33]), old_indices_discarded=array([ 2, 4, 5, 6, 8, 9, 13, 17, 20, 21, 24, 26, 27, 29]), step_length=0.02851907916079509, relative_step_length=1.0001211322141839, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.55119013, 1.03575803]), radius=0.057031250000000006, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([18, 22, 23, 25, 28, 30, 32, 33, 34]), model=ScalarModel(intercept=364.9072282243883, linear_terms=array([-3.26192872, 2.61273054]), square_terms=array([[ 4.37832370e-01, -3.06121074e+01], + [-3.06121074e+01, 2.26542364e+03]]), scale=0.057031250000000006, shift=array([2.55119013, 1.03575803])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=35, candidate_x=array([2.60821707, 1.03646186]), index=35, x=array([2.60821707, 1.03646186]), fval=360.46133848486267, rho=1.1039196224950625, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([18, 22, 23, 25, 28, 30, 32, 33, 34]), old_indices_discarded=array([ 0, 2, 4, 5, 6, 8, 9, 11, 12, 13, 15, 16, 17, 19, 20, 21, 24, + 26, 27, 29, 31]), step_length=0.05703128908111204, relative_step_length=1.000000685257855, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.60821707, 1.03646186]), radius=0.11406250000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([18, 20, 22, 23, 28, 30, 33, 34, 35]), model=ScalarModel(intercept=472.7497163528278, linear_terms=array([ 1.61862158, -1001.29687401]), square_terms=array([[ 4.18155924e-01, -3.73402550e+01], + [-3.73402550e+01, 4.47783593e+03]]), scale=array([0.10108526, 0.0823117 ]), shift=array([2.60821707, 1.0176883 ])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=36, candidate_x=array([2.70930233, 1.03678055]), index=36, x=array([2.70930233, 1.03678055]), fval=356.02846339667883, rho=0.6594198389402943, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([18, 20, 22, 23, 28, 30, 33, 34, 35]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 19, 21, 24, 25, 26, 27, 29, 31, 32]), step_length=0.10108576107481237, relative_step_length=0.8862313299709578, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.70930233, 1.03678055]), radius=0.22812500000000002, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 8, 18, 20, 22, 31, 33, 34, 35, 36]), model=ScalarModel(intercept=2328.858771992395, linear_terms=array([ 176.48815546, -7465.59201409]), square_terms=array([[ 8.96681086e+00, -3.52247560e+02], + [-3.52247560e+02, 1.41126999e+04]]), scale=array([0.20217052, 0.13269498]), shift=array([2.70930233, 0.96730502])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=37, candidate_x=array([2.91147285, 1.04081243]), index=37, x=array([2.91147285, 1.04081243]), fval=346.25080137140253, rho=0.9806695703894485, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 8, 18, 20, 22, 31, 33, 34, 35, 36]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, + 19, 21, 23, 24, 25, 26, 27, 28, 29, 30, 32]), step_length=0.20221071716411682, relative_step_length=0.8864031437331147, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.91147285, 1.04081243]), radius=0.45625000000000004, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 4, 6, 8, 13, 24, 34, 35, 36, 37]), model=ScalarModel(intercept=6481.986941233925, linear_terms=array([ 1729.55658056, -16063.43402533]), square_terms=array([[ 247.29559738, -2255.09046193], + [-2255.09046193, 21052.2958623 ]]), scale=array([0.40434103, 0.2317643 ]), shift=array([2.91147285, 0.8682357 ])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=38, candidate_x=array([2.50713182, 1.02025148]), index=37, x=array([2.91147285, 1.04081243]), fval=346.25080137140253, rho=-10.676742898485362, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 4, 6, 8, 13, 24, 34, 35, 36, 37]), old_indices_discarded=array([ 0, 1, 2, 3, 5, 7, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, + 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 32, 33]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.91147285, 1.04081243]), radius=0.22812500000000002, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([18, 20, 23, 28, 33, 34, 35, 36, 37]), model=ScalarModel(intercept=1962.758747734596, linear_terms=array([ 112.45080748, -5929.7591617 ]), square_terms=array([[ 4.95927050e+00, -2.25104290e+02], + [-2.25104290e+02, 1.08726025e+04]]), scale=array([0.20217052, 0.13067904]), shift=array([2.91147285, 0.96932096])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=39, candidate_x=array([3.11364337, 1.04329696]), index=39, x=array([3.11364337, 1.04329696]), fval=339.6154241917441, rho=0.6515510774987106, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([18, 20, 23, 28, 33, 34, 35, 36, 37]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 38]), step_length=0.20218578329281842, relative_step_length=0.8862938445712587, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.11364337, 1.04329696]), radius=0.45625000000000004, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([18, 20, 23, 28, 34, 35, 36, 37, 39]), model=ScalarModel(intercept=9922.820348705192, linear_terms=array([ 584.39718757, -25402.16735429]), square_terms=array([[ 1.99722647e+01, -7.98222990e+02], + [-7.98222990e+02, 3.36621511e+04]]), scale=array([0.40434103, 0.23052204]), shift=array([3.11364337, 0.86947796])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=40, candidate_x=array([3.5179844 , 1.04890106]), index=40, x=array([3.5179844 , 1.04890106]), fval=332.1534899338143, rho=0.42779959920249483, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([18, 20, 23, 28, 34, 35, 36, 37, 39]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 38]), step_length=0.404379868807441, relative_step_length=0.8863120412217883, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.5179844 , 1.04890106]), radius=0.9125000000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([20, 23, 28, 34, 35, 36, 37, 39, 40]), model=ScalarModel(intercept=19431.021025825365, linear_terms=array([ 1658.92998002, -46026.40383004]), square_terms=array([[ 77.68471224, -2030.75914914], + [-2030.75914914, 55449.81654902]]), scale=array([0.80868207, 0.3 ]), shift=array([3.5179844, 0.8 ])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=40, candidate_x=array([3.5179844 , 1.04890106]), index=40, x=array([3.5179844 , 1.04890106]), fval=332.1534899338143, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([20, 23, 28, 34, 35, 36, 37, 39, 40]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.5179844 , 1.04890106]), radius=0.45625000000000004, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([20, 23, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=7298.848379344904, linear_terms=array([ 94.97700137, -19092.84480929]), square_terms=array([[ 30.43275754, -144.42092896], + [ -144.42092896, 25991.90536408]]), scale=array([0.40434103, 0.22771999]), shift=array([3.5179844 , 0.87228001])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=42, candidate_x=array([3.66959532, 1.04003046]), index=40, x=array([3.5179844 , 1.04890106]), fval=332.1534899338143, rho=-0.9936039728659589, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([20, 23, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.5179844 , 1.04890106]), radius=0.22812500000000002, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([20, 23, 35, 36, 37, 39, 40, 41, 42]), model=ScalarModel(intercept=1727.5592545498296, linear_terms=array([ 13.85204754, -5264.93521515]), square_terms=array([[ 4.38377190e+00, -4.40679296e+01], + [-4.40679296e+01, 9.66076416e+03]]), scale=array([0.20217052, 0.12663473]), shift=array([3.5179844 , 0.97336527])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=43, candidate_x=array([3.72015492, 1.04295647]), index=40, x=array([3.5179844 , 1.04890106]), fval=332.1534899338143, rho=-0.6433159047575661, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([20, 23, 35, 36, 37, 39, 40, 41, 42]), old_indices_discarded=array([ 2, 4, 5, 6, 8, 9, 12, 13, 16, 17, 18, 21, 22, 24, 25, 26, 27, + 28, 29, 30, 31, 32, 33, 34, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.5179844 , 1.04890106]), radius=0.11406250000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([39, 40, 42, 43]), model=ScalarModel(intercept=466.89452123574836, linear_terms=array([ 12.25710368, -808.14619285]), square_terms=array([[ 7.62584626e-01, -4.20205065e+01], + [-4.20205065e+01, 2.42435898e+03]]), scale=array([0.10108526, 0.0760921 ]), shift=array([3.5179844, 1.0239079])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=44, candidate_x=array([3.61906966, 1.05059164]), index=44, x=array([3.61906966, 1.05059164]), fval=331.264805944376, rho=0.5043642670342623, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([39, 40, 42, 43]), old_indices_discarded=array([], dtype=int64), step_length=0.10109939463814678, relative_step_length=0.8863508571015607, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.61906966, 1.05059164]), radius=0.22812500000000002, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([20, 36, 37, 39, 40, 41, 42, 43, 44]), model=ScalarModel(intercept=1710.5748133970885, linear_terms=array([ 9.66477497, -5032.5737487 ]), square_terms=array([[ 4.20712319e+00, -3.76943990e+01], + [-3.76943990e+01, 8.95671426e+03]]), scale=array([0.20217052, 0.12578944]), shift=array([3.61906966, 0.97421056])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=45, candidate_x=array([3.82124018, 1.04541817]), index=44, x=array([3.61906966, 1.05059164]), fval=331.264805944376, rho=-0.4811149565431463, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([20, 36, 37, 39, 40, 41, 42, 43, 44]), old_indices_discarded=array([ 2, 4, 5, 8, 9, 13, 18, 22, 23, 25, 28, 30, 31, 32, 33, 34, 35, + 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.61906966, 1.05059164]), radius=0.11406250000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([39, 40, 42, 43, 44, 45]), model=ScalarModel(intercept=469.5023206480024, linear_terms=array([ 12.44644701, -812.3012272 ]), square_terms=array([[ 7.39423845e-01, -4.10646083e+01], + [-4.10646083e+01, 2.38432798e+03]]), scale=array([0.10108526, 0.07524681]), shift=array([3.61906966, 1.02475319])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=46, candidate_x=array([3.72015492, 1.05168449]), index=46, x=array([3.72015492, 1.05168449]), fval=330.6429285017878, rho=0.40481975074141474, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([39, 40, 42, 43, 44, 45]), old_indices_discarded=array([], dtype=int64), step_length=0.10109116601850233, relative_step_length=0.8862787157786505, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.72015492, 1.05168449]), radius=0.22812500000000002, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([37, 39, 40, 41, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=2776.289476462935, linear_terms=array([ 72.38826402, -8031.03712542]), square_terms=array([[ 2.33438275e+00, -1.31600939e+02], + [-1.31600939e+02, 1.30913375e+04]]), scale=array([0.20217052, 0.12524301]), shift=array([3.72015492, 0.97475699])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=47, candidate_x=array([3.92232544, 1.05284782]), index=47, x=array([3.92232544, 1.05284782]), fval=329.8974806948583, rho=0.09505847922825468, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([37, 39, 40, 41, 42, 43, 44, 45, 46]), old_indices_discarded=array([20, 35, 36]), step_length=0.2021738643158704, relative_step_length=0.8862415970010756, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.92232544, 1.05284782]), radius=0.11406250000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([40, 41, 42, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=499.52315975920084, linear_terms=array([ -9.18218289, -1086.97670374]), square_terms=array([[9.74549599e-01, 5.75390675e+00], + [5.75390675e+00, 3.07558611e+03]]), scale=array([0.10108526, 0.07411872]), shift=array([3.92232544, 1.02588128])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=47, candidate_x=array([3.92232544, 1.05284782]), index=47, x=array([3.92232544, 1.05284782]), fval=329.8974806948583, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([40, 41, 42, 43, 44, 45, 46, 47]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.92232544, 1.05284782]), radius=0.057031250000000006, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([42, 43, 45, 46, 47, 48]), model=ScalarModel(intercept=307.9096219725444, linear_terms=array([-7.14761267, 5.65340426]), square_terms=array([[ 4.92479867e-01, -1.03584540e+01], + [-1.03584540e+01, 9.57884707e+02]]), scale=array([0.05054263, 0.04884741]), shift=array([3.92232544, 1.05115259])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=49, candidate_x=array([3.97286807, 1.05139253]), index=47, x=array([3.92232544, 1.05284782]), fval=329.8974806948583, rho=-0.17384512797919074, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 45, 46, 47, 48]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.92232544, 1.05284782]), radius=0.028515625000000003, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([45, 47, 48, 49]), model=ScalarModel(intercept=334.33247447888357, linear_terms=array([ -8.61214154, 115.74225681]), square_terms=array([[ 0.4246031 , -7.42917186], + [ -7.42917186, 350.46509675]]), scale=0.028515625000000003, shift=array([3.92232544, 1.05284782])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=50, candidate_x=array([3.95102273, 1.04418437]), index=47, x=array([3.92232544, 1.05284782]), fval=329.8974806948583, rho=-0.7253227658517244, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([45, 47, 48, 49]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.92232544, 1.05284782]), radius=0.014257812500000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([47, 49, 50]), model=ScalarModel(intercept=329.8974806948583, linear_terms=array([ 2.23564875e-03, -4.95582209e+00]), square_terms=array([[ 1.24945118e-02, -9.61026201e-01], + [-9.61026201e-01, 7.60541785e+01]]), scale=0.014257812500000001, shift=array([3.92232544, 1.05284782])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=51, candidate_x=array([3.93657038, 1.053956 ]), index=51, x=array([3.93657038, 1.053956 ]), fval=329.71144182431374, rho=0.8394434658153274, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([47, 49, 50]), old_indices_discarded=array([], dtype=int64), step_length=0.014287985375240203, relative_step_length=1.002116234537395, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.93657038, 1.053956 ]), radius=0.028515625000000003, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([45, 47, 48, 49, 50, 51]), model=ScalarModel(intercept=322.6539815389261, linear_terms=array([-5.69111829, 10.03281839]), square_terms=array([[ 2.14196085e-01, -2.08885521e+00], + [-2.08885521e+00, 3.05353159e+02]]), scale=0.028515625000000003, shift=array([3.93657038, 1.053956 ])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=51, candidate_x=array([3.93657038, 1.053956 ]), index=51, x=array([3.93657038, 1.053956 ]), fval=329.71144182431374, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([45, 47, 48, 49, 50, 51]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.93657038, 1.053956 ]), radius=0.014257812500000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([47, 49, 50, 51, 52]), model=ScalarModel(intercept=321.03895339166985, linear_terms=array([ -6.96284555, -26.26307215]), square_terms=array([[ 0.325053 , 1.33117126], + [ 1.33117126, 89.89828657]]), scale=0.014257812500000001, shift=array([3.93657038, 1.053956 ])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.93657038, 1.053956 ]), radius=0.0035644531250000003, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([47, 50, 51, 53, 54]), model=ScalarModel(intercept=315.91078800295094, linear_terms=array([ -3.58652841, -10.16878723]), square_terms=array([[0.09555102, 0.39266515], + [0.39266515, 6.63786641]]), scale=0.0035644531250000003, shift=array([3.93657038, 1.053956 ])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 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State(trustregion=Region(center=array([3.93657038, 1.053956 ]), radius=0.00044555664062500004, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([51, 56, 57]), model=ScalarModel(intercept=329.71144182431374, linear_terms=array([ 2.48192071, -8.59197548]), square_terms=array([[ 0.04013635, -0.14963807], + [-0.14963807, 0.6284637 ]]), scale=0.00044555664062500004, shift=array([3.93657038, 1.053956 ])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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scale=0.00022277832031250002, shift=array([3.93657038, 1.053956 ])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=59, candidate_x=array([3.93678711, 1.05390442]), index=51, x=array([3.93657038, 1.053956 ]), fval=329.71144182431374, rho=-2.411878603086549e-05, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([51, 57, 58]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.93657038, 1.053956 ]), radius=0.00011138916015625001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([51, 58, 59]), model=ScalarModel(intercept=329.7114418243135, linear_terms=array([0.00598092, 0.02278487]), square_terms=array([[ 7.75238359e-07, -4.85669807e-05], + [-4.85669807e-05, 4.99611280e-03]]), scale=0.00011138916015625001, shift=array([3.93657038, 1.053956 ])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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0.5]), upper=array([20. , 1.1]))), model_indices=array([51, 59, 60]), model=ScalarModel(intercept=329.71144182431397, linear_terms=array([-0.00053022, -0.0034001 ]), square_terms=array([[ 1.75578404e-07, -1.43330845e-05], + [-1.43330845e-05, 1.23093573e-03]]), scale=5.5694580078125005e-05, shift=array([3.93657038, 1.053956 ])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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square_terms=array([[ 618.64306394, -2054.1553554 ], + [-2054.1553554 , 6823.68157553]]), scale=array([0.20217052, 0.15611386]), shift=array([2.48342052, 0.94388614])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=15, candidate_x=array([2.28125 , 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.48342052, 0.9899428 ]), radius=0.057031250000000006, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 4, 5, 6, 8, 9, 12, 13, 16]), model=ScalarModel(intercept=439.60724813400134, linear_terms=array([ 29.06098493, -581.19536117]), square_terms=array([[ 4.7075158 , -74.8572189 ], + [ -74.8572189 , 1594.56453251]]), scale=0.057031250000000006, shift=array([2.48342052, 0.9899428 ])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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linear_terms=array([ -1.43610402, -455.95002481]), square_terms=array([[ 3.41974498e+00, -3.77425452e+01], + [-3.77425452e+01, 4.45506833e+03]]), scale=array([0.10108526, 0.09647596]), shift=array([2.42826986, 1.00352404])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 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2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=19, candidate_x=array([2.3271846 , 1.00428002]), index=18, x=array([2.52935512, 1.01421511]), fval=515.4916303449894, rho=-4.58101497389655, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 4, 5, 6, 8, 12, 13, 16, 17, 18]), old_indices_discarded=array([ 0, 1, 2, 3, 7, 9, 10, 11, 14, 15]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.52935512, 1.01421511]), radius=0.11406250000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 4, 5, 6, 8, 9, 13, 17, 18]), model=ScalarModel(intercept=356.13502417264516, linear_terms=array([ -21.86571545, -371.31406634]), square_terms=array([[ 5.32733963, 144.20725695], + [ 144.20725695, 4022.4388802 ]]), scale=array([0.10108526, 0.09343507]), shift=array([2.52935512, 1.00656493])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=20, candidate_x=array([2.63044038, 1.01184027]), index=18, x=array([2.52935512, 1.01421511]), fval=515.4916303449894, rho=-3.937360051298474, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 4, 5, 6, 8, 9, 13, 17, 18]), old_indices_discarded=array([ 0, 1, 3, 7, 10, 11, 12, 14, 15, 16, 19]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.52935512, 1.01421511]), radius=0.057031250000000006, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 4, 5, 6, 8, 9, 13, 18, 20]), model=ScalarModel(intercept=363.9485394385331, linear_terms=array([ 63.83466939, -205.61803243]), square_terms=array([[ 76.34919354, -329.30910554], + [-329.30910554, 1448.28366523]]), scale=0.057031250000000006, shift=array([2.52935512, 1.01421511])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=21, candidate_x=array([2.47199113, 1.00932011]), index=18, x=array([2.52935512, 1.01421511]), fval=515.4916303449894, rho=-1.876806075442851, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 4, 5, 6, 8, 9, 13, 18, 20]), old_indices_discarded=array([ 0, 11, 12, 15, 16, 17, 19]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.52935512, 1.01421511]), radius=0.028515625000000003, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 4, 5, 6, 8, 9, 13, 18, 21]), model=ScalarModel(intercept=437.8465274491715, linear_terms=array([ 100.6571281 , -233.15172692]), square_terms=array([[ 50.52435959, -135.50013132], + [-135.50013132, 372.32698017]]), scale=0.028515625000000003, shift=array([2.52935512, 1.01421511])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=22, candidate_x=array([2.50153674, 1.02161775]), index=22, x=array([2.50153674, 1.02161775]), fval=435.1912542062541, rho=0.9143899853131772, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 4, 5, 6, 8, 9, 13, 18, 21]), old_indices_discarded=array([17, 20]), step_length=0.02878648030503738, relative_step_length=1.0094984874095299, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.50153674, 1.02161775]), radius=0.057031250000000006, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 5, 8, 9, 13, 17, 18, 21, 22]), model=ScalarModel(intercept=362.50552881448345, linear_terms=array([ 0.21162612, -185.90090938]), square_terms=array([[ 2.37422818e-01, -1.88009021e+01], + [-1.88009021e+01, 1.94454231e+03]]), scale=0.057031250000000006, shift=array([2.50153674, 1.02161775])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=23, candidate_x=array([2.55851172, 1.02761616]), index=23, x=array([2.55851172, 1.02761616]), fval=391.26096822653744, rho=4.206827026062353, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 5, 8, 9, 13, 17, 18, 21, 22]), old_indices_discarded=array([ 0, 4, 6, 11, 12, 15, 16, 19, 20]), step_length=0.05728986898430706, relative_step_length=1.0045346890399045, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.55851172, 1.02761616]), radius=0.11406250000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 4, 5, 8, 13, 18, 20, 21, 22, 23]), model=ScalarModel(intercept=475.91436029922244, linear_terms=array([ 146.37643596, -1002.24191137]), square_terms=array([[ 75.34609329, -574.12412706], + [-574.12412706, 4492.7084406 ]]), scale=array([0.10108526, 0.08673455]), shift=array([2.55851172, 1.01326545])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=24, candidate_x=array([2.45742646, 1.02153053]), index=23, x=array([2.55851172, 1.02761616]), fval=391.26096822653744, rho=-1.7281556396495996, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 4, 5, 8, 13, 18, 20, 21, 22, 23]), old_indices_discarded=array([ 0, 1, 2, 3, 6, 7, 9, 10, 11, 12, 14, 15, 16, 17, 19]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.55851172, 1.02761616]), radius=0.057031250000000006, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 8, 13, 18, 20, 21, 22, 23, 24]), model=ScalarModel(intercept=376.95231493068496, linear_terms=array([ 7.67927515, -262.62815684]), square_terms=array([[ 1.43467190e+00, -5.72964126e+01], + [-5.72964126e+01, 2.30456392e+03]]), scale=0.057031250000000006, shift=array([2.55851172, 1.02761616])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=25, candidate_x=array([2.50133675, 1.03269144]), index=25, x=array([2.50133675, 1.03269144]), fval=369.99789296880266, rho=1.3196944736104015, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 8, 13, 18, 20, 21, 22, 23, 24]), old_indices_discarded=array([ 0, 4, 5, 6, 9, 11, 12, 15, 16, 17, 19]), step_length=0.05739978853949947, relative_step_length=1.0064620456241002, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.50133675, 1.03269144]), radius=0.11406250000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 4, 6, 8, 13, 17, 18, 23, 24, 25]), model=ScalarModel(intercept=376.9565890121687, linear_terms=array([ 26.48679953, -345.52089772]), square_terms=array([[ 14.62220936, -198.46876477], + [-198.46876477, 2898.25266642]]), scale=array([0.10108526, 0.08419691]), shift=array([2.50133675, 1.01580309])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=26, candidate_x=array([2.40025149, 1.02007509]), index=25, x=array([2.50133675, 1.03269144]), fval=369.99789296880266, rho=-6.312050224003466, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 4, 6, 8, 13, 17, 18, 23, 24, 25]), old_indices_discarded=array([ 0, 1, 2, 3, 5, 7, 9, 10, 11, 12, 14, 15, 16, 19, 20, 21, 22]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.50133675, 1.03269144]), radius=0.057031250000000006, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 8, 13, 18, 21, 22, 23, 24, 25]), model=ScalarModel(intercept=362.22667160322885, linear_terms=array([ 4.61005975, -18.04304404]), square_terms=array([[ 4.46594012, -100.28453856], + [-100.28453856, 2301.28686072]]), scale=0.057031250000000006, shift=array([2.50133675, 1.03269144])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=27, candidate_x=array([2.44433998, 1.0306581 ]), index=25, x=array([2.50133675, 1.03269144]), fval=369.99789296880266, rho=-1.740901072399487, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 8, 13, 18, 21, 22, 23, 24, 25]), old_indices_discarded=array([ 0, 4, 5, 6, 9, 11, 12, 15, 16, 17, 19, 20, 26]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.50133675, 1.03269144]), radius=0.028515625000000003, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 8, 13, 18, 21, 22, 24, 25, 27]), model=ScalarModel(intercept=361.65660924410525, linear_terms=array([-1.30104938, -4.97478383]), square_terms=array([[ 7.96168857e-02, -6.28732324e+00], + [-6.28732324e+00, 5.74231721e+02]]), scale=0.028515625000000003, shift=array([2.50133675, 1.03269144])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=28, candidate_x=array([2.52984797, 1.03324935]), index=28, x=array([2.52984797, 1.03324935]), fval=368.0234155462132, rho=1.4396777758036234, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 8, 13, 18, 21, 22, 24, 25, 27]), old_indices_discarded=array([ 4, 5, 6, 9, 12, 16, 17, 20, 23, 26]), step_length=0.02851668342057767, relative_step_length=1.0000371172147784, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.52984797, 1.03324935]), radius=0.057031250000000006, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 8, 13, 18, 21, 22, 23, 25, 28]), model=ScalarModel(intercept=366.55770728049407, linear_terms=array([ 14.5612219 , -63.39484606]), square_terms=array([[ 12.58220499, -165.74806918], + [-165.74806918, 2268.97440442]]), scale=0.057031250000000006, shift=array([2.52984797, 1.03324935])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=29, candidate_x=array([2.47285185, 1.03068988]), index=28, x=array([2.52984797, 1.03324935]), fval=368.0234155462132, rho=-0.7726027898783459, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 8, 13, 18, 21, 22, 23, 25, 28]), old_indices_discarded=array([ 0, 4, 5, 6, 9, 11, 12, 15, 16, 17, 19, 20, 24, 26, 27]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.52984797, 1.03324935]), radius=0.028515625000000003, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 8, 18, 21, 22, 23, 25, 28, 29]), model=ScalarModel(intercept=365.0305729249685, linear_terms=array([ 1.44330514, -37.34903706]), square_terms=array([[ 5.87745365e-01, -1.98667496e+01], + [-1.98667496e+01, 6.79541800e+02]]), scale=0.028515625000000003, shift=array([2.52984797, 1.03324935])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=30, candidate_x=array([2.5012988 , 1.03398161]), index=30, x=array([2.5012988 , 1.03398161]), fval=367.923628769737, rho=0.07258712397424556, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 8, 18, 21, 22, 23, 25, 28, 29]), old_indices_discarded=array([ 4, 5, 6, 9, 13, 17, 20, 24, 26, 27]), step_length=0.02855855688673584, relative_step_length=1.0015055565759416, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.5012988 , 1.03398161]), radius=0.014257812500000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 8, 18, 21, 22, 25, 28, 29, 30]), model=ScalarModel(intercept=364.1222320916586, linear_terms=array([ 0.32732752, -1.55141702]), square_terms=array([[ 1.61489154e-01, -5.18747063e+00], + [-5.18747063e+00, 1.69455215e+02]]), scale=0.014257812500000001, shift=array([2.5012988 , 1.03398161])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=31, candidate_x=array([2.48704366, 1.03367625]), index=30, x=array([2.5012988 , 1.03398161]), fval=367.923628769737, rho=-3.4652427297692596, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 8, 18, 21, 22, 25, 28, 29, 30]), old_indices_discarded=array([ 4, 9, 13, 23, 24, 27]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.5012988 , 1.03398161]), radius=0.007128906250000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 8, 18, 22, 25, 28, 29, 30, 31]), model=ScalarModel(intercept=365.04854475169265, linear_terms=array([-0.75068331, -3.51804325]), square_terms=array([[2.82154940e-03, 1.06048737e-01], + [1.06048737e-01, 4.62554460e+01]]), scale=0.007128906250000001, shift=array([2.5012988 , 1.03398161])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=32, candidate_x=array([2.50842892, 1.03449918]), index=32, x=array([2.50842892, 1.03449918]), fval=367.07558654605555, rho=0.9689609326985643, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 8, 18, 22, 25, 28, 29, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.007148871612329106, relative_step_length=1.0028006206883566, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.50842892, 1.03449918]), radius=0.014257812500000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 18, 22, 25, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=365.01877144878074, linear_terms=array([-1.23284448, -3.51419515]), square_terms=array([[ 9.05393247e-03, -8.87054726e-01], + [-8.87054726e-01, 1.69894400e+02]]), scale=0.014257812500000001, shift=array([2.50842892, 1.03449918])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=33, candidate_x=array([2.52268501, 1.03486584]), index=33, x=array([2.52268501, 1.03486584]), fval=366.07487118769455, rho=0.7786619814704517, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 18, 22, 25, 28, 29, 30, 31, 32]), old_indices_discarded=array([ 4, 8, 9, 13, 21, 23, 24, 27]), step_length=0.014260806665657823, relative_step_length=1.0002100017557267, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.52268501, 1.03486584]), radius=0.028515625000000003, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([18, 22, 23, 25, 28, 30, 31, 32, 33]), model=ScalarModel(intercept=366.89662617731534, linear_terms=array([-1.15714064, -8.80524388]), square_terms=array([[ 1.43053818e-01, -8.85407788e+00], + [-8.85407788e+00, 5.63019267e+02]]), scale=0.028515625000000003, shift=array([2.52268501, 1.03486584])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=34, candidate_x=array([2.55119013, 1.03575803]), index=34, x=array([2.55119013, 1.03575803]), fval=364.01132196159205, rho=1.514996115362972, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([18, 22, 23, 25, 28, 30, 31, 32, 33]), old_indices_discarded=array([ 2, 4, 5, 6, 8, 9, 13, 17, 20, 21, 24, 26, 27, 29]), step_length=0.02851907916079509, relative_step_length=1.0001211322141839, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.55119013, 1.03575803]), radius=0.057031250000000006, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([18, 22, 23, 25, 28, 30, 32, 33, 34]), model=ScalarModel(intercept=364.9072282243883, linear_terms=array([-3.26192872, 2.61273054]), square_terms=array([[ 4.37832370e-01, -3.06121074e+01], + [-3.06121074e+01, 2.26542364e+03]]), scale=0.057031250000000006, shift=array([2.55119013, 1.03575803])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=35, candidate_x=array([2.60821707, 1.03646186]), index=35, x=array([2.60821707, 1.03646186]), fval=360.46133848486267, rho=1.1039196224950625, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([18, 22, 23, 25, 28, 30, 32, 33, 34]), old_indices_discarded=array([ 0, 2, 4, 5, 6, 8, 9, 11, 12, 13, 15, 16, 17, 19, 20, 21, 24, + 26, 27, 29, 31]), step_length=0.05703128908111204, relative_step_length=1.000000685257855, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.60821707, 1.03646186]), radius=0.11406250000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([18, 20, 22, 23, 28, 30, 33, 34, 35]), model=ScalarModel(intercept=472.7497163528278, linear_terms=array([ 1.61862158, -1001.29687401]), square_terms=array([[ 4.18155924e-01, -3.73402550e+01], + [-3.73402550e+01, 4.47783593e+03]]), scale=array([0.10108526, 0.0823117 ]), shift=array([2.60821707, 1.0176883 ])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=36, candidate_x=array([2.70930233, 1.03678055]), index=36, x=array([2.70930233, 1.03678055]), fval=356.02846339667883, rho=0.6594198389402943, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([18, 20, 22, 23, 28, 30, 33, 34, 35]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 19, 21, 24, 25, 26, 27, 29, 31, 32]), step_length=0.10108576107481237, relative_step_length=0.8862313299709578, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.70930233, 1.03678055]), radius=0.22812500000000002, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 8, 18, 20, 22, 31, 33, 34, 35, 36]), model=ScalarModel(intercept=2328.858771992395, linear_terms=array([ 176.48815546, -7465.59201409]), square_terms=array([[ 8.96681086e+00, -3.52247560e+02], + [-3.52247560e+02, 1.41126999e+04]]), scale=array([0.20217052, 0.13269498]), shift=array([2.70930233, 0.96730502])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=37, candidate_x=array([2.91147285, 1.04081243]), index=37, x=array([2.91147285, 1.04081243]), fval=346.25080137140253, rho=0.9806695703894485, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 8, 18, 20, 22, 31, 33, 34, 35, 36]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, + 19, 21, 23, 24, 25, 26, 27, 28, 29, 30, 32]), step_length=0.20221071716411682, relative_step_length=0.8864031437331147, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.91147285, 1.04081243]), radius=0.45625000000000004, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 4, 6, 8, 13, 24, 34, 35, 36, 37]), model=ScalarModel(intercept=6481.986941233925, linear_terms=array([ 1729.55658056, -16063.43402533]), square_terms=array([[ 247.29559738, -2255.09046193], + [-2255.09046193, 21052.2958623 ]]), scale=array([0.40434103, 0.2317643 ]), shift=array([2.91147285, 0.8682357 ])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=38, candidate_x=array([2.50713182, 1.02025148]), index=37, x=array([2.91147285, 1.04081243]), fval=346.25080137140253, rho=-10.676742898485362, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 4, 6, 8, 13, 24, 34, 35, 36, 37]), old_indices_discarded=array([ 0, 1, 2, 3, 5, 7, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, + 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 32, 33]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.91147285, 1.04081243]), radius=0.22812500000000002, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([18, 20, 23, 28, 33, 34, 35, 36, 37]), model=ScalarModel(intercept=1962.758747734596, linear_terms=array([ 112.45080748, -5929.7591617 ]), square_terms=array([[ 4.95927050e+00, -2.25104290e+02], + [-2.25104290e+02, 1.08726025e+04]]), scale=array([0.20217052, 0.13067904]), shift=array([2.91147285, 0.96932096])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=39, candidate_x=array([3.11364337, 1.04329696]), index=39, x=array([3.11364337, 1.04329696]), fval=339.6154241917441, rho=0.6515510774987106, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([18, 20, 23, 28, 33, 34, 35, 36, 37]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 38]), step_length=0.20218578329281842, relative_step_length=0.8862938445712587, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.11364337, 1.04329696]), radius=0.45625000000000004, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([18, 20, 23, 28, 34, 35, 36, 37, 39]), model=ScalarModel(intercept=9922.820348705192, linear_terms=array([ 584.39718757, -25402.16735429]), square_terms=array([[ 1.99722647e+01, -7.98222990e+02], + [-7.98222990e+02, 3.36621511e+04]]), scale=array([0.40434103, 0.23052204]), shift=array([3.11364337, 0.86947796])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=40, candidate_x=array([3.5179844 , 1.04890106]), index=40, x=array([3.5179844 , 1.04890106]), fval=332.1534899338143, rho=0.42779959920249483, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([18, 20, 23, 28, 34, 35, 36, 37, 39]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 38]), step_length=0.404379868807441, relative_step_length=0.8863120412217883, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.5179844 , 1.04890106]), radius=0.9125000000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([20, 23, 28, 34, 35, 36, 37, 39, 40]), model=ScalarModel(intercept=19431.021025825365, linear_terms=array([ 1658.92998002, -46026.40383004]), square_terms=array([[ 77.68471224, -2030.75914914], + [-2030.75914914, 55449.81654902]]), scale=array([0.80868207, 0.3 ]), shift=array([3.5179844, 0.8 ])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=40, candidate_x=array([3.5179844 , 1.04890106]), index=40, x=array([3.5179844 , 1.04890106]), fval=332.1534899338143, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([20, 23, 28, 34, 35, 36, 37, 39, 40]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.5179844 , 1.04890106]), radius=0.45625000000000004, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([20, 23, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=7298.848379344904, linear_terms=array([ 94.97700137, -19092.84480929]), square_terms=array([[ 30.43275754, -144.42092896], + [ -144.42092896, 25991.90536408]]), scale=array([0.40434103, 0.22771999]), shift=array([3.5179844 , 0.87228001])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=42, candidate_x=array([3.66959532, 1.04003046]), index=40, x=array([3.5179844 , 1.04890106]), fval=332.1534899338143, rho=-0.9936039728659589, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([20, 23, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.5179844 , 1.04890106]), radius=0.22812500000000002, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([20, 23, 35, 36, 37, 39, 40, 41, 42]), model=ScalarModel(intercept=1727.5592545498296, linear_terms=array([ 13.85204754, -5264.93521515]), square_terms=array([[ 4.38377190e+00, -4.40679296e+01], + [-4.40679296e+01, 9.66076416e+03]]), scale=array([0.20217052, 0.12663473]), shift=array([3.5179844 , 0.97336527])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=43, candidate_x=array([3.72015492, 1.04295647]), index=40, x=array([3.5179844 , 1.04890106]), fval=332.1534899338143, rho=-0.6433159047575661, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([20, 23, 35, 36, 37, 39, 40, 41, 42]), old_indices_discarded=array([ 2, 4, 5, 6, 8, 9, 12, 13, 16, 17, 18, 21, 22, 24, 25, 26, 27, + 28, 29, 30, 31, 32, 33, 34, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.5179844 , 1.04890106]), radius=0.11406250000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([39, 40, 42, 43]), model=ScalarModel(intercept=466.89452123574836, linear_terms=array([ 12.25710368, -808.14619285]), square_terms=array([[ 7.62584626e-01, -4.20205065e+01], + [-4.20205065e+01, 2.42435898e+03]]), scale=array([0.10108526, 0.0760921 ]), shift=array([3.5179844, 1.0239079])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=44, candidate_x=array([3.61906966, 1.05059164]), index=44, x=array([3.61906966, 1.05059164]), fval=331.264805944376, rho=0.5043642670342623, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([39, 40, 42, 43]), old_indices_discarded=array([], dtype=int64), step_length=0.10109939463814678, relative_step_length=0.8863508571015607, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.61906966, 1.05059164]), radius=0.22812500000000002, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([20, 36, 37, 39, 40, 41, 42, 43, 44]), model=ScalarModel(intercept=1710.5748133970885, linear_terms=array([ 9.66477497, -5032.5737487 ]), square_terms=array([[ 4.20712319e+00, -3.76943990e+01], + [-3.76943990e+01, 8.95671426e+03]]), scale=array([0.20217052, 0.12578944]), shift=array([3.61906966, 0.97421056])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=45, candidate_x=array([3.82124018, 1.04541817]), index=44, x=array([3.61906966, 1.05059164]), fval=331.264805944376, rho=-0.4811149565431463, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([20, 36, 37, 39, 40, 41, 42, 43, 44]), old_indices_discarded=array([ 2, 4, 5, 8, 9, 13, 18, 22, 23, 25, 28, 30, 31, 32, 33, 34, 35, + 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.61906966, 1.05059164]), radius=0.11406250000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([39, 40, 42, 43, 44, 45]), model=ScalarModel(intercept=469.5023206480024, linear_terms=array([ 12.44644701, -812.3012272 ]), square_terms=array([[ 7.39423845e-01, -4.10646083e+01], + [-4.10646083e+01, 2.38432798e+03]]), scale=array([0.10108526, 0.07524681]), shift=array([3.61906966, 1.02475319])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=46, candidate_x=array([3.72015492, 1.05168449]), index=46, x=array([3.72015492, 1.05168449]), fval=330.6429285017878, rho=0.40481975074141474, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([39, 40, 42, 43, 44, 45]), old_indices_discarded=array([], dtype=int64), step_length=0.10109116601850233, relative_step_length=0.8862787157786505, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.72015492, 1.05168449]), radius=0.22812500000000002, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([37, 39, 40, 41, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=2776.289476462935, linear_terms=array([ 72.38826402, -8031.03712542]), square_terms=array([[ 2.33438275e+00, -1.31600939e+02], + [-1.31600939e+02, 1.30913375e+04]]), scale=array([0.20217052, 0.12524301]), shift=array([3.72015492, 0.97475699])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=47, candidate_x=array([3.92232544, 1.05284782]), index=47, x=array([3.92232544, 1.05284782]), fval=329.8974806948583, rho=0.09505847922825468, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([37, 39, 40, 41, 42, 43, 44, 45, 46]), old_indices_discarded=array([20, 35, 36]), step_length=0.2021738643158704, relative_step_length=0.8862415970010756, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.92232544, 1.05284782]), radius=0.11406250000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([40, 41, 42, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=499.52315975920084, linear_terms=array([ -9.18218289, -1086.97670374]), square_terms=array([[9.74549599e-01, 5.75390675e+00], + [5.75390675e+00, 3.07558611e+03]]), scale=array([0.10108526, 0.07411872]), shift=array([3.92232544, 1.02588128])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=47, candidate_x=array([3.92232544, 1.05284782]), index=47, x=array([3.92232544, 1.05284782]), fval=329.8974806948583, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([40, 41, 42, 43, 44, 45, 46, 47]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.92232544, 1.05284782]), radius=0.057031250000000006, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([42, 43, 45, 46, 47, 48]), model=ScalarModel(intercept=307.9096219725444, linear_terms=array([-7.14761267, 5.65340426]), square_terms=array([[ 4.92479867e-01, -1.03584540e+01], + [-1.03584540e+01, 9.57884707e+02]]), scale=array([0.05054263, 0.04884741]), shift=array([3.92232544, 1.05115259])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=49, candidate_x=array([3.97286807, 1.05139253]), index=47, x=array([3.92232544, 1.05284782]), fval=329.8974806948583, rho=-0.17384512797919074, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 45, 46, 47, 48]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.92232544, 1.05284782]), radius=0.028515625000000003, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([45, 47, 48, 49]), model=ScalarModel(intercept=334.33247447888357, linear_terms=array([ -8.61214154, 115.74225681]), square_terms=array([[ 0.4246031 , -7.42917186], + [ -7.42917186, 350.46509675]]), scale=0.028515625000000003, shift=array([3.92232544, 1.05284782])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=50, candidate_x=array([3.95102273, 1.04418437]), index=47, x=array([3.92232544, 1.05284782]), fval=329.8974806948583, rho=-0.7253227658517244, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([45, 47, 48, 49]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.92232544, 1.05284782]), radius=0.014257812500000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([47, 49, 50]), model=ScalarModel(intercept=329.8974806948583, linear_terms=array([ 2.23564875e-03, -4.95582209e+00]), square_terms=array([[ 1.24945118e-02, -9.61026201e-01], + [-9.61026201e-01, 7.60541785e+01]]), scale=0.014257812500000001, shift=array([3.92232544, 1.05284782])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=51, candidate_x=array([3.93657038, 1.053956 ]), index=51, x=array([3.93657038, 1.053956 ]), fval=329.71144182431374, rho=0.8394434658153274, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([47, 49, 50]), old_indices_discarded=array([], dtype=int64), step_length=0.014287985375240203, relative_step_length=1.002116234537395, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.93657038, 1.053956 ]), radius=0.028515625000000003, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([45, 47, 48, 49, 50, 51]), model=ScalarModel(intercept=322.6539815389261, linear_terms=array([-5.69111829, 10.03281839]), square_terms=array([[ 2.14196085e-01, -2.08885521e+00], + [-2.08885521e+00, 3.05353159e+02]]), scale=0.028515625000000003, shift=array([3.93657038, 1.053956 ])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=51, candidate_x=array([3.93657038, 1.053956 ]), index=51, x=array([3.93657038, 1.053956 ]), fval=329.71144182431374, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([45, 47, 48, 49, 50, 51]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.93657038, 1.053956 ]), radius=0.014257812500000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([47, 49, 50, 51, 52]), model=ScalarModel(intercept=321.03895339166985, linear_terms=array([ -6.96284555, -26.26307215]), square_terms=array([[ 0.325053 , 1.33117126], + [ 1.33117126, 89.89828657]]), scale=0.014257812500000001, shift=array([3.93657038, 1.053956 ])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=51, candidate_x=array([3.93657038, 1.053956 ]), index=51, x=array([3.93657038, 1.053956 ]), fval=329.71144182431374, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([47, 49, 50, 51, 52]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.93657038, 1.053956 ]), radius=0.007128906250000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([47, 50, 51, 52, 53]), model=ScalarModel(intercept=317.80178265489246, linear_terms=array([ -7.99322872, -18.49913967]), square_terms=array([[ 0.46249261, 1.5242433 ], + [ 1.5242433 , 25.27614788]]), scale=0.007128906250000001, shift=array([3.93657038, 1.053956 ])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=51, candidate_x=array([3.93657038, 1.053956 ]), index=51, x=array([3.93657038, 1.053956 ]), fval=329.71144182431374, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([47, 50, 51, 52, 53]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.93657038, 1.053956 ]), radius=0.0035644531250000003, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([47, 50, 51, 53, 54]), model=ScalarModel(intercept=315.91078800295094, linear_terms=array([ -3.58652841, -10.16878723]), square_terms=array([[0.09555102, 0.39266515], + [0.39266515, 6.63786641]]), scale=0.0035644531250000003, shift=array([3.93657038, 1.053956 ])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=51, candidate_x=array([3.93657038, 1.053956 ]), index=51, x=array([3.93657038, 1.053956 ]), fval=329.71144182431374, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([47, 50, 51, 53, 54]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.93657038, 1.053956 ]), radius=0.0017822265625000002, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([51, 54, 55]), model=ScalarModel(intercept=329.7114418243134, linear_terms=array([ 355.90719429, -673.90758745]), square_terms=array([[ 808.1789343 , -1536.25082243], + [-1536.25082243, 2921.44848597]]), scale=0.0017822265625000002, shift=array([3.93657038, 1.053956 ])), vector_model=VectorModel(intercepts=array([ 1.18237758, 2.88046011, 4.19639954, 6.20021635, + 8.47356014, 11.34931212, 13.85093767, 14.46284022, + 10.78229684, 5.37335013, -4.05030559, -10.30828054]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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rho=inf, accepted=True, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.4009573535569566, relative_step_length=0.8930621314711684, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.88758014, 0.9740326 ]), radius=0.8979383167808209, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 4, 6, 7, 10, 11, 12, 13]), model=ScalarModel(intercept=686.755906097821, linear_terms=array([ 23.77518092, -1230.19278225]), square_terms=array([[ 10.09518421, -22.78009116], + [ -22.78009116, 1899.11691324]]), scale=array([0.79577711, 0.3 ]), shift=array([4.88758014, 0.8 ])), vector_model=VectorModel(intercepts=array([ -0.05745288, -0.07803936, -0.39242693, -0.13914907, + 0.02524866, 0.33134428, -0.07416937, -4.6503072 , + -6.54897699, -8.06242487, inf, -15.42808523]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.44896915839041046, shift=array([4.48969158, 1.02354518])), candidate_index=14, candidate_x=array([4.15686528, 0.99102697]), index=14, x=array([4.15686528, 0.99102697]), fval=795.0937908039798, rho=13.025375501482692, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 4, 6, 7, 10, 11, 12, 13]), old_indices_discarded=array([2, 3, 5, 8, 9]), step_length=0.730912458727012, relative_step_length=0.8139896082699649, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.15686528, 0.99102697]), radius=1.7958766335616418, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 4, 6, 7, 8, 11, 12, 14]), model=ScalarModel(intercept=877.9219390852705, linear_terms=array([ -846.09300603, -1209.71993244]), square_terms=array([[ 809.65248847, 860.74128516], + [ 860.74128516, 1441.57047605]]), scale=array([1.59155423, 0.3 ]), shift=array([4.15686528, 0.8 ])), vector_model=VectorModel(intercepts=array([ -0.05745288, -0.07803936, -0.39242693, -0.13914907, + 0.02524866, 0.33134428, -0.07416937, -4.6503072 , + -6.54897699, -8.06242487, inf, -15.42808523]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.44896915839041046, shift=array([4.48969158, 1.02354518])), candidate_index=15, candidate_x=array([4.82308795, 0.9767686 ]), index=14, x=array([4.15686528, 0.99102697]), fval=795.0937908039798, rho=-1.7076109743560726, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 4, 6, 7, 8, 11, 12, 14]), old_indices_discarded=array([ 2, 3, 5, 9, 10, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.15686528, 0.99102697]), radius=0.8979383167808209, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 4, 6, 7, 11, 12, 14, 15]), model=ScalarModel(intercept=834.1486406992972, linear_terms=array([ -123.38740148, -1156.74663813]), square_terms=array([[ 21.079421 , 151.84787199], + [ 151.84787199, 1419.49099714]]), scale=array([0.79577711, 0.3 ]), shift=array([4.15686528, 0.8 ])), vector_model=VectorModel(intercepts=array([ -0.05745288, -0.07803936, -0.39242693, -0.13914907, + 0.02524866, 0.33134428, -0.07416937, -4.6503072 , + -6.54897699, -8.06242487, inf, -15.42808523]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.44896915839041046, shift=array([4.48969158, 1.02354518])), candidate_index=14, candidate_x=array([4.15686528, 0.99102697]), index=14, x=array([4.15686528, 0.99102697]), fval=795.0937908039798, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 4, 6, 7, 11, 12, 14, 15]), old_indices_discarded=array([ 2, 3, 5, 8, 9, 10, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.15686528, 0.99102697]), radius=0.44896915839041046, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 3, 7, 10, 11, 12, 14, 16]), model=ScalarModel(intercept=802.6351883847453, linear_terms=array([ -890.8858697 , -1153.28834488]), square_terms=array([[ 807.48784349, 994.25447279], + [ 994.25447279, 1287.55389773]]), scale=array([0.39788856, 0.25343079]), shift=array([4.15686528, 0.84656921])), vector_model=VectorModel(intercepts=array([ -0.05745288, -0.07803936, -0.39242693, -0.13914907, + 0.02524866, 0.33134428, -0.07416937, -4.6503072 , + -6.54897699, -8.06242487, inf, -15.42808523]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.44896915839041046, shift=array([4.48969158, 1.02354518])), candidate_index=14, candidate_x=array([4.15686528, 0.99102697]), index=14, x=array([4.15686528, 0.99102697]), fval=795.0937908039798, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 7, 10, 11, 12, 14, 16]), old_indices_discarded=array([ 2, 4, 5, 6, 8, 9, 13, 15]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.15686528, 0.99102697]), radius=0.22448457919520523, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 3, 7, 10, 12, 14, 16, 17]), model=ScalarModel(intercept=355.90446462246524, linear_terms=array([-185.55157098, -261.96418948]), square_terms=array([[388.31026539, 380.60290036], + [380.60290036, 467.61231213]]), scale=array([0.19894428, 0.15395865]), shift=array([4.15686528, 0.94604135])), vector_model=VectorModel(intercepts=array([ -0.05745288, -0.07803936, -0.39242693, -0.13914907, + 0.02524866, 0.33134428, -0.07416937, -4.6503072 , + -6.54897699, -8.06242487, inf, -15.42808523]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.44896915839041046, shift=array([4.48969158, 1.02354518])), candidate_index=14, candidate_x=array([4.15686528, 0.99102697]), index=14, x=array([4.15686528, 0.99102697]), fval=795.0937908039798, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 7, 10, 12, 14, 16, 17]), old_indices_discarded=array([ 2, 4, 5, 6, 8, 9, 11, 13, 15]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.15686528, 0.99102697]), radius=0.11224228959760262, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 3, 7, 10, 14, 16, 17, 18]), model=ScalarModel(intercept=281.97127524158844, linear_terms=array([-10.58864881, -15.3664489 ]), square_terms=array([[ 39.25876782, 60.3995863 ], + [ 60.3995863 , 256.97832576]]), scale=array([0.09947214, 0.09947214]), shift=array([4.15686528, 0.99102697])), vector_model=VectorModel(intercepts=array([ -0.05745288, -0.07803936, -0.39242693, -0.13914907, + 0.02524866, 0.33134428, -0.07416937, -4.6503072 , + -6.54897699, -8.06242487, inf, -15.42808523]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.44896915839041046, shift=array([4.48969158, 1.02354518])), candidate_index=19, candidate_x=array([4.18455645, 0.99046661]), index=14, x=array([4.15686528, 0.99102697]), fval=795.0937908039798, rho=-3.1629480300683412, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 7, 10, 14, 16, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.15686528, 0.99102697]), radius=0.05612114479880131, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 14, 16, 17, 18, 19]), model=ScalarModel(intercept=906.0798218881114, linear_terms=array([ -76.83534399, -1362.9872284 ]), square_terms=array([[ 52.02179127, 136.40322135], + [ 136.40322135, 1574.02617223]]), scale=0.05612114479880131, shift=array([4.15686528, 0.99102697])), vector_model=VectorModel(intercepts=array([ -0.05745288, -0.07803936, -0.39242693, -0.13914907, + 0.02524866, 0.33134428, -0.07416937, -4.6503072 , + -6.54897699, -8.06242487, inf, -15.42808523]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.44896915839041046, shift=array([4.48969158, 1.02354518])), candidate_index=20, candidate_x=array([4.12321979, 1.04172302]), index=20, x=array([4.12321979, 1.04172302]), fval=364.74200004612067, rho=0.7084231872172889, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 10, 14, 16, 17, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.06084494726048229, relative_step_length=1.0841715271243342, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12321979, 1.04172302]), radius=0.11224228959760262, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 14, 16, 17, 18, 19, 20]), model=ScalarModel(intercept=417.8674481290178, linear_terms=array([ -72.75630392, -934.97901545]), square_terms=array([[ 166.69387998, 359.68989571], + [ 359.68989571, 3159.9121006 ]]), scale=array([0.09947214, 0.07887456]), shift=array([4.12321979, 1.02112544])), vector_model=VectorModel(intercepts=array([ -0.05745288, -0.07803936, -0.39242693, -0.13914907, + 0.02524866, 0.33134428, -0.07416937, -4.6503072 , + -6.54897699, -8.06242487, inf, -15.42808523]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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square_terms=array([[ 82.1216678 , 348.28602694], + [ 348.28602694, 1871.02846271]]), scale=0.05612114479880131, shift=array([4.12321979, 1.04172302])), vector_model=VectorModel(intercepts=array([ -0.05745288, -0.07803936, -0.39242693, -0.13914907, + 0.02524866, 0.33134428, -0.07416937, -4.6503072 , + -6.54897699, -8.06242487, inf, -15.42808523]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.44896915839041046, shift=array([4.48969158, 1.02354518])), candidate_index=20, candidate_x=array([4.12321979, 1.04172302]), index=20, x=array([4.12321979, 1.04172302]), fval=364.74200004612067, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 10, 14, 16, 17, 18, 19, 20, 21]), old_indices_discarded=array([7]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12321979, 1.04172302]), radius=0.028060572399400654, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 10, 14, 16, 17, 18, 20, 21, 22]), model=ScalarModel(intercept=276.61199866631875, linear_terms=array([24.33108097, 13.3713441 ]), square_terms=array([[ 41.70814373, 135.60752599], + [135.60752599, 597.75730523]]), scale=0.028060572399400654, shift=array([4.12321979, 1.04172302])), vector_model=VectorModel(intercepts=array([ -0.05745288, -0.07803936, -0.39242693, -0.13914907, + 0.02524866, 0.33134428, -0.07416937, -4.6503072 , + -6.54897699, -8.06242487, inf, -15.42808523]), 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-0.13914907, + 0.02524866, 0.33134428, -0.07416937, -4.6503072 , + -6.54897699, -8.06242487, inf, -15.42808523]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.44896915839041046, shift=array([4.48969158, 1.02354518])), candidate_index=28, candidate_x=array([4.12238657, 1.04144974]), index=20, x=array([4.12321979, 1.04172302]), fval=364.74200004612067, rho=-0.012951244495601396, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([20, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12321979, 1.04172302]), radius=0.0004384464437406352, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([20, 27, 28]), model=ScalarModel(intercept=364.7420000461208, linear_terms=array([ 1.56893669, -6.7344342 ]), square_terms=array([[ 0.01166578, -0.0412127 ], + [-0.0412127 , 0.19845266]]), scale=0.0004384464437406352, shift=array([4.12321979, 1.04172302])), vector_model=VectorModel(intercepts=array([ -0.05745288, -0.07803936, -0.39242693, -0.13914907, + 0.02524866, 0.33134428, -0.07416937, -4.6503072 , + -6.54897699, -8.06242487, inf, -15.42808523]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.44896915839041046, shift=array([4.48969158, 1.02354518])), candidate_index=29, candidate_x=array([4.12313163, 1.04215251]), index=29, x=array([4.12313163, 1.04215251]), fval=362.70327585316227, rho=0.29942496732236035, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([20, 27, 28]), old_indices_discarded=array([], dtype=int64), step_length=0.00043844644374061765, relative_step_length=0.9999999999999599, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute params change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 30 entries., 'history': {'params': [{'CRRA': 4.489691583904104, 'DiscFac': 1.0235451798335713}, {'CRRA': 4.148838926505437, 'DiscFac': 0.6256566229701255}, {'CRRA': 4.88758014076755, 'DiscFac': 1.0085622394168912}, {'CRRA': 4.091803027040658, 'DiscFac': 1.0164244142983376}, {'CRRA': 4.88758014076755, 'DiscFac': 1.099469972899596}, {'CRRA': 4.88758014076755, 'DiscFac': 0.8095032422727262}, {'CRRA': 4.88758014076755, 'DiscFac': 0.7530848261156642}, {'CRRA': 4.370499261127387, 'DiscFac': 1.1}, {'CRRA': 4.88758014076755, 'DiscFac': 1.0230970952959988}, {'CRRA': 4.88758014076755, 'DiscFac': 1.094306518980592}, {'CRRA': 4.091803027040658, 'DiscFac': 1.030316188493455}, {'CRRA': 4.592059653712614, 'DiscFac': 0.6256566229701255}, {'CRRA': 4.713065097063642, 'DiscFac': 1.1}, {'CRRA': 4.88758014076755, 'DiscFac': 0.9740325987200549}, {'CRRA': 4.156865275992871, 'DiscFac': 0.9910269718973239}, {'CRRA': 4.823087954290904, 'DiscFac': 0.9767686041716913}, {'CRRA': 4.0986439359814515, 'DiscFac': 1.0468186695947106}, {'CRRA': 4.159988799689999, 'DiscFac': 1.0720360527765176}, {'CRRA': 4.086768308814167, 'DiscFac': 1.0764443212984682}, {'CRRA': 4.184556452027686, 'DiscFac': 0.9904666054461131}, {'CRRA': 4.12321979390894, 'DiscFac': 1.0417230185131179}, {'CRRA': 4.096584746432777, 'DiscFac': 1.0468674920513872}, {'CRRA': 4.068538483259733, 'DiscFac': 1.0543538205150842}, {'CRRA': 4.095701700856493, 'DiscFac': 1.0472429398263814}, {'CRRA': 4.109922480523975, 'DiscFac': 1.0492056481639367}, {'CRRA': 4.123696391831634, 'DiscFac': 1.0487219532271626}, {'CRRA': 4.122373413553136, 'DiscFac': 1.0451269424945184}, {'CRRA': 4.122194866474128, 'DiscFac': 1.043146144754121}, {'CRRA': 4.122386571705684, 'DiscFac': 1.0414497386270734}, {'CRRA': 4.123131625916622, 'DiscFac': 1.042152508546948}], 'criterion': [nan, 1226.4490465556805, nan, 553.6435505558949, nan, 1171.6637343332839, 1188.9230246157163, nan, nan, nan, 434.3714550376725, 1217.936942397526, nan, 905.6533779307806, 795.0937908039798, 889.762531741871, nan, nan, nan, 799.618570882734, 364.74200004612067, nan, nan, nan, nan, nan, nan, nan, 365.968710281642, 362.70327585316227], 'runtime': [0.0, 1.8893596660000185, 1.9254749279998578, 1.9607912299998134, 1.9952709869999126, 2.031054889999723, 2.068451605000064, 2.1080323689998295, 2.145541043999856, 2.182866938999723, 2.2252518159998544, 2.255448136999803, 2.292968175999704, 4.501134815999649, 6.416764320999846, 8.185432758999923, 9.948060636999799, 11.645774880999852, 13.325634909999735, 14.999888390999786, 16.89481569999998, 18.722493776999727, 20.67351689299994, 22.545289429999684, 24.252850029, 25.96147833899977, 27.657194549999986, 29.387423359999957, 31.07909649899966, 32.79469605799977], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]}}], 'exploration_sample': array([[2.28125 , 1.0625 ], + [5.825 , 0.95 ], + [5. , 0.95 ], + [7.00625 , 0.6125 ], + [3.4625 , 0.875 ], + [4.64375 , 0.6875 ], + [2.871875, 0.78125 ]]), 'exploration_results': array([ 935.17428758, 989.46212984, 1021.90990852, 1165.31803535, + 1180.04944214, 1208.05004997, 1224.63960931])}}" diff --git a/content/tables/min/WarmGlowSub(Stock)(Labor)Market_estimate_results.csv b/content/tables/min/WarmGlowSub(Stock)(Labor)Market_estimate_results.csv new file mode 100644 index 0000000..91c3f77 --- /dev/null +++ b/content/tables/min/WarmGlowSub(Stock)(Labor)Market_estimate_results.csv @@ -0,0 +1,4623 @@ +CRRA,4.6561465823330614 +DiscFac,1.0762986516574313 +time_to_estimate,136.76599597930908 +params,"{'CRRA': 4.6561465823330614, 'DiscFac': 1.0762986516574313}" +criterion,324.50808131884514 +start_criterion,1061.7656193736902 +start_params,"{'CRRA': 5.0, 'DiscFac': 0.95}" +algorithm,multistart_tranquilo_ls +direction,minimize +n_free,2 +message,Absolute criterion change smaller than tolerance. +success, +n_criterion_evaluations, +n_derivative_evaluations, +n_iterations, +history,"{'params': [{'CRRA': 2.28125, 'DiscFac': 1.0625}, {'CRRA': 2.1080599021593467, 'DiscFac': 0.8603294826310895}, {'CRRA': 2.4834205173689106, 'DiscFac': 1.0537994279097354}, {'CRRA': 2.0790794826310894, 'DiscFac': 1.057771933087904}, {'CRRA': 2.4834205173689106, 'DiscFac': 1.0997321942805047}, {'CRRA': 2.4834205173689106, 'DiscFac': 0.9532212916001106}, {'CRRA': 2.4834205173689106, 'DiscFac': 0.9247148727392314}, {'CRRA': 2.220687379772545, 'DiscFac': 1.1}, {'CRRA': 2.4834205173689106, 'DiscFac': 1.061143425100633}, {'CRRA': 2.4834205173689106, 'DiscFac': 1.097123266376995}, {'CRRA': 2.0790794826310894, 'DiscFac': 1.0647910016988402}, {'CRRA': 2.3332640759975307, 'DiscFac': 0.8603294826310895}, {'CRRA': 2.394747958016102, 'DiscFac': 1.1}, {'CRRA': 2.4834205173689106, 'DiscFac': 1.0109704370801267}, {'CRRA': 2.382335258684455, 'DiscFac': 1.0476834403560513}, {'CRRA': 2.1801647413155445, 'DiscFac': 0.9647165064215529}, {'CRRA': 2.48342051736891, 'DiscFac': 1.0347177543831159}, {'CRRA': 2.432877888026683, 'DiscFac': 1.0455642528505245}, {'CRRA': 2.533963146711138, 'DiscFac': 1.0405222480655252}, {'CRRA': 2.4834205173689106, 'DiscFac': 1.0465256644325633}, {'CRRA': 2.3823352586844555, 'DiscFac': 1.033564664572611}, {'CRRA': 2.5339631467111383, 'DiscFac': 1.047469944628167}, {'CRRA': 2.6350484053955934, 'DiscFac': 1.0428685385969303}, {'CRRA': 2.4834205173689106, 'DiscFac': 1.0463589722083482}, {'CRRA': 2.5054215502834296, 'DiscFac': 1.0475071778724756}, {'CRRA': 2.5196979549221816, 'DiscFac': 1.0474502989833212}, {'CRRA': 2.5410765204759427, 'DiscFac': 1.0485469431138206}, {'CRRA': 2.5553265167200228, 'DiscFac': 1.0491997883920992}, {'CRRA': 2.583842487058223, 'DiscFac': 1.0493972612466624}, {'CRRA': 2.6343851164004506, 'DiscFac': 1.0499149483606702}, {'CRRA': 2.7354703750849056, 'DiscFac': 1.0514149796124799}, {'CRRA': 2.937640892453816, 'DiscFac': 1.0544155720791926}, {'CRRA': 3.341981927191637, 'DiscFac': 1.0616360120621342}, {'CRRA': 4.150663996667278, 'DiscFac': 1.085446070296119}, {'CRRA': 3.746322961929458, 'DiscFac': 1.0660934014490961}, {'CRRA': 4.5550050314051, 'DiscFac': 1.0761847385700731}, {'CRRA': 6.172369170356384, 'DiscFac': 1.0972678112700958}, {'CRRA': 3.7463229619294585, 'DiscFac': 1.0483494241971596}, {'CRRA': 4.474340324764396, 'DiscFac': 1.053485161647703}, {'CRRA': 4.75717554877401, 'DiscFac': 1.0764850403494255}, {'CRRA': 4.656090290089556, 'DiscFac': 1.0762985807282335}, {'CRRA': 4.706632919431783, 'DiscFac': 1.069696017703279}, {'CRRA': 4.68136160476067, 'DiscFac': 1.0832949257173223}, {'CRRA': 4.670456130935367, 'DiscFac': 1.0824152990387697}, {'CRRA': 4.6498535218331565, 'DiscFac': 1.0798882535131364}, {'CRRA': 4.658926719589502, 'DiscFac': 1.0784666951442767}, {'CRRA': 4.6578790211563765, 'DiscFac': 1.0757221459705013}, {'CRRA': 4.65520301478792, 'DiscFac': 1.0759821254367556}, {'CRRA': 4.656534705087294, 'DiscFac': 1.0764167570708383}, {'CRRA': 4.656029646033997, 'DiscFac': 1.0765129460204956}, {'CRRA': 4.656080386488486, 'DiscFac': 1.0761876327057809}, {'CRRA': 4.6561465823330614, 'DiscFac': 1.0762986516574313}], 'criterion': [539.4929192700604, 1221.8675258714557, 404.7045013140681, 556.5981636048194, 1418.2180360595958, 1134.4079728308398, 1175.1490350385895, 1830.3844633609278, 454.37828850926013, 1316.6223528136734, 692.7723876600156, 1217.1599081251106, 1550.0771663964833, 713.5134538090916, 401.2841593554285, 1114.6909165038717, 446.76005820979447, 397.62883382383677, 410.77183910073745, 393.7396485524217, 452.8256804660464, 390.1399050831211, 399.8443617809469, 393.9257331434714, 391.8213338797376, 390.9412217644086, 389.277834841791, 388.2655022854925, 386.3917605626402, 382.93906954428303, 376.31868391130615, 365.062918550816, 346.7329948411579, 368.2369752259499, 334.90086244002987, 325.2668555198778, nan, 390.1447848010262, 382.7525586574866, nan, 324.50875500144707, 329.99954100363095, nan, nan, 327.0902292511706, 325.4438253771558, 324.5641619351112, 324.53943466025123, 324.5344057526708, 324.5566121642871, 324.5174940975544, 324.50808131884514], 'runtime': [0.0, 1.8009274670002924, 1.8349953599999935, 1.8716977379999662, 1.9064695870001742, 1.9587920620001569, 1.988055123000322, 2.0242426620002334, 2.0628871050003, 2.1053071480000654, 2.1416214290002245, 2.1893475360002412, 2.2281548040000416, 4.36073855099994, 6.131201134000094, 7.8724270480001906, 9.600155464000181, 11.310955472999922, 13.038677657000335, 14.74292541900013, 16.4423814390002, 18.136036501000035, 19.83283611500019, 21.537421882000217, 23.253812039999957, 25.083194824000202, 26.778671699999904, 28.46434648500008, 30.143333340000027, 31.836918702000276, 33.52446875500027, 35.22875840500001, 36.934571169000264, 38.67663603100027, 40.39671845900011, 42.13699109700019, 43.857527524000034, 45.570228391, 47.2664019140002, 49.09642402999998, 50.78836033000016, 52.4552653300002, 54.15450059300019, 55.86215718199992, 57.58588246599993, 59.26718887700008, 60.95021729000018, 62.6536614659999, 64.33571088600002, 66.06470411600003, 67.73840497099991, 69.41452023800002], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40]}" +convergence_report, +multistart_info,"{'start_parameters': [{'CRRA': 2.28125, 'DiscFac': 1.0625}, {'CRRA': 5.517465994239721, 'DiscFac': 1.033814885188935}], 'local_optima': [Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 0.02962 0.1773 +relative_params_change 0.1778 0.4224 +absolute_criterion_change 9.634 57.67 +absolute_params_change 0.8087 1.921 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute params change smaller than tolerance.], 'exploration_sample': [{'CRRA': 2.28125, 'DiscFac': 1.0625}, {'CRRA': 7.596874999999999, 'DiscFac': 0.9312500000000001}, {'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 5.0, 'DiscFac': 0.95}, {'CRRA': 8.1875, 'DiscFac': 0.7250000000000001}, {'CRRA': 7.00625, 'DiscFac': 0.6125}, {'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 2.871875, 'DiscFac': 0.78125}], 'exploration_results': array([ 539.49291927, 1005.15883524, 1029.6319182 , 1061.73760943, + 1115.48338813, 1167.602755 , 1188.73516988, 1211.80249333, + 1228.66192563])}" +algorithm_output,"{'states': [State(trustregion=Region(center=array([2.28125, 1.0625 ]), radius=0.22812500000000002, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=539.4929192700604, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=0, candidate_x=array([2.28125, 1.0625 ]), index=0, x=array([2.28125, 1.0625 ]), fval=539.4929192700604, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([2.28125, 1.0625 ]), radius=0.22812500000000002, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=369.28064669953756, linear_terms=array([ 23.20797239, -331.32409918]), square_terms=array([[ 30.60434353, -262.86738644], + [-262.86738644, 2311.42613793]]), scale=array([0.20217052, 0.11983526]), shift=array([2.28125 , 0.98016474])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + 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fval=401.2841593554285, rho=-4.787419416129048, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 4, 6, 7, 8, 11, 12, 13, 14]), old_indices_discarded=array([ 1, 2, 3, 5, 9, 10]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.38233526, 1.04768344]), radius=0.11406250000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 2, 4, 5, 8, 9, 12, 13, 14]), model=ScalarModel(intercept=397.48435278501233, linear_terms=array([ -19.88002875, -373.31052501]), square_terms=array([[1.48512547e+00, 1.03320709e+01], + [1.03320709e+01, 2.43818178e+03]]), scale=array([0.10108526, 0.07670091]), shift=array([2.38233526, 1.02329909])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=16, candidate_x=array([2.48342052, 1.03471775]), index=14, x=array([2.38233526, 1.04768344]), fval=401.2841593554285, rho=-0.8971627859072555, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 2, 4, 5, 8, 9, 12, 13, 14]), old_indices_discarded=array([ 1, 3, 6, 7, 10, 11, 15]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.38233526, 1.04768344]), radius=0.057031250000000006, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 2, 4, 8, 9, 12, 13, 14, 16]), model=ScalarModel(intercept=393.8792118849582, linear_terms=array([ -8.62705232, 104.33160222]), square_terms=array([[ 6.22696152e-01, -2.71796860e+01], + [-2.71796860e+01, 1.84007347e+03]]), scale=array([0.05054263, 0.05054263]), shift=array([2.38233526, 1.04768344])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=17, candidate_x=array([2.43287789, 1.04556425]), index=17, x=array([2.43287789, 1.04556425]), fval=397.62883382383677, rho=0.3679927833871514, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 2, 4, 8, 9, 12, 13, 14, 16]), old_indices_discarded=array([ 5, 6, 7, 11, 15]), step_length=0.05058703723790713, relative_step_length=0.8870055844454948, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.43287789, 1.04556425]), radius=0.11406250000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 4, 5, 8, 12, 13, 14, 16, 17]), model=ScalarModel(intercept=414.4434968026508, linear_terms=array([ 9.86021543, -471.90609025]), square_terms=array([[ 3.74635264, -92.83139049], + [ -92.83139049, 2401.95091869]]), scale=array([0.10108526, 0.0777605 ]), shift=array([2.43287789, 1.0222395 ])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=18, candidate_x=array([2.53396315, 1.04052225]), index=17, x=array([2.43287789, 1.04556425]), fval=397.62883382383677, rho=-0.6210906230657142, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 4, 5, 8, 12, 13, 14, 16, 17]), old_indices_discarded=array([ 0, 1, 3, 6, 7, 9, 10, 11, 15]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.43287789, 1.04556425]), radius=0.057031250000000006, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 4, 8, 9, 12, 13, 14, 16, 17]), model=ScalarModel(intercept=385.9217089848088, linear_terms=array([ -7.31126247, -14.99370301]), square_terms=array([[ 3.54256234e-01, -2.03158571e+01], + [-2.03158571e+01, 1.85626847e+03]]), scale=array([0.05054263, 0.05054263]), shift=array([2.43287789, 1.04556425])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=19, candidate_x=array([2.48342052, 1.04652566]), index=19, x=array([2.48342052, 1.04652566]), fval=393.7396485524217, rho=0.5206433882198473, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 4, 8, 9, 12, 13, 14, 16, 17]), old_indices_discarded=array([ 0, 5, 6, 7, 11, 15, 18]), step_length=0.05055177240271502, relative_step_length=0.8863872421297976, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.48342052, 1.04652566]), radius=0.11406250000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 4, 5, 8, 12, 13, 14, 17, 18, 19]), model=ScalarModel(intercept=421.2323159643066, linear_terms=array([ 41.05412659, -502.6472471 ]), square_terms=array([[ 13.73189863, -177.79563733], + [-177.79563733, 2314.95736687]]), scale=array([0.10108526, 0.0772798 ]), shift=array([2.48342052, 1.0227202 ])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=20, candidate_x=array([2.38233526, 1.03356466]), index=19, x=array([2.48342052, 1.04652566]), fval=393.7396485524217, rho=-4.933047820071923, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 4, 5, 8, 12, 13, 14, 17, 18, 19]), old_indices_discarded=array([ 0, 1, 2, 3, 6, 7, 9, 10, 11, 15, 16]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.48342052, 1.04652566]), radius=0.057031250000000006, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 4, 8, 9, 13, 16, 17, 18, 19]), model=ScalarModel(intercept=382.0647107078109, linear_terms=array([ -4.3670562 , -11.49855929]), square_terms=array([[ 3.75364769e-01, -2.23406779e+01], + [-2.23406779e+01, 1.81124631e+03]]), scale=array([0.05054263, 0.05054263]), shift=array([2.48342052, 1.04652566])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=21, candidate_x=array([2.53396315, 1.04746994]), index=21, x=array([2.53396315, 1.04746994]), fval=390.1399050831211, rho=0.8007472184276522, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 4, 8, 9, 13, 16, 17, 18, 19]), old_indices_discarded=array([ 0, 5, 6, 7, 11, 12, 14, 20]), step_length=0.05055144949369534, relative_step_length=0.886381580163425, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.53396315, 1.04746994]), radius=0.11406250000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 4, 5, 8, 13, 16, 17, 19, 21]), model=ScalarModel(intercept=426.84646205225044, linear_terms=array([ 1.15989452, -480.3723676 ]), square_terms=array([[ 7.00906111e-01, -3.46047728e+01], + [-3.46047728e+01, 2.01025586e+03]]), scale=array([0.10108526, 0.07680766]), shift=array([2.53396315, 1.02319234])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=22, candidate_x=array([2.63504841, 1.04286854]), index=21, x=array([2.53396315, 1.04746994]), fval=390.1399050831211, rho=-0.7444906283211007, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 4, 5, 8, 13, 16, 17, 19, 21]), old_indices_discarded=array([ 0, 1, 3, 6, 7, 9, 10, 11, 12, 14, 15, 18, 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.53396315, 1.04746994]), radius=0.057031250000000006, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 4, 8, 9, 13, 16, 18, 19, 21]), model=ScalarModel(intercept=388.9970240954775, linear_terms=array([ 12.00200522, -46.69029483]), square_terms=array([[ 4.53977618, -86.02708251], + [ -86.02708251, 1789.58959189]]), scale=array([0.05054263, 0.05054263]), shift=array([2.53396315, 1.04746994])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=23, candidate_x=array([2.48342052, 1.04635897]), index=21, x=array([2.53396315, 1.04746994]), fval=390.1399050831211, rho=-0.3724578812009339, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 4, 8, 9, 13, 16, 18, 19, 21]), old_indices_discarded=array([ 0, 5, 6, 11, 12, 14, 17, 20, 22]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.53396315, 1.04746994]), radius=0.028515625000000003, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 8, 9, 13, 16, 18, 19, 21, 23]), model=ScalarModel(intercept=389.4436319427866, linear_terms=array([ 5.02814649, -23.66664595]), square_terms=array([[ 1.01446646, -22.91801796], + [-22.91801796, 553.38241538]]), scale=0.028515625000000003, shift=array([2.53396315, 1.04746994])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=24, candidate_x=array([2.50542155, 1.04750718]), index=21, x=array([2.53396315, 1.04746994]), fval=390.1399050831211, rho=-0.37158262078118054, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 8, 9, 13, 16, 18, 19, 21, 23]), old_indices_discarded=array([ 4, 5, 6, 17, 22]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.53396315, 1.04746994]), radius=0.014257812500000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 8, 13, 16, 18, 19, 21, 23, 24]), model=ScalarModel(intercept=392.4947159374246, linear_terms=array([ 1.27932535, -3.76638647]), square_terms=array([[ 0.1356221 , -3.92705052], + [ -3.92705052, 116.92844993]]), scale=0.014257812500000001, shift=array([2.53396315, 1.04746994])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=25, candidate_x=array([2.51969795, 1.0474503 ]), index=21, x=array([2.53396315, 1.04746994]), fval=390.1399050831211, rho=-0.6610327156620669, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 8, 13, 16, 18, 19, 21, 23, 24]), old_indices_discarded=array([9]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.53396315, 1.04746994]), radius=0.007128906250000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([18, 21, 24, 25]), model=ScalarModel(intercept=390.1270373870984, linear_terms=array([-0.40666892, -4.67642507]), square_terms=array([[ 8.24163964e-03, -5.14376616e-01], + [-5.14376616e-01, 3.38745244e+01]]), scale=0.007128906250000001, shift=array([2.53396315, 1.04746994])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=26, candidate_x=array([2.54107652, 1.04854694]), index=26, x=array([2.54107652, 1.04854694]), fval=389.277834841791, rho=1.0787463888831172, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([18, 21, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.007194443137304451, relative_step_length=1.0091931195342132, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.54107652, 1.04854694]), radius=0.014257812500000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 8, 18, 19, 21, 23, 24, 25, 26]), model=ScalarModel(intercept=388.3114278890381, linear_terms=array([-0.97062407, -4.79051289]), square_terms=array([[ 3.23021015e-02, -2.12345521e+00], + [-2.12345521e+00, 1.49936025e+02]]), scale=0.014257812500000001, shift=array([2.54107652, 1.04854694])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=27, candidate_x=array([2.55532652, 1.04919979]), index=27, x=array([2.55532652, 1.04919979]), fval=388.2655022854925, rho=0.9093009321394735, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 8, 18, 19, 21, 23, 24, 25, 26]), old_indices_discarded=array([13, 16]), step_length=0.014264943039271727, relative_step_length=1.0005001145352224, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.55532652, 1.04919979]), radius=0.028515625000000003, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 18, 19, 21, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=388.15196116706716, linear_terms=array([-1.87600935, 4.8110453 ]), square_terms=array([[ 1.41233061e-01, -8.75463669e+00], + [-8.75463669e+00, 5.67685407e+02]]), scale=0.028515625000000003, shift=array([2.55532652, 1.04919979])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=28, candidate_x=array([2.58384249, 1.04939726]), index=28, x=array([2.58384249, 1.04939726]), fval=386.39176056264023, rho=1.030030940972948, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 18, 19, 21, 23, 24, 25, 26, 27]), old_indices_discarded=array([ 4, 5, 8, 9, 13, 16, 17, 22]), step_length=0.028516654078930722, relative_step_length=1.0000360882474335, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.58384249, 1.04939726]), radius=0.057031250000000006, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([18, 19, 21, 22, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=386.5853634477402, linear_terms=array([-3.2942653 , 6.19453001]), square_terms=array([[ 3.46423682e-01, -2.34674124e+01], + [-2.34674124e+01, 1.68637941e+03]]), scale=array([0.05054263, 0.05054263]), shift=array([2.58384249, 1.04939726])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=29, candidate_x=array([2.63438512, 1.04991495]), index=29, x=array([2.63438512, 1.04991495]), fval=382.93906954428303, rho=1.0757679087893275, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([18, 19, 21, 22, 24, 25, 26, 27, 28]), old_indices_discarded=array([ 2, 4, 5, 6, 8, 9, 12, 13, 14, 16, 17, 20, 23]), step_length=0.05054528049950689, relative_step_length=0.8862734114982029, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.63438512, 1.04991495]), radius=0.11406250000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([18, 21, 22, 24, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=597.5812627596855, linear_terms=array([ 17.62359378, -1267.78086801]), square_terms=array([[ 1.43464082e+00, -7.12704676e+01], + [-7.12704676e+01, 3.74858881e+03]]), scale=array([0.10108526, 0.07558516]), shift=array([2.63438512, 1.02441484])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=30, candidate_x=array([2.73547038, 1.05141498]), index=30, x=array([2.73547038, 1.05141498]), fval=376.31868391130615, rho=1.0277337960420385, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([18, 21, 22, 24, 25, 26, 27, 28, 29]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 19, 20, 23]), step_length=0.10109638775475414, relative_step_length=0.8863244953841458, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.73547038, 1.05141498]), radius=0.22812500000000002, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([18, 21, 22, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=2307.7763833225085, linear_terms=array([ 134.13086451, -6300.49433818]), square_terms=array([[ 5.93425604e+00, -2.40087569e+02], + [-2.40087569e+02, 1.02770928e+04]]), scale=array([0.20217052, 0.12537777]), shift=array([2.73547038, 0.97462223])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=31, candidate_x=array([2.93764089, 1.05441557]), index=31, x=array([2.93764089, 1.05441557]), fval=365.062918550816, rho=0.8727682132841054, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([18, 21, 22, 25, 26, 27, 28, 29, 30]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 19, 20, 23, 24]), step_length=0.20219278337360178, relative_step_length=0.8863245298568844, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.93764089, 1.05441557]), radius=0.45625000000000004, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 8, 9, 16, 18, 24, 28, 29, 30, 31]), model=ScalarModel(intercept=12865.218421931559, linear_terms=array([ 855.20260599, -31193.62316319]), square_terms=array([[ 3.13263792e+01, -1.09220775e+03], + [-1.09220775e+03, 3.89236743e+04]]), scale=array([0.40434103, 0.22496273]), shift=array([2.93764089, 0.87503727])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=32, candidate_x=array([3.34198193, 1.06163601]), index=32, x=array([3.34198193, 1.06163601]), fval=346.7329948411579, rho=0.9130421801358641, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 8, 9, 16, 18, 24, 28, 29, 30, 31]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 10, 11, 12, 13, 14, 15, 17, 19, 20, + 21, 22, 23, 25, 26, 27]), step_length=0.40440549838794076, relative_step_length=0.8863682156448016, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.34198193, 1.06163601]), radius=0.9125000000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 4, 6, 13, 17, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=7965.115933100144, linear_terms=array([ 1401.52538742, -17482.46466521]), square_terms=array([[ 132.13425913, -1621.72179802], + [-1621.72179802, 20078.24431782]]), scale=array([0.80868207, 0.3 ]), shift=array([3.34198193, 0.8 ])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=33, candidate_x=array([4.150664 , 1.08544607]), index=32, x=array([3.34198193, 1.06163601]), fval=346.7329948411579, rho=-2.1544952554641625, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 4, 6, 13, 17, 28, 29, 30, 31, 32]), old_indices_discarded=array([ 0, 1, 2, 3, 5, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 19, 20, + 21, 22, 23, 24, 25, 26, 27]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.34198193, 1.06163601]), radius=0.45625000000000004, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([21, 22, 26, 27, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=10898.29333061804, linear_terms=array([ 657.86330033, -25725.95620283]), square_terms=array([[ 2.28531456e+01, -8.27401705e+02], + [-8.27401705e+02, 3.13565250e+04]]), scale=array([0.40434103, 0.22135251]), shift=array([3.34198193, 0.87864749])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=34, candidate_x=array([3.74632296, 1.0660934 ]), index=34, x=array([3.74632296, 1.0660934 ]), fval=334.90086244002987, rho=0.5616369864914499, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([21, 22, 26, 27, 28, 29, 30, 31, 32]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 23, 24, 25, 33]), step_length=0.4043656027569591, relative_step_length=0.8862807731659377, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.74632296, 1.0660934 ]), radius=0.9125000000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 27, 28, 29, 30, 31, 32, 33, 34]), model=ScalarModel(intercept=16391.3697228818, linear_terms=array([ 1360.7428243 , -36399.16797702]), square_terms=array([[ 63.68375989, -1580.10411546], + [-1580.10411546, 41254.20429359]]), scale=array([0.80868207, 0.3 ]), shift=array([3.74632296, 0.8 ])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=35, candidate_x=array([4.55500503, 1.07618474]), index=35, x=array([4.55500503, 1.07618474]), fval=325.2668555198778, rho=0.29852083892327547, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([22, 27, 28, 29, 30, 31, 32, 33, 34]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 23, 24, 25, 26]), step_length=0.8087450306346845, relative_step_length=0.8862959239832158, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.55500503, 1.07618474]), radius=1.8250000000000002, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 28, 29, 30, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=14968.578144708577, linear_terms=array([ 2168.15459365, -31802.26159431]), square_terms=array([[ 175.52773738, -2402.58546272], + [-2402.58546272, 34519.22383816]]), scale=array([1.61736414, 0.3 ]), shift=array([4.55500503, 0.8 ])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=35, candidate_x=array([4.55500503, 1.07618474]), index=35, x=array([4.55500503, 1.07618474]), fval=325.2668555198778, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 28, 29, 30, 31, 32, 33, 34, 35]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 23, 24, 25, 26, 27]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.55500503, 1.07618474]), radius=0.9125000000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 29, 30, 31, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=4358.048308908504, linear_terms=array([ 710.92585749, -8891.08879205]), square_terms=array([[ 105.26910053, -721.38451611], + [-721.38451611, 9868.80195397]]), scale=array([0.80868207, 0.3 ]), shift=array([4.55500503, 0.8 ])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=37, candidate_x=array([3.74632296, 1.04834942]), index=35, x=array([4.55500503, 1.07618474]), fval=325.2668555198778, rho=-1.7700435194524402, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 29, 30, 31, 32, 33, 34, 35, 36]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.55500503, 1.07618474]), radius=0.45625000000000004, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 30, 31, 32, 33, 34, 35, 36, 37]), model=ScalarModel(intercept=3032.083850662671, linear_terms=array([ -31.18380912, -6979.28199582]), square_terms=array([[ 12.72294677, 43.08307281], + [ 43.08307281, 8927.68085089]]), scale=array([0.40434103, 0.21407815]), shift=array([4.55500503, 0.88592185])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=38, candidate_x=array([4.47434032, 1.05348516]), index=35, x=array([4.55500503, 1.07618474]), fval=325.2668555198778, rho=-1.1194335774644109, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 30, 31, 32, 33, 34, 35, 36, 37]), old_indices_discarded=array([ 0, 2, 4, 5, 6, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, + 23, 24, 25, 26, 27, 28, 29]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.55500503, 1.07618474]), radius=0.22812500000000002, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([33, 34, 35, 37, 38]), model=ScalarModel(intercept=1511.3714089512966, linear_terms=array([ 53.97025663, -3068.72012489]), square_terms=array([[ 1.41838555e+00, -7.33292095e+01], + [-7.33292095e+01, 3.96778551e+03]]), scale=array([0.20217052, 0.11299289]), shift=array([4.55500503, 0.98700711])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=35, candidate_x=array([4.55500503, 1.07618474]), index=35, x=array([4.55500503, 1.07618474]), fval=325.2668555198778, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([33, 34, 35, 37, 38]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.55500503, 1.07618474]), radius=0.11406250000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([33, 35, 38, 39]), model=ScalarModel(intercept=550.1980177751707, linear_terms=array([ -21.34314356, -861.36430433]), square_terms=array([[ 3.79220296, 8.8597605 ], + [ 8.8597605 , 1373.95414632]]), scale=array([0.10108526, 0.06245026]), shift=array([4.55500503, 1.03754974])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=40, candidate_x=array([4.65609029, 1.07629858]), index=40, x=array([4.65609029, 1.07629858]), fval=324.50875500144707, rho=0.054273262543122716, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([33, 35, 38, 39]), old_indices_discarded=array([], dtype=int64), step_length=0.10108532278892056, relative_step_length=0.886227487464509, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65609029, 1.07629858]), radius=0.057031250000000006, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([35, 38, 39, 40]), model=ScalarModel(intercept=297.5496123744371, linear_terms=array([-13.8623804, -56.2490704]), square_terms=array([[ 2.70771094, -8.34250059], + [ -8.34250059, 351.68012528]]), scale=array([0.05054263, 0.03712202]), shift=array([4.65609029, 1.06287798])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=41, candidate_x=array([4.70663292, 1.06969602]), index=40, x=array([4.65609029, 1.07629858]), fval=324.50875500144707, rho=-0.2603849652107475, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 38, 39, 40]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65609029, 1.07629858]), radius=0.028515625000000003, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([35, 39, 40, 41]), model=ScalarModel(intercept=314.0366355784512, linear_terms=array([ -4.77698475, -80.97859446]), square_terms=array([[1.67269568e-01, 2.07585986e+00], + [2.07585986e+00, 2.48293104e+02]]), scale=array([0.02527131, 0.02448637]), shift=array([4.65609029, 1.07551363])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=40, candidate_x=array([4.65609029, 1.07629858]), index=40, x=array([4.65609029, 1.07629858]), fval=324.50875500144707, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 39, 40, 41]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65609029, 1.07629858]), radius=0.014257812500000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([40, 41, 42]), model=ScalarModel(intercept=324.50875500144684, linear_terms=array([ -4.87203941, -34.71671033]), square_terms=array([[ 0.15185929, 1.32260597], + [ 1.32260597, 73.69648928]]), scale=0.014257812500000001, shift=array([4.65609029, 1.07629858])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=40, candidate_x=array([4.65609029, 1.07629858]), index=40, x=array([4.65609029, 1.07629858]), fval=324.50875500144707, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([40, 41, 42]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65609029, 1.07629858]), radius=0.007128906250000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([40, 42, 43]), model=ScalarModel(intercept=324.50875500144696, linear_terms=array([ 18.41374889, -220.21494744]), square_terms=array([[ 2.41113424, -30.02159224], + [-30.02159224, 383.81830261]]), scale=0.007128906250000001, shift=array([4.65609029, 1.07629858])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=44, candidate_x=array([4.64985352, 1.07988825]), index=40, x=array([4.65609029, 1.07629858]), fval=324.50875500144707, rho=-0.040216525204197696, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([40, 42, 43]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65609029, 1.07629858]), radius=0.0035644531250000003, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([40, 43, 44]), model=ScalarModel(intercept=324.50875500144764, linear_terms=array([-3.32369605, -5.03665033]), square_terms=array([[0.07385999, 0.19295634], + [0.19295634, 4.07255454]]), scale=0.0035644531250000003, shift=array([4.65609029, 1.07629858])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=45, candidate_x=array([4.65892672, 1.0784667 ]), index=40, x=array([4.65609029, 1.07629858]), fval=324.50875500144707, rho=-0.1932652041870447, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([40, 43, 44]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65609029, 1.07629858]), radius=0.0017822265625000002, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([40, 44, 45]), model=ScalarModel(intercept=324.5087550014469, linear_terms=array([-0.03797236, 0.30302842]), square_terms=array([[ 1.16167799e-04, -9.67679355e-03], + [-9.67679355e-03, 8.72278531e-01]]), scale=0.0017822265625000002, shift=array([4.65609029, 1.07629858])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=46, candidate_x=array([4.65787902, 1.07572215]), index=40, x=array([4.65609029, 1.07629858]), fval=324.50875500144707, rho=-0.6346975789894941, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([40, 44, 45]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65609029, 1.07629858]), radius=0.0008911132812500001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([40, 45, 46]), model=ScalarModel(intercept=324.50875500144684, linear_terms=array([0.03083558, 0.08633504]), square_terms=array([[ 3.80279615e-05, -2.70035490e-03], + [-2.70035490e-03, 2.18789898e-01]]), scale=0.0008911132812500001, shift=array([4.65609029, 1.07629858])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=47, candidate_x=array([4.65520301, 1.07598213]), index=40, x=array([4.65609029, 1.07629858]), fval=324.50875500144707, rho=-0.6325408571369623, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([40, 45, 46]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65609029, 1.07629858]), radius=0.00044555664062500004, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([40, 46, 47]), model=ScalarModel(intercept=324.5087550014472, linear_terms=array([-0.00336429, -0.01511503]), square_terms=array([[ 5.78841731e-06, -5.46592851e-04], + [-5.46592851e-04, 5.55307507e-02]]), scale=0.00044555664062500004, shift=array([4.65609029, 1.07629858])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=48, candidate_x=array([4.65653471, 1.07641676]), index=40, x=array([4.65609029, 1.07629858]), fval=324.50875500144707, rho=-4.619141553298749, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([40, 46, 47]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65609029, 1.07629858]), radius=0.00022277832031250002, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([40, 47, 48]), model=ScalarModel(intercept=324.50875500144707, linear_terms=array([ 0.05986855, -0.18013296]), square_terms=array([[ 3.60665718e-05, -3.86428183e-04], + [-3.86428183e-04, 1.50111980e-02]]), scale=0.00022277832031250002, shift=array([4.65609029, 1.07629858])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=49, candidate_x=array([4.65602965, 1.07651295]), index=40, x=array([4.65609029, 1.07629858]), fval=324.50875500144707, rho=-0.26212236296537095, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([40, 47, 48]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65609029, 1.07629858]), radius=0.00011138916015625001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([40, 48, 49]), model=ScalarModel(intercept=324.5087550014472, linear_terms=array([0.00021849, 0.02168299]), square_terms=array([[ 3.31928202e-07, -2.78076427e-05], + [-2.78076427e-05, 3.35816501e-03]]), scale=0.00011138916015625001, shift=array([4.65609029, 1.07629858])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=50, candidate_x=array([4.65608039, 1.07618763]), index=40, x=array([4.65609029, 1.07629858]), fval=324.50875500144707, rho=-0.43797987064481597, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([40, 48, 49]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65609029, 1.07629858]), radius=5.5694580078125005e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([40, 49, 50]), model=ScalarModel(intercept=324.5087550014474, linear_terms=array([-3.85122473e-02, -8.45473122e-05]), square_terms=array([[1.57897714e-05, 3.45728674e-05], + [3.45728674e-05, 8.61778960e-04]]), scale=5.5694580078125005e-05, shift=array([4.65609029, 1.07629858])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=51, candidate_x=array([4.65614658, 1.07629865]), index=51, x=array([4.65614658, 1.07629865]), fval=324.50808131884514, rho=0.01731052112460427, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([40, 49, 50]), old_indices_discarded=array([], dtype=int64), step_length=5.629228819166208e-05, relative_step_length=1.01073189011747, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 52 entries., 'multistart_info': {'start_parameters': [array([2.28125, 1.0625 ]), array([5.51746599, 1.03381489])], 'local_optima': [{'solution_x': array([4.65614658, 1.07629865]), 'solution_criterion': 324.50808131884514, 'states': [State(trustregion=Region(center=array([2.28125, 1.0625 ]), radius=0.22812500000000002, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=539.4929192700604, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=0, candidate_x=array([2.28125, 1.0625 ]), index=0, x=array([2.28125, 1.0625 ]), fval=539.4929192700604, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([2.28125, 1.0625 ]), radius=0.22812500000000002, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=369.28064669953756, linear_terms=array([ 23.20797239, -331.32409918]), square_terms=array([[ 30.60434353, -262.86738644], + [-262.86738644, 2311.42613793]]), scale=array([0.20217052, 0.11983526]), shift=array([2.28125 , 0.98016474])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=13, candidate_x=array([2.48342052, 1.01097044]), index=0, x=array([2.28125, 1.0625 ]), fval=539.4929192700604, rho=-0.4891062139230318, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.28125, 1.0625 ]), radius=0.11406250000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 2, 3, 4, 7, 8, 9, 10, 12]), model=ScalarModel(intercept=509.6122393186843, linear_terms=array([ 19.08883257, -858.76997919]), square_terms=array([[ 5.03729945, -139.92580294], + [-139.92580294, 4076.45926807]]), scale=array([0.10108526, 0.06929263]), shift=array([2.28125 , 1.03070737])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=14, candidate_x=array([2.38233526, 1.04768344]), index=14, x=array([2.38233526, 1.04768344]), fval=401.2841593554285, rho=1.0178589872619395, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 2, 3, 4, 7, 8, 9, 10, 12]), old_indices_discarded=array([ 1, 5, 6, 11, 13]), step_length=0.10216535598227945, relative_step_length=0.8956962716254636, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.38233526, 1.04768344]), radius=0.22812500000000002, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 4, 6, 7, 8, 11, 12, 13, 14]), model=ScalarModel(intercept=435.28307937263514, linear_terms=array([ 234.63528144, -687.02908869]), square_terms=array([[ 278.9237627 , -852.19265508], + [-852.19265508, 2613.94453482]]), scale=array([0.20217052, 0.12724354]), shift=array([2.38233526, 0.97275646])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=15, candidate_x=array([2.18016474, 0.96471651]), index=14, x=array([2.38233526, 1.04768344]), fval=401.2841593554285, rho=-4.787419416129048, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 4, 6, 7, 8, 11, 12, 13, 14]), old_indices_discarded=array([ 1, 2, 3, 5, 9, 10]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.38233526, 1.04768344]), radius=0.11406250000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 2, 4, 5, 8, 9, 12, 13, 14]), model=ScalarModel(intercept=397.48435278501233, linear_terms=array([ -19.88002875, -373.31052501]), square_terms=array([[1.48512547e+00, 1.03320709e+01], + [1.03320709e+01, 2.43818178e+03]]), scale=array([0.10108526, 0.07670091]), shift=array([2.38233526, 1.02329909])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=16, candidate_x=array([2.48342052, 1.03471775]), index=14, x=array([2.38233526, 1.04768344]), fval=401.2841593554285, rho=-0.8971627859072555, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 2, 4, 5, 8, 9, 12, 13, 14]), old_indices_discarded=array([ 1, 3, 6, 7, 10, 11, 15]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.38233526, 1.04768344]), radius=0.057031250000000006, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 2, 4, 8, 9, 12, 13, 14, 16]), model=ScalarModel(intercept=393.8792118849582, linear_terms=array([ -8.62705232, 104.33160222]), square_terms=array([[ 6.22696152e-01, -2.71796860e+01], + [-2.71796860e+01, 1.84007347e+03]]), scale=array([0.05054263, 0.05054263]), shift=array([2.38233526, 1.04768344])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=17, candidate_x=array([2.43287789, 1.04556425]), index=17, x=array([2.43287789, 1.04556425]), fval=397.62883382383677, rho=0.3679927833871514, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 2, 4, 8, 9, 12, 13, 14, 16]), old_indices_discarded=array([ 5, 6, 7, 11, 15]), step_length=0.05058703723790713, relative_step_length=0.8870055844454948, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.43287789, 1.04556425]), radius=0.11406250000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 4, 5, 8, 12, 13, 14, 16, 17]), model=ScalarModel(intercept=414.4434968026508, linear_terms=array([ 9.86021543, -471.90609025]), square_terms=array([[ 3.74635264, -92.83139049], + [ -92.83139049, 2401.95091869]]), scale=array([0.10108526, 0.0777605 ]), shift=array([2.43287789, 1.0222395 ])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=18, candidate_x=array([2.53396315, 1.04052225]), index=17, x=array([2.43287789, 1.04556425]), fval=397.62883382383677, rho=-0.6210906230657142, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 4, 5, 8, 12, 13, 14, 16, 17]), old_indices_discarded=array([ 0, 1, 3, 6, 7, 9, 10, 11, 15]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.43287789, 1.04556425]), radius=0.057031250000000006, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 4, 8, 9, 12, 13, 14, 16, 17]), model=ScalarModel(intercept=385.9217089848088, linear_terms=array([ -7.31126247, -14.99370301]), square_terms=array([[ 3.54256234e-01, -2.03158571e+01], + [-2.03158571e+01, 1.85626847e+03]]), scale=array([0.05054263, 0.05054263]), shift=array([2.43287789, 1.04556425])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=19, candidate_x=array([2.48342052, 1.04652566]), index=19, x=array([2.48342052, 1.04652566]), fval=393.7396485524217, rho=0.5206433882198473, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 4, 8, 9, 12, 13, 14, 16, 17]), old_indices_discarded=array([ 0, 5, 6, 7, 11, 15, 18]), step_length=0.05055177240271502, relative_step_length=0.8863872421297976, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.48342052, 1.04652566]), radius=0.11406250000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 4, 5, 8, 12, 13, 14, 17, 18, 19]), model=ScalarModel(intercept=421.2323159643066, linear_terms=array([ 41.05412659, -502.6472471 ]), square_terms=array([[ 13.73189863, -177.79563733], + [-177.79563733, 2314.95736687]]), scale=array([0.10108526, 0.0772798 ]), shift=array([2.48342052, 1.0227202 ])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=20, candidate_x=array([2.38233526, 1.03356466]), index=19, x=array([2.48342052, 1.04652566]), fval=393.7396485524217, rho=-4.933047820071923, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 4, 5, 8, 12, 13, 14, 17, 18, 19]), old_indices_discarded=array([ 0, 1, 2, 3, 6, 7, 9, 10, 11, 15, 16]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.48342052, 1.04652566]), radius=0.057031250000000006, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 4, 8, 9, 13, 16, 17, 18, 19]), model=ScalarModel(intercept=382.0647107078109, linear_terms=array([ -4.3670562 , -11.49855929]), square_terms=array([[ 3.75364769e-01, -2.23406779e+01], + [-2.23406779e+01, 1.81124631e+03]]), scale=array([0.05054263, 0.05054263]), shift=array([2.48342052, 1.04652566])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=21, candidate_x=array([2.53396315, 1.04746994]), index=21, x=array([2.53396315, 1.04746994]), fval=390.1399050831211, rho=0.8007472184276522, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 4, 8, 9, 13, 16, 17, 18, 19]), old_indices_discarded=array([ 0, 5, 6, 7, 11, 12, 14, 20]), step_length=0.05055144949369534, relative_step_length=0.886381580163425, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.53396315, 1.04746994]), radius=0.11406250000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 4, 5, 8, 13, 16, 17, 19, 21]), model=ScalarModel(intercept=426.84646205225044, linear_terms=array([ 1.15989452, -480.3723676 ]), square_terms=array([[ 7.00906111e-01, -3.46047728e+01], + [-3.46047728e+01, 2.01025586e+03]]), scale=array([0.10108526, 0.07680766]), shift=array([2.53396315, 1.02319234])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=22, candidate_x=array([2.63504841, 1.04286854]), index=21, x=array([2.53396315, 1.04746994]), fval=390.1399050831211, rho=-0.7444906283211007, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 4, 5, 8, 13, 16, 17, 19, 21]), old_indices_discarded=array([ 0, 1, 3, 6, 7, 9, 10, 11, 12, 14, 15, 18, 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.53396315, 1.04746994]), radius=0.057031250000000006, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 4, 8, 9, 13, 16, 18, 19, 21]), model=ScalarModel(intercept=388.9970240954775, linear_terms=array([ 12.00200522, -46.69029483]), square_terms=array([[ 4.53977618, -86.02708251], + [ -86.02708251, 1789.58959189]]), scale=array([0.05054263, 0.05054263]), shift=array([2.53396315, 1.04746994])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=23, candidate_x=array([2.48342052, 1.04635897]), index=21, x=array([2.53396315, 1.04746994]), fval=390.1399050831211, rho=-0.3724578812009339, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 4, 8, 9, 13, 16, 18, 19, 21]), old_indices_discarded=array([ 0, 5, 6, 11, 12, 14, 17, 20, 22]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.53396315, 1.04746994]), radius=0.028515625000000003, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 8, 9, 13, 16, 18, 19, 21, 23]), model=ScalarModel(intercept=389.4436319427866, linear_terms=array([ 5.02814649, -23.66664595]), square_terms=array([[ 1.01446646, -22.91801796], + [-22.91801796, 553.38241538]]), scale=0.028515625000000003, shift=array([2.53396315, 1.04746994])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=24, candidate_x=array([2.50542155, 1.04750718]), index=21, x=array([2.53396315, 1.04746994]), fval=390.1399050831211, rho=-0.37158262078118054, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 8, 9, 13, 16, 18, 19, 21, 23]), old_indices_discarded=array([ 4, 5, 6, 17, 22]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.53396315, 1.04746994]), radius=0.014257812500000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 8, 13, 16, 18, 19, 21, 23, 24]), model=ScalarModel(intercept=392.4947159374246, linear_terms=array([ 1.27932535, -3.76638647]), square_terms=array([[ 0.1356221 , -3.92705052], + [ -3.92705052, 116.92844993]]), scale=0.014257812500000001, shift=array([2.53396315, 1.04746994])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=25, candidate_x=array([2.51969795, 1.0474503 ]), index=21, x=array([2.53396315, 1.04746994]), fval=390.1399050831211, rho=-0.6610327156620669, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 8, 13, 16, 18, 19, 21, 23, 24]), old_indices_discarded=array([9]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.53396315, 1.04746994]), radius=0.007128906250000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([18, 21, 24, 25]), model=ScalarModel(intercept=390.1270373870984, linear_terms=array([-0.40666892, -4.67642507]), square_terms=array([[ 8.24163964e-03, -5.14376616e-01], + [-5.14376616e-01, 3.38745244e+01]]), scale=0.007128906250000001, shift=array([2.53396315, 1.04746994])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=26, candidate_x=array([2.54107652, 1.04854694]), index=26, x=array([2.54107652, 1.04854694]), fval=389.277834841791, rho=1.0787463888831172, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([18, 21, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.007194443137304451, relative_step_length=1.0091931195342132, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.54107652, 1.04854694]), radius=0.014257812500000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 8, 18, 19, 21, 23, 24, 25, 26]), model=ScalarModel(intercept=388.3114278890381, linear_terms=array([-0.97062407, -4.79051289]), square_terms=array([[ 3.23021015e-02, -2.12345521e+00], + [-2.12345521e+00, 1.49936025e+02]]), scale=0.014257812500000001, shift=array([2.54107652, 1.04854694])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=27, candidate_x=array([2.55532652, 1.04919979]), index=27, x=array([2.55532652, 1.04919979]), fval=388.2655022854925, rho=0.9093009321394735, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 8, 18, 19, 21, 23, 24, 25, 26]), old_indices_discarded=array([13, 16]), step_length=0.014264943039271727, relative_step_length=1.0005001145352224, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.55532652, 1.04919979]), radius=0.028515625000000003, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 2, 18, 19, 21, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=388.15196116706716, linear_terms=array([-1.87600935, 4.8110453 ]), square_terms=array([[ 1.41233061e-01, -8.75463669e+00], + [-8.75463669e+00, 5.67685407e+02]]), scale=0.028515625000000003, shift=array([2.55532652, 1.04919979])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=28, candidate_x=array([2.58384249, 1.04939726]), index=28, x=array([2.58384249, 1.04939726]), fval=386.39176056264023, rho=1.030030940972948, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 18, 19, 21, 23, 24, 25, 26, 27]), old_indices_discarded=array([ 4, 5, 8, 9, 13, 16, 17, 22]), step_length=0.028516654078930722, relative_step_length=1.0000360882474335, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.58384249, 1.04939726]), radius=0.057031250000000006, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([18, 19, 21, 22, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=386.5853634477402, linear_terms=array([-3.2942653 , 6.19453001]), square_terms=array([[ 3.46423682e-01, -2.34674124e+01], + [-2.34674124e+01, 1.68637941e+03]]), scale=array([0.05054263, 0.05054263]), shift=array([2.58384249, 1.04939726])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=29, candidate_x=array([2.63438512, 1.04991495]), index=29, x=array([2.63438512, 1.04991495]), fval=382.93906954428303, rho=1.0757679087893275, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([18, 19, 21, 22, 24, 25, 26, 27, 28]), old_indices_discarded=array([ 2, 4, 5, 6, 8, 9, 12, 13, 14, 16, 17, 20, 23]), step_length=0.05054528049950689, relative_step_length=0.8862734114982029, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.63438512, 1.04991495]), radius=0.11406250000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([18, 21, 22, 24, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=597.5812627596855, linear_terms=array([ 17.62359378, -1267.78086801]), square_terms=array([[ 1.43464082e+00, -7.12704676e+01], + [-7.12704676e+01, 3.74858881e+03]]), scale=array([0.10108526, 0.07558516]), shift=array([2.63438512, 1.02441484])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=30, candidate_x=array([2.73547038, 1.05141498]), index=30, x=array([2.73547038, 1.05141498]), fval=376.31868391130615, rho=1.0277337960420385, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([18, 21, 22, 24, 25, 26, 27, 28, 29]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 19, 20, 23]), step_length=0.10109638775475414, relative_step_length=0.8863244953841458, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.73547038, 1.05141498]), radius=0.22812500000000002, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([18, 21, 22, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=2307.7763833225085, linear_terms=array([ 134.13086451, -6300.49433818]), square_terms=array([[ 5.93425604e+00, -2.40087569e+02], + [-2.40087569e+02, 1.02770928e+04]]), scale=array([0.20217052, 0.12537777]), shift=array([2.73547038, 0.97462223])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=31, candidate_x=array([2.93764089, 1.05441557]), index=31, x=array([2.93764089, 1.05441557]), fval=365.062918550816, rho=0.8727682132841054, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([18, 21, 22, 25, 26, 27, 28, 29, 30]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 19, 20, 23, 24]), step_length=0.20219278337360178, relative_step_length=0.8863245298568844, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.93764089, 1.05441557]), radius=0.45625000000000004, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 8, 9, 16, 18, 24, 28, 29, 30, 31]), model=ScalarModel(intercept=12865.218421931559, linear_terms=array([ 855.20260599, -31193.62316319]), square_terms=array([[ 3.13263792e+01, -1.09220775e+03], + [-1.09220775e+03, 3.89236743e+04]]), scale=array([0.40434103, 0.22496273]), shift=array([2.93764089, 0.87503727])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=32, candidate_x=array([3.34198193, 1.06163601]), index=32, x=array([3.34198193, 1.06163601]), fval=346.7329948411579, rho=0.9130421801358641, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 8, 9, 16, 18, 24, 28, 29, 30, 31]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 10, 11, 12, 13, 14, 15, 17, 19, 20, + 21, 22, 23, 25, 26, 27]), step_length=0.40440549838794076, relative_step_length=0.8863682156448016, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.34198193, 1.06163601]), radius=0.9125000000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 4, 6, 13, 17, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=7965.115933100144, linear_terms=array([ 1401.52538742, -17482.46466521]), square_terms=array([[ 132.13425913, -1621.72179802], + [-1621.72179802, 20078.24431782]]), scale=array([0.80868207, 0.3 ]), shift=array([3.34198193, 0.8 ])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=33, candidate_x=array([4.150664 , 1.08544607]), index=32, x=array([3.34198193, 1.06163601]), fval=346.7329948411579, rho=-2.1544952554641625, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 4, 6, 13, 17, 28, 29, 30, 31, 32]), old_indices_discarded=array([ 0, 1, 2, 3, 5, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 19, 20, + 21, 22, 23, 24, 25, 26, 27]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.34198193, 1.06163601]), radius=0.45625000000000004, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([21, 22, 26, 27, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=10898.29333061804, linear_terms=array([ 657.86330033, -25725.95620283]), square_terms=array([[ 2.28531456e+01, -8.27401705e+02], + [-8.27401705e+02, 3.13565250e+04]]), scale=array([0.40434103, 0.22135251]), shift=array([3.34198193, 0.87864749])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=34, candidate_x=array([3.74632296, 1.0660934 ]), index=34, x=array([3.74632296, 1.0660934 ]), fval=334.90086244002987, rho=0.5616369864914499, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([21, 22, 26, 27, 28, 29, 30, 31, 32]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 23, 24, 25, 33]), step_length=0.4043656027569591, relative_step_length=0.8862807731659377, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.74632296, 1.0660934 ]), radius=0.9125000000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 27, 28, 29, 30, 31, 32, 33, 34]), model=ScalarModel(intercept=16391.3697228818, linear_terms=array([ 1360.7428243 , -36399.16797702]), square_terms=array([[ 63.68375989, -1580.10411546], + [-1580.10411546, 41254.20429359]]), scale=array([0.80868207, 0.3 ]), shift=array([3.74632296, 0.8 ])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=35, candidate_x=array([4.55500503, 1.07618474]), index=35, x=array([4.55500503, 1.07618474]), fval=325.2668555198778, rho=0.29852083892327547, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([22, 27, 28, 29, 30, 31, 32, 33, 34]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 23, 24, 25, 26]), step_length=0.8087450306346845, relative_step_length=0.8862959239832158, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.55500503, 1.07618474]), radius=1.8250000000000002, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 28, 29, 30, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=14968.578144708577, linear_terms=array([ 2168.15459365, -31802.26159431]), square_terms=array([[ 175.52773738, -2402.58546272], + [-2402.58546272, 34519.22383816]]), scale=array([1.61736414, 0.3 ]), shift=array([4.55500503, 0.8 ])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=35, candidate_x=array([4.55500503, 1.07618474]), index=35, x=array([4.55500503, 1.07618474]), fval=325.2668555198778, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 28, 29, 30, 31, 32, 33, 34, 35]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 23, 24, 25, 26, 27]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.55500503, 1.07618474]), radius=0.9125000000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 29, 30, 31, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=4358.048308908504, linear_terms=array([ 710.92585749, -8891.08879205]), square_terms=array([[ 105.26910053, -721.38451611], + [-721.38451611, 9868.80195397]]), scale=array([0.80868207, 0.3 ]), shift=array([4.55500503, 0.8 ])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=37, candidate_x=array([3.74632296, 1.04834942]), index=35, x=array([4.55500503, 1.07618474]), fval=325.2668555198778, rho=-1.7700435194524402, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 29, 30, 31, 32, 33, 34, 35, 36]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.55500503, 1.07618474]), radius=0.45625000000000004, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 30, 31, 32, 33, 34, 35, 36, 37]), model=ScalarModel(intercept=3032.083850662671, linear_terms=array([ -31.18380912, -6979.28199582]), square_terms=array([[ 12.72294677, 43.08307281], + [ 43.08307281, 8927.68085089]]), scale=array([0.40434103, 0.21407815]), shift=array([4.55500503, 0.88592185])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=38, candidate_x=array([4.47434032, 1.05348516]), index=35, x=array([4.55500503, 1.07618474]), fval=325.2668555198778, rho=-1.1194335774644109, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 30, 31, 32, 33, 34, 35, 36, 37]), old_indices_discarded=array([ 0, 2, 4, 5, 6, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, + 23, 24, 25, 26, 27, 28, 29]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.55500503, 1.07618474]), radius=0.22812500000000002, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([33, 34, 35, 37, 38]), model=ScalarModel(intercept=1511.3714089512966, linear_terms=array([ 53.97025663, -3068.72012489]), square_terms=array([[ 1.41838555e+00, -7.33292095e+01], + [-7.33292095e+01, 3.96778551e+03]]), scale=array([0.20217052, 0.11299289]), shift=array([4.55500503, 0.98700711])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=35, candidate_x=array([4.55500503, 1.07618474]), index=35, x=array([4.55500503, 1.07618474]), fval=325.2668555198778, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([33, 34, 35, 37, 38]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.55500503, 1.07618474]), radius=0.11406250000000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([33, 35, 38, 39]), model=ScalarModel(intercept=550.1980177751707, linear_terms=array([ -21.34314356, -861.36430433]), square_terms=array([[ 3.79220296, 8.8597605 ], + [ 8.8597605 , 1373.95414632]]), scale=array([0.10108526, 0.06245026]), shift=array([4.55500503, 1.03754974])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=40, candidate_x=array([4.65609029, 1.07629858]), index=40, x=array([4.65609029, 1.07629858]), fval=324.50875500144707, rho=0.054273262543122716, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([33, 35, 38, 39]), old_indices_discarded=array([], dtype=int64), step_length=0.10108532278892056, relative_step_length=0.886227487464509, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65609029, 1.07629858]), radius=0.057031250000000006, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([35, 38, 39, 40]), model=ScalarModel(intercept=297.5496123744371, linear_terms=array([-13.8623804, -56.2490704]), square_terms=array([[ 2.70771094, -8.34250059], + [ -8.34250059, 351.68012528]]), scale=array([0.05054263, 0.03712202]), shift=array([4.65609029, 1.06287798])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=41, candidate_x=array([4.70663292, 1.06969602]), index=40, x=array([4.65609029, 1.07629858]), fval=324.50875500144707, rho=-0.2603849652107475, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 38, 39, 40]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65609029, 1.07629858]), radius=0.028515625000000003, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([35, 39, 40, 41]), model=ScalarModel(intercept=314.0366355784512, linear_terms=array([ -4.77698475, -80.97859446]), square_terms=array([[1.67269568e-01, 2.07585986e+00], + [2.07585986e+00, 2.48293104e+02]]), scale=array([0.02527131, 0.02448637]), shift=array([4.65609029, 1.07551363])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=40, candidate_x=array([4.65609029, 1.07629858]), index=40, x=array([4.65609029, 1.07629858]), fval=324.50875500144707, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 39, 40, 41]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65609029, 1.07629858]), radius=0.014257812500000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([40, 41, 42]), model=ScalarModel(intercept=324.50875500144684, linear_terms=array([ -4.87203941, -34.71671033]), square_terms=array([[ 0.15185929, 1.32260597], + [ 1.32260597, 73.69648928]]), scale=0.014257812500000001, shift=array([4.65609029, 1.07629858])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=40, candidate_x=array([4.65609029, 1.07629858]), index=40, x=array([4.65609029, 1.07629858]), fval=324.50875500144707, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([40, 41, 42]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65609029, 1.07629858]), radius=0.007128906250000001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([40, 42, 43]), model=ScalarModel(intercept=324.50875500144696, linear_terms=array([ 18.41374889, -220.21494744]), square_terms=array([[ 2.41113424, -30.02159224], + [-30.02159224, 383.81830261]]), scale=0.007128906250000001, shift=array([4.65609029, 1.07629858])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=44, candidate_x=array([4.64985352, 1.07988825]), index=40, x=array([4.65609029, 1.07629858]), fval=324.50875500144707, rho=-0.040216525204197696, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([40, 42, 43]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65609029, 1.07629858]), radius=0.0035644531250000003, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([40, 43, 44]), model=ScalarModel(intercept=324.50875500144764, linear_terms=array([-3.32369605, -5.03665033]), square_terms=array([[0.07385999, 0.19295634], + [0.19295634, 4.07255454]]), scale=0.0035644531250000003, shift=array([4.65609029, 1.07629858])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=45, candidate_x=array([4.65892672, 1.0784667 ]), index=40, x=array([4.65609029, 1.07629858]), fval=324.50875500144707, rho=-0.1932652041870447, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([40, 43, 44]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65609029, 1.07629858]), radius=0.0017822265625000002, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([40, 44, 45]), model=ScalarModel(intercept=324.5087550014469, linear_terms=array([-0.03797236, 0.30302842]), square_terms=array([[ 1.16167799e-04, -9.67679355e-03], + [-9.67679355e-03, 8.72278531e-01]]), scale=0.0017822265625000002, shift=array([4.65609029, 1.07629858])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=46, candidate_x=array([4.65787902, 1.07572215]), index=40, x=array([4.65609029, 1.07629858]), fval=324.50875500144707, rho=-0.6346975789894941, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([40, 44, 45]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65609029, 1.07629858]), radius=0.0008911132812500001, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([40, 45, 46]), model=ScalarModel(intercept=324.50875500144684, linear_terms=array([0.03083558, 0.08633504]), square_terms=array([[ 3.80279615e-05, -2.70035490e-03], + [-2.70035490e-03, 2.18789898e-01]]), scale=0.0008911132812500001, shift=array([4.65609029, 1.07629858])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=47, candidate_x=array([4.65520301, 1.07598213]), index=40, x=array([4.65609029, 1.07629858]), fval=324.50875500144707, rho=-0.6325408571369623, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([40, 45, 46]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65609029, 1.07629858]), radius=0.00044555664062500004, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([40, 46, 47]), model=ScalarModel(intercept=324.5087550014472, linear_terms=array([-0.00336429, -0.01511503]), square_terms=array([[ 5.78841731e-06, -5.46592851e-04], + [-5.46592851e-04, 5.55307507e-02]]), scale=0.00044555664062500004, shift=array([4.65609029, 1.07629858])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), candidate_index=48, candidate_x=array([4.65653471, 1.07641676]), index=40, x=array([4.65609029, 1.07629858]), fval=324.50875500144707, rho=-4.619141553298749, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([40, 46, 47]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65609029, 1.07629858]), radius=0.00022277832031250002, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([40, 47, 48]), model=ScalarModel(intercept=324.50875500144707, linear_terms=array([ 0.05986855, -0.18013296]), square_terms=array([[ 3.60665718e-05, -3.86428183e-04], + [-3.86428183e-04, 1.50111980e-02]]), scale=0.00022277832031250002, shift=array([4.65609029, 1.07629858])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + 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linear_terms=array([0.00021849, 0.02168299]), square_terms=array([[ 3.31928202e-07, -2.78076427e-05], + [-2.78076427e-05, 3.35816501e-03]]), scale=0.00011138916015625001, shift=array([4.65609029, 1.07629858])), vector_model=VectorModel(intercepts=array([ 1.06075516, 2.58156846, 3.64128145, 5.24819755, + 6.92536426, 8.95136781, 10.32540209, 7.6435337 , + 4.01190161, -0.21040033, -6.53830743, -11.78500893]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.22812500000000002, shift=array([2.28125, 1.0625 ])), 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old_indices_used=array([ 0, 1, 3, 7, 10, 11, 12, 13, 14]), old_indices_discarded=array([2, 4, 5, 6, 8, 9]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.0284933 , 1.05977204]), radius=0.2758732997119861, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 3, 7, 10, 12, 13, 14, 15]), model=ScalarModel(intercept=344.4262998626739, linear_terms=array([-103.33496267, -358.05574996]), square_terms=array([[233.09976544, 195.08421045], + [195.08421045, 660.9008632 ]]), scale=array([0.24448635, 0.14235715]), shift=array([5.0284933 , 0.95764285])), vector_model=VectorModel(intercepts=array([ -0.02225619, -0.05624546, -0.37762923, -0.15484397, + -0.06288249, 0.11478383, -0.47188773, -5.53607639, + -7.36495864, -8.65695488, -11.81735926, -15.45100705]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 3, 7, 10, 13, 14, 15, 16]), model=ScalarModel(intercept=297.24961226552693, linear_terms=array([ 52.07074594, -103.62885495]), square_terms=array([[ 31.29738499, -61.65107113], + [-61.65107113, 132.96113088]]), scale=array([0.12224317, 0.08123557]), shift=array([5.0284933 , 1.01876443])), vector_model=VectorModel(intercepts=array([ -0.02225619, -0.05624546, -0.37762923, -0.15484397, + -0.06288249, 0.11478383, -0.47188773, -5.53607639, + -7.36495864, -8.65695488, -11.81735926, -15.45100705]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5517465994239722, shift=array([5.51746599, 1.03381489])), candidate_index=17, candidate_x=array([4.90625013, 1.0444117 ]), index=13, x=array([5.0284933 , 1.05977204]), fval=368.13340247051497, rho=-10.838318991970116, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 7, 10, 13, 14, 15, 16]), old_indices_discarded=array([11, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.0284933 , 1.05977204]), radius=0.06896832492799652, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=293.56988094891074, linear_terms=array([ 29.67365192, -97.46367199]), square_terms=array([[ 11.19394409, -40.20717012], + [-40.20717012, 146.04835721]]), scale=array([0.06112159, 0.05067477]), shift=array([5.0284933 , 1.04932523])), vector_model=VectorModel(intercepts=array([ -0.02225619, -0.05624546, -0.37762923, -0.15484397, + -0.06288249, 0.11478383, -0.47188773, -5.53607639, + -7.36495864, -8.65695488, -11.81735926, -15.45100705]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5517465994239722, shift=array([5.51746599, 1.03381489])), candidate_index=13, candidate_x=array([5.0284933 , 1.05977204]), index=13, x=array([5.0284933 , 1.05977204]), fval=368.13340247051497, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 10, 13, 14, 15, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.0284933 , 1.05977204]), radius=0.03448416246399826, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 13, 15, 16, 17, 18]), model=ScalarModel(intercept=260.788742057204, linear_terms=array([ 1.71724763, -32.27030502]), square_terms=array([[ 1.78754012, 15.57016339], + [ 15.57016339, 211.22032532]]), scale=0.03448416246399826, shift=array([5.0284933 , 1.05977204])), vector_model=VectorModel(intercepts=array([ -0.02225619, -0.05624546, -0.37762923, -0.15484397, + -0.06288249, 0.11478383, -0.47188773, -5.53607639, + -7.36495864, -8.65695488, -11.81735926, -15.45100705]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5517465994239722, shift=array([5.51746599, 1.03381489])), candidate_index=13, candidate_x=array([5.0284933 , 1.05977204]), index=13, x=array([5.0284933 , 1.05977204]), fval=368.13340247051497, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 7, 10, 13, 15, 16, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.0284933 , 1.05977204]), radius=0.01724208123199913, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 13, 15, 16, 18, 19]), model=ScalarModel(intercept=327.468372759335, linear_terms=array([ 7.64746385, -98.50375161]), square_terms=array([[ 0.41227786, -1.77258536], + [ -1.77258536, 105.83658586]]), scale=0.01724208123199913, shift=array([5.0284933 , 1.05977204])), vector_model=VectorModel(intercepts=array([ -0.02225619, -0.05624546, -0.37762923, -0.15484397, + -0.06288249, 0.11478383, -0.47188773, -5.53607639, + -7.36495864, -8.65695488, -11.81735926, -15.45100705]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5517465994239722, shift=array([5.51746599, 1.03381489])), candidate_index=13, candidate_x=array([5.0284933 , 1.05977204]), index=13, x=array([5.0284933 , 1.05977204]), fval=368.13340247051497, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 7, 13, 15, 16, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.0284933 , 1.05977204]), radius=0.008621040615999566, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 13, 16, 19, 20]), model=ScalarModel(intercept=322.9788723346509, linear_terms=array([ 8.24259191, -44.7820302 ]), square_terms=array([[ 2.12879495, 2.3481151 ], + [ 2.3481151 , 26.74049239]]), scale=0.008621040615999566, shift=array([5.0284933 , 1.05977204])), vector_model=VectorModel(intercepts=array([ -0.02225619, -0.05624546, -0.37762923, -0.15484397, + -0.06288249, 0.11478383, -0.47188773, -5.53607639, + -7.36495864, -8.65695488, -11.81735926, -15.45100705]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5517465994239722, shift=array([5.51746599, 1.03381489])), candidate_index=13, candidate_x=array([5.0284933 , 1.05977204]), index=13, x=array([5.0284933 , 1.05977204]), fval=368.13340247051497, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 7, 13, 16, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.0284933 , 1.05977204]), radius=0.004310520307999783, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 20, 21]), model=ScalarModel(intercept=368.133402470515, linear_terms=array([-199.44712466, -237.14896958]), square_terms=array([[274.86171608, 278.30241889], + [278.30241889, 292.02907544]]), scale=0.004310520307999783, shift=array([5.0284933 , 1.05977204])), vector_model=VectorModel(intercepts=array([ -0.02225619, -0.05624546, -0.37762923, -0.15484397, + -0.06288249, 0.11478383, -0.47188773, -5.53607639, + -7.36495864, -8.65695488, -11.81735926, -15.45100705]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5517465994239722, shift=array([5.51746599, 1.03381489])), candidate_index=13, candidate_x=array([5.0284933 , 1.05977204]), index=13, x=array([5.0284933 , 1.05977204]), fval=368.13340247051497, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([13, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.0284933 , 1.05977204]), radius=0.0021552601539998914, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 21, 22]), model=ScalarModel(intercept=368.13340247051474, linear_terms=array([-193.38087729, -166.42544912]), square_terms=array([[189.10001475, 156.36395405], + [156.36395405, 130.2657415 ]]), scale=0.0021552601539998914, shift=array([5.0284933 , 1.05977204])), vector_model=VectorModel(intercepts=array([ -0.02225619, -0.05624546, -0.37762923, -0.15484397, + -0.06288249, 0.11478383, -0.47188773, -5.53607639, + -7.36495864, -8.65695488, -11.81735926, -15.45100705]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5517465994239722, shift=array([5.51746599, 1.03381489])), candidate_index=13, candidate_x=array([5.0284933 , 1.05977204]), index=13, x=array([5.0284933 , 1.05977204]), fval=368.13340247051497, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([13, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.0284933 , 1.05977204]), radius=0.0010776300769999457, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 22, 23]), model=ScalarModel(intercept=368.1334024705151, linear_terms=array([-121.77128912, -89.46990361]), square_terms=array([[74.83904246, 53.02348565], + [53.02348565, 37.80260569]]), scale=0.0010776300769999457, shift=array([5.0284933 , 1.05977204])), vector_model=VectorModel(intercepts=array([ -0.02225619, -0.05624546, -0.37762923, -0.15484397, + -0.06288249, 0.11478383, -0.47188773, -5.53607639, + -7.36495864, -8.65695488, -11.81735926, -15.45100705]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5517465994239722, shift=array([5.51746599, 1.03381489])), candidate_index=24, candidate_x=array([5.02957073, 1.05979264]), index=24, x=array([5.02957073, 1.05979264]), fval=368.0789159332997, rho=0.0006407679428498435, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([13, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0010776300769999947, relative_step_length=1.0000000000000455, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.02957073, 1.05979264]), radius=0.0005388150384999728, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 23, 24]), model=ScalarModel(intercept=368.07891593329987, linear_terms=array([ 0.09596626, -6.44397386]), square_terms=array([[ 4.07596002e-05, -2.52111338e-03], + [-2.52111338e-03, 1.75132715e-01]]), scale=0.0005388150384999728, shift=array([5.02957073, 1.05979264])), vector_model=VectorModel(intercepts=array([ -0.02225619, -0.05624546, -0.37762923, -0.15484397, + -0.06288249, 0.11478383, -0.47188773, -5.53607639, + -7.36495864, -8.65695488, -11.81735926, -15.45100705]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5517465994239722, shift=array([5.51746599, 1.03381489])), candidate_index=24, candidate_x=array([5.02957073, 1.05979264]), index=24, x=array([5.02957073, 1.05979264]), fval=368.0789159332997, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([13, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.02957073, 1.05979264]), radius=0.0002694075192499864, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 24, 25]), model=ScalarModel(intercept=368.07891593329975, linear_terms=array([ 0.19218091, -10.76387371]), square_terms=array([[ 1.78044414e-04, -9.52846298e-03], + [-9.52846298e-03, 5.15403084e-01]]), scale=0.0002694075192499864, shift=array([5.02957073, 1.05979264])), vector_model=VectorModel(intercepts=array([ -0.02225619, -0.05624546, -0.37762923, -0.15484397, + -0.06288249, 0.11478383, -0.47188773, -5.53607639, + -7.36495864, -8.65695488, -11.81735926, -15.45100705]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5517465994239722, shift=array([5.51746599, 1.03381489])), candidate_index=26, candidate_x=array([5.02957273, 1.06006204]), index=26, x=array([5.02957273, 1.06006204]), fval=366.87528253519326, rho=0.11458219192083548, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([13, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.00026940751925008617, relative_step_length=1.0000000000003701, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute params change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 27 entries., 'history': {'params': [{'CRRA': 5.517465994239721, 'DiscFac': 1.033814885188935}, {'CRRA': 5.0284933018032, 'DiscFac': 0.6725853496580942}, {'CRRA': 6.006438686676242, 'DiscFac': 0.8721525193681212}, {'CRRA': 5.0284933018032, 'DiscFac': 0.8598585044827023}, {'CRRA': 6.003150985497998, 'DiscFac': 1.1}, {'CRRA': 6.003723415402228, 'DiscFac': 0.5448421927524136}, {'CRRA': 5.875570018368002, 'DiscFac': 0.5448421927524136}, {'CRRA': 5.0284933018032, 'DiscFac': 1.0841298140700542}, {'CRRA': 6.006438686676242, 'DiscFac': 0.8560223071219775}, {'CRRA': 6.006438686676242, 'DiscFac': 1.0787353944401201}, {'CRRA': 5.0284933018032, 'DiscFac': 0.9539233627068902}, {'CRRA': 5.4793182749825435, 'DiscFac': 0.5448421927524136}, {'CRRA': 5.58224025873283, 'DiscFac': 1.1}, {'CRRA': 5.0284933018032, 'DiscFac': 1.0597720400630486}, {'CRRA': 4.815564277393826, 'DiscFac': 1.1}, {'CRRA': 4.9521194074901675, 'DiscFac': 1.07820661289853}, {'CRRA': 5.025211948819775, 'DiscFac': 1.0353315513948331}, {'CRRA': 4.90625012869407, 'DiscFac': 1.0444116961562104}, {'CRRA': 4.967371715248635, 'DiscFac': 1.0691916621218414}, {'CRRA': 4.994506081339591, 'DiscFac': 1.0674206694925654}, {'CRRA': 5.017310168631569, 'DiscFac': 1.074381214376818}, {'CRRA': 5.024570962217909, 'DiscFac': 1.0674491224177174}, {'CRRA': 5.027444881430467, 'DiscFac': 1.063974456905384}, {'CRRA': 5.028845694420258, 'DiscFac': 1.0619974969043793}, {'CRRA': 5.029570734965658, 'DiscFac': 1.05979264014295}, {'CRRA': 5.029570348293394, 'DiscFac': 1.0603314550427054}, {'CRRA': 5.029572726871201, 'DiscFac': 1.060062040298371}], 'criterion': [538.62727091007, 1207.3436736673395, 1123.9803553992947, 1153.7734293359576, nan, 1201.89122143663, 1205.5170771601368, nan, 1131.9637387201965, nan, 1049.373832121595, 1216.205567519383, nan, 368.13340247051497, nan, nan, 519.7482497133558, 451.3450004894141, nan, nan, nan, nan, nan, nan, 368.07891593329975, nan, 366.87528253519326], 'runtime': [0.0, 1.8393604930001857, 1.8744840399999703, 2.039817265000238, 2.0769789700002548, 2.1107370730001094, 2.1483123210000485, 2.184727404000114, 2.22076628800005, 2.2565912400000343, 2.298254896000344, 2.3342033870003434, 2.3783006880003086, 4.538819075999982, 6.2668982250002045, 7.9595935800002735, 9.7146636880002, 11.55998853400024, 13.313542281000082, 15.018736404000265, 16.70739796700036, 18.446162047000144, 20.202962799000034, 21.98702071600019, 23.762159325000084, 25.538437361999968, 27.283528622000176], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]}}], 'exploration_sample': array([[2.28125 , 1.0625 ], + [7.596875, 0.93125 ], + [5.825 , 0.95 ], + [5. , 0.95 ], + [8.1875 , 0.725 ], + [7.00625 , 0.6125 ], + [3.4625 , 0.875 ], + [4.64375 , 0.6875 ], + [2.871875, 0.78125 ]]), 'exploration_results': array([ 539.49291927, 1005.15883524, 1029.6319182 , 1061.73760943, + 1115.48338813, 1167.602755 , 1188.73516988, 1211.80249333, + 1228.66192563])}}" diff --git a/content/tables/min/WarmGlowSub(Stock)Market_estimate_results.csv b/content/tables/min/WarmGlowSub(Stock)Market_estimate_results.csv new file mode 100644 index 0000000..4267bd8 --- /dev/null +++ b/content/tables/min/WarmGlowSub(Stock)Market_estimate_results.csv @@ -0,0 +1,5854 @@ +CRRA,3.632033659027645 +DiscFac,1.0045131333350326 +time_to_estimate,127.7090654373169 +params,"{'CRRA': 3.632033659027645, 'DiscFac': 1.0045131333350326}" +criterion,506.5420877431311 +start_criterion,nan +start_params,"{'CRRA': 5.0, 'DiscFac': 0.95}" +algorithm,multistart_tranquilo_ls +direction,minimize +n_free,2 +message, +success, +n_criterion_evaluations, +n_derivative_evaluations, +n_iterations, +history,"{'params': [{'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 3.2062782538015053, 'DiscFac': 0.5681439270619826}, {'CRRA': 3.7693560729380176, 'DiscFac': 0.5931914643258382}, {'CRRA': 3.1556439270619823, 'DiscFac': 0.7823187305648491}, {'CRRA': 3.7693560729380176, 'DiscFac': 0.9758796723452919}, {'CRRA': 3.714766521848815, 'DiscFac': 0.5681439270619826}, {'CRRA': 3.768715210658644, 'DiscFac': 0.5681439270619826}, {'CRRA': 3.1673286626320794, 'DiscFac': 1.1}, {'CRRA': 3.7693560729380176, 'DiscFac': 1.0908579040628261}, {'CRRA': 3.7693560729380176, 'DiscFac': 1.0877513222446453}, {'CRRA': 3.252410164125509, 'DiscFac': 1.1}, {'CRRA': 3.1556439270619823, 'DiscFac': 0.5770816471886374}, {'CRRA': 3.1556439270619823, 'DiscFac': 1.0962012275916377}, {'CRRA': 3.1556439270619823, 'DiscFac': 0.8201933907462482}, {'CRRA': 3.2920493494325482, 'DiscFac': 0.9108273227394859}, {'CRRA': 3.160571139998799, 'DiscFac': 0.807765310815706}, {'CRRA': 3.1322566785634134, 'DiscFac': 0.8442047442731109}, {'CRRA': 3.206006823747242, 'DiscFac': 0.9012372812986622}, {'CRRA': 3.324122769792849, 'DiscFac': 0.94050444244772}, {'CRRA': 3.2391386660046924, 'DiscFac': 0.9219394382392962}, {'CRRA': 3.3494377971872664, 'DiscFac': 0.9782059786083586}, {'CRRA': 3.2615524258410584, 'DiscFac': 0.9691159577848923}, {'CRRA': 3.393356772129813, 'DiscFac': 0.9850173754868813}, {'CRRA': 3.3071340001020997, 'DiscFac': 0.9947529704388474}, {'CRRA': 3.153705963633091, 'DiscFac': 0.9805743660572339}, {'CRRA': 3.2206552956753014, 'DiscFac': 0.9888399406371133}, {'CRRA': 3.2636519253036167, 'DiscFac': 0.9986319288051465}, {'CRRA': 3.1770929869388467, 'DiscFac': 0.9972962995518269}, {'CRRA': 3.220321553588347, 'DiscFac': 1.0049324766653562}, {'CRRA': 3.3068310482149306, 'DiscFac': 1.0018892497690481}, {'CRRA': 3.2635882377878542, 'DiscFac': 1.0038023212563407}, 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'DiscFac': 1.0041929153363525}, {'CRRA': 3.5232882324430506, 'DiscFac': 1.003801085804102}, {'CRRA': 3.609557853803056, 'DiscFac': 0.9920242278585506}, {'CRRA': 3.566567668205597, 'DiscFac': 1.0033991492985908}, {'CRRA': 3.4800962789711423, 'DiscFac': 0.9950521150234775}, {'CRRA': 3.5233576120652317, 'DiscFac': 0.9959407701024888}, {'CRRA': 3.5882132469444326, 'DiscFac': 1.0036923734943635}, {'CRRA': 3.6314925157301436, 'DiscFac': 1.0032735665368495}, {'CRRA': 3.7180390378033135, 'DiscFac': 1.001577761816608}, {'CRRA': 3.6748818878252374, 'DiscFac': 1.0067504600642396}, {'CRRA': 3.6532315306052783, 'DiscFac': 1.0062108341092992}, {'CRRA': 3.6424186953925117, 'DiscFac': 1.0058218568602928}, {'CRRA': 3.6358209164977016, 'DiscFac': 1.0000278405094685}, {'CRRA': 3.6337900895987514, 'DiscFac': 1.0047013627340162}, {'CRRA': 3.632033659027645, 'DiscFac': 1.0045131333350326}, {'CRRA': 3.6326709899857446, 'DiscFac': 1.0042869702771626}], 'criterion': [1093.6287094958684, 1245.8554091061123, 1228.437199283229, 1196.2513338061253, 565.8944718863357, 1233.6495058027178, 1232.384369245551, nan, nan, nan, nan, 1245.6273609138634, nan, 1178.0531105990904, 1000.7509434811079, 1184.8727376133124, 1162.300118973653, 1052.3986832064866, 827.2850544940472, 955.1473772312156, 589.2387386757741, 649.1795468814067, 555.0773535680186, 527.5324137105392, 594.1974555608165, 550.7345811743, 519.9309270334896, 526.019227462011, 513.1513126909756, 513.8507302331603, 513.0194722961687, 513.0648520982796, 512.0809014214674, 511.7979374568167, 512.3793529360063, 512.1816475873786, 511.21798874189403, 510.83913420009407, 510.59082554232384, 513.206656921827, 511.9848725771735, 510.27441229833755, 509.73756488700667, 511.8350218971264, 511.1217913384364, 509.3715980078564, 532.8031876140645, 508.9279337370762, 508.3645034963132, 522.3950926634641, 507.8890405609745, 520.1419585533414, 517.012345192252, 507.4336026486255, 506.99784644540586, nan, nan, nan, nan, 508.9720174810991, nan, 506.5420877431311, 506.6849493107312], 'runtime': [0.0, 1.3804252599998108, 1.4150906029999533, 1.4489653229998112, 1.488080220000029, 1.5404481979999218, 1.5700747070000034, 1.611183871999856, 1.6490961620002054, 1.6923267430001943, 1.7307125020001877, 1.7700176570001531, 1.8183009529998344, 3.5063388779999514, 4.789909709999847, 6.110572930000217, 7.373552889000166, 8.65480244299988, 9.997954282000137, 11.292315917999986, 12.581388307999987, 13.893260515999827, 15.192520904000048, 16.508465176999835, 17.83462647999977, 19.14974713699985, 20.485998517000098, 21.850103854999816, 23.2152417100001, 24.547226562000105, 25.87641709599984, 27.20560083700002, 28.524041861000114, 29.84154418900016, 31.16551884699993, 32.48429570400003, 33.79176999499987, 35.09562726000013, 36.45809206500007, 37.80630384899996, 39.149301641999955, 40.46359727900017, 41.79980468799977, 43.11513492699987, 44.429547774000184, 45.73663632199987, 47.042997473000014, 48.38298036600008, 49.69725927399986, 51.02970168999991, 52.40143981099982, 53.727951674999986, 55.20516782000004, 56.5488178720002, 57.90847202099985, 59.24165819200016, 60.596691159999864, 61.908351636000134, 63.24388750400021, 64.57625644300015, 65.95520391699984, 67.28008590299987, 68.63086242999998], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51]}" +convergence_report, +multistart_info,"{'start_parameters': [{'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 3.9283585146612063, 'DiscFac': 0.9116621363063967}], 'local_optima': [Minimize with 2 free parameters terminated. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 0.9326 0.9326 +relative_params_change 0.1316 0.1316 +absolute_criterion_change 527.7 527.7 +absolute_params_change 0.323 0.323 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute params change smaller than tolerance.], 'exploration_sample': [{'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 2.871875, 'DiscFac': 0.78125}, {'CRRA': 2.28125, 'DiscFac': 1.0625}], 'exploration_results': array([1093.6287095 , 1183.94834133, 1205.66501897, 1353.72863006])}" +algorithm_output,"{'states': [State(trustregion=Region(center=array([3.4625, 0.875 ]), radius=0.34625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=1093.6287094958684, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.34625, shift=array([3.4625, 0.875 ])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=0, candidate_x=array([3.4625, 0.875 ]), index=0, x=array([3.4625, 0.875 ]), fval=1093.6287094958684, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([3.4625, 0.875 ]), radius=0.34625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=651.4449578029853, linear_terms=array([281.18601875, 919.56705343]), square_terms=array([[ 186.56083108, 697.51852997], + [ 697.51852997, 4254.6830109 ]]), scale=array([0.30685607, 0.26592804]), shift=array([3.4625 , 0.83407196])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=13, candidate_x=array([3.15564393, 0.82019339]), index=0, x=array([3.4625, 0.875 ]), fval=1093.6287094958684, rho=-0.21893278324197663, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.4625, 0.875 ]), radius=0.173125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 4, 7, 8, 9, 10, 12, 13]), model=ScalarModel(intercept=697.61707876083, linear_terms=array([ 86.6112369 , -859.52858173]), square_terms=array([[ 88.19328217, 426.09698529], + [ 426.09698529, 6091.26225323]]), scale=0.173125, shift=array([3.4625, 0.875 ])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=14, candidate_x=array([3.29204935, 0.91082732]), index=14, x=array([3.29204935, 0.91082732]), fval=1000.7509434811079, rho=0.525364637884901, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 3, 4, 7, 8, 9, 10, 12, 13]), old_indices_discarded=array([ 1, 2, 5, 6, 11]), step_length=0.17417526039465764, relative_step_length=1.0060664860341235, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.29204935, 0.91082732]), radius=0.34625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 3, 7, 10, 11, 12, 13, 14]), model=ScalarModel(intercept=823.8622722142668, linear_terms=array([ 80.18328931, -88.8613507 ]), square_terms=array([[ 762.08731871, -1381.66492468], + [-1381.66492468, 2821.90772929]]), scale=array([0.30685607, 0.24801438]), shift=array([3.29204935, 0.85198562])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=15, candidate_x=array([3.16057114, 0.80776531]), index=14, x=array([3.29204935, 0.91082732]), fval=1000.7509434811079, rho=-2.7239526213953136, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 7, 10, 11, 12, 13, 14]), old_indices_discarded=array([2, 4, 5, 6, 8, 9]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.29204935, 0.91082732]), radius=0.173125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 3, 7, 10, 12, 13, 14, 15]), model=ScalarModel(intercept=859.1595008143797, linear_terms=array([-49.00694728, 377.59149803]), square_terms=array([[ 99.87726874, -357.72244111], + [-357.72244111, 1848.37553732]]), scale=0.173125, shift=array([3.29204935, 0.91082732])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=16, candidate_x=array([3.13225668, 0.84420474]), index=14, x=array([3.29204935, 0.91082732]), fval=1000.7509434811079, rho=-3.384890709638498, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 7, 10, 12, 13, 14, 15]), old_indices_discarded=array([ 2, 4, 5, 6, 8, 9, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.29204935, 0.91082732]), radius=0.0865625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 7, 10, 12, 13, 14, 15, 16]), model=ScalarModel(intercept=827.6928291025154, linear_terms=array([ 5.35234931, 50.7864861 ]), square_terms=array([[ 10.55179511, -63.25662137], + [ -63.25662137, 1024.07313652]]), scale=0.0865625, shift=array([3.29204935, 0.91082732])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=17, candidate_x=array([3.20600682, 0.90123728]), index=14, x=array([3.29204935, 0.91082732]), fval=1000.7509434811079, rho=-8.050741085388342, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 3, 7, 10, 12, 13, 14, 15, 16]), old_indices_discarded=array([ 1, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.29204935, 0.91082732]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 10, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=964.3969823484208, linear_terms=array([ 5.87336302, -56.20626673]), square_terms=array([[ 1.90128134, -13.33600414], + [-13.33600414, 93.87172932]]), scale=0.04328125, shift=array([3.29204935, 0.91082732])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=18, candidate_x=array([3.32412277, 0.94050444]), index=18, x=array([3.32412277, 0.94050444]), fval=827.2850544940472, rho=9.440779127990067, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 3, 10, 13, 14, 15, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0436970906100786, relative_step_length=1.0096078696913466, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.32412277, 0.94050444]), radius=0.0865625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 7, 10, 13, 14, 15, 16, 17, 18]), model=ScalarModel(intercept=754.9629712965816, linear_terms=array([-11.33853356, 149.73382686]), square_terms=array([[ 19.81434331, -166.92704745], + [-166.92704745, 1457.19103375]]), scale=0.0865625, shift=array([3.32412277, 0.94050444])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=19, candidate_x=array([3.23913867, 0.92193944]), index=18, x=array([3.32412277, 0.94050444]), fval=827.2850544940472, rho=-9.785116917650237, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 7, 10, 13, 14, 15, 16, 17, 18]), old_indices_discarded=array([ 1, 3, 11, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.32412277, 0.94050444]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 10, 13, 14, 15, 16, 17, 18, 19]), model=ScalarModel(intercept=832.6203197168809, linear_terms=array([ 11.43225451, -88.93217292]), square_terms=array([[ 1.89207866, -14.51433349], + [-14.51433349, 111.66139431]]), scale=0.04328125, shift=array([3.32412277, 0.94050444])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=20, candidate_x=array([3.3494378 , 0.97820598]), index=20, x=array([3.3494378 , 0.97820598]), fval=589.2387386757741, rho=6.707721298454917, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 10, 13, 14, 15, 16, 17, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.045412073734327936, relative_step_length=1.0492320285187682, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.3494378 , 0.97820598]), radius=0.0865625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 7, 10, 12, 14, 17, 18, 19, 20]), model=ScalarModel(intercept=710.921693371805, linear_terms=array([202.27752122, 780.08362564]), square_terms=array([[ 118.12834448, 487.07344 ], + [ 487.07344 , 2688.6689297 ]]), scale=0.0865625, shift=array([3.3494378 , 0.97820598])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=21, candidate_x=array([3.26155243, 0.96911596]), index=20, x=array([3.3494378 , 0.97820598]), fval=589.2387386757741, rho=-0.37545420554346837, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 7, 10, 12, 14, 17, 18, 19, 20]), old_indices_discarded=array([ 3, 4, 13, 15, 16]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.3494378 , 0.97820598]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 10, 14, 17, 18, 19, 20, 21]), model=ScalarModel(intercept=549.8023854149372, linear_terms=array([ -7.52264096, -60.41063775]), square_terms=array([[ 1.47685727, 19.34913644], + [ 19.34913644, 256.1673533 ]]), scale=0.04328125, shift=array([3.3494378 , 0.97820598])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=22, candidate_x=array([3.39335677, 0.98501738]), index=22, x=array([3.39335677, 0.98501738]), fval=555.0773535680186, rho=3.376269726696847, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 10, 14, 17, 18, 19, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.04444402645396528, relative_step_length=1.0268655931602086, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39335677, 0.98501738]), radius=0.0865625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 10, 14, 17, 18, 19, 20, 21, 22]), model=ScalarModel(intercept=509.93359583487626, linear_terms=array([ -2.87140025, -77.0340883 ]), square_terms=array([[ 1.94049629, 45.43808551], + [ 45.43808551, 1087.04904653]]), scale=0.0865625, shift=array([3.39335677, 0.98501738])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=23, candidate_x=array([3.307134 , 0.99475297]), index=23, x=array([3.307134 , 0.99475297]), fval=527.5324137105392, rho=9.012522247815982, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 10, 14, 17, 18, 19, 20, 21, 22]), old_indices_discarded=array([ 3, 4, 7, 8, 9, 12, 13, 15, 16]), step_length=0.08677066454287263, relative_step_length=1.0024047889429328, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.307134 , 0.99475297]), radius=0.173125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([10, 14, 17, 18, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=560.8180421345091, linear_terms=array([ 171.84978397, -706.76473204]), square_terms=array([[ 74.62650725, -461.02969905], + [-461.02969905, 3206.52608661]]), scale=array([0.15342804, 0.12933753]), shift=array([3.307134 , 0.97066247])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=24, candidate_x=array([3.15370596, 0.98057437]), index=23, x=array([3.307134 , 0.99475297]), fval=527.5324137105392, rho=-0.9813470262140938, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([10, 14, 17, 18, 19, 20, 21, 22, 23]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 15, 16]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.307134 , 0.99475297]), radius=0.0865625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([10, 14, 17, 18, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=484.79762730862103, linear_terms=array([ 48.50792314, -73.29564765]), square_terms=array([[ 23.75435503, -174.08413125], + [-174.08413125, 1436.30017384]]), scale=0.0865625, shift=array([3.307134 , 0.99475297])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=25, candidate_x=array([3.2206553 , 0.98883994]), index=23, x=array([3.307134 , 0.99475297]), fval=527.5324137105392, rho=-0.5781888323147932, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([10, 14, 17, 18, 19, 20, 21, 22, 23]), old_indices_discarded=array([ 0, 3, 7, 12, 13, 15, 16, 24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.307134 , 0.99475297]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([10, 14, 18, 19, 20, 21, 22, 23, 25]), model=ScalarModel(intercept=497.9928888365399, linear_terms=array([ 10.19808053, -47.847162 ]), square_terms=array([[ 0.75061409, -15.01172448], + [-15.01172448, 357.53799767]]), scale=0.04328125, shift=array([3.307134 , 0.99475297])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=26, candidate_x=array([3.26365193, 0.99863193]), index=26, x=array([3.26365193, 0.99863193]), fval=519.9309270334896, rho=0.6687184257598502, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([10, 14, 18, 19, 20, 21, 22, 23, 25]), old_indices_discarded=array([ 0, 7, 12, 17, 24]), step_length=0.04365474941845791, relative_step_length=1.008629589451735, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.26365193, 0.99863193]), radius=0.0865625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([10, 14, 18, 19, 20, 21, 23, 25, 26]), model=ScalarModel(intercept=477.3301617595189, linear_terms=array([ 13.68073618, -18.67221002]), square_terms=array([[ 1.51452177, -42.17862452], + [ -42.17862452, 1510.52712184]]), scale=0.0865625, shift=array([3.26365193, 0.99863193])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=27, candidate_x=array([3.17709299, 0.9972963 ]), index=26, x=array([3.26365193, 0.99863193]), fval=519.9309270334896, rho=-0.4645488387784232, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([10, 14, 18, 19, 20, 21, 23, 25, 26]), old_indices_discarded=array([ 0, 3, 7, 12, 13, 15, 16, 17, 22, 24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.26365193, 0.99863193]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 18, 19, 20, 21, 23, 25, 26, 27]), model=ScalarModel(intercept=532.5926218735569, linear_terms=array([ 3.02001572, -34.91119764]), square_terms=array([[ 3.06576932e-02, -1.71386354e+00], + [-1.71386354e+00, 2.25297083e+02]]), scale=0.04328125, shift=array([3.26365193, 0.99863193])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=28, candidate_x=array([3.22032155, 1.00493248]), index=28, x=array([3.22032155, 1.00493248]), fval=513.1513126909756, rho=1.2432267791250464, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 18, 19, 20, 21, 23, 25, 26, 27]), old_indices_discarded=array([ 7, 10, 12, 13, 16, 17, 22, 24]), step_length=0.043786048192572063, relative_step_length=1.011663207337405, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.22032155, 1.00493248]), radius=0.0865625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([10, 19, 21, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=480.1639722811906, linear_terms=array([-9.58819386, -6.16098806]), square_terms=array([[ 2.95376033, 73.23574637], + [ 73.23574637, 1897.40328086]]), scale=0.0865625, shift=array([3.22032155, 1.00493248])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=29, candidate_x=array([3.30683105, 1.00188925]), index=28, x=array([3.22032155, 1.00493248]), fval=513.1513126909756, rho=-0.07527732047828518, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([10, 19, 21, 23, 24, 25, 26, 27, 28]), old_indices_discarded=array([ 0, 3, 7, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.22032155, 1.00493248]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 21, 23, 24, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=523.4679173240145, linear_terms=array([-2.59748377, -0.7171113 ]), square_terms=array([[2.31661478e-01, 7.66177350e+00], + [7.66177350e+00, 2.63292238e+02]]), scale=0.04328125, shift=array([3.22032155, 1.00493248])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=30, candidate_x=array([3.26358824, 1.00380232]), index=30, x=array([3.26358824, 1.00380232]), fval=513.0194722961687, rho=0.0512525527783304, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([19, 21, 23, 24, 25, 26, 27, 28, 29]), old_indices_discarded=array([ 7, 10, 12, 13, 14, 15, 16, 17, 18, 20, 22]), step_length=0.043281441899137574, relative_step_length=1.0000044337706877, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.26358824, 1.00380232]), radius=0.021640625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 21, 23, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=519.3234950395965, linear_terms=array([-0.34493884, 1.06441875]), square_terms=array([[1.02321587e-02, 8.19381474e-01], + [8.19381474e-01, 6.67857826e+01]]), scale=0.021640625, shift=array([3.26358824, 1.00380232])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=31, candidate_x=array([3.28522304, 1.00319524]), index=30, x=array([3.26358824, 1.00380232]), fval=513.0194722961687, rho=-0.12388915381056541, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([19, 21, 23, 25, 26, 27, 28, 29, 30]), old_indices_discarded=array([10, 14, 18, 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.26358824, 1.00380232]), radius=0.0108203125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([21, 23, 25, 26, 28, 29, 30, 31]), model=ScalarModel(intercept=514.1646995371159, linear_terms=array([-0.15345129, -3.50288081]), square_terms=array([[3.05251315e-03, 2.75003243e-01], + [2.75003243e-01, 2.51655902e+01]]), scale=0.0108203125, shift=array([3.26358824, 1.00380232])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=32, candidate_x=array([3.27442429, 1.0051837 ]), index=32, x=array([3.27442429, 1.0051837 ]), fval=512.0809014214674, rho=2.6136687240628853, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([21, 23, 25, 26, 28, 29, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.010923748464293592, relative_step_length=1.0095594248589024, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.27442429, 1.0051837 ]), radius=0.021640625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([21, 23, 25, 26, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=513.4928941324267, linear_terms=array([-0.24707091, 0.03226047]), square_terms=array([[1.26987208e-02, 1.12599639e+00], + [1.12599639e+00, 1.01298380e+02]]), scale=0.021640625, shift=array([3.27442429, 1.0051837 ])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=33, candidate_x=array([3.29606351, 1.00493688]), index=33, x=array([3.29606351, 1.00493688]), fval=511.79793745681667, rho=1.1440872753091216, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([21, 23, 25, 26, 28, 29, 30, 31, 32]), old_indices_discarded=array([10, 14, 18, 19, 20, 27]), step_length=0.021640625913725366, relative_step_length=1.0000000422226885, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.29606351, 1.00493688]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([20, 21, 23, 26, 29, 30, 31, 32, 33]), model=ScalarModel(intercept=513.3709874050701, linear_terms=array([ 1.13482224, -0.44884792]), square_terms=array([[2.17777822e-02, 2.50633392e+00], + [2.50633392e+00, 3.99796932e+02]]), scale=0.04328125, shift=array([3.29606351, 1.00493688])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=34, candidate_x=array([3.25278341, 1.00525589]), index=33, x=array([3.29606351, 1.00493688]), fval=511.79793745681667, rho=-0.512339270174138, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([20, 21, 23, 26, 29, 30, 31, 32, 33]), old_indices_discarded=array([ 0, 7, 10, 12, 14, 17, 18, 19, 22, 24, 25, 27, 28]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.29606351, 1.00493688]), radius=0.021640625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([21, 23, 26, 29, 30, 31, 32, 33, 34]), model=ScalarModel(intercept=513.2806219743039, linear_terms=array([ 0.40201888, -0.14468052]), square_terms=array([[2.11787355e-03, 2.74550213e-01], + [2.74550213e-01, 1.00721574e+02]]), scale=0.021640625, shift=array([3.29606351, 1.00493688])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=35, candidate_x=array([3.27442305, 1.0050266 ]), index=33, x=array([3.29606351, 1.00493688]), fval=511.79793745681667, rho=-0.9549081380658717, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([21, 23, 26, 29, 30, 31, 32, 33, 34]), old_indices_discarded=array([10, 14, 18, 19, 20, 22, 25, 28]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.29606351, 1.00493688]), radius=0.0108203125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 26, 29, 30, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=511.8797516145538, linear_terms=array([-0.15742971, -3.27449424]), square_terms=array([[2.83081088e-03, 2.88272590e-01], + [2.88272590e-01, 2.97786495e+01]]), scale=0.0108203125, shift=array([3.29606351, 1.00493688])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=36, candidate_x=array([3.30689479, 1.00601728]), index=36, x=array([3.30689479, 1.00601728]), fval=511.21798874189403, rho=1.8960681633469345, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([23, 26, 29, 30, 31, 32, 33, 34, 35]), old_indices_discarded=array([21]), step_length=0.010885029063083678, relative_step_length=1.005981025324701, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.30689479, 1.00601728]), radius=0.021640625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 26, 29, 30, 31, 32, 33, 35, 36]), model=ScalarModel(intercept=511.4227564263899, linear_terms=array([-0.3250255 , -0.31495259]), square_terms=array([[1.22560006e-02, 1.19816348e+00], + [1.19816348e+00, 1.18877538e+02]]), scale=0.021640625, shift=array([3.30689479, 1.00601728])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=37, candidate_x=array([3.3285349 , 1.00585694]), index=37, x=array([3.3285349 , 1.00585694]), fval=510.83913420009407, rho=1.1759433626717288, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([23, 26, 29, 30, 31, 32, 33, 35, 36]), old_indices_discarded=array([14, 18, 19, 20, 21, 22, 25, 28, 34]), step_length=0.02164070209710628, relative_step_length=1.0000035626099653, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.3285349 , 1.00585694]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([20, 23, 29, 31, 32, 33, 35, 36, 37]), model=ScalarModel(intercept=510.92606818398167, linear_terms=array([-1.25261185, 5.63856144]), square_terms=array([[6.91279361e-02, 5.32869751e+00], + [5.32869751e+00, 4.22474316e+02]]), scale=0.04328125, shift=array([3.3285349 , 1.00585694])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=38, candidate_x=array([3.37180546, 1.00473701]), index=38, x=array([3.37180546, 1.00473701]), fval=510.59082554232384, rho=0.18257009624743023, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([20, 23, 29, 31, 32, 33, 35, 36, 37]), old_indices_discarded=array([ 0, 7, 10, 12, 14, 17, 18, 19, 21, 22, 24, 25, 26, 27, 28, 30, 34]), step_length=0.0432850573848669, relative_step_length=1.0000879684590185, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.37180546, 1.00473701]), radius=0.0865625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([18, 20, 22, 23, 29, 33, 36, 37, 38]), model=ScalarModel(intercept=516.8290415340703, linear_terms=array([ 5.75246416, 11.16476668]), square_terms=array([[ 2.64009594e-01, -1.44214956e+01], + [-1.44214956e+01, 1.26850502e+03]]), scale=0.0865625, shift=array([3.37180546, 1.00473701])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=39, candidate_x=array([3.28525713, 1.00299909]), index=38, x=array([3.37180546, 1.00473701]), fval=510.59082554232384, rho=-0.44505466092410517, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([18, 20, 22, 23, 29, 33, 36, 37, 38]), old_indices_discarded=array([ 0, 3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 19, 21, 24, 25, + 26, 27, 28, 30, 31, 32, 34, 35]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.37180546, 1.00473701]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([18, 20, 22, 23, 29, 33, 36, 37, 38]), model=ScalarModel(intercept=516.8290415340703, linear_terms=array([2.87623208, 5.58238334]), square_terms=array([[ 6.60023984e-02, -3.60537390e+00], + [-3.60537390e+00, 3.17126254e+02]]), scale=0.04328125, shift=array([3.37180546, 1.00473701])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=40, candidate_x=array([3.32853554, 1.00349462]), index=38, x=array([3.37180546, 1.00473701]), fval=510.59082554232384, rho=-0.468500568265753, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([18, 20, 22, 23, 29, 33, 36, 37, 38]), old_indices_discarded=array([ 0, 10, 14, 17, 19, 21, 25, 26, 27, 28, 30, 31, 32, 34, 35, 39]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.37180546, 1.00473701]), radius=0.021640625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([20, 22, 23, 29, 33, 36, 37, 38, 40]), model=ScalarModel(intercept=510.83094836315786, linear_terms=array([-0.31356388, -0.52607831]), square_terms=array([[1.43542942e-02, 1.22048177e+00], + [1.22048177e+00, 1.05162098e+02]]), scale=0.021640625, shift=array([3.37180546, 1.00473701])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=41, candidate_x=array([3.39344589, 1.00459454]), index=41, x=array([3.39344589, 1.00459454]), fval=510.27441229833755, rho=1.0250640374729652, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([20, 22, 23, 29, 33, 36, 37, 38, 40]), old_indices_discarded=array([18, 31, 32, 35, 39]), step_length=0.021640897833367097, relative_step_length=1.0000126074624507, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39344589, 1.00459454]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([20, 22, 23, 29, 36, 37, 38, 40, 41]), model=ScalarModel(intercept=510.388199569147, linear_terms=array([-0.70976972, -0.38780663]), square_terms=array([[5.87854987e-02, 4.93351716e+00], + [4.93351716e+00, 4.19243045e+02]]), scale=0.04328125, shift=array([3.39344589, 1.00459454])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=42, candidate_x=array([3.43672463, 1.00412607]), index=42, x=array([3.43672463, 1.00412607]), fval=509.73756488700667, rho=0.7615075639324983, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([20, 22, 23, 29, 36, 37, 38, 40, 41]), old_indices_discarded=array([ 0, 10, 14, 17, 18, 19, 21, 25, 26, 28, 30, 31, 32, 33, 34, 35, 39]), step_length=0.04328126925028541, relative_step_length=1.0000004447719373, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43672463, 1.00412607]), radius=0.0865625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([18, 20, 22, 36, 37, 38, 40, 41, 42]), model=ScalarModel(intercept=513.6632160611562, linear_terms=array([ 1.66952951, 16.46923676]), square_terms=array([[2.01619093e-02, 1.39077252e+00], + [1.39077252e+00, 1.30355320e+03]]), scale=0.0865625, shift=array([3.43672463, 1.00412607])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=43, candidate_x=array([3.35016101, 1.00312604]), index=42, x=array([3.43672463, 1.00412607]), fval=509.73756488700667, rho=-1.2008268877931576, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([18, 20, 22, 36, 37, 38, 40, 41, 42]), old_indices_discarded=array([ 0, 3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 19, 21, 23, 24, + 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 39]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43672463, 1.00412607]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([18, 20, 22, 37, 38, 40, 41, 42, 43]), model=ScalarModel(intercept=513.482630783711, linear_terms=array([0.65403347, 8.91106369]), square_terms=array([[5.63212301e-03, 7.99992087e-01], + [7.99992087e-01, 3.24875610e+02]]), scale=0.04328125, shift=array([3.43672463, 1.00412607])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=44, candidate_x=array([3.39344059, 1.00304757]), index=42, x=array([3.43672463, 1.00412607]), fval=509.73756488700667, rho=-1.839477153136199, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([18, 20, 22, 37, 38, 40, 41, 42, 43]), old_indices_discarded=array([ 0, 10, 14, 19, 21, 23, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 39]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43672463, 1.00412607]), radius=0.021640625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([20, 22, 38, 40, 41, 42, 43, 44]), model=ScalarModel(intercept=510.12883784081237, linear_terms=array([-0.25127194, -0.58488428]), square_terms=array([[9.67696849e-03, 9.89759993e-01], + [9.89759993e-01, 1.03384944e+02]]), scale=0.021640625, shift=array([3.43672463, 1.00412607])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=45, candidate_x=array([3.45836543, 1.00404152]), index=45, x=array([3.45836543, 1.00404152]), fval=509.3715980078564, rho=1.4802790196268736, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([20, 22, 38, 40, 41, 42, 43, 44]), old_indices_discarded=array([], dtype=int64), step_length=0.021640972413607785, relative_step_length=1.0000160537696017, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.45836543, 1.00404152]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 20, 22, 38, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=506.56672858420234, linear_terms=array([-4.47747831, 43.24748363]), square_terms=array([[ 1.64747112, 16.06601525], + [ 16.06601525, 169.95443376]]), scale=0.04328125, shift=array([3.45836543, 1.00404152])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=46, candidate_x=array([3.50048727, 0.98975262]), index=45, x=array([3.45836543, 1.00404152]), fval=509.3715980078564, rho=-1.7034780941746406, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 20, 22, 38, 41, 42, 43, 44, 45]), old_indices_discarded=array([14, 18, 21, 23, 26, 29, 30, 31, 32, 33, 34, 35, 36, 37, 39, 40]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.45836543, 1.00404152]), radius=0.021640625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 38, 41, 42, 44, 45, 46]), model=ScalarModel(intercept=509.90462027267597, linear_terms=array([-0.12472648, -1.80799561]), square_terms=array([[1.12107334e-02, 1.07164529e+00], + [1.07164529e+00, 1.05129465e+02]]), scale=0.021640625, shift=array([3.45836543, 1.00404152])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=47, candidate_x=array([3.48000873, 1.00419292]), index=47, x=array([3.48000873, 1.00419292]), fval=508.9279337370762, rho=3.6451687787620792, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([22, 38, 41, 42, 44, 45, 46]), old_indices_discarded=array([], dtype=int64), step_length=0.021643822010034636, relative_step_length=1.0001477318716365, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.48000873, 1.00419292]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 38, 41, 42, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=509.4211138668626, linear_terms=array([-0.43693822, -0.49379518]), square_terms=array([[4.49138891e-02, 4.30131411e+00], + [4.30131411e+00, 4.20126086e+02]]), scale=0.04328125, shift=array([3.48000873, 1.00419292])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=48, candidate_x=array([3.52328823, 1.00380109]), index=48, x=array([3.52328823, 1.00380109]), fval=508.3645034963132, rho=1.3050907569087495, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([22, 38, 41, 42, 43, 44, 45, 46, 47]), old_indices_discarded=array([ 0, 14, 18, 20, 23, 29, 31, 32, 33, 35, 36, 37, 39, 40]), step_length=0.04328128036721916, relative_step_length=1.0000007016252803, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.52328823, 1.00380109]), radius=0.0865625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 22, 41, 42, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=510.9361846246444, linear_terms=array([-2.56354502, 68.23315716]), square_terms=array([[5.02760540e-01, 1.72586847e+01], + [1.72586847e+01, 6.23529154e+02]]), scale=0.0865625, shift=array([3.52328823, 1.00380109])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=49, candidate_x=array([3.60955785, 0.99202423]), index=48, x=array([3.52328823, 1.00380109]), fval=508.3645034963132, rho=-1.7199045949987042, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 22, 41, 42, 44, 45, 46, 47, 48]), old_indices_discarded=array([ 3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, + 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, + 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.52328823, 1.00380109]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 41, 42, 44, 45, 46, 47, 48, 49]), model=ScalarModel(intercept=509.1888444032537, linear_terms=array([-0.2211104 , -0.64677861]), square_terms=array([[5.00518952e-02, 4.53096392e+00], + [4.53096392e+00, 4.18022502e+02]]), scale=0.04328125, shift=array([3.52328823, 1.00380109])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=50, candidate_x=array([3.56656767, 1.00339915]), index=50, x=array([3.56656767, 1.00339915]), fval=507.88904056097454, rho=2.2205332256943504, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([22, 41, 42, 44, 45, 46, 47, 48, 49]), old_indices_discarded=array([ 0, 18, 20, 23, 36, 37, 38, 40, 43]), step_length=0.04328130211625866, relative_step_length=1.000001204130164, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.56656767, 1.00339915]), radius=0.0865625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 41, 42, 45, 46, 47, 48, 49, 50]), model=ScalarModel(intercept=514.5638756314247, linear_terms=array([ 3.48562996, 53.35987525]), square_terms=array([[ 1.37261425e-01, -7.23686414e+00], + [-7.23686414e+00, 6.24285840e+02]]), scale=0.0865625, shift=array([3.56656767, 1.00339915])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=51, candidate_x=array([3.48009628, 0.99505212]), index=50, x=array([3.56656767, 1.00339915]), fval=507.88904056097454, rho=-1.9285165450030315, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 41, 42, 45, 46, 47, 48, 49, 50]), old_indices_discarded=array([ 4, 7, 8, 9, 10, 12, 14, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, + 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 43, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.56656767, 1.00339915]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 42, 45, 46, 47, 48, 49, 50, 51]), model=ScalarModel(intercept=515.3264858581856, linear_terms=array([ 1.71158 , 25.56690401]), square_terms=array([[ 2.71276458e-02, -1.52375230e+00], + [-1.52375230e+00, 1.55243251e+02]]), scale=0.04328125, shift=array([3.56656767, 1.00339915])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=52, candidate_x=array([3.52335761, 0.99594077]), index=50, x=array([3.56656767, 1.00339915]), fval=507.88904056097454, rho=-2.248134988786441, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 42, 45, 46, 47, 48, 49, 50, 51]), old_indices_discarded=array([ 4, 22, 38, 41, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.56656767, 1.00339915]), radius=0.021640625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([46, 47, 48, 49, 50, 51, 52]), model=ScalarModel(intercept=508.17663344776184, linear_terms=array([-0.18431719, -2.63810816]), square_terms=array([[1.45940369e-02, 1.22560823e+00], + [1.22560823e+00, 1.04071984e+02]]), scale=0.021640625, shift=array([3.56656767, 1.00339915])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=53, candidate_x=array([3.58821325, 1.00369237]), index=53, x=array([3.58821325, 1.00369237]), fval=507.4336026486255, rho=2.4401860410878675, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([46, 47, 48, 49, 50, 51, 52]), old_indices_discarded=array([], dtype=int64), step_length=0.021647564744518292, relative_step_length=1.0003206813351413, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.58821325, 1.00369237]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([45, 46, 47, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=507.76912581454695, linear_terms=array([-0.42677272, -0.59726462]), square_terms=array([[5.22032739e-02, 4.63172328e+00], + [4.63172328e+00, 4.16496389e+02]]), scale=0.04328125, shift=array([3.58821325, 1.00369237])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=54, candidate_x=array([3.63149252, 1.00327357]), index=54, x=array([3.63149252, 1.00327357]), fval=506.99784644540586, rho=1.037040511159352, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([45, 46, 47, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([ 0, 4, 8, 9, 22, 41, 42, 44]), step_length=0.0432812951041612, relative_step_length=1.0000010421178038, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.63149252, 1.00327357]), radius=0.0865625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 4, 46, 47, 48, 49, 50, 52, 53, 54]), model=ScalarModel(intercept=507.0456308378341, linear_terms=array([-1.09355637, 5.86222255]), square_terms=array([[3.81131556e-01, 2.40396513e+01], + [2.40396513e+01, 1.52493360e+03]]), scale=0.0865625, shift=array([3.63149252, 1.00327357])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=54, candidate_x=array([3.63149252, 1.00327357]), index=54, x=array([3.63149252, 1.00327357]), fval=506.99784644540586, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 4, 46, 47, 48, 49, 50, 52, 53, 54]), old_indices_discarded=array([ 0, 2, 8, 9, 10, 14, 18, 19, 20, 21, 22, 23, 25, 26, 28, 29, 30, + 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 51]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.63149252, 1.00327357]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 4, 46, 48, 49, 50, 52, 53, 54, 55]), model=ScalarModel(intercept=483.6460160115828, linear_terms=array([ -8.37903947, -46.37591995]), square_terms=array([[ 0.605033 , 11.49832029], + [ 11.49832029, 426.97587575]]), scale=0.04328125, shift=array([3.63149252, 1.00327357])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=54, candidate_x=array([3.63149252, 1.00327357]), index=54, x=array([3.63149252, 1.00327357]), fval=506.99784644540586, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 4, 46, 48, 49, 50, 52, 53, 54, 55]), old_indices_discarded=array([ 0, 8, 9, 42, 45, 47, 51]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.63149252, 1.00327357]), radius=0.021640625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([49, 50, 53, 54, 55, 56]), model=ScalarModel(intercept=489.81991575860064, linear_terms=array([ -7.9974817 , -19.32900869]), square_terms=array([[ 0.42136964, 3.37341025], + [ 3.37341025, 110.35741782]]), scale=0.021640625, shift=array([3.63149252, 1.00327357])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=54, candidate_x=array([3.63149252, 1.00327357]), index=54, x=array([3.63149252, 1.00327357]), fval=506.99784644540586, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([49, 50, 53, 54, 55, 56]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.63149252, 1.00327357]), radius=0.0108203125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([49, 53, 54, 56, 57]), model=ScalarModel(intercept=488.20806659594507, linear_terms=array([-5.57735135, -8.72033931]), square_terms=array([[ 0.19829904, 1.06047186], + [ 1.06047186, 27.40287752]]), scale=0.0108203125, shift=array([3.63149252, 1.00327357])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=54, candidate_x=array([3.63149252, 1.00327357]), index=54, x=array([3.63149252, 1.00327357]), fval=506.99784644540586, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([49, 53, 54, 56, 57]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.63149252, 1.00327357]), radius=0.00541015625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([49, 54, 57, 58]), model=ScalarModel(intercept=494.0113764663675, linear_terms=array([-11.65580487, 15.6297121 ]), square_terms=array([[ 0.78526236, -0.48868618], + [-0.48868618, 6.38068787]]), scale=0.00541015625, shift=array([3.63149252, 1.00327357])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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square_terms=array([[0.11698178, 0.26813838], + [0.26813838, 2.07607229]]), scale=0.002705078125, shift=array([3.63149252, 1.00327357])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=54, candidate_x=array([3.63149252, 1.00327357]), index=54, x=array([3.63149252, 1.00327357]), fval=506.99784644540586, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([54, 58, 59]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.63149252, 1.00327357]), radius=0.0013525390625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([54, 59, 60]), model=ScalarModel(intercept=506.99784644540597, linear_terms=array([ -8.71304558, -11.97148297]), square_terms=array([[0.3825836 , 0.59999497], + [0.59999497, 1.3121182 ]]), scale=0.0013525390625, shift=array([3.63149252, 1.00327357])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=61, candidate_x=array([3.63203366, 1.00451313]), index=61, x=array([3.63203366, 1.00451313]), fval=506.5420877431311, rho=0.03337442243196882, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([54, 59, 60]), old_indices_discarded=array([], dtype=int64), step_length=0.0013525390625000859, relative_step_length=1.0000000000000635, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.63203366, 1.00451313]), radius=0.00067626953125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([54, 60, 61]), model=ScalarModel(intercept=506.54208774313054, linear_terms=array([-9.21303399, 3.86970875]), square_terms=array([[ 0.44560218, -0.146486 ], + [-0.146486 , 0.1480837 ]]), scale=0.00067626953125, shift=array([3.63203366, 1.00451313])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=62, candidate_x=array([3.63267099, 1.00428697]), index=61, x=array([3.63203366, 1.00451313]), fval=506.5420877431311, rho=-0.01469109283770332, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([54, 60, 61]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 63 entries., 'multistart_info': {'start_parameters': [array([3.4625, 0.875 ]), array([3.92835851, 0.91166214])], 'local_optima': [{'solution_x': array([3.63203366, 1.00451313]), 'solution_criterion': 506.5420877431311, 'states': [State(trustregion=Region(center=array([3.4625, 0.875 ]), radius=0.34625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=1093.6287094958684, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.34625, shift=array([3.4625, 0.875 ])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=0, candidate_x=array([3.4625, 0.875 ]), index=0, x=array([3.4625, 0.875 ]), fval=1093.6287094958684, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([3.4625, 0.875 ]), radius=0.34625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=651.4449578029853, linear_terms=array([281.18601875, 919.56705343]), square_terms=array([[ 186.56083108, 697.51852997], + [ 697.51852997, 4254.6830109 ]]), scale=array([0.30685607, 0.26592804]), shift=array([3.4625 , 0.83407196])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=13, candidate_x=array([3.15564393, 0.82019339]), index=0, x=array([3.4625, 0.875 ]), fval=1093.6287094958684, rho=-0.21893278324197663, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.4625, 0.875 ]), radius=0.173125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 4, 7, 8, 9, 10, 12, 13]), model=ScalarModel(intercept=697.61707876083, linear_terms=array([ 86.6112369 , -859.52858173]), square_terms=array([[ 88.19328217, 426.09698529], + [ 426.09698529, 6091.26225323]]), scale=0.173125, shift=array([3.4625, 0.875 ])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=14, candidate_x=array([3.29204935, 0.91082732]), index=14, x=array([3.29204935, 0.91082732]), fval=1000.7509434811079, rho=0.525364637884901, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 3, 4, 7, 8, 9, 10, 12, 13]), old_indices_discarded=array([ 1, 2, 5, 6, 11]), step_length=0.17417526039465764, relative_step_length=1.0060664860341235, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.29204935, 0.91082732]), radius=0.34625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 3, 7, 10, 11, 12, 13, 14]), model=ScalarModel(intercept=823.8622722142668, linear_terms=array([ 80.18328931, -88.8613507 ]), square_terms=array([[ 762.08731871, -1381.66492468], + [-1381.66492468, 2821.90772929]]), scale=array([0.30685607, 0.24801438]), shift=array([3.29204935, 0.85198562])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=15, candidate_x=array([3.16057114, 0.80776531]), index=14, x=array([3.29204935, 0.91082732]), fval=1000.7509434811079, rho=-2.7239526213953136, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 7, 10, 11, 12, 13, 14]), old_indices_discarded=array([2, 4, 5, 6, 8, 9]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.29204935, 0.91082732]), radius=0.173125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 3, 7, 10, 12, 13, 14, 15]), model=ScalarModel(intercept=859.1595008143797, linear_terms=array([-49.00694728, 377.59149803]), square_terms=array([[ 99.87726874, -357.72244111], + [-357.72244111, 1848.37553732]]), scale=0.173125, shift=array([3.29204935, 0.91082732])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=16, candidate_x=array([3.13225668, 0.84420474]), index=14, x=array([3.29204935, 0.91082732]), fval=1000.7509434811079, rho=-3.384890709638498, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 7, 10, 12, 13, 14, 15]), old_indices_discarded=array([ 2, 4, 5, 6, 8, 9, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.29204935, 0.91082732]), radius=0.0865625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 7, 10, 12, 13, 14, 15, 16]), model=ScalarModel(intercept=827.6928291025154, linear_terms=array([ 5.35234931, 50.7864861 ]), square_terms=array([[ 10.55179511, -63.25662137], + [ -63.25662137, 1024.07313652]]), scale=0.0865625, shift=array([3.29204935, 0.91082732])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=17, candidate_x=array([3.20600682, 0.90123728]), index=14, x=array([3.29204935, 0.91082732]), fval=1000.7509434811079, rho=-8.050741085388342, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 3, 7, 10, 12, 13, 14, 15, 16]), old_indices_discarded=array([ 1, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.29204935, 0.91082732]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 10, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=964.3969823484208, linear_terms=array([ 5.87336302, -56.20626673]), square_terms=array([[ 1.90128134, -13.33600414], + [-13.33600414, 93.87172932]]), scale=0.04328125, shift=array([3.29204935, 0.91082732])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=18, candidate_x=array([3.32412277, 0.94050444]), index=18, x=array([3.32412277, 0.94050444]), fval=827.2850544940472, rho=9.440779127990067, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 3, 10, 13, 14, 15, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0436970906100786, relative_step_length=1.0096078696913466, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.32412277, 0.94050444]), radius=0.0865625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 7, 10, 13, 14, 15, 16, 17, 18]), model=ScalarModel(intercept=754.9629712965816, linear_terms=array([-11.33853356, 149.73382686]), square_terms=array([[ 19.81434331, -166.92704745], + [-166.92704745, 1457.19103375]]), scale=0.0865625, shift=array([3.32412277, 0.94050444])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=19, candidate_x=array([3.23913867, 0.92193944]), index=18, x=array([3.32412277, 0.94050444]), fval=827.2850544940472, rho=-9.785116917650237, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 7, 10, 13, 14, 15, 16, 17, 18]), old_indices_discarded=array([ 1, 3, 11, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.32412277, 0.94050444]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 10, 13, 14, 15, 16, 17, 18, 19]), model=ScalarModel(intercept=832.6203197168809, linear_terms=array([ 11.43225451, -88.93217292]), square_terms=array([[ 1.89207866, -14.51433349], + [-14.51433349, 111.66139431]]), scale=0.04328125, shift=array([3.32412277, 0.94050444])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=20, candidate_x=array([3.3494378 , 0.97820598]), index=20, x=array([3.3494378 , 0.97820598]), fval=589.2387386757741, rho=6.707721298454917, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 10, 13, 14, 15, 16, 17, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.045412073734327936, relative_step_length=1.0492320285187682, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.3494378 , 0.97820598]), radius=0.0865625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 7, 10, 12, 14, 17, 18, 19, 20]), model=ScalarModel(intercept=710.921693371805, linear_terms=array([202.27752122, 780.08362564]), square_terms=array([[ 118.12834448, 487.07344 ], + [ 487.07344 , 2688.6689297 ]]), scale=0.0865625, shift=array([3.3494378 , 0.97820598])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=21, candidate_x=array([3.26155243, 0.96911596]), index=20, x=array([3.3494378 , 0.97820598]), fval=589.2387386757741, rho=-0.37545420554346837, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 7, 10, 12, 14, 17, 18, 19, 20]), old_indices_discarded=array([ 3, 4, 13, 15, 16]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.3494378 , 0.97820598]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 10, 14, 17, 18, 19, 20, 21]), model=ScalarModel(intercept=549.8023854149372, linear_terms=array([ -7.52264096, -60.41063775]), square_terms=array([[ 1.47685727, 19.34913644], + [ 19.34913644, 256.1673533 ]]), scale=0.04328125, shift=array([3.3494378 , 0.97820598])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=22, candidate_x=array([3.39335677, 0.98501738]), index=22, x=array([3.39335677, 0.98501738]), fval=555.0773535680186, rho=3.376269726696847, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 10, 14, 17, 18, 19, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.04444402645396528, relative_step_length=1.0268655931602086, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39335677, 0.98501738]), radius=0.0865625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 10, 14, 17, 18, 19, 20, 21, 22]), model=ScalarModel(intercept=509.93359583487626, linear_terms=array([ -2.87140025, -77.0340883 ]), square_terms=array([[ 1.94049629, 45.43808551], + [ 45.43808551, 1087.04904653]]), scale=0.0865625, shift=array([3.39335677, 0.98501738])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=23, candidate_x=array([3.307134 , 0.99475297]), index=23, x=array([3.307134 , 0.99475297]), fval=527.5324137105392, rho=9.012522247815982, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 10, 14, 17, 18, 19, 20, 21, 22]), old_indices_discarded=array([ 3, 4, 7, 8, 9, 12, 13, 15, 16]), step_length=0.08677066454287263, relative_step_length=1.0024047889429328, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.307134 , 0.99475297]), radius=0.173125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([10, 14, 17, 18, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=560.8180421345091, linear_terms=array([ 171.84978397, -706.76473204]), square_terms=array([[ 74.62650725, -461.02969905], + [-461.02969905, 3206.52608661]]), scale=array([0.15342804, 0.12933753]), shift=array([3.307134 , 0.97066247])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=24, candidate_x=array([3.15370596, 0.98057437]), index=23, x=array([3.307134 , 0.99475297]), fval=527.5324137105392, rho=-0.9813470262140938, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([10, 14, 17, 18, 19, 20, 21, 22, 23]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 15, 16]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.307134 , 0.99475297]), radius=0.0865625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([10, 14, 17, 18, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=484.79762730862103, linear_terms=array([ 48.50792314, -73.29564765]), square_terms=array([[ 23.75435503, -174.08413125], + [-174.08413125, 1436.30017384]]), scale=0.0865625, shift=array([3.307134 , 0.99475297])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=25, candidate_x=array([3.2206553 , 0.98883994]), index=23, x=array([3.307134 , 0.99475297]), fval=527.5324137105392, rho=-0.5781888323147932, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([10, 14, 17, 18, 19, 20, 21, 22, 23]), old_indices_discarded=array([ 0, 3, 7, 12, 13, 15, 16, 24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.307134 , 0.99475297]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([10, 14, 18, 19, 20, 21, 22, 23, 25]), model=ScalarModel(intercept=497.9928888365399, linear_terms=array([ 10.19808053, -47.847162 ]), square_terms=array([[ 0.75061409, -15.01172448], + [-15.01172448, 357.53799767]]), scale=0.04328125, shift=array([3.307134 , 0.99475297])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=26, candidate_x=array([3.26365193, 0.99863193]), index=26, x=array([3.26365193, 0.99863193]), fval=519.9309270334896, rho=0.6687184257598502, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([10, 14, 18, 19, 20, 21, 22, 23, 25]), old_indices_discarded=array([ 0, 7, 12, 17, 24]), step_length=0.04365474941845791, relative_step_length=1.008629589451735, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.26365193, 0.99863193]), radius=0.0865625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([10, 14, 18, 19, 20, 21, 23, 25, 26]), model=ScalarModel(intercept=477.3301617595189, linear_terms=array([ 13.68073618, -18.67221002]), square_terms=array([[ 1.51452177, -42.17862452], + [ -42.17862452, 1510.52712184]]), scale=0.0865625, shift=array([3.26365193, 0.99863193])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=27, candidate_x=array([3.17709299, 0.9972963 ]), index=26, x=array([3.26365193, 0.99863193]), fval=519.9309270334896, rho=-0.4645488387784232, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([10, 14, 18, 19, 20, 21, 23, 25, 26]), old_indices_discarded=array([ 0, 3, 7, 12, 13, 15, 16, 17, 22, 24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.26365193, 0.99863193]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 18, 19, 20, 21, 23, 25, 26, 27]), model=ScalarModel(intercept=532.5926218735569, linear_terms=array([ 3.02001572, -34.91119764]), square_terms=array([[ 3.06576932e-02, -1.71386354e+00], + [-1.71386354e+00, 2.25297083e+02]]), scale=0.04328125, shift=array([3.26365193, 0.99863193])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=28, candidate_x=array([3.22032155, 1.00493248]), index=28, x=array([3.22032155, 1.00493248]), fval=513.1513126909756, rho=1.2432267791250464, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 18, 19, 20, 21, 23, 25, 26, 27]), old_indices_discarded=array([ 7, 10, 12, 13, 16, 17, 22, 24]), step_length=0.043786048192572063, relative_step_length=1.011663207337405, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.22032155, 1.00493248]), radius=0.0865625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([10, 19, 21, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=480.1639722811906, linear_terms=array([-9.58819386, -6.16098806]), square_terms=array([[ 2.95376033, 73.23574637], + [ 73.23574637, 1897.40328086]]), scale=0.0865625, shift=array([3.22032155, 1.00493248])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=29, candidate_x=array([3.30683105, 1.00188925]), index=28, x=array([3.22032155, 1.00493248]), fval=513.1513126909756, rho=-0.07527732047828518, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([10, 19, 21, 23, 24, 25, 26, 27, 28]), old_indices_discarded=array([ 0, 3, 7, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.22032155, 1.00493248]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 21, 23, 24, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=523.4679173240145, linear_terms=array([-2.59748377, -0.7171113 ]), square_terms=array([[2.31661478e-01, 7.66177350e+00], + [7.66177350e+00, 2.63292238e+02]]), scale=0.04328125, shift=array([3.22032155, 1.00493248])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=30, candidate_x=array([3.26358824, 1.00380232]), index=30, x=array([3.26358824, 1.00380232]), fval=513.0194722961687, rho=0.0512525527783304, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([19, 21, 23, 24, 25, 26, 27, 28, 29]), old_indices_discarded=array([ 7, 10, 12, 13, 14, 15, 16, 17, 18, 20, 22]), step_length=0.043281441899137574, relative_step_length=1.0000044337706877, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.26358824, 1.00380232]), radius=0.021640625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 21, 23, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=519.3234950395965, linear_terms=array([-0.34493884, 1.06441875]), square_terms=array([[1.02321587e-02, 8.19381474e-01], + [8.19381474e-01, 6.67857826e+01]]), scale=0.021640625, shift=array([3.26358824, 1.00380232])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=31, candidate_x=array([3.28522304, 1.00319524]), index=30, x=array([3.26358824, 1.00380232]), fval=513.0194722961687, rho=-0.12388915381056541, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([19, 21, 23, 25, 26, 27, 28, 29, 30]), old_indices_discarded=array([10, 14, 18, 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.26358824, 1.00380232]), radius=0.0108203125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([21, 23, 25, 26, 28, 29, 30, 31]), model=ScalarModel(intercept=514.1646995371159, linear_terms=array([-0.15345129, -3.50288081]), square_terms=array([[3.05251315e-03, 2.75003243e-01], + [2.75003243e-01, 2.51655902e+01]]), scale=0.0108203125, shift=array([3.26358824, 1.00380232])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=32, candidate_x=array([3.27442429, 1.0051837 ]), index=32, x=array([3.27442429, 1.0051837 ]), fval=512.0809014214674, rho=2.6136687240628853, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([21, 23, 25, 26, 28, 29, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.010923748464293592, relative_step_length=1.0095594248589024, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.27442429, 1.0051837 ]), radius=0.021640625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([21, 23, 25, 26, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=513.4928941324267, linear_terms=array([-0.24707091, 0.03226047]), square_terms=array([[1.26987208e-02, 1.12599639e+00], + [1.12599639e+00, 1.01298380e+02]]), scale=0.021640625, shift=array([3.27442429, 1.0051837 ])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=33, candidate_x=array([3.29606351, 1.00493688]), index=33, x=array([3.29606351, 1.00493688]), fval=511.79793745681667, rho=1.1440872753091216, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([21, 23, 25, 26, 28, 29, 30, 31, 32]), old_indices_discarded=array([10, 14, 18, 19, 20, 27]), step_length=0.021640625913725366, relative_step_length=1.0000000422226885, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.29606351, 1.00493688]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([20, 21, 23, 26, 29, 30, 31, 32, 33]), model=ScalarModel(intercept=513.3709874050701, linear_terms=array([ 1.13482224, -0.44884792]), square_terms=array([[2.17777822e-02, 2.50633392e+00], + [2.50633392e+00, 3.99796932e+02]]), scale=0.04328125, shift=array([3.29606351, 1.00493688])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=34, candidate_x=array([3.25278341, 1.00525589]), index=33, x=array([3.29606351, 1.00493688]), fval=511.79793745681667, rho=-0.512339270174138, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([20, 21, 23, 26, 29, 30, 31, 32, 33]), old_indices_discarded=array([ 0, 7, 10, 12, 14, 17, 18, 19, 22, 24, 25, 27, 28]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.29606351, 1.00493688]), radius=0.021640625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([21, 23, 26, 29, 30, 31, 32, 33, 34]), model=ScalarModel(intercept=513.2806219743039, linear_terms=array([ 0.40201888, -0.14468052]), square_terms=array([[2.11787355e-03, 2.74550213e-01], + [2.74550213e-01, 1.00721574e+02]]), scale=0.021640625, shift=array([3.29606351, 1.00493688])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=35, candidate_x=array([3.27442305, 1.0050266 ]), index=33, x=array([3.29606351, 1.00493688]), fval=511.79793745681667, rho=-0.9549081380658717, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([21, 23, 26, 29, 30, 31, 32, 33, 34]), old_indices_discarded=array([10, 14, 18, 19, 20, 22, 25, 28]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.29606351, 1.00493688]), radius=0.0108203125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 26, 29, 30, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=511.8797516145538, linear_terms=array([-0.15742971, -3.27449424]), square_terms=array([[2.83081088e-03, 2.88272590e-01], + [2.88272590e-01, 2.97786495e+01]]), scale=0.0108203125, shift=array([3.29606351, 1.00493688])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=36, candidate_x=array([3.30689479, 1.00601728]), index=36, x=array([3.30689479, 1.00601728]), fval=511.21798874189403, rho=1.8960681633469345, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([23, 26, 29, 30, 31, 32, 33, 34, 35]), old_indices_discarded=array([21]), step_length=0.010885029063083678, relative_step_length=1.005981025324701, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.30689479, 1.00601728]), radius=0.021640625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 26, 29, 30, 31, 32, 33, 35, 36]), model=ScalarModel(intercept=511.4227564263899, linear_terms=array([-0.3250255 , -0.31495259]), square_terms=array([[1.22560006e-02, 1.19816348e+00], + [1.19816348e+00, 1.18877538e+02]]), scale=0.021640625, shift=array([3.30689479, 1.00601728])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=37, candidate_x=array([3.3285349 , 1.00585694]), index=37, x=array([3.3285349 , 1.00585694]), fval=510.83913420009407, rho=1.1759433626717288, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([23, 26, 29, 30, 31, 32, 33, 35, 36]), old_indices_discarded=array([14, 18, 19, 20, 21, 22, 25, 28, 34]), step_length=0.02164070209710628, relative_step_length=1.0000035626099653, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.3285349 , 1.00585694]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([20, 23, 29, 31, 32, 33, 35, 36, 37]), model=ScalarModel(intercept=510.92606818398167, linear_terms=array([-1.25261185, 5.63856144]), square_terms=array([[6.91279361e-02, 5.32869751e+00], + [5.32869751e+00, 4.22474316e+02]]), scale=0.04328125, shift=array([3.3285349 , 1.00585694])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=38, candidate_x=array([3.37180546, 1.00473701]), index=38, x=array([3.37180546, 1.00473701]), fval=510.59082554232384, rho=0.18257009624743023, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([20, 23, 29, 31, 32, 33, 35, 36, 37]), old_indices_discarded=array([ 0, 7, 10, 12, 14, 17, 18, 19, 21, 22, 24, 25, 26, 27, 28, 30, 34]), step_length=0.0432850573848669, relative_step_length=1.0000879684590185, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.37180546, 1.00473701]), radius=0.0865625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([18, 20, 22, 23, 29, 33, 36, 37, 38]), model=ScalarModel(intercept=516.8290415340703, linear_terms=array([ 5.75246416, 11.16476668]), square_terms=array([[ 2.64009594e-01, -1.44214956e+01], + [-1.44214956e+01, 1.26850502e+03]]), scale=0.0865625, shift=array([3.37180546, 1.00473701])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=39, candidate_x=array([3.28525713, 1.00299909]), index=38, x=array([3.37180546, 1.00473701]), fval=510.59082554232384, rho=-0.44505466092410517, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([18, 20, 22, 23, 29, 33, 36, 37, 38]), old_indices_discarded=array([ 0, 3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 19, 21, 24, 25, + 26, 27, 28, 30, 31, 32, 34, 35]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.37180546, 1.00473701]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([18, 20, 22, 23, 29, 33, 36, 37, 38]), model=ScalarModel(intercept=516.8290415340703, linear_terms=array([2.87623208, 5.58238334]), square_terms=array([[ 6.60023984e-02, -3.60537390e+00], + [-3.60537390e+00, 3.17126254e+02]]), scale=0.04328125, shift=array([3.37180546, 1.00473701])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=40, candidate_x=array([3.32853554, 1.00349462]), index=38, x=array([3.37180546, 1.00473701]), fval=510.59082554232384, rho=-0.468500568265753, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([18, 20, 22, 23, 29, 33, 36, 37, 38]), old_indices_discarded=array([ 0, 10, 14, 17, 19, 21, 25, 26, 27, 28, 30, 31, 32, 34, 35, 39]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.37180546, 1.00473701]), radius=0.021640625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([20, 22, 23, 29, 33, 36, 37, 38, 40]), model=ScalarModel(intercept=510.83094836315786, linear_terms=array([-0.31356388, -0.52607831]), square_terms=array([[1.43542942e-02, 1.22048177e+00], + [1.22048177e+00, 1.05162098e+02]]), scale=0.021640625, shift=array([3.37180546, 1.00473701])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=41, candidate_x=array([3.39344589, 1.00459454]), index=41, x=array([3.39344589, 1.00459454]), fval=510.27441229833755, rho=1.0250640374729652, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([20, 22, 23, 29, 33, 36, 37, 38, 40]), old_indices_discarded=array([18, 31, 32, 35, 39]), step_length=0.021640897833367097, relative_step_length=1.0000126074624507, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39344589, 1.00459454]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([20, 22, 23, 29, 36, 37, 38, 40, 41]), model=ScalarModel(intercept=510.388199569147, linear_terms=array([-0.70976972, -0.38780663]), square_terms=array([[5.87854987e-02, 4.93351716e+00], + [4.93351716e+00, 4.19243045e+02]]), scale=0.04328125, shift=array([3.39344589, 1.00459454])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=42, candidate_x=array([3.43672463, 1.00412607]), index=42, x=array([3.43672463, 1.00412607]), fval=509.73756488700667, rho=0.7615075639324983, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([20, 22, 23, 29, 36, 37, 38, 40, 41]), old_indices_discarded=array([ 0, 10, 14, 17, 18, 19, 21, 25, 26, 28, 30, 31, 32, 33, 34, 35, 39]), step_length=0.04328126925028541, relative_step_length=1.0000004447719373, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43672463, 1.00412607]), radius=0.0865625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([18, 20, 22, 36, 37, 38, 40, 41, 42]), model=ScalarModel(intercept=513.6632160611562, linear_terms=array([ 1.66952951, 16.46923676]), square_terms=array([[2.01619093e-02, 1.39077252e+00], + [1.39077252e+00, 1.30355320e+03]]), scale=0.0865625, shift=array([3.43672463, 1.00412607])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=43, candidate_x=array([3.35016101, 1.00312604]), index=42, x=array([3.43672463, 1.00412607]), fval=509.73756488700667, rho=-1.2008268877931576, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([18, 20, 22, 36, 37, 38, 40, 41, 42]), old_indices_discarded=array([ 0, 3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 19, 21, 23, 24, + 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 39]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43672463, 1.00412607]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([18, 20, 22, 37, 38, 40, 41, 42, 43]), model=ScalarModel(intercept=513.482630783711, linear_terms=array([0.65403347, 8.91106369]), square_terms=array([[5.63212301e-03, 7.99992087e-01], + [7.99992087e-01, 3.24875610e+02]]), scale=0.04328125, shift=array([3.43672463, 1.00412607])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=44, candidate_x=array([3.39344059, 1.00304757]), index=42, x=array([3.43672463, 1.00412607]), fval=509.73756488700667, rho=-1.839477153136199, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([18, 20, 22, 37, 38, 40, 41, 42, 43]), old_indices_discarded=array([ 0, 10, 14, 19, 21, 23, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 39]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43672463, 1.00412607]), radius=0.021640625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([20, 22, 38, 40, 41, 42, 43, 44]), model=ScalarModel(intercept=510.12883784081237, linear_terms=array([-0.25127194, -0.58488428]), square_terms=array([[9.67696849e-03, 9.89759993e-01], + [9.89759993e-01, 1.03384944e+02]]), scale=0.021640625, shift=array([3.43672463, 1.00412607])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=45, candidate_x=array([3.45836543, 1.00404152]), index=45, x=array([3.45836543, 1.00404152]), fval=509.3715980078564, rho=1.4802790196268736, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([20, 22, 38, 40, 41, 42, 43, 44]), old_indices_discarded=array([], dtype=int64), step_length=0.021640972413607785, relative_step_length=1.0000160537696017, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.45836543, 1.00404152]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 20, 22, 38, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=506.56672858420234, linear_terms=array([-4.47747831, 43.24748363]), square_terms=array([[ 1.64747112, 16.06601525], + [ 16.06601525, 169.95443376]]), scale=0.04328125, shift=array([3.45836543, 1.00404152])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=46, candidate_x=array([3.50048727, 0.98975262]), index=45, x=array([3.45836543, 1.00404152]), fval=509.3715980078564, rho=-1.7034780941746406, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 20, 22, 38, 41, 42, 43, 44, 45]), old_indices_discarded=array([14, 18, 21, 23, 26, 29, 30, 31, 32, 33, 34, 35, 36, 37, 39, 40]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.45836543, 1.00404152]), radius=0.021640625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 38, 41, 42, 44, 45, 46]), model=ScalarModel(intercept=509.90462027267597, linear_terms=array([-0.12472648, -1.80799561]), square_terms=array([[1.12107334e-02, 1.07164529e+00], + [1.07164529e+00, 1.05129465e+02]]), scale=0.021640625, shift=array([3.45836543, 1.00404152])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=47, candidate_x=array([3.48000873, 1.00419292]), index=47, x=array([3.48000873, 1.00419292]), fval=508.9279337370762, rho=3.6451687787620792, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([22, 38, 41, 42, 44, 45, 46]), old_indices_discarded=array([], dtype=int64), step_length=0.021643822010034636, relative_step_length=1.0001477318716365, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.48000873, 1.00419292]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 38, 41, 42, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=509.4211138668626, linear_terms=array([-0.43693822, -0.49379518]), square_terms=array([[4.49138891e-02, 4.30131411e+00], + [4.30131411e+00, 4.20126086e+02]]), scale=0.04328125, shift=array([3.48000873, 1.00419292])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=48, candidate_x=array([3.52328823, 1.00380109]), index=48, x=array([3.52328823, 1.00380109]), fval=508.3645034963132, rho=1.3050907569087495, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([22, 38, 41, 42, 43, 44, 45, 46, 47]), old_indices_discarded=array([ 0, 14, 18, 20, 23, 29, 31, 32, 33, 35, 36, 37, 39, 40]), step_length=0.04328128036721916, relative_step_length=1.0000007016252803, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.52328823, 1.00380109]), radius=0.0865625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 22, 41, 42, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=510.9361846246444, linear_terms=array([-2.56354502, 68.23315716]), square_terms=array([[5.02760540e-01, 1.72586847e+01], + [1.72586847e+01, 6.23529154e+02]]), scale=0.0865625, shift=array([3.52328823, 1.00380109])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=49, candidate_x=array([3.60955785, 0.99202423]), index=48, x=array([3.52328823, 1.00380109]), fval=508.3645034963132, rho=-1.7199045949987042, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 22, 41, 42, 44, 45, 46, 47, 48]), old_indices_discarded=array([ 3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, + 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, + 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.52328823, 1.00380109]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 41, 42, 44, 45, 46, 47, 48, 49]), model=ScalarModel(intercept=509.1888444032537, linear_terms=array([-0.2211104 , -0.64677861]), square_terms=array([[5.00518952e-02, 4.53096392e+00], + [4.53096392e+00, 4.18022502e+02]]), scale=0.04328125, shift=array([3.52328823, 1.00380109])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=50, candidate_x=array([3.56656767, 1.00339915]), index=50, x=array([3.56656767, 1.00339915]), fval=507.88904056097454, rho=2.2205332256943504, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([22, 41, 42, 44, 45, 46, 47, 48, 49]), old_indices_discarded=array([ 0, 18, 20, 23, 36, 37, 38, 40, 43]), step_length=0.04328130211625866, relative_step_length=1.000001204130164, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.56656767, 1.00339915]), radius=0.0865625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 41, 42, 45, 46, 47, 48, 49, 50]), model=ScalarModel(intercept=514.5638756314247, linear_terms=array([ 3.48562996, 53.35987525]), square_terms=array([[ 1.37261425e-01, -7.23686414e+00], + [-7.23686414e+00, 6.24285840e+02]]), scale=0.0865625, shift=array([3.56656767, 1.00339915])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=51, candidate_x=array([3.48009628, 0.99505212]), index=50, x=array([3.56656767, 1.00339915]), fval=507.88904056097454, rho=-1.9285165450030315, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 41, 42, 45, 46, 47, 48, 49, 50]), old_indices_discarded=array([ 4, 7, 8, 9, 10, 12, 14, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, + 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 43, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.56656767, 1.00339915]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 42, 45, 46, 47, 48, 49, 50, 51]), model=ScalarModel(intercept=515.3264858581856, linear_terms=array([ 1.71158 , 25.56690401]), square_terms=array([[ 2.71276458e-02, -1.52375230e+00], + [-1.52375230e+00, 1.55243251e+02]]), scale=0.04328125, shift=array([3.56656767, 1.00339915])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=52, candidate_x=array([3.52335761, 0.99594077]), index=50, x=array([3.56656767, 1.00339915]), fval=507.88904056097454, rho=-2.248134988786441, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 42, 45, 46, 47, 48, 49, 50, 51]), old_indices_discarded=array([ 4, 22, 38, 41, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.56656767, 1.00339915]), radius=0.021640625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([46, 47, 48, 49, 50, 51, 52]), model=ScalarModel(intercept=508.17663344776184, linear_terms=array([-0.18431719, -2.63810816]), square_terms=array([[1.45940369e-02, 1.22560823e+00], + [1.22560823e+00, 1.04071984e+02]]), scale=0.021640625, shift=array([3.56656767, 1.00339915])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=53, candidate_x=array([3.58821325, 1.00369237]), index=53, x=array([3.58821325, 1.00369237]), fval=507.4336026486255, rho=2.4401860410878675, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([46, 47, 48, 49, 50, 51, 52]), old_indices_discarded=array([], dtype=int64), step_length=0.021647564744518292, relative_step_length=1.0003206813351413, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.58821325, 1.00369237]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([45, 46, 47, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=507.76912581454695, linear_terms=array([-0.42677272, -0.59726462]), square_terms=array([[5.22032739e-02, 4.63172328e+00], + [4.63172328e+00, 4.16496389e+02]]), scale=0.04328125, shift=array([3.58821325, 1.00369237])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=54, candidate_x=array([3.63149252, 1.00327357]), index=54, x=array([3.63149252, 1.00327357]), fval=506.99784644540586, rho=1.037040511159352, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([45, 46, 47, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([ 0, 4, 8, 9, 22, 41, 42, 44]), step_length=0.0432812951041612, relative_step_length=1.0000010421178038, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.63149252, 1.00327357]), radius=0.0865625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 4, 46, 47, 48, 49, 50, 52, 53, 54]), model=ScalarModel(intercept=507.0456308378341, linear_terms=array([-1.09355637, 5.86222255]), square_terms=array([[3.81131556e-01, 2.40396513e+01], + [2.40396513e+01, 1.52493360e+03]]), scale=0.0865625, shift=array([3.63149252, 1.00327357])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=54, candidate_x=array([3.63149252, 1.00327357]), index=54, x=array([3.63149252, 1.00327357]), fval=506.99784644540586, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 4, 46, 47, 48, 49, 50, 52, 53, 54]), old_indices_discarded=array([ 0, 2, 8, 9, 10, 14, 18, 19, 20, 21, 22, 23, 25, 26, 28, 29, 30, + 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 51]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.63149252, 1.00327357]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 4, 46, 48, 49, 50, 52, 53, 54, 55]), model=ScalarModel(intercept=483.6460160115828, linear_terms=array([ -8.37903947, -46.37591995]), square_terms=array([[ 0.605033 , 11.49832029], + [ 11.49832029, 426.97587575]]), scale=0.04328125, shift=array([3.63149252, 1.00327357])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=54, candidate_x=array([3.63149252, 1.00327357]), index=54, x=array([3.63149252, 1.00327357]), fval=506.99784644540586, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 4, 46, 48, 49, 50, 52, 53, 54, 55]), old_indices_discarded=array([ 0, 8, 9, 42, 45, 47, 51]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.63149252, 1.00327357]), radius=0.021640625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([49, 50, 53, 54, 55, 56]), model=ScalarModel(intercept=489.81991575860064, linear_terms=array([ -7.9974817 , -19.32900869]), square_terms=array([[ 0.42136964, 3.37341025], + [ 3.37341025, 110.35741782]]), scale=0.021640625, shift=array([3.63149252, 1.00327357])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=54, candidate_x=array([3.63149252, 1.00327357]), index=54, x=array([3.63149252, 1.00327357]), fval=506.99784644540586, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([49, 50, 53, 54, 55, 56]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.63149252, 1.00327357]), radius=0.0108203125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([49, 53, 54, 56, 57]), model=ScalarModel(intercept=488.20806659594507, linear_terms=array([-5.57735135, -8.72033931]), square_terms=array([[ 0.19829904, 1.06047186], + [ 1.06047186, 27.40287752]]), scale=0.0108203125, shift=array([3.63149252, 1.00327357])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=54, candidate_x=array([3.63149252, 1.00327357]), index=54, x=array([3.63149252, 1.00327357]), fval=506.99784644540586, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([49, 53, 54, 56, 57]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.63149252, 1.00327357]), radius=0.00541015625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([49, 54, 57, 58]), model=ScalarModel(intercept=494.0113764663675, linear_terms=array([-11.65580487, 15.6297121 ]), square_terms=array([[ 0.78526236, -0.48868618], + [-0.48868618, 6.38068787]]), scale=0.00541015625, shift=array([3.63149252, 1.00327357])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=59, candidate_x=array([3.63582092, 1.00002784]), index=54, x=array([3.63149252, 1.00327357]), fval=506.99784644540586, rho=-0.11566595855501426, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([49, 54, 57, 58]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.63149252, 1.00327357]), radius=0.002705078125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([54, 58, 59]), model=ScalarModel(intercept=506.997846445406, linear_terms=array([-4.77796857, -7.07581725]), square_terms=array([[0.11698178, 0.26813838], + [0.26813838, 2.07607229]]), scale=0.002705078125, shift=array([3.63149252, 1.00327357])), vector_model=VectorModel(intercepts=array([ 0.17006451, 0.02956994, -0.9361252 , -1.61351554, + -2.52174659, -3.40422327, -5.21498224, -12.83276861, + -13.72721231, -13.55879483, -14.8535869 , -16.95254847]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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fval=709.5920774337976, rho=6.284152384779159, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 2, 3, 4, 6, 7, 9, 12, 13]), old_indices_discarded=array([ 1, 5, 8, 10, 11]), step_length=0.17624549117969177, relative_step_length=0.8972984035032338, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.10242937, 0.93926306]), radius=0.39283585146612066, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 2, 4, 5, 8, 9, 12, 13, 14]), model=ScalarModel(intercept=703.0395545815431, linear_terms=array([-160.81523137, -813.85973953]), square_terms=array([[ 23.80260467, 130.21587045], + [ 130.21587045, 1976.08778378]]), scale=array([0.34814171, 0.25443933]), shift=array([4.10242937, 0.84556067])), vector_model=VectorModel(intercepts=array([ 0.49652143, 0.84772779, 0.35898903, 0.18625493, + -0.21246767, -0.548112 , -1.96085423, -10.76245674, + -12.3146449 , -12.62045897, -14.08604359, 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State(trustregion=Region(center=array([4.10242937, 0.93926306]), radius=0.19641792573306033, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 2, 4, 8, 9, 12, 13, 14, 15]), model=ScalarModel(intercept=573.1710443089172, linear_terms=array([ -70.2999988 , -362.33652641]), square_terms=array([[ 6.7024952 , 36.51035483], + [ 36.51035483, 1758.79957809]]), scale=array([0.17407085, 0.1674039 ]), shift=array([4.10242937, 0.9325961 ])), vector_model=VectorModel(intercepts=array([ 0.49652143, 0.84772779, 0.35898903, 0.18625493, + -0.21246767, -0.548112 , -1.96085423, -10.76245674, + -12.3146449 , -12.62045897, -14.08604359, -15.95795125]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.39283585146612066, shift=array([3.92835851, 0.91166214])), candidate_index=14, candidate_x=array([4.10242937, 0.93926306]), index=14, x=array([4.10242937, 0.93926306]), fval=709.5920774337976, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 2, 4, 8, 9, 12, 13, 14, 15]), old_indices_discarded=array([ 1, 3, 5, 6, 7, 10, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.10242937, 0.93926306]), radius=0.09820896286653016, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 2, 4, 8, 9, 13, 14, 15, 16]), model=ScalarModel(intercept=592.6543914686597, linear_terms=array([ -74.26778817, -252.56460606]), square_terms=array([[ 10.82595238, 61.08368861], + [ 61.08368861, 515.52510087]]), scale=0.09820896286653016, shift=array([4.10242937, 0.93926306])), vector_model=VectorModel(intercepts=array([ 0.49652143, 0.84772779, 0.35898903, 0.18625493, + -0.21246767, -0.548112 , -1.96085423, -10.76245674, + -12.3146449 , -12.62045897, -14.08604359, -15.95795125]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.39283585146612066, shift=array([3.92835851, 0.91166214])), candidate_index=14, candidate_x=array([4.10242937, 0.93926306]), index=14, x=array([4.10242937, 0.93926306]), fval=709.5920774337976, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 2, 4, 8, 9, 13, 14, 15, 16]), old_indices_discarded=array([ 5, 6, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.10242937, 0.93926306]), radius=0.04910448143326508, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 4, 8, 9, 13, 14, 16, 17]), model=ScalarModel(intercept=647.7854134729351, linear_terms=array([ -28.30343399, -165.4032875 ]), square_terms=array([[ 1.18820393, 8.18165452], + [ 8.18165452, 231.06687257]]), scale=0.04910448143326508, shift=array([4.10242937, 0.93926306])), vector_model=VectorModel(intercepts=array([ 0.49652143, 0.84772779, 0.35898903, 0.18625493, + -0.21246767, -0.548112 , -1.96085423, -10.76245674, + -12.3146449 , -12.62045897, -14.08604359, -15.95795125]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.39283585146612066, shift=array([3.92835851, 0.91166214])), candidate_index=14, candidate_x=array([4.10242937, 0.93926306]), index=14, x=array([4.10242937, 0.93926306]), fval=709.5920774337976, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 4, 8, 9, 13, 14, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.10242937, 0.93926306]), radius=0.02455224071663254, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 17, 18]), model=ScalarModel(intercept=709.5920774337975, linear_terms=array([ -25.35221022, -575.03625503]), square_terms=array([[ 13.95957634, -31.10967033], + [-31.10967033, 572.70300511]]), scale=0.02455224071663254, shift=array([4.10242937, 0.93926306])), vector_model=VectorModel(intercepts=array([ 0.49652143, 0.84772779, 0.35898903, 0.18625493, + -0.21246767, -0.548112 , -1.96085423, -10.76245674, + -12.3146449 , -12.62045897, -14.08604359, -15.95795125]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.39283585146612066, shift=array([3.92835851, 0.91166214])), candidate_index=14, candidate_x=array([4.10242937, 0.93926306]), index=14, x=array([4.10242937, 0.93926306]), fval=709.5920774337976, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.10242937, 0.93926306]), radius=0.01227612035831627, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 18, 19]), model=ScalarModel(intercept=709.5920774337985, linear_terms=array([ 32.20028076, -282.80159432]), square_terms=array([[ 22.82924084, -54.81188866], + [-54.81188866, 193.24414789]]), scale=0.01227612035831627, shift=array([4.10242937, 0.93926306])), vector_model=VectorModel(intercepts=array([ 0.49652143, 0.84772779, 0.35898903, 0.18625493, + -0.21246767, -0.548112 , -1.96085423, -10.76245674, + -12.3146449 , -12.62045897, -14.08604359, -15.95795125]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.39283585146612066, shift=array([3.92835851, 0.91166214])), candidate_index=20, candidate_x=array([4.10608546, 0.95098211]), index=20, x=array([4.10608546, 0.95098211]), fval=648.6690709013858, rho=0.32596998290269796, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.012276120358316323, relative_step_length=1.0000000000000042, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.10608546, 0.95098211]), radius=0.02455224071663254, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 17, 18, 19, 20]), model=ScalarModel(intercept=634.8446467033286, linear_terms=array([ -45.92106717, -141.73701279]), square_terms=array([[ 9.00323906, 2.41866819], + [ 2.41866819, 76.65075564]]), scale=0.02455224071663254, shift=array([4.10608546, 0.95098211])), vector_model=VectorModel(intercepts=array([ 0.49652143, 0.84772779, 0.35898903, 0.18625493, + -0.21246767, -0.548112 , -1.96085423, -10.76245674, + -12.3146449 , -12.62045897, -14.08604359, -15.95795125]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.39283585146612066, shift=array([3.92835851, 0.91166214])), candidate_index=20, candidate_x=array([4.10608546, 0.95098211]), index=20, x=array([4.10608546, 0.95098211]), fval=648.6690709013858, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.10608546, 0.95098211]), radius=0.01227612035831627, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 18, 19, 20, 21]), model=ScalarModel(intercept=616.6512546416332, linear_terms=array([ 14.81758717, -126.85375063]), square_terms=array([[ 5.00889551, -8.40865233], + [-8.40865233, 42.10350276]]), scale=0.01227612035831627, shift=array([4.10608546, 0.95098211])), vector_model=VectorModel(intercepts=array([ 0.49652143, 0.84772779, 0.35898903, 0.18625493, + -0.21246767, -0.548112 , -1.96085423, -10.76245674, + -12.3146449 , -12.62045897, -14.08604359, -15.95795125]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.39283585146612066, shift=array([3.92835851, 0.91166214])), candidate_index=20, candidate_x=array([4.10608546, 0.95098211]), index=20, x=array([4.10608546, 0.95098211]), fval=648.6690709013858, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 18, 19, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.10608546, 0.95098211]), radius=0.006138060179158135, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 19, 20, 21, 22]), model=ScalarModel(intercept=626.3886121528963, linear_terms=array([ 31.9745401 , -61.83962378]), square_terms=array([[ 6.31182423, -5.30340037], + [-5.30340037, 9.14042468]]), scale=0.006138060179158135, shift=array([4.10608546, 0.95098211])), vector_model=VectorModel(intercepts=array([ 0.49652143, 0.84772779, 0.35898903, 0.18625493, + -0.21246767, -0.548112 , -1.96085423, -10.76245674, + -12.3146449 , -12.62045897, -14.08604359, -15.95795125]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.39283585146612066, shift=array([3.92835851, 0.91166214])), candidate_index=23, candidate_x=array([4.10440383, 0.95688532]), index=23, x=array([4.10440383, 0.95688532]), fval=620.2878620321094, rho=0.45503025929577373, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 19, 20, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.006138060179158198, relative_step_length=1.0000000000000102, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.10440383, 0.95688532]), radius=0.01227612035831627, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 18, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=570.2820883152913, linear_terms=array([ -0.63687268, -115.46232547]), square_terms=array([[ 3.19576866, -3.44091438], + [-3.44091438, 39.37098378]]), scale=0.01227612035831627, shift=array([4.10440383, 0.95688532])), vector_model=VectorModel(intercepts=array([ 0.49652143, 0.84772779, 0.35898903, 0.18625493, + -0.21246767, -0.548112 , -1.96085423, -10.76245674, + -12.3146449 , -12.62045897, -14.08604359, -15.95795125]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.39283585146612066, shift=array([3.92835851, 0.91166214])), candidate_index=23, candidate_x=array([4.10440383, 0.95688532]), index=23, x=array([4.10440383, 0.95688532]), fval=620.2878620321094, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 18, 19, 20, 21, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.10440383, 0.95688532]), radius=0.006138060179158135, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 19, 20, 21, 22, 23, 24]), model=ScalarModel(intercept=565.2350874755684, linear_terms=array([ 13.42917918, -58.85087105]), square_terms=array([[ 5.65506944, -3.96090155], + [-3.96090155, 10.02210554]]), scale=0.006138060179158135, shift=array([4.10440383, 0.95688532])), vector_model=VectorModel(intercepts=array([ 0.49652143, 0.84772779, 0.35898903, 0.18625493, + -0.21246767, -0.548112 , -1.96085423, -10.76245674, + -12.3146449 , -12.62045897, -14.08604359, -15.95795125]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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0.18625493, + -0.21246767, -0.548112 , -1.96085423, -10.76245674, + -12.3146449 , -12.62045897, -14.08604359, -15.95795125]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.39283585146612066, shift=array([3.92835851, 0.91166214])), candidate_index=23, candidate_x=array([4.10440383, 0.95688532]), index=23, x=array([4.10440383, 0.95688532]), fval=620.2878620321094, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([19, 20, 22, 23, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.10440383, 0.95688532]), radius=0.0015345150447895338, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([20, 22, 23, 25, 26]), model=ScalarModel(intercept=607.3049943215983, linear_terms=array([ -1.17893353, -11.37519598]), square_terms=array([[0.00320547, 0.0303136 ], + [0.0303136 , 0.38382998]]), scale=0.0015345150447895338, shift=array([4.10440383, 0.95688532])), vector_model=VectorModel(intercepts=array([ 0.49652143, 0.84772779, 0.35898903, 0.18625493, + -0.21246767, -0.548112 , -1.96085423, -10.76245674, + -12.3146449 , -12.62045897, -14.08604359, -15.95795125]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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model=ScalarModel(intercept=595.6643413349246, linear_terms=array([ -3.45218014, -26.63767134]), square_terms=array([[0.07749722, 0.13329762], + [0.13329762, 1.87298856]]), scale=0.0030690300895790676, shift=array([4.10452951, 0.95841468])), vector_model=VectorModel(intercepts=array([ 0.49652143, 0.84772779, 0.35898903, 0.18625493, + -0.21246767, -0.548112 , -1.96085423, -10.76245674, + -12.3146449 , -12.62045897, -14.08604359, -15.95795125]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.39283585146612066, shift=array([3.92835851, 0.91166214])), candidate_index=27, candidate_x=array([4.10452951, 0.95841468]), index=27, x=array([4.10452951, 0.95841468]), fval=613.2288018488726, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([19, 20, 22, 23, 24, 25, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.10452951, 0.95841468]), radius=0.0015345150447895338, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([20, 22, 23, 25, 26, 27, 28]), model=ScalarModel(intercept=595.907158344846, linear_terms=array([ -3.08249489, -12.20590735]), square_terms=array([[0.02934606, 0.08729316], + [0.08729316, 0.44531894]]), scale=0.0015345150447895338, shift=array([4.10452951, 0.95841468])), vector_model=VectorModel(intercepts=array([ 0.49652143, 0.84772779, 0.35898903, 0.18625493, + -0.21246767, -0.548112 , -1.96085423, -10.76245674, + -12.3146449 , -12.62045897, -14.08604359, -15.95795125]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.39283585146612066, shift=array([3.92835851, 0.91166214])), candidate_index=27, candidate_x=array([4.10452951, 0.95841468]), index=27, x=array([4.10452951, 0.95841468]), fval=613.2288018488726, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([20, 22, 23, 25, 26, 27, 28]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.10452951, 0.95841468]), radius=0.0007672575223947669, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 26, 27, 28, 29]), model=ScalarModel(intercept=604.9203462717749, linear_terms=array([-18.68076427, -6.90750075]), square_terms=array([[1.2707043 , 0.2933156 ], + [0.2933156 , 0.13226964]]), scale=0.0007672575223947669, shift=array([4.10452951, 0.95841468])), vector_model=VectorModel(intercepts=array([ 0.49652143, 0.84772779, 0.35898903, 0.18625493, + -0.21246767, -0.548112 , -1.96085423, -10.76245674, + -12.3146449 , -12.62045897, -14.08604359, -15.95795125]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.39283585146612066, shift=array([3.92835851, 0.91166214])), candidate_index=30, candidate_x=array([4.10526572, 0.95863073]), index=30, x=array([4.10526572, 0.95863073]), fval=612.1663712949527, rho=0.05533358464722446, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([23, 26, 27, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=0.0007672575223946103, relative_step_length=0.9999999999997959, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute params change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 31 entries., 'history': {'params': [{'CRRA': 3.9283585146612063, 'DiscFac': 0.9116621363063967}, {'CRRA': 3.58021680580877, 'DiscFac': 0.5640908738961512}, {'CRRA': 4.276500223513643, 'DiscFac': 0.7473801239067223}, {'CRRA': 3.58021680580877, 'DiscFac': 0.8703686602196827}, {'CRRA': 4.276500223513643, 'DiscFac': 0.9642710795378783}, {'CRRA': 4.276500223513643, 'DiscFac': 0.6027326498042208}, {'CRRA': 3.9574335869652093, 'DiscFac': 0.5635204274539602}, {'CRRA': 3.58021680580877, 'DiscFac': 1.0819158438847456}, {'CRRA': 4.276500223513643, 'DiscFac': 1.097682967347001}, {'CRRA': 4.276500223513643, 'DiscFac': 1.053710114882074}, {'CRRA': 3.58021680580877, 'DiscFac': 1.0940747949541754}, {'CRRA': 3.7311830324920114, 'DiscFac': 0.5635204274539602}, {'CRRA': 3.7690658533787085, 'DiscFac': 1.1}, {'CRRA': 4.276500223513643, 'DiscFac': 0.8925220331576068}, {'CRRA': 4.1024293690874245, 'DiscFac': 0.9392630565899157}, {'CRRA': 4.450571077939861, 'DiscFac': 0.9335860575541611}, {'CRRA': 4.276500223513643, 'DiscFac': 0.963608487127061}, {'CRRA': 4.2050881969414124, 'DiscFac': 0.972601738111027}, {'CRRA': 4.142133526792268, 'DiscFac': 0.9694062696783321}, {'CRRA': 4.114779365849994, 'DiscFac': 0.9610994250037553}, {'CRRA': 4.106085457101602, 'DiscFac': 0.9509821073827255}, {'CRRA': 4.107824517137357, 'DiscFac': 0.9754726809652186}, {'CRRA': 4.107403747175251, 'DiscFac': 0.9631872391859296}, {'CRRA': 4.104403827709157, 'DiscFac': 0.9568853188264594}, {'CRRA': 4.106739391371688, 'DiscFac': 0.9689372179892525}, {'CRRA': 4.103838108958621, 'DiscFac': 0.9629972534676561}, {'CRRA': 4.104602246127849, 'DiscFac': 0.9599479281423641}, {'CRRA': 4.104529507007806, 'DiscFac': 0.9584146785410382}, {'CRRA': 4.10479485740129, 'DiscFac': 0.9614722159258333}, {'CRRA': 4.10485727203945, 'DiscFac': 0.9599137804410828}, {'CRRA': 4.105265716135248, 'DiscFac': 0.958630734613255}], 'criterion': [885.1490295235632, 1237.3831227272105, 1173.4747124052076, 1091.2858399384895, nan, 1214.2023923754682, 1228.6185193350634, nan, nan, nan, nan, 1233.935273500313, nan, 916.291791025054, 709.5920774337976, nan, nan, nan, nan, nan, 648.6690709013858, nan, nan, 620.2878620321094, nan, nan, nan, 613.2288018488726, nan, nan, 612.1663712949527], 'runtime': [0.0, 1.4403331720000097, 1.4767484219996732, 1.512743013999625, 1.5560416649996114, 1.5917235949996211, 1.6404006269999627, 1.6671121529998345, 1.705849116999616, 1.7473428230000536, 1.7893147419999877, 1.832734428999629, 1.878267821999998, 3.587017901999843, 4.94060757699981, 6.261645380999653, 7.591948510999828, 8.923523973999636, 10.256098466999902, 11.575847744999919, 12.899013525999635, 14.1988267429997, 15.56263835899972, 16.87367188600001, 18.229436776000057, 19.58007070099984, 20.950962564999827, 22.30214271299974, 23.663406628999837, 25.00892779900005, 26.44043202900002], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]}}], 'exploration_sample': array([[3.4625 , 0.875 ], + [4.64375 , 0.6875 ], + [2.871875, 0.78125 ], + [2.28125 , 1.0625 ]]), 'exploration_results': array([1093.6287095 , 1183.94834133, 1205.66501897, 1353.72863006])}}" diff --git a/content/tables/min/WarmGlow_estimate_results.csv b/content/tables/min/WarmGlow_estimate_results.csv new file mode 100644 index 0000000..aaa9d20 --- /dev/null +++ b/content/tables/min/WarmGlow_estimate_results.csv @@ -0,0 +1,2964 @@ +CRRA,3.2492019027639434 +DiscFac,0.9947689143590757 +time_to_estimate,92.65805292129517 +params,"{'CRRA': 3.2492019027639434, 'DiscFac': 0.9947689143590757}" +criterion,483.3042437482078 +start_criterion,nan +start_params,"{'CRRA': 5.0, 'DiscFac': 0.95}" +algorithm,multistart_tranquilo_ls +direction,minimize +n_free,2 +message,Absolute params change smaller than tolerance. +success, +n_criterion_evaluations, +n_derivative_evaluations, +n_iterations, +history,"{'params': [{'CRRA': 3.787666272489316, 'DiscFac': 0.8916692958468136}, {'CRRA': 3.4522702301564188, 'DiscFac': 0.5559961123158821}, {'CRRA': 4.123339456020248, 'DiscFac': 0.9023086310261734}, {'CRRA': 3.4519930889583845, 'DiscFac': 0.9621907100872018}, {'CRRA': 4.123339456020248, 'DiscFac': 1.089750383978294}, {'CRRA': 4.123339456020248, 'DiscFac': 0.6017993841903007}, {'CRRA': 4.123339456020248, 'DiscFac': 0.5569865537153984}, {'CRRA': 3.4683844729376645, 'DiscFac': 1.1}, {'CRRA': 4.123339456020248, 'DiscFac': 1.045584977176946}, {'CRRA': 4.10451029867194, 'DiscFac': 1.1}, {'CRRA': 3.4519930889583845, 'DiscFac': 1.054282457648922}, {'CRRA': 3.7954582435001956, 'DiscFac': 0.5559961123158821}, {'CRRA': 3.8035398561153393, 'DiscFac': 1.1}, {'CRRA': 3.4519930889583845, 'DiscFac': 0.864627335810008}, {'CRRA': 3.592010414630564, 'DiscFac': 0.894292791173227}, {'CRRA': 3.6888912528562816, 'DiscFac': 0.8878074252872034}, {'CRRA': 3.823561879152503, 'DiscFac': 0.9246859419813435}, {'CRRA': 3.730859920510897, 'DiscFac': 0.947729381130338}, {'CRRA': 3.563023328745431, 'DiscFac': 0.9551359206790517}, {'CRRA': 3.2477863535466858, 'DiscFac': 0.9938493219885415}, {'CRRA': 3.1978636230600257, 'DiscFac': 1.0105906789642098}, {'CRRA': 3.3468786473767884, 'DiscFac': 0.9769704795680376}, {'CRRA': 3.3483016413066986, 'DiscFac': 0.9803419003819871}, {'CRRA': 3.341936549287567, 'DiscFac': 0.9837374224736428}, {'CRRA': 3.2009950294051723, 'DiscFac': 1.001074240849349}, {'CRRA': 3.2710390033805554, 'DiscFac': 0.9991449061243276}, {'CRRA': 3.257841194054621, 'DiscFac': 1.0003218222323884}, {'CRRA': 3.245971813012713, 'DiscFac': 0.9994825157389511}, {'CRRA': 3.248525695788145, 'DiscFac': 0.996714584689371}, {'CRRA': 3.2492547111506704, 'DiscFac': 0.9940310230356313}, {'CRRA': 3.2492019027639434, 'DiscFac': 0.9947689143590757}], 'criterion': [960.8060529870695, 1240.2921504697779, 850.859898892532, 596.4110585596852, nan, 1216.050562892, 1223.9931083911201, nan, nan, nan, nan, 1231.906846531173, nan, 1096.360653117792, 981.7508031032233, 993.7795220807161, 776.1278971480874, 648.6747263507989, 625.5055756735769, 484.1337317628459, nan, 528.5042475772016, 514.6948394166868, 503.03465345715216, nan, nan, nan, nan, nan, 483.968970933012, 483.3042437482078], 'runtime': [0.0, 1.4134274120001464, 1.4513098170000376, 1.4883478880001348, 1.5293199889997595, 1.5646587159999399, 1.618685972000094, 1.649523740999939, 1.6883285939998132, 1.7261191950001376, 1.7632402599997476, 1.8103470779997224, 1.8507548039997346, 3.590426523000133, 4.847393539999757, 6.175072521999937, 7.528106565000144, 8.883646622000015, 10.280463337000128, 11.652836511999794, 13.009232939999947, 14.359365203999914, 15.72169381699996, 17.094576741999845, 18.500274525999885, 19.861051937999946, 21.18866636199982, 22.520675393000147, 23.882492001999708, 25.24515795699972, 26.623506488999737], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]}" +convergence_report,"{'one_step': {'relative_criterion_change': 0.08786861410107832, 'relative_params_change': 0.059574135069656635, 'absolute_criterion_change': 42.46727408732477, 'absolute_params_change': 0.18479425470330055}, 'five_steps': {'relative_criterion_change': 0.08786861410107832, 'relative_params_change': 0.059574135069656635, 'absolute_criterion_change': 42.46727408732477, 'absolute_params_change': 0.18479425470330055}}" +multistart_info,"{'start_parameters': [{'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 3.787666272489316, 'DiscFac': 0.8916692958468136}], 'local_optima': [Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute params change smaller than tolerance., Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute params change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 0.4266 0.611 +relative_params_change 0.2042 0.2042 +absolute_criterion_change 254.4 364.4 +absolute_params_change 0.674 0.674 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 2.871875, 'DiscFac': 0.78125}, {'CRRA': 2.28125, 'DiscFac': 1.0625}], 'exploration_results': array([1069.96402275, 1180.14464257, 1201.13045726, 2198.69465259])}" +algorithm_output,"{'states': [State(trustregion=Region(center=array([3.78766627, 0.8916693 ]), radius=0.3787666272489316, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=960.8060529870695, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.3787666272489316, shift=array([3.78766627, 0.8916693 ])), vector_model=VectorModel(intercepts=array([ 0.35338954, 0.47806807, -0.22056614, -0.58388504, + -1.16360651, -1.67357266, -3.18317315, -11.54070577, + -12.88671729, -13.03004323, -14.43344133, -16.38334533]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.3787666272489316, shift=array([3.78766627, 0.8916693 ])), candidate_index=0, candidate_x=array([3.78766627, 0.8916693 ]), index=0, x=array([3.78766627, 0.8916693 ]), fval=960.8060529870695, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([3.78766627, 0.8916693 ]), radius=0.3787666272489316, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=874.7353270796168, linear_terms=array([ 873.49602125, 1350.09023191]), square_terms=array([[ 985.59998281, 2047.15687 ], + [2047.15687 , 5176.28198934]]), scale=array([0.33567318, 0.27200194]), shift=array([3.78766627, 0.82799806])), vector_model=VectorModel(intercepts=array([ 0.35338954, 0.47806807, -0.22056614, -0.58388504, + -1.16360651, -1.67357266, -3.18317315, -11.54070577, + -12.88671729, -13.03004323, -14.43344133, -16.38334533]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.3787666272489316, shift=array([3.78766627, 0.8916693 ])), candidate_index=13, candidate_x=array([3.45199309, 0.86462734]), index=0, x=array([3.78766627, 0.8916693 ]), fval=960.8060529870695, rho=-0.15308542515227241, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.78766627, 0.8916693 ]), radius=0.1893833136244658, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 2, 3, 8, 9, 10, 11, 12, 13]), model=ScalarModel(intercept=808.6912637597069, linear_terms=array([350.32639779, 685.42785942]), square_terms=array([[ 234.14892793, 697.30904451], + [ 697.30904451, 2410.5795072 ]]), scale=0.1893833136244658, shift=array([3.78766627, 0.8916693 ])), vector_model=VectorModel(intercepts=array([ 0.35338954, 0.47806807, -0.22056614, -0.58388504, + -1.16360651, -1.67357266, -3.18317315, -11.54070577, + -12.88671729, -13.03004323, -14.43344133, -16.38334533]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.3787666272489316, shift=array([3.78766627, 0.8916693 ])), candidate_index=14, candidate_x=array([3.59201041, 0.89429279]), index=0, x=array([3.78766627, 0.8916693 ]), fval=960.8060529870695, rho=-0.08829078860833003, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 2, 3, 8, 9, 10, 11, 12, 13]), old_indices_discarded=array([1, 4, 5, 6, 7]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.78766627, 0.8916693 ]), radius=0.0946916568122329, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 2, 3, 8, 10, 11, 12, 13, 14]), model=ScalarModel(intercept=730.9325872858446, linear_terms=array([106.27869552, 159.7730584 ]), square_terms=array([[ 42.38597082, 131.40554341], + [131.40554341, 502.25747585]]), scale=0.0946916568122329, shift=array([3.78766627, 0.8916693 ])), vector_model=VectorModel(intercepts=array([ 0.35338954, 0.47806807, -0.22056614, -0.58388504, + -1.16360651, -1.67357266, -3.18317315, -11.54070577, + -12.88671729, -13.03004323, -14.43344133, -16.38334533]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.3787666272489316, shift=array([3.78766627, 0.8916693 ])), candidate_index=15, candidate_x=array([3.68889125, 0.88780743]), index=0, x=array([3.78766627, 0.8916693 ]), fval=960.8060529870695, rho=-0.373384654091654, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 2, 3, 8, 10, 11, 12, 13, 14]), old_indices_discarded=array([4, 5, 7, 9]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.78766627, 0.8916693 ]), radius=0.04734582840611645, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 14, 15]), model=ScalarModel(intercept=965.5477767842316, linear_terms=array([ -5.90849634, -72.44081687]), square_terms=array([[ 0.10420462, 2.89237224], + [ 2.89237224, 95.70682949]]), scale=0.04734582840611645, shift=array([3.78766627, 0.8916693 ])), vector_model=VectorModel(intercepts=array([ 0.35338954, 0.47806807, -0.22056614, -0.58388504, + -1.16360651, -1.67357266, -3.18317315, -11.54070577, + -12.88671729, -13.03004323, -14.43344133, -16.38334533]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.3787666272489316, shift=array([3.78766627, 0.8916693 ])), candidate_index=16, candidate_x=array([3.82356188, 0.92468594]), index=16, x=array([3.82356188, 0.92468594]), fval=776.1278971480874, rho=6.122051811394392, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.04877082631749214, relative_step_length=1.0300976444883916, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.82356188, 0.92468594]), radius=0.0946916568122329, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 2, 4, 8, 9, 12, 14, 15, 16]), model=ScalarModel(intercept=828.8020397225383, linear_terms=array([ -4.39761005, -143.69587127]), square_terms=array([[ 10.86877388, 86.76352764], + [ 86.76352764, 933.32116784]]), scale=0.0946916568122329, shift=array([3.82356188, 0.92468594])), vector_model=VectorModel(intercepts=array([ 0.35338954, 0.47806807, -0.22056614, -0.58388504, + -1.16360651, -1.67357266, -3.18317315, -11.54070577, + -12.88671729, -13.03004323, -14.43344133, -16.38334533]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.3787666272489316, shift=array([3.78766627, 0.8916693 ])), candidate_index=17, candidate_x=array([3.73085992, 0.94772938]), index=17, x=array([3.73085992, 0.94772938]), fval=648.6747263507989, rho=6.893178067282503, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 2, 4, 8, 9, 12, 14, 15, 16]), old_indices_discarded=array([ 3, 5, 7, 10, 11, 13]), step_length=0.09552305074589823, relative_step_length=1.0087800125338806, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.73085992, 0.94772938]), radius=0.1893833136244658, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 10, 12, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=720.6246885108825, linear_terms=array([ 145.98231008, -194.8708089 ]), square_terms=array([[ 53.34554651, 318.31554452], + [ 318.31554452, 5407.49764562]]), scale=array([0.16783659, 0.16005361]), shift=array([3.73085992, 0.93994639])), vector_model=VectorModel(intercepts=array([ 0.35338954, 0.47806807, -0.22056614, -0.58388504, + -1.16360651, -1.67357266, -3.18317315, -11.54070577, + -12.88671729, -13.03004323, -14.43344133, -16.38334533]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.3787666272489316, shift=array([3.78766627, 0.8916693 ])), candidate_index=18, candidate_x=array([3.56302333, 0.95513592]), index=18, x=array([3.56302333, 0.95513592]), fval=625.5055756735769, rho=0.16481323155383745, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 3, 10, 12, 13, 14, 15, 16, 17]), old_indices_discarded=array([ 1, 2, 4, 5, 6, 7, 8, 9, 11]), step_length=0.1679999356057445, relative_step_length=0.8870894293194009, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.56302333, 0.95513592]), radius=0.3787666272489316, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 12, 13, 14, 15, 16, 17, 18]), model=ScalarModel(intercept=1699.230811865631, linear_terms=array([ -733.78876689, -5525.46893289]), square_terms=array([[ 801.51635682, 2663.03790063], + [ 2663.03790063, 14379.03531788]]), scale=array([0.33567318, 0.24026863]), shift=array([3.56302333, 0.85973137])), vector_model=VectorModel(intercepts=array([ 0.35338954, 0.47806807, -0.22056614, -0.58388504, + -1.16360651, -1.67357266, -3.18317315, -11.54070577, + -12.88671729, -13.03004323, -14.43344133, -16.38334533]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.3787666272489316, shift=array([3.78766627, 0.8916693 ])), candidate_index=19, candidate_x=array([3.24778635, 0.99384932]), index=19, x=array([3.24778635, 0.99384932]), fval=484.1337317628459, rho=1.030886182642824, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 12, 13, 14, 15, 16, 17, 18]), old_indices_discarded=array([ 0, 1, 2, 4, 5, 6, 8, 9, 10, 11]), step_length=0.3176052234668127, relative_step_length=0.8385248346023826, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.24778635, 0.99384932]), radius=0.7575332544978632, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 1, 3, 7, 11, 12, 13, 14, 16, 19]), model=ScalarModel(intercept=1209.299534643605, linear_terms=array([-2242.238234 , -2587.12158589]), square_terms=array([[4221.05213969, 3641.36424949], + [3641.36424949, 4071.26438615]]), scale=array([0.67134637, 0.3 ]), shift=array([3.24778635, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.35338954, 0.47806807, -0.22056614, -0.58388504, + -1.16360651, -1.67357266, -3.18317315, -11.54070577, + -12.88671729, -13.03004323, -14.43344133, -16.38334533]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.3787666272489316, shift=array([3.78766627, 0.8916693 ])), candidate_index=19, candidate_x=array([3.24778635, 0.99384932]), index=19, x=array([3.24778635, 0.99384932]), fval=484.1337317628459, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 1, 3, 7, 11, 12, 13, 14, 16, 19]), old_indices_discarded=array([ 0, 2, 4, 5, 6, 8, 9, 10, 15, 17, 18]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.24778635, 0.99384932]), radius=0.3787666272489316, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 13, 14, 15, 18, 19, 20]), model=ScalarModel(intercept=2124.417481740468, linear_terms=array([ -942.92590417, -7426.11137923]), square_terms=array([[ 377.95674671, 1876.28626265], + [ 1876.28626265, 15509.99841478]]), scale=array([0.33567318, 0.22091193]), shift=array([3.24778635, 0.87908807])), vector_model=VectorModel(intercepts=array([ 0.35338954, 0.47806807, -0.22056614, -0.58388504, + -1.16360651, -1.67357266, -3.18317315, -11.54070577, + -12.88671729, -13.03004323, -14.43344133, -16.38334533]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.3787666272489316, shift=array([3.78766627, 0.8916693 ])), candidate_index=21, candidate_x=array([3.34687865, 0.97697048]), index=19, x=array([3.24778635, 0.99384932]), fval=484.1337317628459, rho=-2.284718989594501, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 10, 13, 14, 15, 18, 19, 20]), old_indices_discarded=array([ 0, 1, 2, 4, 5, 6, 8, 9, 11, 12, 16, 17]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.24778635, 0.99384932]), radius=0.1893833136244658, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 13, 14, 18, 19, 20, 21]), model=ScalarModel(intercept=477.7179817875052, linear_terms=array([ -163.3710089 , -1175.98868957]), square_terms=array([[ 127.74286605, 686.46717998], + [ 686.46717998, 6044.38085127]]), scale=array([0.16783659, 0.13699363]), shift=array([3.24778635, 0.96300637])), vector_model=VectorModel(intercepts=array([ 0.35338954, 0.47806807, -0.22056614, -0.58388504, + -1.16360651, -1.67357266, -3.18317315, -11.54070577, + -12.88671729, -13.03004323, -14.43344133, -16.38334533]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.3787666272489316, shift=array([3.78766627, 0.8916693 ])), candidate_index=22, candidate_x=array([3.34830164, 0.9803419 ]), index=19, x=array([3.24778635, 0.99384932]), fval=484.1337317628459, rho=-2.6000951258679503, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 10, 13, 14, 18, 19, 20, 21]), old_indices_discarded=array([ 0, 1, 2, 4, 8, 9, 11, 12, 15, 16, 17]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.24778635, 0.99384932]), radius=0.0946916568122329, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 13, 18, 19, 20, 21, 22]), model=ScalarModel(intercept=374.72280604462765, linear_terms=array([-14.1761097, 29.2396918]), square_terms=array([[ 46.90834448, 284.30156756], + [ 284.30156756, 2940.59930772]]), scale=0.0946916568122329, shift=array([3.24778635, 0.99384932])), vector_model=VectorModel(intercepts=array([ 0.35338954, 0.47806807, -0.22056614, -0.58388504, + -1.16360651, -1.67357266, -3.18317315, -11.54070577, + -12.88671729, -13.03004323, -14.43344133, -16.38334533]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.3787666272489316, shift=array([3.78766627, 0.8916693 ])), candidate_index=23, candidate_x=array([3.34193655, 0.98373742]), index=19, x=array([3.24778635, 0.99384932]), fval=484.1337317628459, rho=-2.5369146064038564, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 10, 13, 18, 19, 20, 21, 22]), old_indices_discarded=array([14, 15]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.24778635, 0.99384932]), radius=0.04734582840611645, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 10, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=441.2649652868354, linear_terms=array([ -2.43397956, -116.38232897]), square_terms=array([[ 4.67788775, 43.93335349], + [ 43.93335349, 1049.51019283]]), scale=0.04734582840611645, shift=array([3.24778635, 0.99384932])), vector_model=VectorModel(intercepts=array([ 0.35338954, 0.47806807, -0.22056614, -0.58388504, + -1.16360651, -1.67357266, -3.18317315, -11.54070577, + -12.88671729, -13.03004323, -14.43344133, -16.38334533]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.3787666272489316, shift=array([3.78766627, 0.8916693 ])), candidate_index=19, candidate_x=array([3.24778635, 0.99384932]), index=19, x=array([3.24778635, 0.99384932]), fval=484.1337317628459, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 10, 19, 20, 21, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.24778635, 0.99384932]), radius=0.023672914203058226, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 20, 21, 22, 23, 24]), model=ScalarModel(intercept=456.6727886617666, linear_terms=array([ -11.56483252, -141.69457592]), square_terms=array([[ 3.5660652 , 34.18551936], + [ 34.18551936, 482.88971038]]), scale=0.023672914203058226, shift=array([3.24778635, 0.99384932])), vector_model=VectorModel(intercepts=array([ 0.35338954, 0.47806807, -0.22056614, -0.58388504, + -1.16360651, -1.67357266, -3.18317315, -11.54070577, + -12.88671729, -13.03004323, -14.43344133, -16.38334533]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.3787666272489316, shift=array([3.78766627, 0.8916693 ])), candidate_index=19, candidate_x=array([3.24778635, 0.99384932]), index=19, x=array([3.24778635, 0.99384932]), fval=484.1337317628459, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([19, 20, 21, 22, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.24778635, 0.99384932]), radius=0.011836457101529113, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 20, 24, 25]), model=ScalarModel(intercept=424.46586880900963, linear_terms=array([ -5.90684488, -111.62112051]), square_terms=array([[ 0.42710782, 7.90575832], + [ 7.90575832, 190.40627925]]), scale=0.011836457101529113, shift=array([3.24778635, 0.99384932])), vector_model=VectorModel(intercepts=array([ 0.35338954, 0.47806807, -0.22056614, -0.58388504, + -1.16360651, -1.67357266, -3.18317315, -11.54070577, + -12.88671729, -13.03004323, -14.43344133, -16.38334533]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.3787666272489316, shift=array([3.78766627, 0.8916693 ])), candidate_index=19, candidate_x=array([3.24778635, 0.99384932]), index=19, x=array([3.24778635, 0.99384932]), fval=484.1337317628459, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([19, 20, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.24778635, 0.99384932]), radius=0.0059182285507645566, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 25, 26]), model=ScalarModel(intercept=484.1337317628462, linear_terms=array([ -16.06805906, -176.33191042]), square_terms=array([[ 1.26390583, 14.22749645], + [ 14.22749645, 169.71406618]]), scale=0.0059182285507645566, shift=array([3.24778635, 0.99384932])), vector_model=VectorModel(intercepts=array([ 0.35338954, 0.47806807, -0.22056614, -0.58388504, + -1.16360651, -1.67357266, -3.18317315, -11.54070577, + -12.88671729, -13.03004323, -14.43344133, -16.38334533]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.3787666272489316, shift=array([3.78766627, 0.8916693 ])), candidate_index=19, candidate_x=array([3.24778635, 0.99384932]), index=19, x=array([3.24778635, 0.99384932]), fval=484.1337317628459, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([19, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.24778635, 0.99384932]), radius=0.0029591142753822783, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 26, 27]), model=ScalarModel(intercept=484.13373176284574, linear_terms=array([ 8.10776251, -113.24175874]), square_terms=array([[ 0.32512291, -4.57313309], + [-4.57313309, 67.6659022 ]]), scale=0.0029591142753822783, shift=array([3.24778635, 0.99384932])), vector_model=VectorModel(intercepts=array([ 0.35338954, 0.47806807, -0.22056614, -0.58388504, + -1.16360651, -1.67357266, -3.18317315, -11.54070577, + -12.88671729, -13.03004323, -14.43344133, -16.38334533]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.3787666272489316, shift=array([3.78766627, 0.8916693 ])), candidate_index=19, candidate_x=array([3.24778635, 0.99384932]), index=19, x=array([3.24778635, 0.99384932]), fval=484.1337317628459, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([19, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.24778635, 0.99384932]), radius=0.0014795571376911391, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 27, 28]), model=ScalarModel(intercept=484.1337317628462, linear_terms=array([-97.84517191, -89.44417119]), square_terms=array([[47.12079293, 43.46749574], + [43.46749574, 40.77539384]]), scale=0.0014795571376911391, shift=array([3.24778635, 0.99384932])), vector_model=VectorModel(intercepts=array([ 0.35338954, 0.47806807, -0.22056614, -0.58388504, + -1.16360651, -1.67357266, -3.18317315, -11.54070577, + -12.88671729, -13.03004323, -14.43344133, -16.38334533]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.3787666272489316, shift=array([3.78766627, 0.8916693 ])), candidate_index=29, candidate_x=array([3.24925471, 0.99403102]), index=29, x=array([3.24925471, 0.99403102]), fval=483.968970933012, rho=0.0020782488349921106, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([19, 27, 28]), old_indices_discarded=array([], dtype=int64), step_length=0.0014795571376912547, relative_step_length=1.0000000000000782, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.24925471, 0.99403102]), radius=0.0007397785688455696, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 28, 29]), model=ScalarModel(intercept=483.968970933012, linear_terms=array([ 0.5408325 , -5.01774165]), square_terms=array([[ 0.00141837, -0.01343874], + [-0.01343874, 0.31696815]]), scale=0.0007397785688455696, shift=array([3.24925471, 0.99403102])), vector_model=VectorModel(intercepts=array([ 0.35338954, 0.47806807, -0.22056614, -0.58388504, + -1.16360651, -1.67357266, -3.18317315, -11.54070577, + -12.88671729, -13.03004323, -14.43344133, -16.38334533]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.3787666272489316, shift=array([3.78766627, 0.8916693 ])), candidate_index=30, candidate_x=array([3.2492019 , 0.99476891]), index=30, x=array([3.2492019 , 0.99476891]), fval=483.3042437482078, rho=0.1360776516278881, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([19, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=0.0007397785688455717, relative_step_length=1.0000000000000029, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 31 entries., 'multistart_info': {'start_parameters': [array([3.4625, 0.875 ]), array([3.78766627, 0.8916693 ])], 'local_optima': [{'solution_x': array([3.43306478, 0.97623899]), 'solution_criterion': 525.7715178355326, 'states': [State(trustregion=Region(center=array([3.4625, 0.875 ]), radius=0.34625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=1069.9640227451446, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.34625, shift=array([3.4625, 0.875 ])), vector_model=VectorModel(intercepts=array([ 0.19831535, 0.09313108, -0.83564003, -1.46504524, + -2.31136791, -3.11563805, -4.84487237, -12.59469794, + -13.59264665, -13.47998451, -14.80009701, -16.86444915]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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449.16579325, 1001.63970094]), square_terms=array([[ 416.11481305, 908.53755792], + [ 908.53755792, 2817.29885557]]), scale=array([0.30685607, 0.26592804]), shift=array([3.4625 , 0.83407196])), vector_model=VectorModel(intercepts=array([ 0.19831535, 0.09313108, -0.83564003, -1.46504524, + -2.31136791, -3.11563805, -4.84487237, -12.59469794, + -13.59264665, -13.47998451, -14.80009701, -16.86444915]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=13, candidate_x=array([3.15564393, 0.82528395]), index=0, x=array([3.4625, 0.875 ]), fval=1069.9640227451446, rho=-0.22575496413768242, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.4625, 0.875 ]), radius=0.173125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 4, 7, 8, 9, 10, 12, 13]), model=ScalarModel(intercept=1086.9499711683493, linear_terms=array([264.6525083 , 309.56375141]), square_terms=array([[ 258.19170206, 645.97073698], + [ 645.97073698, 3178.90944329]]), scale=0.173125, shift=array([3.4625, 0.875 ])), vector_model=VectorModel(intercepts=array([ 0.19831535, 0.09313108, -0.83564003, -1.46504524, + -2.31136791, -3.11563805, -4.84487237, -12.59469794, + -13.59264665, -13.47998451, -14.80009701, 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n_evals_acceptance=1), State(trustregion=Region(center=array([3.28999947, 0.8927801 ]), radius=0.34625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 3, 7, 10, 11, 12, 13, 14]), model=ScalarModel(intercept=981.9299518820028, linear_terms=array([ 92.88342123, -198.31600617]), square_terms=array([[ 673.07971505, -1016.35064949], + [-1016.35064949, 1537.99538979]]), scale=array([0.30685607, 0.25703799]), shift=array([3.28999947, 0.84296201])), vector_model=VectorModel(intercepts=array([ 0.19831535, 0.09313108, -0.83564003, -1.46504524, + -2.31136791, -3.11563805, -4.84487237, -12.59469794, + -13.59264665, -13.47998451, -14.80009701, -16.86444915]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=14, candidate_x=array([3.28999947, 0.8927801 ]), index=14, x=array([3.28999947, 0.8927801 ]), fval=1036.0802202087962, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 7, 10, 11, 12, 13, 14]), old_indices_discarded=array([2, 4, 5, 6, 8, 9]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.28999947, 0.8927801 ]), radius=0.173125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 3, 7, 10, 12, 13, 14, 15]), model=ScalarModel(intercept=969.8153066839036, linear_terms=array([-69.46896655, 146.71728994]), square_terms=array([[ 33.35620996, -170.9070801 ], + [-170.9070801 , 912.29598504]]), scale=0.173125, shift=array([3.28999947, 0.8927801 ])), vector_model=VectorModel(intercepts=array([ 0.19831535, 0.09313108, -0.83564003, -1.46504524, + -2.31136791, -3.11563805, -4.84487237, -12.59469794, + -13.59264665, -13.47998451, -14.80009701, -16.86444915]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=16, candidate_x=array([3.46524131, 0.89755798]), index=16, x=array([3.46524131, 0.89755798]), fval=988.3435182697583, rho=0.8904811108198745, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 7, 10, 12, 13, 14, 15]), old_indices_discarded=array([ 2, 4, 5, 6, 8, 9, 11]), step_length=0.17530696401757798, relative_step_length=1.0126034022675985, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.46524131, 0.89755798]), radius=0.34625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 4, 9, 10, 13, 14, 15, 16]), model=ScalarModel(intercept=1090.1393984252868, linear_terms=array([ -31.27559889, -1619.41763547]), square_terms=array([[ 159.31076854, 538.37461974], + [ 538.37461974, 7188.13411139]]), scale=array([0.30685607, 0.25464904]), shift=array([3.46524131, 0.84535096])), vector_model=VectorModel(intercepts=array([ 0.19831535, 0.09313108, -0.83564003, -1.46504524, + -2.31136791, -3.11563805, -4.84487237, -12.59469794, + -13.59264665, -13.47998451, -14.80009701, -16.86444915]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=17, candidate_x=array([3.23310263, 0.91714951]), index=17, x=array([3.23310263, 0.91714951]), fval=929.1268307173862, rho=1.6668598892956832, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 3, 4, 9, 10, 13, 14, 15, 16]), old_indices_discarded=array([ 1, 2, 5, 6, 7, 8, 11, 12]), step_length=0.23296393669926482, relative_step_length=0.6728200337884904, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.23310263, 0.91714951]), radius=0.6925, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 3, 4, 6, 8, 12, 15, 17]), model=ScalarModel(intercept=914.9614856430516, linear_terms=array([-369.51480627, -760.34494131]), square_terms=array([[1112.09805767, 1547.08881929], + [1547.08881929, 2687.3703418 ]]), scale=array([0.61371215, 0.3 ]), shift=array([3.23310263, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.19831535, 0.09313108, -0.83564003, -1.46504524, + -2.31136791, -3.11563805, -4.84487237, -12.59469794, + -13.59264665, -13.47998451, -14.80009701, -16.86444915]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=18, candidate_x=array([3.04408312, 0.93807237]), index=18, x=array([3.04408312, 0.93807237]), fval=826.8463175953099, rho=3.926219426473953, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 4, 6, 8, 12, 15, 17]), old_indices_discarded=array([ 2, 5, 7, 9, 10, 11, 13, 14, 16]), step_length=0.1901739765043008, relative_step_length=0.27461946065603005, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.04408312, 0.93807237]), radius=0.6925, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 3, 7, 10, 14, 15, 17, 18]), model=ScalarModel(intercept=1023.1832806367438, linear_terms=array([-164.64803316, -933.7815826 ]), square_terms=array([[ 22.05432563, -19.89084209], + [ -19.89084209, 2342.07776399]]), scale=array([0.61371215, 0.3 ]), shift=array([3.04408312, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.19831535, 0.09313108, -0.83564003, -1.46504524, + -2.31136791, -3.11563805, -4.84487237, -12.59469794, + -13.59264665, -13.47998451, -14.80009701, -16.86444915]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=19, candidate_x=array([3.65779526, 0.92215723]), index=19, x=array([3.65779526, 0.92215723]), fval=820.069252724014, rho=0.040808195473632104, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 7, 10, 14, 15, 17, 18]), old_indices_discarded=array([ 2, 4, 5, 6, 8, 9, 11, 12, 13, 16]), step_length=0.6139184715764755, relative_step_length=0.8865248687024917, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.65779526, 0.92215723]), radius=0.34625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 2, 4, 5, 8, 9, 15, 16, 19]), model=ScalarModel(intercept=764.8274038991793, linear_terms=array([233.00958338, 111.77321685]), square_terms=array([[2719.46308331, 2358.66329209], + [2358.66329209, 2092.74157235]]), scale=array([0.30685607, 0.24234942]), shift=array([3.65779526, 0.85765058])), vector_model=VectorModel(intercepts=array([ 0.19831535, 0.09313108, -0.83564003, -1.46504524, + -2.31136791, -3.11563805, -4.84487237, -12.59469794, + -13.59264665, -13.47998451, -14.80009701, -16.86444915]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=19, candidate_x=array([3.65779526, 0.92215723]), index=19, x=array([3.65779526, 0.92215723]), fval=820.069252724014, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 2, 4, 5, 8, 9, 15, 16, 19]), old_indices_discarded=array([ 1, 3, 6, 7, 10, 11, 12, 13, 14, 17, 18]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.65779526, 0.92215723]), radius=0.173125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 2, 4, 8, 9, 15, 16, 19, 20]), model=ScalarModel(intercept=1143.969631411414, linear_terms=array([ 974.12770479, 1356.59865903]), square_terms=array([[1070.60848213, 1603.16173076], + [1603.16173076, 2679.55654902]]), scale=0.173125, shift=array([3.65779526, 0.92215723])), vector_model=VectorModel(intercepts=array([ 0.19831535, 0.09313108, -0.83564003, -1.46504524, + -2.31136791, -3.11563805, -4.84487237, -12.59469794, + -13.59264665, -13.47998451, -14.80009701, -16.86444915]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=21, candidate_x=array([3.4810213 , 0.93997173]), index=21, x=array([3.4810213 , 0.93997173]), fval=735.6146704571191, rho=0.1871717652839075, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 2, 4, 8, 9, 15, 16, 19, 20]), old_indices_discarded=array([ 1, 3, 5, 6, 7, 10, 11, 12, 13, 14, 17, 18]), step_length=0.177669328044831, relative_step_length=1.0262488262517313, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.4810213 , 0.93997173]), radius=0.34625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 4, 8, 10, 13, 15, 17, 19, 20, 21]), model=ScalarModel(intercept=1052.2838042107119, linear_terms=array([ -224.99208977, -2540.23365723]), square_terms=array([[ 215.06317243, 892.6934914 ], + [ 892.6934914 , 8177.43986672]]), scale=array([0.30685607, 0.23344217]), shift=array([3.4810213 , 0.86655783])), vector_model=VectorModel(intercepts=array([ 0.19831535, 0.09313108, -0.83564003, -1.46504524, + -2.31136791, -3.11563805, -4.84487237, -12.59469794, + -13.59264665, -13.47998451, -14.80009701, -16.86444915]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=22, candidate_x=array([3.34453238, 0.95040927]), index=22, x=array([3.34453238, 0.95040927]), fval=686.0263608994728, rho=4.240156738562875, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 4, 8, 10, 13, 15, 17, 19, 20, 21]), old_indices_discarded=array([ 0, 1, 2, 3, 5, 6, 7, 9, 11, 12, 14, 16, 18]), step_length=0.13688742461719516, relative_step_length=0.3953427425767369, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.34453238, 0.95040927]), radius=0.34625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 12, 15, 17, 18, 19, 20, 22]), model=ScalarModel(intercept=993.9676127670127, linear_terms=array([ -18.11531437, -1795.38622931]), square_terms=array([[ 7.21241047, -110.90483964], + [-110.90483964, 4497.78771025]]), scale=array([0.30685607, 0.2282234 ]), shift=array([3.34453238, 0.8717766 ])), vector_model=VectorModel(intercepts=array([ 0.19831535, 0.09313108, -0.83564003, -1.46504524, + -2.31136791, -3.11563805, -4.84487237, -12.59469794, + -13.59264665, -13.47998451, -14.80009701, -16.86444915]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=22, candidate_x=array([3.34453238, 0.95040927]), index=22, x=array([3.34453238, 0.95040927]), fval=686.0263608994728, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 3, 12, 15, 17, 18, 19, 20, 22]), old_indices_discarded=array([ 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 16, 21]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.34453238, 0.95040927]), radius=0.173125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 10, 13, 14, 16, 17, 20, 21, 22]), model=ScalarModel(intercept=694.5285134193795, linear_terms=array([ 18.42946457, -260.61575238]), square_terms=array([[ 16.8071809 , -199.68326366], + [-199.68326366, 2388.44859099]]), scale=array([0.15342804, 0.15150938]), shift=array([3.34453238, 0.94849062])), vector_model=VectorModel(intercepts=array([ 0.19831535, 0.09313108, -0.83564003, -1.46504524, + -2.31136791, -3.11563805, -4.84487237, -12.59469794, + -13.59264665, -13.47998451, -14.80009701, -16.86444915]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=22, candidate_x=array([3.34453238, 0.95040927]), index=22, x=array([3.34453238, 0.95040927]), fval=686.0263608994728, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 10, 13, 14, 16, 17, 20, 21, 22]), old_indices_discarded=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 15, 18, 19, 23]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.34453238, 0.95040927]), radius=0.0865625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 10, 14, 16, 17, 20, 21, 22, 24]), model=ScalarModel(intercept=613.8256355640807, linear_terms=array([ -33.04326989, -329.58101587]), square_terms=array([[ 2.25709624, 2.24823194], + [ 2.24823194, 1241.2576458 ]]), scale=0.0865625, shift=array([3.34453238, 0.95040927])), vector_model=VectorModel(intercepts=array([ 0.19831535, 0.09313108, -0.83564003, -1.46504524, + -2.31136791, -3.11563805, -4.84487237, -12.59469794, + -13.59264665, -13.47998451, -14.80009701, -16.86444915]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=25, candidate_x=array([3.43109342, 0.97269438]), index=25, x=array([3.43109342, 0.97269438]), fval=542.0913678929571, rho=1.9178442391178265, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 10, 14, 16, 17, 20, 21, 22, 24]), old_indices_discarded=array([ 1, 3, 4, 7, 11, 12, 13, 15, 18, 19, 23]), step_length=0.08938365884857186, relative_step_length=1.0325910047488445, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43109342, 0.97269438]), radius=0.173125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 14, 15, 16, 20, 21, 22, 24, 25]), model=ScalarModel(intercept=471.34423653235615, linear_terms=array([-165.73687498, -445.61811571]), square_terms=array([[ 48.87440154, 239.81531061], + [ 239.81531061, 3437.88931188]]), scale=array([0.15342804, 0.14036683]), shift=array([3.43109342, 0.95963317])), vector_model=VectorModel(intercepts=array([ 0.19831535, 0.09313108, -0.83564003, -1.46504524, + -2.31136791, -3.11563805, -4.84487237, -12.59469794, + -13.59264665, -13.47998451, -14.80009701, -16.86444915]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=25, candidate_x=array([3.43109342, 0.97269438]), index=25, x=array([3.43109342, 0.97269438]), fval=542.0913678929571, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 15, 16, 20, 21, 22, 24, 25]), old_indices_discarded=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 17, 18, 19, 23]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43109342, 0.97269438]), radius=0.0865625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 14, 16, 20, 21, 22, 24, 25, 26]), model=ScalarModel(intercept=465.39512295126724, linear_terms=array([ -49.12355559, -113.5362643 ]), square_terms=array([[ 11.48227515, -14.677596 ], + [ -14.677596 , 1195.45030439]]), scale=0.0865625, shift=array([3.43109342, 0.97269438])), vector_model=VectorModel(intercepts=array([ 0.19831535, 0.09313108, -0.83564003, -1.46504524, + -2.31136791, -3.11563805, -4.84487237, -12.59469794, + -13.59264665, -13.47998451, -14.80009701, -16.86444915]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=25, candidate_x=array([3.43109342, 0.97269438]), index=25, x=array([3.43109342, 0.97269438]), fval=542.0913678929571, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 16, 20, 21, 22, 24, 25, 26]), old_indices_discarded=array([ 3, 4, 7, 8, 9, 10, 12, 13, 15, 17, 18, 19, 23]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43109342, 0.97269438]), radius=0.04328125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 16, 20, 21, 22, 24, 25, 26, 27]), model=ScalarModel(intercept=457.82639585630363, linear_terms=array([-47.71090329, -78.74164566]), square_terms=array([[ 9.31587254, 2.82729766], + [ 2.82729766, 323.54173297]]), scale=0.04328125, shift=array([3.43109342, 0.97269438])), vector_model=VectorModel(intercepts=array([ 0.19831535, 0.09313108, -0.83564003, -1.46504524, + -2.31136791, -3.11563805, -4.84487237, -12.59469794, + -13.59264665, -13.47998451, -14.80009701, -16.86444915]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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vector_model=VectorModel(intercepts=array([ 0.19831535, 0.09313108, -0.83564003, -1.46504524, + -2.31136791, -3.11563805, -4.84487237, -12.59469794, + -13.59264665, -13.47998451, -14.80009701, -16.86444915]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=25, candidate_x=array([3.43109342, 0.97269438]), index=25, x=array([3.43109342, 0.97269438]), fval=542.0913678929571, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 21, 22, 24, 25, 27, 28]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43109342, 0.97269438]), radius=0.0108203125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 28, 29]), model=ScalarModel(intercept=542.0913678929574, linear_terms=array([ -17.54329943, -487.32084576]), square_terms=array([[ 0.71548366, 19.50465628], + [ 19.50465628, 542.78547871]]), scale=0.0108203125, shift=array([3.43109342, 0.97269438])), vector_model=VectorModel(intercepts=array([ 0.19831535, 0.09313108, -0.83564003, -1.46504524, + -2.31136791, -3.11563805, -4.84487237, -12.59469794, + -13.59264665, -13.47998451, -14.80009701, -16.86444915]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + 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0.97269438]), index=25, x=array([3.43109342, 0.97269438]), fval=542.0913678929571, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 29, 30]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43109342, 0.97269438]), radius=0.002705078125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 30, 31]), model=ScalarModel(intercept=542.0913678929522, linear_terms=array([ 320.8067255 , -309.07107234]), square_terms=array([[ 393.7717483 , -367.10671651], + [-367.10671651, 343.89444956]]), scale=0.002705078125, shift=array([3.43109342, 0.97269438])), vector_model=VectorModel(intercepts=array([ 0.19831535, 0.09313108, -0.83564003, -1.46504524, + -2.31136791, -3.11563805, -4.84487237, -12.59469794, + -13.59264665, -13.47998451, -14.80009701, -16.86444915]), linear_terms=array([[0., 0.], + [0., 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0.97541122]), radius=0.0013525390625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 31, 32]), model=ScalarModel(intercept=529.497519760253, linear_terms=array([-23.33593094, -3.11168899]), square_terms=array([[ 2.08911234, -0.0421325 ], + [-0.0421325 , 0.36323846]]), scale=0.0013525390625, shift=array([3.43141632, 0.97541122])), vector_model=VectorModel(intercepts=array([ 0.19831535, 0.09313108, -0.83564003, -1.46504524, + -2.31136791, -3.11563805, -4.84487237, -12.59469794, + -13.59264665, -13.47998451, -14.80009701, -16.86444915]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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vector_model=VectorModel(intercepts=array([ 0.19831535, 0.09313108, -0.83564003, -1.46504524, + -2.31136791, -3.11563805, -4.84487237, -12.59469794, + -13.59264665, -13.47998451, -14.80009701, -16.86444915]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.34625, shift=array([3.4625, 0.875 ])), candidate_index=34, candidate_x=array([3.43306478, 0.97623899]), index=34, x=array([3.43306478, 0.97623899]), fval=525.7715178355326, rho=0.46959425776937647, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([25, 31, 32, 33]), old_indices_discarded=array([], dtype=int64), step_length=0.0006762695312499009, relative_step_length=0.9999999999998535, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute params change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 35 entries., 'history': {'params': [{'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 3.2062782538015053, 'DiscFac': 0.5681439270619826}, {'CRRA': 3.7693560729380176, 'DiscFac': 0.5931914643258382}, {'CRRA': 3.1556439270619823, 'DiscFac': 0.7823187305648491}, {'CRRA': 3.7693560729380176, 'DiscFac': 0.9758796723452919}, {'CRRA': 3.714766521848815, 'DiscFac': 0.5681439270619826}, {'CRRA': 3.768715210658644, 'DiscFac': 0.5681439270619826}, {'CRRA': 3.1673286626320794, 'DiscFac': 1.1}, {'CRRA': 3.7693560729380176, 'DiscFac': 1.0908579040628261}, {'CRRA': 3.7693560729380176, 'DiscFac': 1.0877513222446453}, {'CRRA': 3.252410164125509, 'DiscFac': 1.1}, {'CRRA': 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old_indices_discarded=array([ 0, 1, 2, 4, 5, 6, 8, 9, 10, 11]), step_length=0.3176052234668127, relative_step_length=0.8385248346023826, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.24778635, 0.99384932]), radius=0.7575332544978632, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 1, 3, 7, 11, 12, 13, 14, 16, 19]), model=ScalarModel(intercept=1209.299534643605, linear_terms=array([-2242.238234 , -2587.12158589]), square_terms=array([[4221.05213969, 3641.36424949], + [3641.36424949, 4071.26438615]]), scale=array([0.67134637, 0.3 ]), shift=array([3.24778635, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.35338954, 0.47806807, -0.22056614, -0.58388504, + -1.16360651, -1.67357266, -3.18317315, -11.54070577, + -12.88671729, -13.03004323, -14.43344133, -16.38334533]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.3787666272489316, shift=array([3.78766627, 0.8916693 ])), candidate_index=19, candidate_x=array([3.24778635, 0.99384932]), index=19, x=array([3.24778635, 0.99384932]), fval=484.1337317628459, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 1, 3, 7, 11, 12, 13, 14, 16, 19]), old_indices_discarded=array([ 0, 2, 4, 5, 6, 8, 9, 10, 15, 17, 18]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.24778635, 0.99384932]), radius=0.3787666272489316, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 13, 14, 15, 18, 19, 20]), model=ScalarModel(intercept=2124.417481740468, linear_terms=array([ -942.92590417, -7426.11137923]), square_terms=array([[ 377.95674671, 1876.28626265], + [ 1876.28626265, 15509.99841478]]), scale=array([0.33567318, 0.22091193]), shift=array([3.24778635, 0.87908807])), vector_model=VectorModel(intercepts=array([ 0.35338954, 0.47806807, -0.22056614, -0.58388504, + -1.16360651, -1.67357266, -3.18317315, -11.54070577, + -12.88671729, -13.03004323, -14.43344133, -16.38334533]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.3787666272489316, shift=array([3.78766627, 0.8916693 ])), candidate_index=21, candidate_x=array([3.34687865, 0.97697048]), index=19, x=array([3.24778635, 0.99384932]), fval=484.1337317628459, rho=-2.284718989594501, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 10, 13, 14, 15, 18, 19, 20]), old_indices_discarded=array([ 0, 1, 2, 4, 5, 6, 8, 9, 11, 12, 16, 17]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.24778635, 0.99384932]), radius=0.1893833136244658, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 13, 14, 18, 19, 20, 21]), model=ScalarModel(intercept=477.7179817875052, linear_terms=array([ -163.3710089 , -1175.98868957]), square_terms=array([[ 127.74286605, 686.46717998], + [ 686.46717998, 6044.38085127]]), scale=array([0.16783659, 0.13699363]), shift=array([3.24778635, 0.96300637])), vector_model=VectorModel(intercepts=array([ 0.35338954, 0.47806807, -0.22056614, -0.58388504, + -1.16360651, -1.67357266, -3.18317315, -11.54070577, + -12.88671729, -13.03004323, -14.43344133, -16.38334533]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.3787666272489316, shift=array([3.78766627, 0.8916693 ])), candidate_index=22, candidate_x=array([3.34830164, 0.9803419 ]), index=19, x=array([3.24778635, 0.99384932]), fval=484.1337317628459, rho=-2.6000951258679503, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 10, 13, 14, 18, 19, 20, 21]), old_indices_discarded=array([ 0, 1, 2, 4, 8, 9, 11, 12, 15, 16, 17]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.24778635, 0.99384932]), radius=0.0946916568122329, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 13, 18, 19, 20, 21, 22]), model=ScalarModel(intercept=374.72280604462765, linear_terms=array([-14.1761097, 29.2396918]), square_terms=array([[ 46.90834448, 284.30156756], + [ 284.30156756, 2940.59930772]]), scale=0.0946916568122329, shift=array([3.24778635, 0.99384932])), vector_model=VectorModel(intercepts=array([ 0.35338954, 0.47806807, -0.22056614, -0.58388504, + -1.16360651, -1.67357266, -3.18317315, -11.54070577, + -12.88671729, -13.03004323, -14.43344133, -16.38334533]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.3787666272489316, shift=array([3.78766627, 0.8916693 ])), candidate_index=23, candidate_x=array([3.34193655, 0.98373742]), index=19, x=array([3.24778635, 0.99384932]), fval=484.1337317628459, rho=-2.5369146064038564, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 10, 13, 18, 19, 20, 21, 22]), old_indices_discarded=array([14, 15]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.24778635, 0.99384932]), radius=0.04734582840611645, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 10, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=441.2649652868354, linear_terms=array([ -2.43397956, -116.38232897]), square_terms=array([[ 4.67788775, 43.93335349], + [ 43.93335349, 1049.51019283]]), scale=0.04734582840611645, shift=array([3.24778635, 0.99384932])), vector_model=VectorModel(intercepts=array([ 0.35338954, 0.47806807, -0.22056614, -0.58388504, + -1.16360651, -1.67357266, -3.18317315, -11.54070577, + -12.88671729, -13.03004323, -14.43344133, -16.38334533]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.3787666272489316, shift=array([3.78766627, 0.8916693 ])), candidate_index=19, candidate_x=array([3.24778635, 0.99384932]), index=19, x=array([3.24778635, 0.99384932]), fval=484.1337317628459, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 10, 19, 20, 21, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.24778635, 0.99384932]), radius=0.023672914203058226, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 20, 21, 22, 23, 24]), model=ScalarModel(intercept=456.6727886617666, linear_terms=array([ -11.56483252, -141.69457592]), square_terms=array([[ 3.5660652 , 34.18551936], + [ 34.18551936, 482.88971038]]), scale=0.023672914203058226, shift=array([3.24778635, 0.99384932])), vector_model=VectorModel(intercepts=array([ 0.35338954, 0.47806807, -0.22056614, -0.58388504, + -1.16360651, -1.67357266, -3.18317315, -11.54070577, + -12.88671729, -13.03004323, -14.43344133, -16.38334533]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.3787666272489316, shift=array([3.78766627, 0.8916693 ])), candidate_index=19, candidate_x=array([3.24778635, 0.99384932]), index=19, x=array([3.24778635, 0.99384932]), fval=484.1337317628459, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([19, 20, 21, 22, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.24778635, 0.99384932]), radius=0.011836457101529113, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 20, 24, 25]), model=ScalarModel(intercept=424.46586880900963, linear_terms=array([ -5.90684488, -111.62112051]), square_terms=array([[ 0.42710782, 7.90575832], + [ 7.90575832, 190.40627925]]), scale=0.011836457101529113, shift=array([3.24778635, 0.99384932])), vector_model=VectorModel(intercepts=array([ 0.35338954, 0.47806807, -0.22056614, -0.58388504, + -1.16360651, -1.67357266, -3.18317315, -11.54070577, + -12.88671729, -13.03004323, -14.43344133, -16.38334533]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.3787666272489316, shift=array([3.78766627, 0.8916693 ])), candidate_index=19, candidate_x=array([3.24778635, 0.99384932]), index=19, x=array([3.24778635, 0.99384932]), fval=484.1337317628459, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([19, 20, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.24778635, 0.99384932]), radius=0.0059182285507645566, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 25, 26]), model=ScalarModel(intercept=484.1337317628462, linear_terms=array([ -16.06805906, -176.33191042]), square_terms=array([[ 1.26390583, 14.22749645], + [ 14.22749645, 169.71406618]]), scale=0.0059182285507645566, shift=array([3.24778635, 0.99384932])), vector_model=VectorModel(intercepts=array([ 0.35338954, 0.47806807, -0.22056614, -0.58388504, + -1.16360651, -1.67357266, -3.18317315, -11.54070577, + -12.88671729, -13.03004323, -14.43344133, -16.38334533]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.3787666272489316, shift=array([3.78766627, 0.8916693 ])), candidate_index=19, candidate_x=array([3.24778635, 0.99384932]), index=19, x=array([3.24778635, 0.99384932]), fval=484.1337317628459, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([19, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.24778635, 0.99384932]), radius=0.0029591142753822783, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 26, 27]), model=ScalarModel(intercept=484.13373176284574, linear_terms=array([ 8.10776251, -113.24175874]), square_terms=array([[ 0.32512291, -4.57313309], + [-4.57313309, 67.6659022 ]]), scale=0.0029591142753822783, shift=array([3.24778635, 0.99384932])), vector_model=VectorModel(intercepts=array([ 0.35338954, 0.47806807, -0.22056614, -0.58388504, + -1.16360651, -1.67357266, -3.18317315, -11.54070577, + -12.88671729, -13.03004323, -14.43344133, -16.38334533]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.3787666272489316, shift=array([3.78766627, 0.8916693 ])), candidate_index=19, candidate_x=array([3.24778635, 0.99384932]), index=19, x=array([3.24778635, 0.99384932]), fval=484.1337317628459, rho=-inf, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([19, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.24778635, 0.99384932]), radius=0.0014795571376911391, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 27, 28]), model=ScalarModel(intercept=484.1337317628462, linear_terms=array([-97.84517191, -89.44417119]), square_terms=array([[47.12079293, 43.46749574], + [43.46749574, 40.77539384]]), scale=0.0014795571376911391, shift=array([3.24778635, 0.99384932])), vector_model=VectorModel(intercepts=array([ 0.35338954, 0.47806807, -0.22056614, -0.58388504, + -1.16360651, -1.67357266, -3.18317315, -11.54070577, + -12.88671729, -13.03004323, -14.43344133, -16.38334533]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.3787666272489316, shift=array([3.78766627, 0.8916693 ])), candidate_index=29, candidate_x=array([3.24925471, 0.99403102]), index=29, x=array([3.24925471, 0.99403102]), fval=483.968970933012, rho=0.0020782488349921106, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([19, 27, 28]), old_indices_discarded=array([], dtype=int64), step_length=0.0014795571376912547, relative_step_length=1.0000000000000782, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.24925471, 0.99403102]), radius=0.0007397785688455696, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 28, 29]), model=ScalarModel(intercept=483.968970933012, linear_terms=array([ 0.5408325 , -5.01774165]), square_terms=array([[ 0.00141837, -0.01343874], + [-0.01343874, 0.31696815]]), scale=0.0007397785688455696, shift=array([3.24925471, 0.99403102])), vector_model=VectorModel(intercepts=array([ 0.35338954, 0.47806807, -0.22056614, -0.58388504, + -1.16360651, -1.67357266, -3.18317315, -11.54070577, + -12.88671729, -13.03004323, -14.43344133, -16.38334533]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.3787666272489316, shift=array([3.78766627, 0.8916693 ])), candidate_index=30, candidate_x=array([3.2492019 , 0.99476891]), index=30, x=array([3.2492019 , 0.99476891]), fval=483.3042437482078, rho=0.1360776516278881, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([19, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=0.0007397785688455717, relative_step_length=1.0000000000000029, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute params change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 31 entries., 'history': {'params': [{'CRRA': 3.787666272489316, 'DiscFac': 0.8916692958468136}, {'CRRA': 3.4522702301564188, 'DiscFac': 0.5559961123158821}, {'CRRA': 4.123339456020248, 'DiscFac': 0.9023086310261734}, {'CRRA': 3.4519930889583845, 'DiscFac': 0.9621907100872018}, {'CRRA': 4.123339456020248, 'DiscFac': 1.089750383978294}, {'CRRA': 4.123339456020248, 'DiscFac': 0.6017993841903007}, {'CRRA': 4.123339456020248, 'DiscFac': 0.5569865537153984}, {'CRRA': 3.4683844729376645, 'DiscFac': 1.1}, {'CRRA': 4.123339456020248, 'DiscFac': 1.045584977176946}, {'CRRA': 4.10451029867194, 'DiscFac': 1.1}, {'CRRA': 3.4519930889583845, 'DiscFac': 1.054282457648922}, {'CRRA': 3.7954582435001956, 'DiscFac': 0.5559961123158821}, {'CRRA': 3.8035398561153393, 'DiscFac': 1.1}, {'CRRA': 3.4519930889583845, 'DiscFac': 0.864627335810008}, {'CRRA': 3.592010414630564, 'DiscFac': 0.894292791173227}, {'CRRA': 3.6888912528562816, 'DiscFac': 0.8878074252872034}, {'CRRA': 3.823561879152503, 'DiscFac': 0.9246859419813435}, {'CRRA': 3.730859920510897, 'DiscFac': 0.947729381130338}, {'CRRA': 3.563023328745431, 'DiscFac': 0.9551359206790517}, {'CRRA': 3.2477863535466858, 'DiscFac': 0.9938493219885415}, {'CRRA': 3.1978636230600257, 'DiscFac': 1.0105906789642098}, {'CRRA': 3.3468786473767884, 'DiscFac': 0.9769704795680376}, {'CRRA': 3.3483016413066986, 'DiscFac': 0.9803419003819871}, {'CRRA': 3.341936549287567, 'DiscFac': 0.9837374224736428}, {'CRRA': 3.2009950294051723, 'DiscFac': 1.001074240849349}, {'CRRA': 3.2710390033805554, 'DiscFac': 0.9991449061243276}, {'CRRA': 3.257841194054621, 'DiscFac': 1.0003218222323884}, {'CRRA': 3.245971813012713, 'DiscFac': 0.9994825157389511}, {'CRRA': 3.248525695788145, 'DiscFac': 0.996714584689371}, {'CRRA': 3.2492547111506704, 'DiscFac': 0.9940310230356313}, {'CRRA': 3.2492019027639434, 'DiscFac': 0.9947689143590757}], 'criterion': [960.8060529870695, 1240.2921504697779, 850.859898892532, 596.4110585596852, nan, 1216.050562892, 1223.9931083911201, nan, nan, nan, nan, 1231.906846531173, nan, 1096.360653117792, 981.7508031032233, 993.7795220807161, 776.1278971480874, 648.6747263507989, 625.5055756735769, 484.1337317628459, nan, 528.5042475772016, 514.6948394166868, 503.03465345715216, nan, nan, nan, nan, nan, 483.968970933012, 483.3042437482078], 'runtime': [0.0, 1.4134274120001464, 1.4513098170000376, 1.4883478880001348, 1.5293199889997595, 1.5646587159999399, 1.618685972000094, 1.649523740999939, 1.6883285939998132, 1.7261191950001376, 1.7632402599997476, 1.8103470779997224, 1.8507548039997346, 3.590426523000133, 4.847393539999757, 6.175072521999937, 7.528106565000144, 8.883646622000015, 10.280463337000128, 11.652836511999794, 13.009232939999947, 14.359365203999914, 15.72169381699996, 17.094576741999845, 18.500274525999885, 19.861051937999946, 21.18866636199982, 22.520675393000147, 23.882492001999708, 25.24515795699972, 26.623506488999737], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]}, 'multistart_info': {...}}], 'exploration_sample': array([[3.4625 , 0.875 ], + [4.64375 , 0.6875 ], + [2.871875, 0.78125 ], + [2.28125 , 1.0625 ]]), 'exploration_results': array([1069.96402275, 1180.14464257, 1201.13045726, 2198.69465259])}}" diff --git a/content/tables/min/WealthPortfolioSub(Labor)Market_estimate_results.csv b/content/tables/min/WealthPortfolioSub(Labor)Market_estimate_results.csv new file mode 100644 index 0000000..9444b84 --- /dev/null +++ b/content/tables/min/WealthPortfolioSub(Labor)Market_estimate_results.csv @@ -0,0 +1,8207 @@ +CRRA,13.76522943668619 +DiscFac,1.0738842444835321 +time_to_estimate,165.19665384292603 +params,"{'CRRA': 13.76522943668619, 'DiscFac': 1.0738842444835321}" +criterion,0.6882579284467486 +start_criterion,28.714580565529207 +start_params,"{'CRRA': 5.0, 'DiscFac': 0.95}" +algorithm,multistart_tranquilo_ls +direction,minimize +n_free,2 +message, +success, +n_criterion_evaluations, +n_derivative_evaluations, +n_iterations, +history,"{'params': [{'CRRA': 13.597593925796534, 'DiscFac': 1.020747282479346}, {'CRRA': 12.392538539955158, 'DiscFac': 0.6174149114063475}, {'CRRA': 14.80264931163791, 'DiscFac': 0.8326677874318185}, {'CRRA': 12.392538539955158, 'DiscFac': 0.8066232391144652}, {'CRRA': 14.771533894047689, 'DiscFac': 1.1}, {'CRRA': 14.775517168591643, 'DiscFac': 0.5}, {'CRRA': 14.468122155990066, 'DiscFac': 0.5}, {'CRRA': 12.392538539955158, 'DiscFac': 1.0736606786321312}, {'CRRA': 14.80264931163791, 'DiscFac': 0.7851437377134408}, {'CRRA': 14.760718893191527, 'DiscFac': 1.1}, {'CRRA': 12.392538539955158, 'DiscFac': 0.9606688675973344}, {'CRRA': 13.444055859501773, 'DiscFac': 0.5}, {'CRRA': 13.68922978340706, 'DiscFac': 1.1}, {'CRRA': 13.800350112056032, 'DiscFac': 1.1}, {'CRRA': 14.653108579352649, 'DiscFac': 1.1}, {'CRRA': 13.733860806509067, 'DiscFac': 1.1}, {'CRRA': 13.533413460280963, 'DiscFac': 1.0879828070365027}, {'CRRA': 13.654507190717343, 'DiscFac': 1.1}, {'CRRA': 13.741852570001186, 'DiscFac': 1.0555947848542355}, {'CRRA': 13.762692131248489, 'DiscFac': 1.0746702605461693}, {'CRRA': 13.838008092863575, 'DiscFac': 1.0616383225721628}, {'CRRA': 13.725034150440946, 'DiscFac': 1.0803527378804203}, {'CRRA': 13.741370045958632, 'DiscFac': 1.0813751657181843}, {'CRRA': 13.772787944089714, 'DiscFac': 1.070788238644506}, {'CRRA': 13.757620359413389, 'DiscFac': 1.076251839651587}, {'CRRA': 13.76522943668619, 'DiscFac': 1.0738842444835321}, {'CRRA': 13.760176305468253, 'DiscFac': 1.0755211705357692}, {'CRRA': 13.762815908535117, 'DiscFac': 1.074994677092113}, {'CRRA': 13.766465318103393, 'DiscFac': 1.073385240068689}, {'CRRA': 13.765741837404423, 'DiscFac': 1.0734198935341484}, {'CRRA': 13.76498652924187, 'DiscFac': 1.0741464913817635}, {'CRRA': 13.765099214247023, 'DiscFac': 1.073987169470037}, {'CRRA': 13.76529772667114, 'DiscFac': 1.073834078766463}, {'CRRA': 13.765261690170846, 'DiscFac': 1.0738581297466943}, {'CRRA': 13.765213205132117, 'DiscFac': 1.0738971702745834}, {'CRRA': 13.765221385235995, 'DiscFac': 1.0738907865004494}, {'CRRA': 13.76523347183466, 'DiscFac': 1.0738809851467088}, {'CRRA': 13.765227417544464, 'DiscFac': 1.0738858722201654}, {'CRRA': 13.76522842842111, 'DiscFac': 1.0738850599644876}, {'CRRA': 13.76523021512763, 'DiscFac': 1.073883616766271}, {'CRRA': 13.765230214507426, 'DiscFac': 1.073883615997389}, {'CRRA': 13.765230214739557, 'DiscFac': 1.0738836162850556}, {'CRRA': 13.765230214887971, 'DiscFac': 1.0738836164690735}, {'CRRA': 13.765228657845949, 'DiscFac': 1.0738848717058849}, {'CRRA': 13.765230214640988, 'DiscFac': 1.0738836161630856}, {'CRRA': 13.765228657644837, 'DiscFac': 1.0738848714563134}, {'CRRA': 13.765230214734727, 'DiscFac': 1.0738836162788903}, {'CRRA': 13.765228658030406, 'DiscFac': 1.0738848719349061}, {'CRRA': 13.765228658584434, 'DiscFac': 1.0738848726220682}, {'CRRA': 13.765228658553019, 'DiscFac': 1.0738848725830974}, {'CRRA': 13.765230216618574, 'DiscFac': 1.0738836186197498}, {'CRRA': 13.765230216717889, 'DiscFac': 1.073883618741515}, {'CRRA': 13.765230216717889, 'DiscFac': 1.073883618741515}, {'CRRA': 13.765230216717889, 'DiscFac': 1.073883618741515}, {'CRRA': 13.765230216717889, 'DiscFac': 1.073883618741515}, {'CRRA': 13.765230216717889, 'DiscFac': 1.073883618741515}, {'CRRA': 13.765230216717889, 'DiscFac': 1.073883618741515}, {'CRRA': 13.765230216717889, 'DiscFac': 1.073883618741515}, {'CRRA': 13.765230216717889, 'DiscFac': 1.073883618741515}, {'CRRA': 13.765230216717889, 'DiscFac': 1.073883618741515}, {'CRRA': 13.765230216717889, 'DiscFac': 1.073883618741515}, {'CRRA': 13.765230216717889, 'DiscFac': 1.073883618741515}, {'CRRA': 13.765230216717889, 'DiscFac': 1.073883618741515}], 'criterion': [1.5919418620501866, 3.256642791598931, 2.4245720174482632, 2.6770326909650377, 1.375271059626373, 3.932115023884988, 3.8900565091396633, 1.3788040584356367, 2.67342734680254, 1.3747875134503535, 1.7273269565534193, 3.784579404744082, 1.3717696200767946, 1.3671976679432574, 1.5932413313061198, 1.7940144542579126, 1.4714362912230357, 1.4586163616235037, 1.4116247061572644, 1.1237511246855783, 1.817413667189244, 1.7640558941311795, 1.2590902699405484, 2.1087642405633775, 1.6806322555078967, 0.6882579284467486, 1.3619794529851408, 1.7708804039433663, 1.487817536704842, 1.6073083741913023, 1.3515606737064714, 1.5988332872617663, 2.1071882633058205, 1.6046549090592486, 2.4392140191496465, 2.0969547685400998, 1.619697166814026, 1.4177027742030972, 2.7375198870821396, 1.648837292228864, 1.2017087559283488, 1.6123653333088765, 2.1559864840618888, 1.6813226057480248, 1.7555339640362284, 1.5641420916511097, 2.121351535271443, 1.195628783451865, 1.366634904655717, 1.7240618907150989, 0.9835036694297299, 1.5576561839970382, 1.389378348605993, 1.323322900002188, 1.4964292964688457, 1.432272428511995, 1.2033755926739262, 3.1657769462342085, 2.197985075310209, 1.2162154879079494, 2.342003085876411, 1.4622856861257032, 1.2041984771527132], 'runtime': [0.0, 2.0959708729997146, 2.319453641000109, 2.530794336999861, 2.7641818269994474, 2.9907601049999357, 3.247736452999561, 3.470891711999684, 3.6835053400000106, 3.9246623110002474, 4.152454041000055, 4.384807880999688, 4.596234226999513, 6.502221419999842, 8.399830567000208, 10.140461093999875, 11.878436920999775, 13.582565646000148, 15.297765229000106, 17.038238051000008, 18.749509088000195, 20.598889396999766, 22.321903571999428, 24.043425724999906, 25.73815736799952, 27.4287797019997, 29.126953945999958, 30.835123793999628, 32.67733082199993, 34.38814910400015, 36.11709095999959, 37.83038476299953, 39.557416933999775, 41.28646993300026, 43.04241953099972, 44.77638568899965, 46.610105842999474, 48.309058674999505, 50.002209946999756, 51.68927304099998, 53.389748636999684, 55.07344832600029, 56.79775497099945, 58.6573964600002, 60.38401259799957, 62.08020242099974, 63.793128000999786, 65.52371880499959, 67.23712577999959, 68.96224358600011, 70.82533353899998, 72.57404010899972, 74.30988537800022, 76.06118913799946, 77.77554541300015, 79.50649890000022, 81.24803584599977, 83.17606934800006, 85.03626549899946, 86.74029938300009, 88.45210760499958, 90.18602291600018, 91.91770858499967], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51]}" +convergence_report,"{'one_step': {'relative_criterion_change': 0.7585906706214464, 'relative_params_change': 0.04559161987110576, 'absolute_criterion_change': 0.5221060435009465, 'absolute_params_change': 0.37522077252389147}, 'five_steps': {'relative_criterion_change': 0.7585906706214464, 'relative_params_change': 0.04559161987110576, 'absolute_criterion_change': 0.5221060435009465, 'absolute_params_change': 0.37522077252389147}}" +multistart_info,"{'start_parameters': [{'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 13.597593925796534, 'DiscFac': 1.020747282479346}], 'local_optima': [Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute params change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 0.02632 0.2707 +relative_params_change 0.0005553 0.0918 +absolute_criterion_change 0.03186 0.3276 +absolute_params_change 0.0006017 1.071 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 0.6327 0.9982 +relative_params_change 0.0007548 0.07704 +absolute_criterion_change 0.4355 0.687 +absolute_params_change 0.002656 1.007 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 14.093749999999998, 'DiscFac': 0.9875}, {'CRRA': 17.6375, 'DiscFac': 1.0250000000000001}, {'CRRA': 16.45625, 'DiscFac': 0.9125000000000001}, {'CRRA': 11.73125, 'DiscFac': 0.7625000000000001}, {'CRRA': 10.549999999999999, 'DiscFac': 0.8}, {'CRRA': 12.9125, 'DiscFac': 0.575}, {'CRRA': 15.274999999999999, 'DiscFac': 0.65}, {'CRRA': 17.046875, 'DiscFac': 0.6312500000000001}, {'CRRA': 18.81875, 'DiscFac': 0.5375}, {'CRRA': 9.368749999999999, 'DiscFac': 0.8375}, {'CRRA': 8.1875, 'DiscFac': 0.7250000000000001}, {'CRRA': 7.00625, 'DiscFac': 0.6125}, {'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 2.871875, 'DiscFac': 0.78125}, {'CRRA': 5.0, 'DiscFac': 0.95}, {'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 2.28125, 'DiscFac': 1.0625}], 'exploration_results': array([ 1.25226984, 1.77139048, 1.79567843, 2.19243348, 2.99563577, + 3.4003551 , 3.46407238, 3.67340234, 3.72448066, 4.28414873, + 4.65250486, 5.39134448, 6.53886763, 14.94020775, 20.34265339, + 25.6087008 , 28.43667398, 29.29813793, 75.84976584])}" +algorithm_output,"{'states': [State(trustregion=Region(center=array([13.59759393, 1.02074728]), radius=1.3597593925796536, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=1.5919418620501866, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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State(trustregion=Region(center=array([13.59759393, 1.02074728]), radius=1.3597593925796536, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=2.034809583725369, linear_terms=array([ 0.00692503, -1.51067031]), square_terms=array([[ 0.14904974, -0.03200334], + [-0.03200334, 1.29590588]]), scale=array([1.20505539, 0.3 ]), shift=array([13.59759393, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 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radius=1.3597593925796536, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 2, 4, 5, 6, 8, 11, 12, 13]), model=ScalarModel(intercept=1.9544299211503704, linear_terms=array([ 0.19985396, -1.35020304]), square_terms=array([[ 0.11438723, -0.28080018], + [-0.28080018, 1.18331746]]), scale=array([1.20505539, 0.3 ]), shift=array([13.80035011, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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model_indices=array([ 0, 2, 4, 6, 9, 11, 12, 13, 14]), model=ScalarModel(intercept=1.9524600277342483, linear_terms=array([ 0.03980169, -1.41316029]), square_terms=array([[ 0.0307801 , -0.03640509], + [-0.03640509, 1.39773428]]), scale=array([0.60252769, 0.3 ]), shift=array([13.80035011, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], 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model=ScalarModel(intercept=1.4423114067995801, linear_terms=array([ 0.00970986, -0.30971613]), square_terms=array([[0.01737096, 0.00617437], + [0.00617437, 0.34251207]]), scale=array([0.30126385, 0.15063192]), shift=array([13.80035011, 0.94936808])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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-0.07753849]), square_terms=array([[0.53597227, 0.0007038 ], + [0.0007038 , 0.05909585]]), scale=array([0.15063192, 0.07531596]), shift=array([13.80035011, 1.02468404])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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scale=array([0.07531596, 0.03765798]), shift=array([13.80035011, 1.06234202])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=19, candidate_x=array([13.76269213, 1.07467026]), index=19, x=array([13.76269213, 1.07467026]), fval=1.123751124685578, rho=1.7655548058481516, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([12, 13, 15, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.0453841295972527, relative_step_length=1.0680508294610014, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76269213, 1.07467026]), radius=0.08498496203622835, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 13, 15, 16, 17, 18, 19]), model=ScalarModel(intercept=1.15503149521224, linear_terms=array([-0.08309304, -0.2101502 ]), square_terms=array([[0.02472817, 0.0671273 ], + [0.0671273 , 0.60172359]]), scale=array([0.07531596, 0.05032285]), shift=array([13.76269213, 1.04967715])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=20, candidate_x=array([13.83800809, 1.06163832]), index=19, x=array([13.76269213, 1.07467026]), fval=1.123751124685578, rho=-12.049711039797472, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 13, 15, 16, 17, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76269213, 1.07467026]), radius=0.042492481018114175, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 13, 15, 17, 18, 19, 20]), model=ScalarModel(intercept=1.3037825390286737, linear_terms=array([ 0.05958201, -0.15298978]), square_terms=array([[ 0.00838945, -0.02709215], + [-0.02709215, 0.33469544]]), scale=array([0.03765798, 0.03149386]), shift=array([13.76269213, 1.06850614])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=21, candidate_x=array([13.72503415, 1.08035274]), index=19, x=array([13.76269213, 1.07467026]), fval=1.123751124685578, rho=-11.530216603469125, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 13, 15, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76269213, 1.07467026]), radius=0.021246240509057088, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([12, 13, 15, 18, 19, 20, 21]), model=ScalarModel(intercept=1.339765491859563, linear_terms=array([ 0.01289748, -0.05831618]), square_terms=array([[ 0.00337072, -0.00852184], + [-0.00852184, 0.15088885]]), scale=0.021246240509057088, shift=array([13.76269213, 1.07467026])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=22, candidate_x=array([13.74137005, 1.08137517]), index=19, x=array([13.76269213, 1.07467026]), fval=1.123751124685578, rho=-6.962948052081567, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([12, 13, 15, 18, 19, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76269213, 1.07467026]), radius=0.010623120254528544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 15, 18, 19, 21, 22]), model=ScalarModel(intercept=1.0959366501510284, linear_terms=array([-0.07603389, 0.0592279 ]), square_terms=array([[ 0.01482588, -0.01887136], + [-0.01887136, 0.05507578]]), scale=0.010623120254528544, shift=array([13.76269213, 1.07467026])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=23, candidate_x=array([13.77278794, 1.07078824]), index=19, x=array([13.76269213, 1.07467026]), fval=1.123751124685578, rho=-12.796215190602183, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([13, 15, 18, 19, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76269213, 1.07467026]), radius=0.005311560127264272, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 22, 23]), model=ScalarModel(intercept=1.123751124685578, linear_terms=array([2.35075615, 6.72344032]), square_terms=array([[ 34.95552465, 104.4822096 ], + [104.4822096 , 312.38317194]]), scale=0.005311560127264272, shift=array([13.76269213, 1.07467026])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=24, candidate_x=array([13.75762036, 1.07625184]), index=19, x=array([13.76269213, 1.07467026]), fval=1.123751124685578, rho=-3.3674374603538753, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([19, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76269213, 1.07467026]), radius=0.002655780063632136, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 23, 24]), model=ScalarModel(intercept=1.1237511246855785, linear_terms=array([-0.5780775 , -1.19841878]), square_terms=array([[ 5.94858558, 17.60275441], + [17.60275441, 52.66397965]]), scale=0.002655780063632136, shift=array([13.76269213, 1.07467026])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=25, candidate_x=array([13.76522944, 1.07388424]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=2.835499899865532, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([19, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.002656264319473295, relative_step_length=1.0001823403405237, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=0.005311560127264272, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 22, 23, 24, 25]), model=ScalarModel(intercept=0.9444375568015315, linear_terms=array([0.44532753, 1.19395003]), square_terms=array([[ 20.99849631, 63.51413818], + [ 63.51413818, 192.14709976]]), scale=0.005311560127264272, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=26, candidate_x=array([13.76017631, 1.07552117]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-13.438176044734822, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([19, 22, 23, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=0.002655780063632136, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 23, 24, 25, 26]), model=ScalarModel(intercept=0.7549074447561732, linear_terms=array([-1.08491772, -3.21566214]), square_terms=array([[ 4.95581197, 13.40310954], + [13.40310954, 36.80550289]]), scale=0.002655780063632136, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=27, candidate_x=array([13.76281591, 1.07499468]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-5.765434623109165, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([19, 23, 24, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=0.001327890031816068, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 25, 26, 27]), model=ScalarModel(intercept=0.6663131578299749, linear_terms=array([0.31579992, 1.29406005]), square_terms=array([[ 0.97652328, 3.33987753], + [ 3.33987753, 11.68276301]]), scale=0.001327890031816068, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=28, candidate_x=array([13.76646532, 1.07338524]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-7.097581043735553, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([19, 25, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=0.000663945015908034, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 25, 27, 28]), model=ScalarModel(intercept=1.0748283427553293, linear_terms=array([0.74775752, 1.86090971]), square_terms=array([[0.94615569, 2.1520877 ], + [2.1520877 , 5.00035967]]), scale=0.000663945015908034, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=30, candidate_x=array([13.76498653, 1.07414649]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-4.057932281360053, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=0.0001659862539770085, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 29, 30]), model=ScalarModel(intercept=0.6882579284467485, linear_terms=array([2.51110852, 2.58551584]), square_terms=array([[17.31961069, 17.86076585], + [17.86076585, 18.4307605 ]]), scale=0.0001659862539770085, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=31, candidate_x=array([13.76509921, 1.07398717]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-4.996089828139207, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 29, 30]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=8.299312698850425e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 30, 31]), model=ScalarModel(intercept=0.6882579284467492, linear_terms=array([-0.83335016, -0.64209979]), square_terms=array([[2.48376422, 2.0304305 ], + [2.0304305 , 1.6811628 ]]), scale=8.299312698850425e-05, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=32, candidate_x=array([13.76529773, 1.07383408]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-8.895409172081964, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=4.1496563494252124e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 31, 32]), model=ScalarModel(intercept=0.6882579284467484, linear_terms=array([5.4261937 , 7.07144682]), square_terms=array([[196.90079522, 251.86367885], + [251.86367885, 322.26054074]]), scale=4.1496563494252124e-05, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=33, candidate_x=array([13.76526169, 1.07385813]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-6.602379228031406, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 31, 32]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=2.0748281747126062e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 32, 33]), model=ScalarModel(intercept=0.6882579284467489, linear_terms=array([-1.67471137, -2.43734694]), square_terms=array([[44.36150417, 58.4429871 ], + [58.4429871 , 77.26404403]]), scale=2.0748281747126062e-05, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=34, candidate_x=array([13.76521321, 1.07389717]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-14.213590995613345, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 32, 33]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1.0374140873563031e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 33, 34]), model=ScalarModel(intercept=0.6882579284467507, linear_terms=array([-32.53614671, -40.36884666]), square_terms=array([[5704.80115467, 7072.67560803], + [7072.67560803, 8768.54976842]]), scale=1.0374140873563031e-05, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], 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radius=5.1870704367815156e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 34, 35]), model=ScalarModel(intercept=0.6882579284467505, linear_terms=array([ 9.69356396, 12.41688657]), square_terms=array([[531.75055055, 673.71090102], + [673.71090102, 853.87114041]]), scale=5.1870704367815156e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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model_indices=array([25, 35, 36]), model=ScalarModel(intercept=0.688257928446749, linear_terms=array([ 96.92131584, 119.52724588]), square_terms=array([[23233.92339938, 28666.34702734], + [28666.34702734, 35368.97768226]]), scale=2.5935352183907578e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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linear_terms=array([-228.72939942, -283.40527822]), square_terms=array([[149814.22087996, 185594.86902726], + [185594.86902726, 229921.14598203]]), scale=1.2967676091953789e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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[253921.58304407, 315109.6517833 ]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=40, candidate_x=array([13.76523021, 1.07388362]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-2.1629857204623297, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 37, 38, 39]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 37, 38, 39, 40]), model=ScalarModel(intercept=1.1405666279545543, linear_terms=array([205.50981035, 254.96369968]), square_terms=array([[196975.02816638, 244290.03299938], + [244290.03299938, 302970.52114327]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=41, candidate_x=array([13.76523021, 1.07388362]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-5.914600339115848, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 37, 38, 39, 40]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 37, 38, 39, 40, 41]), model=ScalarModel(intercept=1.1856833521849348, linear_terms=array([158.29089716, 196.3772247 ]), square_terms=array([[214418.42009996, 265947.86330119], + [265947.86330119, 329860.98150686]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=42, candidate_x=array([13.76523021, 1.07388362]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-17.707333949313302, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 37, 38, 39, 40, 41]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 37, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=1.3049678277178531, linear_terms=array([220.22289196, 273.08372696]), square_terms=array([[208999.40253 , 259169.5243306 ], + [259169.5243306 , 321382.95662051]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + 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old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 37, 38, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=1.3567974599896127, linear_terms=array([202.98250055, 251.79397558]), square_terms=array([[144095.09085198, 178734.35986993], + [178734.35986993, 221700.63899107]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 37, 38, 39, 40, 41, 42, 43, 44]), model=ScalarModel(intercept=1.4152002732237223, linear_terms=array([188.23186675, 233.45474242]), square_terms=array([[136071.15256904, 168774.1945395 ], + [168774.1945395 , 209337.0286982 ]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=45, candidate_x=array([13.76522866, 1.07388487]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-6.369117061581065, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 37, 38, 39, 40, 41, 42, 43, 44]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 38, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=1.4322930669889091, linear_terms=array([165.85163954, 205.76495158]), square_terms=array([[132937.5754333 , 164910.82906923], + [164910.82906923, 204574.07883395]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 39, 40, 41, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=1.3093308313412153, linear_terms=array([123.65989376, 153.3047285 ]), square_terms=array([[323967.17174465, 401840.36181195], + [401840.36181195, 498432.23105826]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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model_indices=array([25, 39, 41, 42, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=1.0590382139587964, linear_terms=array([-280.9809403 , -348.70796313]), square_terms=array([[266181.47955628, 330177.67116074], + [330177.67116074, 409560.10325178]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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model=ScalarModel(intercept=1.220859614346961, linear_terms=array([-138.3649051 , -171.73746763]), square_terms=array([[158556.44632179, 196651.07147605], + [196651.07147605, 243898.29439121]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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-295.51259223]), square_terms=array([[ 74869.56239741, 92919.65993007], + [ 92919.65993007, 115321.54114857]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=52, candidate_x=array([13.76523022, 1.07388362]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-2.1033304004202726, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 39, 42, 43, 44, 45, 47, 49, 50]), old_indices_discarded=array([37, 38, 40, 41, 46, 48, 51]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 39, 42, 43, 44, 45, 47, 49, 50]), model=ScalarModel(intercept=1.2399950339941943, linear_terms=array([-189.67214848, -235.48113278]), square_terms=array([[ 69951.25959547, 86896.16232258], + [ 86896.16232258, 107945.82494064]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 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candidate_index=53, candidate_x=array([13.76523022, 1.07388362]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-1.9051669620622436, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 39, 42, 43, 44, 45, 47, 49, 50]), old_indices_discarded=array([37, 38, 40, 41, 46, 48, 51, 52]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 39, 42, 43, 44, 45, 47, 49, 50]), model=ScalarModel(intercept=1.2399950339941943, linear_terms=array([-189.67214848, -235.48113278]), square_terms=array([[ 69951.25959547, 86896.16232258], + [ 86896.16232258, 107945.82494064]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + 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rho=-1.5453303195996202, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 39, 42, 43, 44, 45, 47, 49, 50]), old_indices_discarded=array([37, 38, 40, 41, 46, 48, 51, 52, 53, 54, 55]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 39, 42, 43, 44, 45, 47, 49, 50]), model=ScalarModel(intercept=1.2399950339941943, linear_terms=array([-189.67214848, -235.48113278]), square_terms=array([[ 69951.25959547, 86896.16232258], + [ 86896.16232258, 107945.82494064]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=57, candidate_x=array([13.76523022, 1.07388362]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-7.432448004507076, accepted=False, 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old_indices_used=array([25, 39, 42, 43, 44, 45, 47, 49, 50]), old_indices_discarded=array([37, 38, 40, 41, 46, 48, 51, 52, 53, 54, 55, 56, 57]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 39, 42, 43, 44, 45, 47, 49, 50]), model=ScalarModel(intercept=1.2399950339941943, linear_terms=array([-189.67214848, -235.48113278]), square_terms=array([[ 69951.25959547, 86896.16232258], + [ 86896.16232258, 107945.82494064]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + 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45, 47, 49, 50]), old_indices_discarded=array([37, 38, 40, 41, 46, 48, 51, 52, 53, 54, 55, 56, 57, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 39, 42, 43, 44, 45, 47, 49, 50]), model=ScalarModel(intercept=1.2399950339941943, linear_terms=array([-189.67214848, -235.48113278]), square_terms=array([[ 69951.25959547, 86896.16232258], + [ 86896.16232258, 107945.82494064]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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old_indices_discarded=array([37, 38, 40, 41, 46, 48, 51, 52, 53, 54, 55, 56, 57, 58, 59]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 39, 42, 43, 44, 45, 47, 49, 50]), model=ScalarModel(intercept=1.2399950339941943, linear_terms=array([-189.67214848, -235.48113278]), square_terms=array([[ 69951.25959547, 86896.16232258], + [ 86896.16232258, 107945.82494064]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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41, 46, 48, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 39, 42, 43, 44, 45, 47, 49, 50]), model=ScalarModel(intercept=1.2399950339941943, linear_terms=array([-189.67214848, -235.48113278]), square_terms=array([[ 69951.25959547, 86896.16232258], + [ 86896.16232258, 107945.82494064]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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57, 58, 59, 60, 61]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 63 entries., 'multistart_info': {'start_parameters': [array([12.321875, 1.08125 ]), array([13.59759393, 1.02074728])], 'local_optima': [{'solution_x': array([13.39207935, 1.03451876]), 'solution_criterion': 1.210363971947695, 'states': [State(trustregion=Region(center=array([12.321875, 1.08125 ]), radius=1.2321875, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=1.5379709637742367, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=1.2321875, shift=array([12.321875, 1.08125 ])), vector_model=VectorModel(intercepts=array([ 0.01938559, 0.01236126, -0.02541336, 0.06675187, 0.19101415, + 0.15552265, 0.10788637, 0.02151982, -0.00180687, -0.01933219, + -0.56991473, -0.81302909, -0.20768953, -0.13461208, -0.09577825, + 0.25047508, 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 2, 4, 5, 6, 8, 9, 13, 14]), model=ScalarModel(intercept=1.8597631456640993, linear_terms=array([-0.16560094, -1.39509672]), square_terms=array([[ 0.25676534, -0.02422231], + [-0.02422231, 1.5320715 ]]), scale=array([1.09199774, 0.3 ]), shift=array([13.41387274, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.01938559, 0.01236126, -0.02541336, 0.06675187, 0.19101415, + 0.15552265, 0.10788637, 0.02151982, -0.00180687, -0.01933219, + -0.56991473, -0.81302909, -0.20768953, -0.13461208, -0.09577825, + 0.25047508, 0.58326817]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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9, 13, 15]), model=ScalarModel(intercept=1.8893357537559765, linear_terms=array([-0.02772496, -1.3695219 ]), square_terms=array([[ 0.04325758, -0.01687608], + [-0.01687608, 1.53625408]]), scale=array([0.54599887, 0.29895768]), shift=array([13.41387274, 0.80104232])), vector_model=VectorModel(intercepts=array([ 0.01938559, 0.01236126, -0.02541336, 0.06675187, 0.19101415, + 0.15552265, 0.10788637, 0.02151982, -0.00180687, -0.01933219, + -0.56991473, -0.81302909, -0.20768953, -0.13461208, -0.09577825, + 0.25047508, 0.58326817]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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linear_terms=array([-0.11965999, -0.40798019]), square_terms=array([[0.13418385, 0.01559018], + [0.01559018, 0.50991394]]), scale=array([0.27299943, 0.16245797]), shift=array([13.41387274, 0.93754203])), vector_model=VectorModel(intercepts=array([ 0.01938559, 0.01236126, -0.02541336, 0.06675187, 0.19101415, + 0.15552265, 0.10788637, 0.02151982, -0.00180687, -0.01933219, + -0.56991473, -0.81302909, -0.20768953, -0.13461208, -0.09577825, + 0.25047508, 0.58326817]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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0.15141401]]), scale=array([0.13649972, 0.09420811]), shift=array([13.41387274, 1.00579189])), vector_model=VectorModel(intercepts=array([ 0.01938559, 0.01236126, -0.02541336, 0.06675187, 0.19101415, + 0.15552265, 0.10788637, 0.02151982, -0.00180687, -0.01933219, + -0.56991473, -0.81302909, -0.20768953, -0.13461208, -0.09577825, + 0.25047508, 0.58326817]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.2321875, shift=array([12.321875, 1.08125 ])), candidate_index=19, candidate_x=array([13.55037246, 1.1 ]), index=13, x=array([13.41387274, 1.0480835 ]), fval=1.3627041528656627, rho=-9.94771316924612, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 2, 4, 5, 8, 9, 13, 16, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.41387274, 1.0480835 ]), radius=0.07701171875, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 4, 8, 9, 13, 18, 19]), model=ScalarModel(intercept=1.3783908568552679, linear_terms=array([ 0.01021357, -0.00822452]), square_terms=array([[ 0.05843199, -0.08341068], + [-0.08341068, 0.18300197]]), scale=array([0.06824986, 0.06008318]), shift=array([13.41387274, 1.03991682])), vector_model=VectorModel(intercepts=array([ 0.01938559, 0.01236126, -0.02541336, 0.06675187, 0.19101415, + 0.15552265, 0.10788637, 0.02151982, -0.00180687, -0.01933219, + -0.56991473, -0.81302909, -0.20768953, -0.13461208, -0.09577825, + 0.25047508, 0.58326817]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.2321875, shift=array([12.321875, 1.08125 ])), candidate_index=20, candidate_x=array([13.39225885, 1.03394448]), index=20, x=array([13.39225885, 1.03394448]), fval=1.2422204164694501, rho=67.64661076310064, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 4, 8, 9, 13, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.025827737562700674, relative_step_length=0.33537412204166234, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.39225885, 1.03394448]), radius=0.07701171875, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 4, 8, 9, 13, 18, 19, 20]), model=ScalarModel(intercept=1.329063512541693, linear_terms=array([ 0.02464634, -0.02431736]), square_terms=array([[ 0.05297532, -0.08788172], + [-0.08788172, 0.22588201]]), scale=array([0.06824986, 0.06715269]), shift=array([13.39225885, 1.03284731])), vector_model=VectorModel(intercepts=array([ 0.01938559, 0.01236126, -0.02541336, 0.06675187, 0.19101415, + 0.15552265, 0.10788637, 0.02151982, -0.00180687, -0.01933219, + -0.56991473, -0.81302909, -0.20768953, -0.13461208, -0.09577825, + 0.25047508, 0.58326817]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.2321875, shift=array([12.321875, 1.08125 ])), candidate_index=21, candidate_x=array([13.33708417, 1.01895545]), index=20, x=array([13.39225885, 1.03394448]), fval=1.2422204164694501, rho=-106.24217111440103, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 4, 8, 9, 13, 18, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.39225885, 1.03394448]), radius=0.038505859375, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 4, 9, 13, 19, 20, 21]), model=ScalarModel(intercept=1.4558785589476007, linear_terms=array([ 0.03199582, -0.13313381]), square_terms=array([[0.00508526, 0.00270481], + [0.00270481, 0.2748186 ]]), scale=0.038505859375, shift=array([13.39225885, 1.03394448])), vector_model=VectorModel(intercepts=array([ 0.01938559, 0.01236126, -0.02541336, 0.06675187, 0.19101415, + 0.15552265, 0.10788637, 0.02151982, -0.00180687, -0.01933219, + -0.56991473, -0.81302909, -0.20768953, -0.13461208, -0.09577825, + 0.25047508, 0.58326817]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.2321875, shift=array([12.321875, 1.08125 ])), candidate_index=22, candidate_x=array([13.3541983 , 1.05118446]), index=20, x=array([13.39225885, 1.03394448]), fval=1.2422204164694501, rho=-6.452870384435016, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 4, 9, 13, 19, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.39225885, 1.03394448]), radius=0.0192529296875, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 9, 13, 20, 21, 22]), model=ScalarModel(intercept=1.4157454760310837, linear_terms=array([-0.03025277, -0.32371426]), square_terms=array([[ 0.03408775, -0.09379796], + [-0.09379796, 0.50863551]]), scale=0.0192529296875, shift=array([13.39225885, 1.03394448])), vector_model=VectorModel(intercepts=array([ 0.01938559, 0.01236126, -0.02541336, 0.06675187, 0.19101415, + 0.15552265, 0.10788637, 0.02151982, -0.00180687, -0.01933219, + -0.56991473, -0.81302909, -0.20768953, -0.13461208, -0.09577825, + 0.25047508, 0.58326817]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.2321875, shift=array([12.321875, 1.08125 ])), candidate_index=23, candidate_x=array([13.4067861, 1.0466706]), index=20, x=array([13.39225885, 1.03394448]), fval=1.2422204164694501, rho=-1.3455197239916337, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 9, 13, 20, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.39225885, 1.03394448]), radius=0.00962646484375, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 9, 13, 20, 22, 23]), model=ScalarModel(intercept=1.2461213821670756, linear_terms=array([-0.01139901, 0.03479926]), square_terms=array([[ 0.01697025, -0.01299824], + [-0.01299824, 0.05399418]]), scale=0.00962646484375, shift=array([13.39225885, 1.03394448])), vector_model=VectorModel(intercepts=array([ 0.01938559, 0.01236126, -0.02541336, 0.06675187, 0.19101415, + 0.15552265, 0.10788637, 0.02151982, -0.00180687, -0.01933219, + -0.56991473, -0.81302909, -0.20768953, -0.13461208, -0.09577825, + 0.25047508, 0.58326817]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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scale=1.2321875, shift=array([12.321875, 1.08125 ])), candidate_index=28, candidate_x=array([13.39207935, 1.03451876]), index=28, x=array([13.39207935, 1.03451876]), fval=1.210363971947695, rho=1.3634463837141155, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([20, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0006016745527352553, relative_step_length=1.0000340727379582, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute params change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 29 entries., 'history': {'params': [{'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 11.446536902444704, 'DiscFac': 0.5}, {'CRRA': 13.413872739706319, 'DiscFac': 0.5007652721540578}, {'CRRA': 11.229877260293678, 'DiscFac': 0.59273111228441}, {'CRRA': 13.294070386710855, 'DiscFac': 1.1}, {'CRRA': 13.413872739706319, 'DiscFac': 0.5}, {'CRRA': 12.446098642928627, 'DiscFac': 0.5}, {'CRRA': 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0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=14, candidate_x=array([14.65310858, 1.1 ]), index=13, x=array([13.80035011, 1.1 ]), fval=1.3671976679432574, rho=-7.892356930659339, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 2, 4, 5, 6, 8, 11, 12, 13]), old_indices_discarded=array([ 1, 3, 7, 9, 10]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.80035011, 1.1 ]), radius=0.6798796962898268, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 2, 4, 6, 9, 11, 12, 13, 14]), model=ScalarModel(intercept=1.9524600277342483, linear_terms=array([ 0.03980169, -1.41316029]), square_terms=array([[ 0.0307801 , -0.03640509], + [-0.03640509, 1.39773428]]), scale=array([0.60252769, 0.3 ]), shift=array([13.80035011, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], 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old_indices_discarded=array([ 1, 3, 5, 7, 8, 10]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.80035011, 1.1 ]), radius=0.3399398481449134, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 4, 6, 9, 11, 12, 13, 14, 15]), model=ScalarModel(intercept=1.4423114067995801, linear_terms=array([ 0.00970986, -0.30971613]), square_terms=array([[0.01737096, 0.00617437], + [0.00617437, 0.34251207]]), scale=array([0.30126385, 0.15063192]), shift=array([13.80035011, 0.94936808])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.80035011, 1.1 ]), radius=0.1699699240724567, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 11, 12, 13, 15, 16]), model=ScalarModel(intercept=1.5943095812154688, linear_terms=array([ 0.51822844, -0.07753849]), square_terms=array([[0.53597227, 0.0007038 ], + [0.0007038 , 0.05909585]]), scale=array([0.15063192, 0.07531596]), shift=array([13.80035011, 1.02468404])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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State(trustregion=Region(center=array([13.80035011, 1.1 ]), radius=0.08498496203622835, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 13, 15, 16, 17]), model=ScalarModel(intercept=1.2630637427538014, linear_terms=array([0.0673167 , 0.12201243]), square_terms=array([[0.06493105, 0.0942397 ], + [0.0942397 , 0.27245921]]), scale=array([0.07531596, 0.03765798]), shift=array([13.80035011, 1.06234202])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([12, 13, 15, 17, 18]), model=ScalarModel(intercept=1.494530967111558, linear_terms=array([0.102457 , 0.02803027]), square_terms=array([[0.03812849, 0.01170315], + [0.01170315, 0.0472904 ]]), scale=array([0.03765798, 0.01882899]), shift=array([13.80035011, 1.08117101])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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model_indices=array([ 0, 12, 13, 15, 16, 17, 18, 19]), model=ScalarModel(intercept=1.15503149521224, linear_terms=array([-0.08309304, -0.2101502 ]), square_terms=array([[0.02472817, 0.0671273 ], + [0.0671273 , 0.60172359]]), scale=array([0.07531596, 0.05032285]), shift=array([13.76269213, 1.04967715])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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model=ScalarModel(intercept=1.3037825390286737, linear_terms=array([ 0.05958201, -0.15298978]), square_terms=array([[ 0.00838945, -0.02709215], + [-0.02709215, 0.33469544]]), scale=array([0.03765798, 0.03149386]), shift=array([13.76269213, 1.06850614])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=21, candidate_x=array([13.72503415, 1.08035274]), index=19, x=array([13.76269213, 1.07467026]), fval=1.123751124685578, rho=-11.530216603469125, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 13, 15, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76269213, 1.07467026]), radius=0.021246240509057088, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([12, 13, 15, 18, 19, 20, 21]), model=ScalarModel(intercept=1.339765491859563, linear_terms=array([ 0.01289748, -0.05831618]), square_terms=array([[ 0.00337072, -0.00852184], + [-0.00852184, 0.15088885]]), scale=0.021246240509057088, shift=array([13.76269213, 1.07467026])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=22, candidate_x=array([13.74137005, 1.08137517]), index=19, x=array([13.76269213, 1.07467026]), fval=1.123751124685578, rho=-6.962948052081567, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([12, 13, 15, 18, 19, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76269213, 1.07467026]), radius=0.010623120254528544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 15, 18, 19, 21, 22]), model=ScalarModel(intercept=1.0959366501510284, linear_terms=array([-0.07603389, 0.0592279 ]), square_terms=array([[ 0.01482588, -0.01887136], + [-0.01887136, 0.05507578]]), scale=0.010623120254528544, shift=array([13.76269213, 1.07467026])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=23, candidate_x=array([13.77278794, 1.07078824]), index=19, x=array([13.76269213, 1.07467026]), fval=1.123751124685578, rho=-12.796215190602183, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([13, 15, 18, 19, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76269213, 1.07467026]), radius=0.005311560127264272, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 22, 23]), model=ScalarModel(intercept=1.123751124685578, linear_terms=array([2.35075615, 6.72344032]), square_terms=array([[ 34.95552465, 104.4822096 ], + [104.4822096 , 312.38317194]]), scale=0.005311560127264272, shift=array([13.76269213, 1.07467026])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=24, candidate_x=array([13.75762036, 1.07625184]), index=19, x=array([13.76269213, 1.07467026]), fval=1.123751124685578, rho=-3.3674374603538753, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([19, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76269213, 1.07467026]), radius=0.002655780063632136, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 23, 24]), model=ScalarModel(intercept=1.1237511246855785, linear_terms=array([-0.5780775 , -1.19841878]), square_terms=array([[ 5.94858558, 17.60275441], + [17.60275441, 52.66397965]]), scale=0.002655780063632136, shift=array([13.76269213, 1.07467026])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=25, candidate_x=array([13.76522944, 1.07388424]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=2.835499899865532, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([19, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.002656264319473295, relative_step_length=1.0001823403405237, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=0.005311560127264272, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 22, 23, 24, 25]), model=ScalarModel(intercept=0.9444375568015315, linear_terms=array([0.44532753, 1.19395003]), square_terms=array([[ 20.99849631, 63.51413818], + [ 63.51413818, 192.14709976]]), scale=0.005311560127264272, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=26, candidate_x=array([13.76017631, 1.07552117]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-13.438176044734822, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([19, 22, 23, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=0.002655780063632136, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 23, 24, 25, 26]), model=ScalarModel(intercept=0.7549074447561732, linear_terms=array([-1.08491772, -3.21566214]), square_terms=array([[ 4.95581197, 13.40310954], + [13.40310954, 36.80550289]]), scale=0.002655780063632136, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=27, candidate_x=array([13.76281591, 1.07499468]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-5.765434623109165, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([19, 23, 24, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=0.001327890031816068, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 25, 26, 27]), model=ScalarModel(intercept=0.6663131578299749, linear_terms=array([0.31579992, 1.29406005]), square_terms=array([[ 0.97652328, 3.33987753], + [ 3.33987753, 11.68276301]]), scale=0.001327890031816068, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=28, candidate_x=array([13.76646532, 1.07338524]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-7.097581043735553, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([19, 25, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=0.000663945015908034, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([19, 25, 27, 28]), model=ScalarModel(intercept=1.0748283427553293, linear_terms=array([0.74775752, 1.86090971]), square_terms=array([[0.94615569, 2.1520877 ], + [2.1520877 , 5.00035967]]), scale=0.000663945015908034, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 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radius=0.000331972507954017, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 28, 29]), model=ScalarModel(intercept=0.688257928446749, linear_terms=array([-0.05009521, -0.4261496 ]), square_terms=array([[0.05474491, 0.13563449], + [0.13563449, 0.64186031]]), scale=0.000331972507954017, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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30]), model=ScalarModel(intercept=0.6882579284467485, linear_terms=array([2.51110852, 2.58551584]), square_terms=array([[17.31961069, 17.86076585], + [17.86076585, 18.4307605 ]]), scale=0.0001659862539770085, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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square_terms=array([[2.48376422, 2.0304305 ], + [2.0304305 , 1.6811628 ]]), scale=8.299312698850425e-05, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=33, candidate_x=array([13.76526169, 1.07385813]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-6.602379228031406, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 31, 32]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=2.0748281747126062e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 32, 33]), model=ScalarModel(intercept=0.6882579284467489, linear_terms=array([-1.67471137, -2.43734694]), square_terms=array([[44.36150417, 58.4429871 ], + [58.4429871 , 77.26404403]]), scale=2.0748281747126062e-05, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=34, candidate_x=array([13.76521321, 1.07389717]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-14.213590995613345, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 32, 33]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1.0374140873563031e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 33, 34]), model=ScalarModel(intercept=0.6882579284467507, linear_terms=array([-32.53614671, -40.36884666]), square_terms=array([[5704.80115467, 7072.67560803], + [7072.67560803, 8768.54976842]]), scale=1.0374140873563031e-05, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=35, candidate_x=array([13.76522139, 1.07389079]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-12.888292610975698, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 33, 34]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=5.1870704367815156e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 34, 35]), model=ScalarModel(intercept=0.6882579284467505, linear_terms=array([ 9.69356396, 12.41688657]), square_terms=array([[531.75055055, 673.71090102], + [673.71090102, 853.87114041]]), scale=5.1870704367815156e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=36, candidate_x=array([13.76523347, 1.07388099]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-8.167753639507541, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 34, 35]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=2.5935352183907578e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 35, 36]), model=ScalarModel(intercept=0.688257928446749, linear_terms=array([ 96.92131584, 119.52724588]), square_terms=array([[23233.92339938, 28666.34702734], + [28666.34702734, 35368.97768226]]), scale=2.5935352183907578e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1.2967676091953789e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 36, 37]), model=ScalarModel(intercept=0.6882579284467522, linear_terms=array([-228.72939942, -283.40527822]), square_terms=array([[149814.22087996, 185594.86902726], + [185594.86902726, 229921.14598203]]), scale=1.2967676091953789e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 37, 38]), model=ScalarModel(intercept=0.6882579284479667, linear_terms=array([110.01063583, 136.7138673 ]), square_terms=array([[204615.15154828, 253921.58304407], + [253921.58304407, 315109.6517833 ]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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upper=array([20. , 1.1]))), model_indices=array([25, 37, 38, 39]), model=ScalarModel(intercept=1.1577741715871368, linear_terms=array([242.70494131, 301.06849961]), square_terms=array([[155662.83189264, 193036.2230012 ], + [193036.2230012 , 239382.71308459]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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linear_terms=array([205.50981035, 254.96369968]), square_terms=array([[196975.02816638, 244290.03299938], + [244290.03299938, 302970.52114327]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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[265947.86330119, 329860.98150686]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=43, candidate_x=array([13.76522866, 1.07388487]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-8.647410683350707, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 37, 38, 39, 40, 41, 42]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 37, 38, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=1.3567974599896127, linear_terms=array([202.98250055, 251.79397558]), square_terms=array([[144095.09085198, 178734.35986993], + [178734.35986993, 221700.63899107]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, 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candidate_x=array([13.76523021, 1.07388362]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-7.117835688525804, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 37, 38, 39, 40, 41, 42, 43]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 37, 38, 39, 40, 41, 42, 43, 44]), model=ScalarModel(intercept=1.4152002732237223, linear_terms=array([188.23186675, 233.45474242]), square_terms=array([[136071.15256904, 168774.1945395 ], + [168774.1945395 , 209337.0286982 ]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=45, candidate_x=array([13.76522866, 1.07388487]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-6.369117061581065, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 37, 38, 39, 40, 41, 42, 43, 44]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 38, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=1.4322930669889091, linear_terms=array([165.85163954, 205.76495158]), square_terms=array([[132937.5754333 , 164910.82906923], + [164910.82906923, 204574.07883395]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=46, candidate_x=array([13.76523021, 1.07388362]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-12.178849532771347, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 38, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([37]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 39, 40, 41, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=1.3093308313412153, linear_terms=array([123.65989376, 153.3047285 ]), square_terms=array([[323967.17174465, 401840.36181195], + [401840.36181195, 498432.23105826]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + 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43, 44, 45, 46]), old_indices_discarded=array([37, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 39, 41, 42, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=1.0590382139587964, linear_terms=array([-280.9809403 , -348.70796313]), square_terms=array([[266181.47955628, 330177.67116074], + [330177.67116074, 409560.10325178]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=48, candidate_x=array([13.76522866, 1.07388487]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-2.8012851877774967, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 39, 41, 42, 43, 44, 45, 46, 47]), old_indices_discarded=array([37, 38, 40]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 39, 41, 42, 43, 44, 45, 47, 48]), model=ScalarModel(intercept=1.220859614346961, linear_terms=array([-138.3649051 , -171.73746763]), square_terms=array([[158556.44632179, 196651.07147605], + [196651.07147605, 243898.29439121]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=49, candidate_x=array([13.76522866, 1.07388487]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-7.513233363014608, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 39, 41, 42, 43, 44, 45, 47, 48]), old_indices_discarded=array([37, 38, 40, 46]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 39, 42, 43, 44, 45, 47, 48, 49]), model=ScalarModel(intercept=1.2282682420444786, linear_terms=array([-238.11010807, -295.51259223]), square_terms=array([[ 74869.56239741, 92919.65993007], + [ 92919.65993007, 115321.54114857]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=50, candidate_x=array([13.76523022, 1.07388362]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-0.834182990182004, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 39, 42, 43, 44, 45, 47, 48, 49]), old_indices_discarded=array([37, 38, 40, 41, 46]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 39, 42, 43, 44, 45, 47, 49, 50]), model=ScalarModel(intercept=1.2399950339941943, linear_terms=array([-189.67214848, -235.48113278]), square_terms=array([[ 69951.25959547, 86896.16232258], + [ 86896.16232258, 107945.82494064]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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model_indices=array([25, 39, 42, 43, 44, 45, 47, 49, 50]), model=ScalarModel(intercept=1.2399950339941943, linear_terms=array([-189.67214848, -235.48113278]), square_terms=array([[ 69951.25959547, 86896.16232258], + [ 86896.16232258, 107945.82494064]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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model=ScalarModel(intercept=1.2399950339941943, linear_terms=array([-189.67214848, -235.48113278]), square_terms=array([[ 69951.25959547, 86896.16232258], + [ 86896.16232258, 107945.82494064]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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linear_terms=array([-189.67214848, -235.48113278]), square_terms=array([[ 69951.25959547, 86896.16232258], + [ 86896.16232258, 107945.82494064]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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square_terms=array([[ 69951.25959547, 86896.16232258], + [ 86896.16232258, 107945.82494064]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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86896.16232258, 107945.82494064]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=57, candidate_x=array([13.76523022, 1.07388362]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-7.432448004507076, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 39, 42, 43, 44, 45, 47, 49, 50]), old_indices_discarded=array([37, 38, 40, 41, 46, 48, 51, 52, 53, 54, 55, 56]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 39, 42, 43, 44, 45, 47, 49, 50]), model=ScalarModel(intercept=1.2399950339941943, linear_terms=array([-189.67214848, -235.48113278]), square_terms=array([[ 69951.25959547, 86896.16232258], + [ 86896.16232258, 107945.82494064]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=58, candidate_x=array([13.76523022, 1.07388362]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-4.5291149894285505, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 39, 42, 43, 44, 45, 47, 49, 50]), old_indices_discarded=array([37, 38, 40, 41, 46, 48, 51, 52, 53, 54, 55, 56, 57]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 39, 42, 43, 44, 45, 47, 49, 50]), model=ScalarModel(intercept=1.2399950339941943, linear_terms=array([-189.67214848, -235.48113278]), square_terms=array([[ 69951.25959547, 86896.16232258], + [ 86896.16232258, 107945.82494064]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=59, candidate_x=array([13.76523022, 1.07388362]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-1.5838494401492669, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 39, 42, 43, 44, 45, 47, 49, 50]), old_indices_discarded=array([37, 38, 40, 41, 46, 48, 51, 52, 53, 54, 55, 56, 57, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([13.76522944, 1.07388424]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 39, 42, 43, 44, 45, 47, 49, 50]), model=ScalarModel(intercept=1.2399950339941943, linear_terms=array([-189.67214848, -235.48113278]), square_terms=array([[ 69951.25959547, 86896.16232258], + [ 86896.16232258, 107945.82494064]]), scale=1e-06, shift=array([13.76522944, 1.07388424])), vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), 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vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), 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vector_model=VectorModel(intercepts=array([ 0.02005503, 0.0186307 , -0.03444429, -0.01075204, 0.03631109, + -0.01858382, -0.035244 , -0.23998362, -0.32929702, -0.43943744, + -0.66654897, -0.8036659 , -0.15079765, -0.04159839, 0.02704146, + 0.12955665, 0.30935043]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.3597593925796536, shift=array([13.59759393, 1.02074728])), candidate_index=62, candidate_x=array([13.76523022, 1.07388362]), index=25, x=array([13.76522944, 1.07388424]), fval=0.6882579284467486, rho=-1.5477989368164367, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 39, 42, 43, 44, 45, 47, 49, 50]), old_indices_discarded=array([37, 38, 40, 41, 46, 48, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'message': None, 'tranquilo_history': History for least_squares function with 63 entries., 'history': {'params': [{'CRRA': 13.597593925796534, 'DiscFac': 1.020747282479346}, {'CRRA': 12.392538539955158, 'DiscFac': 0.6174149114063475}, {'CRRA': 14.80264931163791, 'DiscFac': 0.8326677874318185}, {'CRRA': 12.392538539955158, 'DiscFac': 0.8066232391144652}, {'CRRA': 14.771533894047689, 'DiscFac': 1.1}, {'CRRA': 14.775517168591643, 'DiscFac': 0.5}, {'CRRA': 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'DiscFac': 1.073883618741515}, {'CRRA': 13.765230216717889, 'DiscFac': 1.073883618741515}, {'CRRA': 13.765230216717889, 'DiscFac': 1.073883618741515}, {'CRRA': 13.765230216717889, 'DiscFac': 1.073883618741515}, {'CRRA': 13.765230216717889, 'DiscFac': 1.073883618741515}, {'CRRA': 13.765230216717889, 'DiscFac': 1.073883618741515}], 'criterion': [1.5919418620501866, 3.256642791598931, 2.4245720174482632, 2.6770326909650377, 1.375271059626373, 3.932115023884988, 3.8900565091396633, 1.3788040584356367, 2.67342734680254, 1.3747875134503535, 1.7273269565534193, 3.784579404744082, 1.3717696200767946, 1.3671976679432574, 1.5932413313061198, 1.7940144542579126, 1.4714362912230357, 1.4586163616235037, 1.4116247061572644, 1.1237511246855783, 1.817413667189244, 1.7640558941311795, 1.2590902699405484, 2.1087642405633775, 1.6806322555078967, 0.6882579284467486, 1.3619794529851408, 1.7708804039433663, 1.487817536704842, 1.6073083741913023, 1.3515606737064714, 1.5988332872617663, 2.1071882633058205, 1.6046549090592486, 2.4392140191496465, 2.0969547685400998, 1.619697166814026, 1.4177027742030972, 2.7375198870821396, 1.648837292228864, 1.2017087559283488, 1.6123653333088765, 2.1559864840618888, 1.6813226057480248, 1.7555339640362284, 1.5641420916511097, 2.121351535271443, 1.195628783451865, 1.366634904655717, 1.7240618907150989, 0.9835036694297299, 1.5576561839970382, 1.389378348605993, 1.323322900002188, 1.4964292964688457, 1.432272428511995, 1.2033755926739262, 3.1657769462342085, 2.197985075310209, 1.2162154879079494, 2.342003085876411, 1.4622856861257032, 1.2041984771527132], 'runtime': [0.0, 2.0959708729997146, 2.319453641000109, 2.530794336999861, 2.7641818269994474, 2.9907601049999357, 3.247736452999561, 3.470891711999684, 3.6835053400000106, 3.9246623110002474, 4.152454041000055, 4.384807880999688, 4.596234226999513, 6.502221419999842, 8.399830567000208, 10.140461093999875, 11.878436920999775, 13.582565646000148, 15.297765229000106, 17.038238051000008, 18.749509088000195, 20.598889396999766, 22.321903571999428, 24.043425724999906, 25.73815736799952, 27.4287797019997, 29.126953945999958, 30.835123793999628, 32.67733082199993, 34.38814910400015, 36.11709095999959, 37.83038476299953, 39.557416933999775, 41.28646993300026, 43.04241953099972, 44.77638568899965, 46.610105842999474, 48.309058674999505, 50.002209946999756, 51.68927304099998, 53.389748636999684, 55.07344832600029, 56.79775497099945, 58.6573964600002, 60.38401259799957, 62.08020242099974, 63.793128000999786, 65.52371880499959, 67.23712577999959, 68.96224358600011, 70.82533353899998, 72.57404010899972, 74.30988537800022, 76.06118913799946, 77.77554541300015, 79.50649890000022, 81.24803584599977, 83.17606934800006, 85.03626549899946, 86.74029938300009, 88.45210760499958, 90.18602291600018, 91.91770858499967], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51]}, 'multistart_info': {...}}], 'exploration_sample': array([[12.321875, 1.08125 ], + [14.09375 , 0.9875 ], + [17.6375 , 1.025 ], + [16.45625 , 0.9125 ], + [11.73125 , 0.7625 ], + [10.55 , 0.8 ], + [12.9125 , 0.575 ], + [15.275 , 0.65 ], + [17.046875, 0.63125 ], + [18.81875 , 0.5375 ], + [ 9.36875 , 0.8375 ], + [ 8.1875 , 0.725 ], + [ 7.00625 , 0.6125 ], + [ 5.825 , 0.95 ], + [ 4.64375 , 0.6875 ], + [ 2.871875, 0.78125 ], + [ 5. , 0.95 ], + [ 3.4625 , 0.875 ], + [ 2.28125 , 1.0625 ]]), 'exploration_results': array([ 1.25226984, 1.77139048, 1.79567843, 2.19243348, 2.99563577, + 3.4003551 , 3.46407238, 3.67340234, 3.72448066, 4.28414873, + 4.65250486, 5.39134448, 6.53886763, 14.94020775, 20.34265339, + 25.6087008 , 28.43667398, 29.29813793, 75.84976584])}}" diff --git a/content/tables/min/WealthPortfolioSub(Stock)(Labor)Market_estimate_results.csv b/content/tables/min/WealthPortfolioSub(Stock)(Labor)Market_estimate_results.csv new file mode 100644 index 0000000..5cae110 --- /dev/null +++ b/content/tables/min/WealthPortfolioSub(Stock)(Labor)Market_estimate_results.csv @@ -0,0 +1,6021 @@ +CRRA,5.573894562325964 +DiscFac,1.0637390075406437 +time_to_estimate,235.58063197135925 +params,"{'CRRA': 5.573894562325964, 'DiscFac': 1.0637390075406437}" +criterion,1.4220519178994522 +start_criterion,3.7528915666217584 +start_params,"{'CRRA': 5.0, 'DiscFac': 0.95}" +algorithm,multistart_tranquilo_ls +direction,minimize +n_free,2 +message,Absolute criterion change smaller than tolerance. +success, +n_criterion_evaluations, +n_derivative_evaluations, +n_iterations, +history,"{'params': [{'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 5.3827721337329075, 'DiscFac': 0.5}, {'CRRA': 6.341227184076231, 'DiscFac': 0.9843397863096757}, {'CRRA': 5.308772815923768, 'DiscFac': 0.9942847020759833}, {'CRRA': 6.341227184076231, 'DiscFac': 1.099329565298806}, {'CRRA': 6.341227184076231, 'DiscFac': 0.7325487756828386}, {'CRRA': 6.341227184076231, 'DiscFac': 0.661184756844424}, {'CRRA': 5.670357912186334, 'DiscFac': 1.1}, {'CRRA': 6.341227184076231, 'DiscFac': 1.0027250193492325}, {'CRRA': 6.341227184076231, 'DiscFac': 1.0927982791010287}, {'CRRA': 5.308772815923768, 'DiscFac': 1.0118564969416792}, {'CRRA': 5.957814024190954, 'DiscFac': 0.5}, {'CRRA': 6.114808484578101, 'DiscFac': 1.1}, {'CRRA': 5.793348226117784, 'DiscFac': 0.8284226490669568}, {'CRRA': 5.75003997251512, 'DiscFac': 0.8880821392041797}, {'CRRA': 5.693161284486315, 'DiscFac': 1.0118481504711487}, {'CRRA': 5.569384429577121, 'DiscFac': 1.0922008146955873}, {'CRRA': 5.5194785395513035, 'DiscFac': 1.0658390858610285}, {'CRRA': 5.600878088644884, 'DiscFac': 1.1}, {'CRRA': 5.77759213158942, 'DiscFac': 1.0321931747072446}, {'CRRA': 5.545693184498602, 'DiscFac': 1.055901773046547}, {'CRRA': 5.454950141541775, 'DiscFac': 1.0580103701909507}, {'CRRA': 5.551742738556068, 'DiscFac': 1.0610052791857207}, {'CRRA': 5.569955078014674, 'DiscFac': 1.0653960027788554}, {'CRRA': 5.560809192249429, 'DiscFac': 1.0668198165422604}, {'CRRA': 5.574179063160669, 'DiscFac': 1.0637026157965719}, {'CRRA': 5.565030737518938, 'DiscFac': 1.0667766565553518}, {'CRRA': 5.578730259698591, 'DiscFac': 1.0636435394860717}, {'CRRA': 5.571905522822675, 'DiscFac': 1.0633495597956906}, {'CRRA': 5.575317879327959, 'DiscFac': 1.063494000480499}, {'CRRA': 5.574535066002697, 'DiscFac': 1.0641709732701494}, {'CRRA': 5.573894562325964, 'DiscFac': 1.0637390075406437}], 'criterion': [3.0263314834387383, 4.314184027187926, nan, 2.6142787419100317, nan, 4.730311355914472, 4.931838606867419, 1.9543423789678298, nan, nan, 2.1984626089974055, 4.789253019202322, nan, 3.940933836887815, 3.4612490178599495, 2.070351234692411, 1.7490483015806304, 1.4289096777146673, 1.9829352389581485, 1.6954301219950216, 1.4443085463640908, 1.4384944542770688, 1.4260383541426693, 1.42426971311261, 1.427741913899455, 1.4220984404550594, 1.427437660317148, 1.422217891677385, 1.4225162577343387, 1.4221438433511406, 1.4221892061726984, 1.4220519178994522], 'runtime': [0.0, 2.075601254000503, 2.3004396590004035, 2.512775510999745, 2.727538692000053, 3.093790787999751, 3.3206908180000028, 3.5342638620004436, 3.778585648000444, 4.001509642000201, 4.260968579999826, 4.507971130000442, 4.722957699999824, 6.59312373099965, 8.327218147000167, 10.04041867900014, 11.774315939999724, 13.478361817000405, 15.181451481000295, 17.012758128000314, 18.715239236000343, 20.42677367000033, 22.141106265000417, 23.86589965300027, 25.620126351999716, 27.356191783999748, 29.185643632999927, 30.88528100500025, 32.59903886000029, 34.286631453000155, 35.98912276600004, 37.70891717300037], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]}" +convergence_report, +multistart_info,"{'start_parameters': [{'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 7.5503322732065214, 'DiscFac': 1.0688678584866826}], 'local_optima': [Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance., Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Maximum number of criterion evaluations reached.], 'exploration_sample': [{'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 5.0, 'DiscFac': 0.95}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 14.093749999999998, 'DiscFac': 0.9875}, {'CRRA': 9.368749999999999, 'DiscFac': 0.8375}, {'CRRA': 17.6375, 'DiscFac': 1.0250000000000001}, {'CRRA': 8.1875, 'DiscFac': 0.7250000000000001}, {'CRRA': 10.549999999999999, 'DiscFac': 0.8}, {'CRRA': 16.45625, 'DiscFac': 0.9125000000000001}, {'CRRA': 7.00625, 'DiscFac': 0.6125}, {'CRRA': 11.73125, 'DiscFac': 0.7625000000000001}, {'CRRA': 15.274999999999999, 'DiscFac': 0.65}, {'CRRA': 12.9125, 'DiscFac': 0.575}, {'CRRA': 17.046875, 'DiscFac': 0.6312500000000001}, {'CRRA': 18.81875, 'DiscFac': 0.5375}, {'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 2.28125, 'DiscFac': 1.0625}, {'CRRA': 2.871875, 'DiscFac': 0.78125}], 'exploration_results': array([ 3.02633148, 3.30050278, 3.74724591, 3.92461378, 4.45534719, + 4.52159416, 4.77507862, 5.06340436, 5.16761845, 5.42393003, + 5.50914405, 5.69522002, 6.68407291, 6.73863805, 6.84560996, + 7.20052264, 7.72130492, 8.47185488, 10.67451262])}" +algorithm_output,"{'states': [State(trustregion=Region(center=array([5.825, 0.95 ]), radius=0.5824999999999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=3.0263314834387383, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=0, candidate_x=array([5.825, 0.95 ]), index=0, x=array([5.825, 0.95 ]), fval=3.0263314834387383, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([5.825, 0.95 ]), radius=0.5824999999999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=1.1269978152790263, linear_terms=array([-0.51127322, -0.63965657]), square_terms=array([[11.76668356, 13.01143564], + [13.01143564, 15.17207492]]), scale=array([0.51622718, 0.3 ]), shift=array([5.825, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.825, 0.95 ]), radius=0.29124999999999995, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 2, 3, 7, 8, 10, 11, 12, 13]), model=ScalarModel(intercept=1.581609097434704, linear_terms=array([2.65278709, 2.28940689]), square_terms=array([[8.20115491, 7.03588261], + [7.03588261, 6.38777128]]), scale=array([0.25811359, 0.2040568 ]), shift=array([5.825 , 0.8959432])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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0.95 ]), radius=0.14562499999999998, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 2, 3, 7, 8, 11, 12, 13, 14]), model=ScalarModel(intercept=1.6064049690312896, linear_terms=array([1.823518 , 1.65703042]), square_terms=array([[3.57574989, 3.17459736], + [3.17459736, 3.01964201]]), scale=0.14562499999999998, shift=array([5.825, 0.95 ])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 7, 10, 11, 12, 13, 14, 15]), model=ScalarModel(intercept=1.8544790200782664, linear_terms=array([-0.67331221, -1.21592495]), square_terms=array([[1.63171239, 1.52446346], + [1.52446346, 2.03881587]]), scale=array([0.25811359, 0.17313272]), shift=array([5.69316128, 0.92686728])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=16, candidate_x=array([5.56938443, 1.09220081]), index=16, x=array([5.56938443, 1.09220081]), fval=1.7490483015806302, rho=4.7314095484198075, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 3, 7, 10, 11, 12, 13, 14, 15]), old_indices_discarded=array([1, 2, 4, 5, 6, 8, 9]), step_length=0.1475712047087003, relative_step_length=0.5066822479268681, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.56938443, 1.09220081]), radius=0.5824999999999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 7, 10, 12, 13, 14, 15, 16]), model=ScalarModel(intercept=7.085140584300428, linear_terms=array([ -5.65921381, -14.17503365]), square_terms=array([[ 5.20815903, 7.08665734], + [ 7.08665734, 17.08804688]]), scale=array([0.51622718, 0.26201318]), shift=array([5.56938443, 0.83798682])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=17, candidate_x=array([5.51947854, 1.06583909]), index=17, x=array([5.51947854, 1.06583909]), fval=1.4289096777146673, rho=1.7809591946937149, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 3, 7, 10, 12, 13, 14, 15, 16]), old_indices_discarded=array([ 1, 2, 4, 5, 6, 8, 9, 11]), step_length=0.05644057588664191, relative_step_length=0.09689369250925652, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.51947854, 1.06583909]), radius=0.5824999999999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 3, 7, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=2.3354605283597563, linear_terms=array([ 1.46424334, -2.36914478]), square_terms=array([[ 1.06094696, -1.63153519], + [-1.63153519, 2.58355658]]), scale=array([0.51622718, 0.27519405]), shift=array([5.51947854, 0.82480595])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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linear_terms=array([-0.92259607, -5.33641639]), square_terms=array([[0.27027798, 1.20759192], + [1.20759192, 7.70294614]]), scale=array([0.25811359, 0.14613725]), shift=array([5.51947854, 0.95386275])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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scale=array([0.0645284 , 0.04934466]), shift=array([5.51947854, 1.05065534])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=22, candidate_x=array([5.55174274, 1.06100528]), index=22, x=array([5.55174274, 1.06100528]), fval=1.426038354142669, rho=0.07310801718365617, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 7, 15, 16, 17, 18, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.0326242888718403, relative_step_length=0.8961178059217939, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.55174274, 1.06100528]), radius=0.018203124999999997, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 17, 18, 20, 22]), model=ScalarModel(intercept=1.4163790473957398, linear_terms=array([-0.00983569, -0.08333704]), square_terms=array([[2.86954770e-04, 8.05036733e-04], + [8.05036733e-04, 3.32811019e-01]]), scale=0.018203124999999997, shift=array([5.55174274, 1.06100528])), vector_model=VectorModel(intercepts=array([ 0.00324884, 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x=array([5.56995508, 1.065396 ]), fval=1.42426971311261, rho=-0.24612567440050911, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([16, 20, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.56995508, 1.065396 ]), radius=0.004550781249999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 23, 24]), model=ScalarModel(intercept=1.42426971311261, linear_terms=array([-4.86697548e-05, 7.42140875e-03]), square_terms=array([[ 7.70644314e-06, -1.20696978e-04], + [-1.20696978e-04, 1.96339837e-02]]), scale=0.004550781249999999, shift=array([5.56995508, 1.065396 ])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=25, candidate_x=array([5.57417906, 1.06370262]), index=25, x=array([5.57417906, 1.06370262]), fval=1.4220984404550594, rho=1.5482217964560738, accepted=True, 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old_indices_used=array([16, 18, 20, 22, 23, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.57417906, 1.06370262]), radius=0.004550781249999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 23, 24, 25, 26]), model=ScalarModel(intercept=1.422282780554977, linear_terms=array([-0.00015294, 0.00037013]), square_terms=array([[ 7.13476990e-06, -1.13770520e-04], + [-1.13770520e-04, 1.96031452e-02]]), scale=0.004550781249999999, shift=array([5.57417906, 1.06370262])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.57417906, 1.06370262]), radius=0.0022753906249999996, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 25, 26, 27]), model=ScalarModel(intercept=1.4221663020015802, linear_terms=array([5.73401438e-05, 7.45830401e-04]), square_terms=array([[ 1.12265319e-06, -2.59043861e-05], + [-2.59043861e-05, 4.91328724e-03]]), scale=0.0022753906249999996, shift=array([5.57417906, 1.06370262])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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State(trustregion=Region(center=array([5.57417906, 1.06370262]), radius=0.0011376953124999998, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 25, 27, 28]), model=ScalarModel(intercept=1.422325827336663, linear_terms=array([-4.61917023e-05, 2.38441492e-04]), square_terms=array([[ 3.55694453e-07, -6.48069226e-06], + [-6.48069226e-06, 1.22037302e-03]]), scale=0.0011376953124999998, shift=array([5.57417906, 1.06370262])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 28, 29]), model=ScalarModel(intercept=1.422098440455059, linear_terms=array([-4.34744633e-05, -3.03707274e-04]), square_terms=array([[ 8.37764168e-08, -1.07708651e-06], + [-1.07708651e-06, 2.98889073e-04]]), scale=0.0005688476562499999, shift=array([5.57417906, 1.06370262])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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model=ScalarModel(intercept=1.422098440455059, linear_terms=array([ 4.17356026e-06, -1.04084673e-05]), square_terms=array([[ 1.93011592e-08, -1.59744779e-07], + [-1.59744779e-07, 7.59670263e-05]]), scale=0.00028442382812499995, shift=array([5.57417906, 1.06370262])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=31, candidate_x=array([5.57389456, 1.06373901]), index=31, x=array([5.57389456, 1.06373901]), fval=1.4220519178994522, rho=9.583354885845637, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([25, 29, 30]), old_indices_discarded=array([], dtype=int64), step_length=0.00028681890451018565, relative_step_length=1.0084208007499749, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 32 entries., 'multistart_info': {'start_parameters': [array([5.825, 0.95 ]), array([7.55033227, 1.06886786])], 'local_optima': [{'solution_x': array([5.57389456, 1.06373901]), 'solution_criterion': 1.4220519178994522, 'states': [State(trustregion=Region(center=array([5.825, 0.95 ]), radius=0.5824999999999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=3.0263314834387383, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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linear_terms=array([-0.51127322, -0.63965657]), square_terms=array([[11.76668356, 13.01143564], + [13.01143564, 15.17207492]]), scale=array([0.51622718, 0.3 ]), shift=array([5.825, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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scale=array([0.25811359, 0.2040568 ]), shift=array([5.825 , 0.8959432])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], 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-0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=15, candidate_x=array([5.69316128, 1.01184815]), index=15, x=array([5.69316128, 1.01184815]), fval=2.070351234692411, rho=2.2229628748013988, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 2, 3, 7, 8, 11, 12, 13, 14]), old_indices_discarded=array([ 1, 4, 5, 6, 9, 10]), step_length=0.1456249999999998, relative_step_length=0.9999999999999989, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.69316128, 1.01184815]), radius=0.29124999999999995, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 7, 10, 11, 12, 13, 14, 15]), model=ScalarModel(intercept=1.8544790200782664, linear_terms=array([-0.67331221, -1.21592495]), square_terms=array([[1.63171239, 1.52446346], + [1.52446346, 2.03881587]]), scale=array([0.25811359, 0.17313272]), shift=array([5.69316128, 0.92686728])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=16, candidate_x=array([5.56938443, 1.09220081]), index=16, x=array([5.56938443, 1.09220081]), fval=1.7490483015806302, rho=4.7314095484198075, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 3, 7, 10, 11, 12, 13, 14, 15]), old_indices_discarded=array([1, 2, 4, 5, 6, 8, 9]), step_length=0.1475712047087003, relative_step_length=0.5066822479268681, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.56938443, 1.09220081]), radius=0.5824999999999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 7, 10, 12, 13, 14, 15, 16]), model=ScalarModel(intercept=7.085140584300428, linear_terms=array([ -5.65921381, -14.17503365]), square_terms=array([[ 5.20815903, 7.08665734], + [ 7.08665734, 17.08804688]]), scale=array([0.51622718, 0.26201318]), shift=array([5.56938443, 0.83798682])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=17, candidate_x=array([5.51947854, 1.06583909]), index=17, x=array([5.51947854, 1.06583909]), fval=1.4289096777146673, rho=1.7809591946937149, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 3, 7, 10, 12, 13, 14, 15, 16]), old_indices_discarded=array([ 1, 2, 4, 5, 6, 8, 9, 11]), step_length=0.05644057588664191, relative_step_length=0.09689369250925652, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.51947854, 1.06583909]), radius=0.5824999999999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 3, 7, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=2.3354605283597563, linear_terms=array([ 1.46424334, -2.36914478]), square_terms=array([[ 1.06094696, -1.63153519], + [-1.63153519, 2.58355658]]), scale=array([0.51622718, 0.27519405]), shift=array([5.51947854, 0.82480595])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=18, candidate_x=array([5.60087809, 1.1 ]), index=17, x=array([5.51947854, 1.06583909]), fval=1.4289096777146673, rho=-85.51150568657764, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 7, 13, 14, 15, 16, 17]), old_indices_discarded=array([ 2, 4, 5, 6, 8, 9, 10, 11, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.51947854, 1.06583909]), radius=0.29124999999999995, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 7, 10, 14, 15, 16, 17, 18]), model=ScalarModel(intercept=3.1012802436018085, linear_terms=array([-0.92259607, -5.33641639]), square_terms=array([[0.27027798, 1.20759192], + [1.20759192, 7.70294614]]), scale=array([0.25811359, 0.14613725]), shift=array([5.51947854, 0.95386275])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=19, candidate_x=array([5.77759213, 1.03219317]), index=17, x=array([5.51947854, 1.06583909]), fval=1.4289096777146673, rho=-4.019293806179108, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 3, 7, 10, 14, 15, 16, 17, 18]), old_indices_discarded=array([ 1, 2, 4, 5, 6, 8, 9, 11, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.51947854, 1.06583909]), radius=0.14562499999999998, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 14, 15, 16, 17, 18, 19]), model=ScalarModel(intercept=1.5213811565094542, linear_terms=array([-0.14355744, -1.03791084]), square_terms=array([[0.06159175, 0.28510759], + [0.28510759, 2.13210366]]), scale=array([0.1290568 , 0.08160886]), shift=array([5.51947854, 1.01839114])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=20, candidate_x=array([5.54569318, 1.05590177]), index=17, x=array([5.51947854, 1.06583909]), fval=1.4289096777146673, rho=-1.535972006541552, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 10, 14, 15, 16, 17, 18, 19]), old_indices_discarded=array([ 0, 1, 11, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.51947854, 1.06583909]), radius=0.07281249999999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 15, 16, 17, 18, 19, 20]), model=ScalarModel(intercept=1.3026835229178233, linear_terms=array([ 0.01296259, -0.29445874]), square_terms=array([[ 0.00193973, -0.00535854], + [-0.00535854, 1.93956479]]), scale=array([0.0645284 , 0.04934466]), shift=array([5.51947854, 1.05065534])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=21, candidate_x=array([5.45495014, 1.05801037]), index=17, x=array([5.51947854, 1.06583909]), fval=1.4289096777146673, rho=-0.27578700766971787, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 10, 15, 16, 17, 18, 19, 20]), old_indices_discarded=array([ 0, 13, 14]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.51947854, 1.06583909]), radius=0.036406249999999994, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 15, 16, 17, 18, 20, 21]), model=ScalarModel(intercept=1.3985870458518956, linear_terms=array([-0.03050627, 0.11095194]), square_terms=array([[0.00179814, 0.01810855], + [0.01810855, 0.86143979]]), scale=array([0.0322642, 0.0322642]), shift=array([5.51947854, 1.06583909])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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relative_step_length=0.8961178059217939, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.55174274, 1.06100528]), radius=0.018203124999999997, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 17, 18, 20, 22]), model=ScalarModel(intercept=1.4163790473957398, linear_terms=array([-0.00983569, -0.08333704]), square_terms=array([[2.86954770e-04, 8.05036733e-04], + [8.05036733e-04, 3.32811019e-01]]), scale=0.018203124999999997, shift=array([5.55174274, 1.06100528])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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n_evals_acceptance=1), State(trustregion=Region(center=array([5.56995508, 1.065396 ]), radius=0.009101562499999999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 20, 22, 23]), model=ScalarModel(intercept=1.4255871683087975, linear_terms=array([ 0.01282382, -0.01799885]), square_terms=array([[ 0.00027616, -0.00248466], + [-0.00248466, 0.08699657]]), scale=0.009101562499999999, shift=array([5.56995508, 1.065396 ])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 23, 24]), model=ScalarModel(intercept=1.42426971311261, linear_terms=array([-4.86697548e-05, 7.42140875e-03]), square_terms=array([[ 7.70644314e-06, -1.20696978e-04], + [-1.20696978e-04, 1.96339837e-02]]), scale=0.004550781249999999, shift=array([5.56995508, 1.065396 ])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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model_indices=array([16, 18, 20, 22, 23, 24, 25]), model=ScalarModel(intercept=1.4247281558811358, linear_terms=array([ 0.00539427, -0.03219716]), square_terms=array([[ 3.96935302e-05, -1.28952506e-03], + [-1.28952506e-03, 8.65974369e-02]]), scale=0.009101562499999999, shift=array([5.57417906, 1.06370262])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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model=ScalarModel(intercept=1.422282780554977, linear_terms=array([-0.00015294, 0.00037013]), square_terms=array([[ 7.13476990e-06, -1.13770520e-04], + [-1.13770520e-04, 1.96031452e-02]]), scale=0.004550781249999999, shift=array([5.57417906, 1.06370262])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=27, candidate_x=array([5.57873026, 1.06364354]), index=25, x=array([5.57417906, 1.06370262]), fval=1.4220984404550594, rho=-0.7907220678531115, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 23, 24, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.57417906, 1.06370262]), radius=0.0022753906249999996, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 25, 26, 27]), model=ScalarModel(intercept=1.4221663020015802, linear_terms=array([5.73401438e-05, 7.45830401e-04]), square_terms=array([[ 1.12265319e-06, -2.59043861e-05], + [-2.59043861e-05, 4.91328724e-03]]), scale=0.0022753906249999996, shift=array([5.57417906, 1.06370262])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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scale=0.0011376953124999998, shift=array([5.57417906, 1.06370262])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=29, candidate_x=array([5.57531788, 1.063494 ]), index=25, x=array([5.57417906, 1.06370262]), fval=1.4220984404550594, rho=-0.6669519353256694, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 25, 27, 28]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.57417906, 1.06370262]), radius=0.0005688476562499999, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 28, 29]), model=ScalarModel(intercept=1.422098440455059, linear_terms=array([-4.34744633e-05, -3.03707274e-04]), square_terms=array([[ 8.37764168e-08, -1.07708651e-06], + [-1.07708651e-06, 2.98889073e-04]]), scale=0.0005688476562499999, shift=array([5.57417906, 1.06370262])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, -0.12292514, + -0.183175 , -0.23977682, -0.60774252, -0.67504492, -0.66762796, + -0.75647431, -0.92995825, -0.00230267, 0.07861212, 0.05982638, + 0.19899139, 0.38776794]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5824999999999999, shift=array([5.825, 0.95 ])), candidate_index=30, candidate_x=array([5.57453507, 1.06417097]), index=25, x=array([5.57417906, 1.06370262]), fval=1.4220984404550594, rho=-0.5142705306272952, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([25, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.57417906, 1.06370262]), radius=0.00028442382812499995, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([25, 29, 30]), model=ScalarModel(intercept=1.422098440455059, linear_terms=array([ 4.17356026e-06, -1.04084673e-05]), square_terms=array([[ 1.93011592e-08, -1.59744779e-07], + [-1.59744779e-07, 7.59670263e-05]]), scale=0.00028442382812499995, shift=array([5.57417906, 1.06370262])), vector_model=VectorModel(intercepts=array([ 0.00324884, -0.00957298, -0.05934558, -0.09479157, 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1.06373901]), index=31, x=array([5.57389456, 1.06373901]), fval=1.4220519178994522, rho=9.583354885845637, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([25, 29, 30]), old_indices_discarded=array([], dtype=int64), step_length=0.00028681890451018565, relative_step_length=1.0084208007499749, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 32 entries., 'history': {'params': [{'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 5.3827721337329075, 'DiscFac': 0.5}, {'CRRA': 6.341227184076231, 'DiscFac': 0.9843397863096757}, {'CRRA': 5.308772815923768, 'DiscFac': 0.9942847020759833}, {'CRRA': 6.341227184076231, 'DiscFac': 1.099329565298806}, {'CRRA': 6.341227184076231, 'DiscFac': 0.7325487756828386}, {'CRRA': 6.341227184076231, 'DiscFac': 0.661184756844424}, {'CRRA': 5.670357912186334, 'DiscFac': 1.1}, {'CRRA': 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State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=0.18875830683016304, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 4, 6, 7, 9, 10, 12, 13, 14]), model=ScalarModel(intercept=19.22375838635329, linear_terms=array([1.32892629, 5.63610224]), square_terms=array([[0.15092368, 0.22405344], + [0.22405344, 0.99645599]]), scale=array([0.16728269, 0.09920742]), shift=array([7.55033227, 1.00079258])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], 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model=ScalarModel(intercept=inf, linear_terms=array([ inf, -inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=array([0.08364135, 0.05738674]), shift=array([7.55033227, 1.04261326])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([16]), old_indices_used=array([ 0, 13, 15]), old_indices_discarded=array([14]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=0.04718957670754076, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=inf, linear_terms=array([inf, inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=array([0.04182067, 0.03647641]), shift=array([7.55033227, 1.06352359])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([18]), old_indices_used=array([ 0, 17]), old_indices_discarded=array([16]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=0.02359478835377038, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 19, 20]), model=ScalarModel(intercept=inf, linear_terms=array([ inf, -inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=0.02359478835377038, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([20]), old_indices_used=array([ 0, 17, 19]), old_indices_discarded=array([18]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=0.01179739417688519, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 19, 20, 21, 22]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=0.01179739417688519, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([22]), old_indices_used=array([ 0, 17, 19, 20, 21]), old_indices_discarded=array([18]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=0.005898697088442595, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 19, 21, 22, 23, 24]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=0.005898697088442595, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([24]), old_indices_used=array([ 0, 17, 19, 21, 22, 23]), old_indices_discarded=array([20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=0.0029493485442212974, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 19, 21, 23, 24, 25, 26]), model=ScalarModel(intercept=inf, linear_terms=array([ inf, -inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=0.0029493485442212974, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([26]), old_indices_used=array([ 0, 17, 19, 21, 23, 24, 25]), old_indices_discarded=array([22]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=0.0014746742721106487, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 19, 21, 23, 25, 26, 27, 28]), model=ScalarModel(intercept=inf, linear_terms=array([ inf, -inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=0.0014746742721106487, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([28]), old_indices_used=array([ 0, 17, 19, 21, 23, 25, 26, 27]), old_indices_discarded=array([24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=0.0007373371360553244, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 19, 21, 23, 25, 27, 29, 30]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=0.0007373371360553244, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([30]), old_indices_used=array([ 0, 17, 19, 21, 23, 25, 27, 29]), old_indices_discarded=array([26, 28]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=0.0003686685680276622, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 19, 21, 23, 25, 27, 29, 31, 32]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=0.0003686685680276622, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([32]), old_indices_used=array([ 0, 19, 21, 23, 25, 27, 29, 31]), old_indices_discarded=array([17, 28, 30]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=0.0001843342840138311, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 21, 23, 25, 27, 29, 31, 33, 34]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=0.0001843342840138311, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([34]), old_indices_used=array([ 0, 21, 23, 25, 27, 29, 31, 33]), old_indices_discarded=array([17, 19, 30, 32]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=9.216714200691555e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 23, 25, 27, 29, 31, 33, 35, 36]), model=ScalarModel(intercept=inf, linear_terms=array([ inf, -inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=9.216714200691555e-05, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([36]), old_indices_used=array([ 0, 23, 25, 27, 29, 31, 33, 35]), old_indices_discarded=array([17, 19, 21, 32, 34]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=4.608357100345777e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 25, 27, 29, 31, 33, 35, 37, 38]), model=ScalarModel(intercept=inf, linear_terms=array([inf, inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=4.608357100345777e-05, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([38]), old_indices_used=array([ 0, 25, 27, 29, 31, 33, 35, 37]), old_indices_discarded=array([17, 19, 21, 23, 34, 36]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=2.3041785501728886e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 27, 29, 31, 33, 35, 37, 39, 40]), model=ScalarModel(intercept=inf, linear_terms=array([ inf, -inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=2.3041785501728886e-05, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([40]), old_indices_used=array([ 0, 27, 29, 31, 33, 35, 37, 39]), old_indices_discarded=array([17, 19, 21, 23, 25, 36, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1.1520892750864443e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 29, 31, 33, 35, 37, 39, 41, 42]), model=ScalarModel(intercept=inf, linear_terms=array([inf, inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1.1520892750864443e-05, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([42]), old_indices_used=array([ 0, 29, 31, 33, 35, 37, 39, 41]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 38, 40]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=5.7604463754322216e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 31, 33, 35, 37, 39, 41, 43, 44]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=5.7604463754322216e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([44]), old_indices_used=array([ 0, 31, 33, 35, 37, 39, 41, 43]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 40, 42]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=2.8802231877161108e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 33, 35, 37, 39, 41, 43, 45, 46]), model=ScalarModel(intercept=inf, linear_terms=array([inf, inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=2.8802231877161108e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([46]), old_indices_used=array([ 0, 33, 35, 37, 39, 41, 43, 45]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 42, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1.4401115938580554e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 35, 37, 39, 41, 43, 45, 47, 48]), model=ScalarModel(intercept=inf, linear_terms=array([inf, inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1.4401115938580554e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([48]), old_indices_used=array([ 0, 35, 37, 39, 41, 43, 45, 47]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 44, 46]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 37, 39, 41, 43, 45, 47, 49, 50]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([50]), old_indices_used=array([ 0, 37, 39, 41, 43, 45, 47, 49]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 46, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 39, 41, 43, 45, 47, 49, 51, 52]), model=ScalarModel(intercept=inf, linear_terms=array([ inf, -inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([52]), old_indices_used=array([ 0, 39, 41, 43, 45, 47, 49, 51]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 46, 48, 50]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 43, 45, 47, 49, 51, 52, 53, 54]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([54]), old_indices_used=array([ 0, 43, 45, 47, 49, 51, 52, 53]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 46, 48, 50]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 45, 47, 49, 51, 52, 53, 55, 56]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([56]), old_indices_used=array([ 0, 45, 47, 49, 51, 52, 53, 55]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 46, 48, 50, + 54]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 49, 51, 52, 53, 55, 56, 57, 58]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([58]), old_indices_used=array([ 0, 49, 51, 52, 53, 55, 56, 57]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 50, 54]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 51, 52, 53, 55, 56, 57, 59, 60]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([60]), old_indices_used=array([ 0, 51, 52, 53, 55, 56, 57, 59]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 54, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 53, 55, 56, 57, 59, 61, 62]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([62]), old_indices_used=array([ 0, 52, 53, 55, 56, 57, 59, 61]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 54, 58, 60]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 57, 59, 61, 62, 63, 64]), model=ScalarModel(intercept=inf, linear_terms=array([ inf, -inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([64]), old_indices_used=array([ 0, 52, 56, 57, 59, 61, 62, 63]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 58, 60]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 59, 61, 62, 63, 65, 66]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([66]), old_indices_used=array([ 0, 52, 56, 59, 61, 62, 63, 65]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 57, 58, 60, 64]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 62, 63, 65, 66, 67, 68]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([68]), old_indices_used=array([ 0, 52, 56, 62, 63, 65, 66, 67]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, 61, 64]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 62, 65, 66, 67, 69, 70]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([70]), old_indices_used=array([ 0, 52, 56, 62, 65, 66, 67, 69]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 68]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 62, 66, 69, 70, 71, 72]), model=ScalarModel(intercept=inf, linear_terms=array([inf, inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([72]), old_indices_used=array([ 0, 52, 56, 62, 66, 69, 70, 71]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 62, 66, 70, 72, 73, 74]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([74]), old_indices_used=array([ 0, 52, 56, 62, 66, 70, 72, 73]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68, + 69, 71]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 62, 66, 70, 72, 75, 76]), model=ScalarModel(intercept=inf, linear_terms=array([inf, inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([76]), old_indices_used=array([ 0, 52, 56, 62, 66, 70, 72, 75]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68, + 69, 71, 73, 74]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 62, 66, 70, 72, 76, 78]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([78]), old_indices_used=array([ 0, 52, 56, 62, 66, 70, 72, 76]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68, + 69, 71, 73, 74, 75, 77]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 62, 66, 70, 72, 78, 80]), model=ScalarModel(intercept=inf, linear_terms=array([-inf, -inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([80]), old_indices_used=array([ 0, 52, 56, 62, 66, 70, 72, 78]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68, + 69, 71, 73, 74, 75, 76, 77, 79]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 62, 66, 70, 78, 80, 82]), model=ScalarModel(intercept=inf, linear_terms=array([ inf, -inf]), square_terms=array([[ inf, -inf], + [-inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7550332273206521, shift=array([7.55033227, 1.06886786])), candidate_index=0, candidate_x=array([7.55033227, 1.06886786]), index=0, x=array([7.55033227, 1.06886786]), fval=inf, rho=-inf, accepted=False, new_indices=array([82]), old_indices_used=array([ 0, 52, 56, 62, 66, 70, 78, 80]), old_indices_discarded=array([17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 46, 47, + 48, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68, + 69, 71, 72, 73, 74, 75, 76, 77, 79, 81]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.55033227, 1.06886786]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 52, 56, 62, 66, 70, 78, 80, 84]), model=ScalarModel(intercept=inf, linear_terms=array([inf, inf]), square_terms=array([[inf, inf], + [inf, inf]]), scale=1e-06, shift=array([7.55033227, 1.06886786])), vector_model=VectorModel(intercepts=array([ 0.015868 , inf, inf, inf, inf, + inf, inf, inf, inf, inf, + inf, -0.74132942, inf, inf, inf, + inf, 0.15441146]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], 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nan, nan, nan, nan, nan, nan, nan, nan, nan], 'runtime': [0.0, 2.208003130999714, 2.4108274589998473, 2.6445564219993685, 2.861070712999208, 3.0903511259994048, 3.3147837339993202, 3.5445771449994936, 3.763165709999157, 4.011760434999815, 4.254563144999338, 4.481855996999911, 4.734949446999963, 6.675738173999889, 8.416725412999767, 10.15450823699939, 11.883843522999996, 13.624447997999596, 15.448351131999516, 17.171726831999877, 18.88588633799918, 20.59283455899913, 22.43239950899988, 24.520430089, 26.3628967249997, 28.565140177999638, 30.343838971999503, 32.09905305399934, 33.847234142999696, 35.59349253599976, 37.29427836799914, 38.98703113099964, 40.82964572799938, 42.576055502999225, 44.29262355499941, 46.017979343999286, 47.73866828999962, 49.43964796899945, 51.183157880999715, 53.015724850999504, 54.71051612499923, 56.412946336999994, 58.13829104699926, 59.860055269999975, 61.558349431999886, 63.287416894999296, 65.13462926899956, 66.84517105899977, 68.56712897499983, 70.26700060699932, 71.96833215499919, 73.73193814099977, 75.56138586099951, 77.46366084499914, 79.22617003699997, 80.9957298469999, 82.7311454009996, 84.47368552699936, 86.21652899799938, 87.92976052999984, 89.79720195199934, 91.55993134299933, 93.31182523299958, 95.08997003399963, 96.81811396099965, 98.61492505799924, 100.3684951639998, 102.22739970999919, 103.94773307499963, 105.69449496999914, 107.46558442999958, 109.16500168999937, 110.86308380299943, 112.61978389399974, 114.47552693799935, 116.21721567699933, 117.93670399699931, 119.68790375899971, 121.4288536539998, 123.14456135699947, 124.85203158899913, 126.56781216999934, 128.38843487799932, 130.10438197299936, 131.83267793599953, 133.55169217000002, 135.25045203299942, 136.96302765499968, 138.67373905899967, 140.50321614399945, 142.19887414299956, 143.88603324799988, 145.59989389599923, 147.33349493199967, 149.06226304499978, 150.77150221800002, 152.6252947109997, 154.3393833299997, 156.03193448599995, 157.7520206069994], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88]}}], 'exploration_sample': array([[ 5.825 , 0.95 ], + [12.321875, 1.08125 ], + [ 5. , 0.95 ], + [ 4.64375 , 0.6875 ], + [14.09375 , 0.9875 ], + [ 9.36875 , 0.8375 ], + [17.6375 , 1.025 ], + [ 8.1875 , 0.725 ], + [10.55 , 0.8 ], + [16.45625 , 0.9125 ], + [ 7.00625 , 0.6125 ], + [11.73125 , 0.7625 ], + [15.275 , 0.65 ], + [12.9125 , 0.575 ], + [17.046875, 0.63125 ], + [18.81875 , 0.5375 ], + [ 3.4625 , 0.875 ], + [ 2.28125 , 1.0625 ], + [ 2.871875, 0.78125 ]]), 'exploration_results': array([ 3.02633148, 3.30050278, 3.74724591, 3.92461378, 4.45534719, + 4.52159416, 4.77507862, 5.06340436, 5.16761845, 5.42393003, + 5.50914405, 5.69522002, 6.68407291, 6.73863805, 6.84560996, + 7.20052264, 7.72130492, 8.47185488, 10.67451262])}}" diff --git a/content/tables/min/WealthPortfolioSub(Stock)Market_estimate_results.csv b/content/tables/min/WealthPortfolioSub(Stock)Market_estimate_results.csv new file mode 100644 index 0000000..6624a23 --- /dev/null +++ b/content/tables/min/WealthPortfolioSub(Stock)Market_estimate_results.csv @@ -0,0 +1,5941 @@ +CRRA,4.272642056859294 +DiscFac,0.9814607088251204 +time_to_estimate,105.39929842948914 +params,"{'CRRA': 4.272642056859294, 'DiscFac': 0.9814607088251204}" +criterion,1.5881921698252235 +start_criterion,1.7417506643900147 +start_params,"{'CRRA': 5.0, 'DiscFac': 0.95}" +algorithm,multistart_tranquilo_ls +direction,minimize +n_free,2 +message,Absolute criterion change smaller than tolerance. +success, +n_criterion_evaluations, +n_derivative_evaluations, +n_iterations, +history,"{'params': [{'CRRA': 4.707203308184448, 'DiscFac': 0.9723293237231975}, {'CRRA': 4.290038276655113, 'DiscFac': 0.5612839565173267}, {'CRRA': 5.124368339713784, 'DiscFac': 0.7815145778668051}, {'CRRA': 4.290038276655113, 'DiscFac': 0.8852911049439405}, {'CRRA': 5.124368339713784, 'DiscFac': 1.0998082225394454}, {'CRRA': 5.124368339713784, 'DiscFac': 0.6528702672894295}, {'CRRA': 5.088882093668508, 'DiscFac': 0.5551642921938619}, {'CRRA': 4.290038276655113, 'DiscFac': 1.0958487440640778}, {'CRRA': 5.124368339713784, 'DiscFac': 0.9644452739308207}, {'CRRA': 4.815289190462201, 'DiscFac': 1.1}, {'CRRA': 4.290038276655113, 'DiscFac': 1.0869940700172442}, {'CRRA': 4.610395613673098, 'DiscFac': 0.5551642921938619}, {'CRRA': 4.712667209754667, 'DiscFac': 1.1}, {'CRRA': 4.290038276655113, 'DiscFac': 0.8302806857872134}, {'CRRA': 4.49862079241978, 'DiscFac': 0.8632291852888655}, {'CRRA': 4.618262130250856, 'DiscFac': 0.892211907379606}, {'CRRA': 4.649360364309195, 'DiscFac': 0.9610417287955715}, {'CRRA': 4.677179125402864, 'DiscFac': 0.9726952599678501}, {'CRRA': 4.662420411739433, 'DiscFac': 0.9709527874372768}, {'CRRA': 4.633143948980592, 'DiscFac': 0.9673433127210762}, {'CRRA': 4.647708424364658, 'DiscFac': 0.9710769920318861}, {'CRRA': 4.618217430273746, 'DiscFac': 0.9715282984145067}, {'CRRA': 4.559385583543956, 'DiscFac': 0.9740437573449842}, {'CRRA': 4.442362348737134, 'DiscFac': 0.9615159200987019}, {'CRRA': 4.6177820933812335, 'DiscFac': 0.966832786178363}, {'CRRA': 4.530168028631268, 'DiscFac': 0.9705981657398585}, {'CRRA': 4.54468016749636, 'DiscFac': 0.9744118507601772}, {'CRRA': 4.515251965828596, 'DiscFac': 0.974531215771109}, {'CRRA': 4.456469672419852, 'DiscFac': 0.9719249832509226}, {'CRRA': 4.54388800665835, 'DiscFac': 0.9677847513502521}, {'CRRA': 4.5005611019825995, 'DiscFac': 0.9754513255437164}, {'CRRA': 4.529485918905441, 'DiscFac': 0.9678147143724265}, {'CRRA': 4.485839431860874, 'DiscFac': 0.9752072544842302}, {'CRRA': 4.456429538458829, 'DiscFac': 0.9759793251278149}, {'CRRA': 4.397608962906071, 'DiscFac': 0.9774938921710231}, {'CRRA': 4.279965862398321, 'DiscFac': 0.9804443756996974}, {'CRRA': 4.122203844999166, 'DiscFac': 0.937104673702021}, {'CRRA': 4.163432700992639, 'DiscFac': 0.9640546179784433}, {'CRRA': 4.336906613655518, 'DiscFac': 0.9656153058500048}, {'CRRA': 4.249020769576481, 'DiscFac': 0.9761381800429344}, {'CRRA': 4.294680071351096, 'DiscFac': 0.9804072400803372}, {'CRRA': 4.272642056859294, 'DiscFac': 0.9814607088251204}], 'criterion': [1.6380478126416131, 4.03723195367052, 3.5440740950812013, 3.2759678353900803, 6.781083894625252, 3.8713442516298198, 3.996890584141768, 7.627891908022631, 1.7551707114922444, 7.144112642232848, 6.6404717126627215, 3.935564366818633, 7.291412101525699, 3.862333195265191, 3.197857033577577, 2.6761443032128733, 1.6565559654451956, 1.631941462430788, 1.6292672840092992, 1.6313284776792012, 1.6269015883981968, 1.6215771152258682, 1.6122243121836672, 1.6696000135319509, 1.6306703543399446, 1.614669208950377, 1.6100467129384297, 1.6064550393910892, 1.6076695381563089, 1.6233520305796163, 1.6045259070805813, 1.6226940047077765, 1.6029216364692263, 1.599716096343115, 1.5939391812466719, 1.5886714368056305, 2.4007693475753995, 1.7378906646063588, 1.6562535862817716, 1.601747168932624, 1.5887715770723967, 1.5881921698252237], 'runtime': [0.0, 1.6408778029999667, 1.879227850999996, 2.101594065000427, 2.313481502000286, 2.55059718099983, 2.782204840999839, 3.0194256559998394, 3.235137909000514, 3.449739153000337, 3.6794994500005487, 3.8966444010002306, 4.28813861800063, 5.764485786000478, 7.000034329999835, 8.24761119699997, 9.516164548999768, 10.76685041500059, 12.019717905000107, 13.265578907999952, 14.518897724000453, 15.760440533999827, 16.995802637999986, 18.22653636799987, 19.587672147000376, 20.82062521500029, 22.057747867000217, 23.28416549299982, 24.62205672500022, 25.949533355999847, 27.21613684300064, 28.504664697999942, 29.771321367999917, 31.040138887000467, 32.30792920600015, 33.793730317000154, 35.13760062700021, 36.411630069999774, 37.71618604300056, 39.015112086000045, 40.31985881299988, 41.661046635000275], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]}" +convergence_report,"{'one_step': {'relative_criterion_change': 0.00015933602220390196, 'relative_params_change': 0.0066586234867468804, 'absolute_criterion_change': 0.000253056222835335, 'absolute_params_change': 0.02844554200489083}, 'five_steps': {'relative_criterion_change': 0.00015933602220390196, 'relative_params_change': 0.0066586234867468804, 'absolute_criterion_change': 0.000253056222835335, 'absolute_params_change': 0.02844554200489083}}" +multistart_info,"{'start_parameters': [{'CRRA': 5.0, 'DiscFac': 0.95}, {'CRRA': 4.707203308184448, 'DiscFac': 0.9723293237231975}], 'local_optima': [Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 5.482e-05 0.01069 +relative_params_change 0.008558 0.06089 +absolute_criterion_change 8.708e-05 0.01698 +absolute_params_change 0.0362 0.2564 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 0.0003018 0.01028 +relative_params_change 0.002003 0.05369 +absolute_criterion_change 0.0004793 0.01633 +absolute_params_change 0.007394 0.228 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 5.0, 'DiscFac': 0.95}, {'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 7.596874999999999, 'DiscFac': 0.9312500000000001}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 9.368749999999999, 'DiscFac': 0.8375}, {'CRRA': 8.1875, 'DiscFac': 0.7250000000000001}, {'CRRA': 10.549999999999999, 'DiscFac': 0.8}, {'CRRA': 11.73125, 'DiscFac': 0.7625000000000001}, {'CRRA': 7.00625, 'DiscFac': 0.6125}, {'CRRA': 15.274999999999999, 'DiscFac': 0.65}, {'CRRA': 12.9125, 'DiscFac': 0.575}, {'CRRA': 17.046875, 'DiscFac': 0.6312500000000001}, {'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 16.45625, 'DiscFac': 0.9125000000000001}, {'CRRA': 14.093749999999998, 'DiscFac': 0.9875}, {'CRRA': 18.81875, 'DiscFac': 0.5375}, {'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 17.6375, 'DiscFac': 1.0250000000000001}, {'CRRA': 2.871875, 'DiscFac': 0.78125}, {'CRRA': 2.28125, 'DiscFac': 1.0625}], 'exploration_results': array([ 1.74175066, 2.04247528, 2.9717037 , 3.76994253, 3.80031518, + 4.27735638, 4.32695188, 4.78264529, 5.25346612, 5.79528191, + 5.94010747, 6.02403907, 6.09730665, 6.10855726, 6.12270544, + 6.45799199, 7.01428788, 7.3683214 , 8.72764877, 13.61935456])}" +algorithm_output,"{'states': [State(trustregion=Region(center=array([4.70720331, 0.97232932]), radius=0.4707203308184449, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=1.6380478126416131, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + 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linear_terms=array([ 0.1685441 , -0.13872507]), square_terms=array([[ 0.05020446, -0.12669357], + [-0.12669357, 0.64471625]]), scale=0.029420020676152805, shift=array([4.70720331, 0.97232932])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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0.97095279])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=19, candidate_x=array([4.63314395, 0.96734331]), index=18, x=array([4.66242041, 0.97095279]), fval=1.6292672840092994, rho=-0.020590492456450688, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 15, 16, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.66242041, 0.97095279]), radius=0.014710010338076403, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 16, 17, 18, 19]), model=ScalarModel(intercept=1.6292156586595015, linear_terms=array([0.00188502, 0.00233158]), square_terms=array([[0.00018003, 0.0034169 ], + [0.0034169 , 0.12685944]]), scale=0.014710010338076403, shift=array([4.66242041, 0.97095279])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=20, candidate_x=array([4.64770842, 0.97107699]), index=20, x=array([4.64770842, 0.97107699]), fval=1.6269015883981965, rho=1.3143613594368355, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.014712511658342373, relative_step_length=1.0001700420467752, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.64770842, 0.97107699]), radius=0.029420020676152805, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 15, 16, 17, 18, 19, 20]), model=ScalarModel(intercept=1.5063160859816729, linear_terms=array([ 0.05041148, -0.04272255]), square_terms=array([[ 0.0075054 , -0.03307161], + [-0.03307161, 0.58165163]]), scale=0.029420020676152805, shift=array([4.64770842, 0.97107699])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=21, candidate_x=array([4.61821743, 0.9715283 ]), index=21, x=array([4.61821743, 0.9715283 ]), fval=1.6215771152258682, rho=0.11367207027105729, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 15, 16, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.029494447103161476, relative_step_length=1.002529788399129, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.61821743, 0.9715283 ]), radius=0.05884004135230561, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 15, 16, 17, 18, 19, 20, 21]), model=ScalarModel(intercept=1.517081598562027, linear_terms=array([ 0.03194748, -0.0915037 ]), square_terms=array([[0.00517109, 0.00423992], + [0.00423992, 2.21259895]]), scale=0.05884004135230561, shift=array([4.61821743, 0.9715283 ])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=22, candidate_x=array([4.55938558, 0.97404376]), index=22, x=array([4.55938558, 0.97404376]), fval=1.6122243121836672, rho=0.2975814223142011, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 15, 16, 17, 18, 19, 20, 21]), old_indices_discarded=array([ 9, 14]), step_length=0.058885598606691694, relative_step_length=1.000774255988593, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.55938558, 0.97404376]), radius=0.11768008270461122, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 15, 16, 17, 18, 19, 20, 21, 22]), model=ScalarModel(intercept=1.6244940972117774, linear_terms=array([0.00768524, 0.3968281 ]), square_terms=array([[ 0.01794061, -0.11175552], + [-0.11175552, 4.76976273]]), scale=0.11768008270461122, shift=array([4.55938558, 0.97404376])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + 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new_indices=array([], dtype=int64), old_indices_used=array([14, 15, 16, 17, 18, 19, 20, 21, 22]), old_indices_discarded=array([ 0, 1, 3, 4, 7, 8, 9, 10, 11, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.55938558, 0.97404376]), radius=0.05884004135230561, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([15, 16, 17, 18, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=1.6256896704319683, linear_terms=array([0.0043886 , 0.10549552]), square_terms=array([[0.00419989, 0.0564693 ], + [0.0564693 , 1.33147903]]), scale=0.05884004135230561, shift=array([4.55938558, 0.97404376])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 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16, 17, 18, 19, 20, 21, 22, 23]), old_indices_discarded=array([ 0, 3, 9, 10, 12, 14]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.55938558, 0.97404376]), radius=0.029420020676152805, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([15, 16, 18, 19, 20, 21, 22, 23, 24]), model=ScalarModel(intercept=1.6269177150442447, linear_terms=array([0.00261458, 0.05266846]), square_terms=array([[0.00103242, 0.01385775], + [0.01385775, 0.33190979]]), scale=0.029420020676152805, shift=array([4.55938558, 0.97404376])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=25, candidate_x=array([4.53016803, 0.97059817]), index=22, x=array([4.55938558, 0.97404376]), fval=1.6122243121836672, rho=-0.5597649420514624, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([15, 16, 18, 19, 20, 21, 22, 23, 24]), old_indices_discarded=array([14, 17]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.55938558, 0.97404376]), radius=0.014710010338076403, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([21, 22, 24, 25]), model=ScalarModel(intercept=1.6125186187197176, linear_terms=array([0.00192027, 0.00012471]), square_terms=array([[0.00019263, 0.00355747], + [0.00355747, 0.13532065]]), scale=0.014710010338076403, shift=array([4.55938558, 0.97404376])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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State(trustregion=Region(center=array([4.54468017, 0.97441185]), radius=0.029420020676152805, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 19, 20, 21, 22, 23, 24, 25, 26]), model=ScalarModel(intercept=1.610617175628848, linear_terms=array([0.00467212, 0.01317766]), square_terms=array([[0.00085989, 0.01539591], + [0.01539591, 0.54391644]]), scale=0.029420020676152805, shift=array([4.54468017, 0.97441185])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=27, candidate_x=array([4.51525197, 0.97453122]), index=27, x=array([4.51525197, 0.97453122]), fval=1.6064550393910895, rho=0.8455422416631787, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([16, 19, 20, 21, 22, 23, 24, 25, 26]), old_indices_discarded=array([14, 15, 17, 18]), step_length=0.029428443747579514, relative_step_length=1.000286304062102, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.51525197, 0.97453122]), radius=0.05884004135230561, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 19, 21, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=1.6171036489097632, linear_terms=array([0.00432157, 0.08549957]), square_terms=array([[0.00404165, 0.04083728], + [0.04083728, 0.9748026 ]]), scale=0.05884004135230561, shift=array([4.51525197, 0.97453122])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=28, candidate_x=array([4.45646967, 0.97192498]), index=27, x=array([4.51525197, 0.97453122]), fval=1.6064550393910895, rho=-0.3653445002297127, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 19, 21, 22, 23, 24, 25, 26, 27]), old_indices_discarded=array([ 0, 3, 7, 10, 12, 13, 15, 16, 17, 18, 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.51525197, 0.97453122]), radius=0.029420020676152805, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 21, 22, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=1.6158549160636237, linear_terms=array([0.0016874, 0.045421 ]), square_terms=array([[0.00106973, 0.01073937], + [0.01073937, 0.24481613]]), scale=0.029420020676152805, shift=array([4.51525197, 0.97453122])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=29, candidate_x=array([4.54388801, 0.96778475]), index=27, x=array([4.51525197, 0.97453122]), fval=1.6064550393910895, rho=-3.9976405851055894, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 21, 22, 23, 24, 25, 26, 27, 28]), old_indices_discarded=array([15, 16, 19, 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.51525197, 0.97453122]), radius=0.014710010338076403, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=1.6066876339598914, linear_terms=array([ 0.00156986, -0.00517551]), square_terms=array([[0.00019907, 0.00364165], + [0.00364165, 0.13928521]]), scale=0.014710010338076403, shift=array([4.51525197, 0.97453122])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=30, candidate_x=array([4.5005611 , 0.97545133]), index=30, x=array([4.5005611 , 0.97545133]), fval=1.6045259070805813, rho=1.104079170574446, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([22, 25, 26, 27, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=0.014719649538465622, relative_step_length=1.0006552816869387, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.5005611 , 0.97545133]), radius=0.029420020676152805, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 22, 23, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=1.6151275211840224, linear_terms=array([-0.00091945, 0.04982336]), square_terms=array([[0.0018448 , 0.01496444], + [0.01496444, 0.24558318]]), scale=0.029420020676152805, shift=array([4.5005611 , 0.97545133])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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0.00619617]), square_terms=array([[0.0002099 , 0.00380383], + [0.00380383, 0.14225083]]), scale=0.014710010338076403, shift=array([4.5005611 , 0.97545133])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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0.01532586], + [0.01532586, 0.57103593]]), scale=0.029420020676152805, shift=array([4.48583943, 0.97520725])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=33, candidate_x=array([4.45642954, 0.97597933]), index=33, x=array([4.45642954, 0.97597933]), fval=1.599716096343115, rho=0.9127111762327604, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([23, 25, 26, 27, 28, 29, 30, 31, 32]), old_indices_discarded=array([14, 21, 22, 24]), step_length=0.0294200258837129, relative_step_length=1.0000001770073568, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.45642954, 0.97597933]), radius=0.05884004135230561, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 25, 27, 28, 29, 30, 31, 32, 33]), model=ScalarModel(intercept=1.5997771172632202, linear_terms=array([0.00705164, 0.0023645 ]), square_terms=array([[0.00344304, 0.06165063], + [0.06165063, 2.29724063]]), scale=0.05884004135230561, shift=array([4.45642954, 0.97597933])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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scale=0.11768008270461122, shift=array([4.39760896, 0.97749389])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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shift=array([4.27996586, 0.98044438])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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fval=1.5881921698252235, rho=1.2960293054666532, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([35, 39, 40]), old_indices_discarded=array([], dtype=int64), step_length=0.007393988138705295, relative_step_length=1.005300196093838, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 42 entries., 'multistart_info': {'start_parameters': [array([5. , 0.95]), array([4.70720331, 0.97232932])], 'local_optima': [{'solution_x': array([4.24419676, 0.98157843]), 'solution_criterion': 1.5884452260480588, 'states': [State(trustregion=Region(center=array([5. , 0.95]), radius=0.5, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=1.7417506643900147, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.5, shift=array([5. , 0.95])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5, shift=array([5. , 0.95])), candidate_index=0, candidate_x=array([5. , 0.95]), index=0, x=array([5. , 0.95]), fval=1.7417506643900145, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([5. , 0.95]), radius=0.5, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=1.525682901381052, linear_terms=array([ 0.35147642, -0.70797287]), square_terms=array([[ 0.17228309, -0.70481542], + [-0.70481542, 7.78269567]]), scale=array([0.44311346, 0.29655673]), shift=array([5. , 0.80344327])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5, shift=array([5. , 0.95])), candidate_index=13, candidate_x=array([4.55688654, 0.80356358]), index=0, x=array([5. , 0.95]), fval=1.7417506643900145, rho=-2.1815597578942127, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5. , 0.95]), radius=0.25, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 4, 7, 8, 9, 10, 12, 13]), model=ScalarModel(intercept=1.688619674041434, linear_terms=array([ 0.20628142, -2.78312447]), square_terms=array([[ 0.04634332, -0.54356827], + [-0.54356827, 15.6553705 ]]), scale=array([0.22155673, 0.18577837]), shift=array([5. , 0.91422163])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 7, 8, 9, 10, 12, 13, 14]), model=ScalarModel(intercept=1.5012663356887093, linear_terms=array([ 0.08935364, -0.18990169]), square_terms=array([[ 0.02022595, -0.24678211], + [-0.24678211, 7.22788319]]), scale=0.125, shift=array([5. , 0.95])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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model=ScalarModel(intercept=1.7089526132207769, linear_terms=array([ 0.02648415, -0.90309879]), square_terms=array([[ 2.01607285e-03, -3.09418837e-02], + [-3.09418837e-02, 2.59137456e+00]]), scale=0.0625, shift=array([4.87496272, 0.94902461])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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square_terms=array([[ 0.02293557, -0.32021801], + [-0.32021801, 9.48068192]]), scale=0.125, shift=array([4.81232469, 0.96994443])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5, shift=array([5. , 0.95])), candidate_index=17, candidate_x=array([4.6875834 , 0.96047652]), index=17, x=array([4.6875834 , 0.96047652]), fval=1.6605566084700965, rho=0.019087760659877613, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 3, 7, 9, 10, 12, 14, 15, 16]), old_indices_discarded=array([ 1, 11, 13]), step_length=0.12510008504138495, relative_step_length=1.0008006803310796, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.6875834 , 0.96047652]), radius=0.0625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 12, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=1.4081104203383767, linear_terms=array([0.02058664, 0.27473715]), square_terms=array([[ 0.02001168, -0.16820066], + [-0.16820066, 1.89038505]]), scale=0.0625, shift=array([4.6875834 , 0.96047652])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5, shift=array([5. , 0.95])), candidate_index=18, candidate_x=array([4.62612848, 0.94622607]), index=17, x=array([4.6875834 , 0.96047652]), fval=1.6605566084700965, rho=-2.0577598152094736, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 10, 12, 13, 14, 15, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.6875834 , 0.96047652]), radius=0.03125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 16, 17, 18]), model=ScalarModel(intercept=1.6564086025715405, linear_terms=array([-0.00040164, -0.13821286]), square_terms=array([[0.0010511 , 0.01749125], + [0.01749125, 0.49318606]]), scale=0.03125, shift=array([4.6875834 , 0.96047652])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5, shift=array([5. , 0.95])), candidate_index=19, candidate_x=array([4.65669195, 0.970249 ]), index=19, x=array([4.65669195, 0.970249 ]), fval=1.6291014270431563, rho=1.332667694520173, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 16, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.03240034924580121, relative_step_length=1.0368111758656386, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65669195, 0.970249 ]), radius=0.0625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 13, 14, 16, 17, 18, 19]), model=ScalarModel(intercept=1.4667362991629962, linear_terms=array([0.03429873, 0.35050851]), square_terms=array([[ 0.07672055, -0.32331271], + [-0.32331271, 1.74181997]]), scale=0.0625, shift=array([4.65669195, 0.970249 ])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, 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dtype=int64), old_indices_used=array([ 3, 7, 10, 13, 14, 16, 17, 18, 19]), old_indices_discarded=array([15]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65669195, 0.970249 ]), radius=0.03125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 14, 16, 17, 18, 19, 20]), model=ScalarModel(intercept=1.476547966942911, linear_terms=array([0.03370192, 0.03763453]), square_terms=array([[ 0.00506063, -0.03670318], + [-0.03670318, 0.56722334]]), scale=0.03125, shift=array([4.65669195, 0.970249 ])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65669195, 0.970249 ]), radius=0.015625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([17, 18, 19, 20, 21]), model=ScalarModel(intercept=1.6322981269191386, linear_terms=array([0.00381601, 0.00635055]), square_terms=array([[0.00024779, 0.00408403], + [0.00408403, 0.13797549]]), scale=0.015625, shift=array([4.65669195, 0.970249 ])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5, shift=array([5. , 0.95])), candidate_index=22, candidate_x=array([4.64105275, 0.96999908]), index=22, x=array([4.64105275, 0.96999908]), fval=1.6271336788414648, rho=0.5298332001777031, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([17, 18, 19, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.015641195495581482, relative_step_length=1.0010365117172149, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.64105275, 0.96999908]), radius=0.03125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 14, 17, 18, 19, 20, 21, 22]), model=ScalarModel(intercept=1.4934936330221924, linear_terms=array([0.03884984, 0.00088909]), square_terms=array([[ 0.00687594, -0.04632293], + [-0.04632293, 0.56349202]]), scale=0.03125, shift=array([4.64105275, 0.96999908])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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1.1]))), model_indices=array([17, 18, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=1.629077033113828, linear_terms=array([0.00316852, 0.0009159 ]), square_terms=array([[0.00023897, 0.00406143], + [0.00406143, 0.13846122]]), scale=0.015625, shift=array([4.64105275, 0.96999908])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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linear_terms=array([0.00458585, 0.02180017]), square_terms=array([[0.00098328, 0.01001956], + [0.01001956, 0.27420015]]), scale=0.03125, shift=array([4.62543122, 0.97034637])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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2.22309204]]), scale=0.0625, shift=array([4.59411882, 0.96902136])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5, shift=array([5. , 0.95])), candidate_index=26, candidate_x=array([4.53171043, 0.97299769]), index=26, x=array([4.53171043, 0.97299769]), fval=1.6090597971175158, rho=1.2503670663900857, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([17, 18, 19, 20, 21, 22, 23, 24, 25]), old_indices_discarded=array([ 3, 7, 10, 13, 14, 15, 16]), step_length=0.06253494026175699, relative_step_length=1.0005590441881118, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.53171043, 0.97299769]), radius=0.125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 18, 20, 21, 22, 23, 24, 25, 26]), model=ScalarModel(intercept=1.6102616360135478, linear_terms=array([0.03712606, 0.18206296]), square_terms=array([[ 0.01015217, -0.01895962], + [-0.01895962, 4.60594093]]), scale=0.125, shift=array([4.53171043, 0.97299769])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5, shift=array([5. , 0.95])), candidate_index=27, candidate_x=array([4.40674873, 0.96757506]), index=26, x=array([4.53171043, 0.97299769]), fval=1.6090597971175158, rho=-0.5639375590226261, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 18, 20, 21, 22, 23, 24, 25, 26]), old_indices_discarded=array([ 0, 1, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.53171043, 0.97299769]), radius=0.0625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 18, 20, 21, 22, 23, 24, 25, 26]), model=ScalarModel(intercept=1.6102616360135507, linear_terms=array([0.01856303, 0.09103148]), square_terms=array([[ 0.00253804, -0.00473991], + [-0.00473991, 1.15148523]]), scale=0.0625, shift=array([4.53171043, 0.97299769])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.5, shift=array([5. , 0.95])), candidate_index=28, candidate_x=array([4.46924045, 0.96787265]), index=26, x=array([4.53171043, 0.97299769]), fval=1.6090597971175158, rho=-0.6660251871790788, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 18, 20, 21, 22, 23, 24, 25, 26]), old_indices_discarded=array([ 7, 10, 13, 14, 16, 17, 19, 27]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.53171043, 0.97299769]), radius=0.03125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([18, 20, 21, 22, 23, 24, 25, 26, 28]), model=ScalarModel(intercept=1.6130550857976087, linear_terms=array([0.00349422, 0.00036862]), square_terms=array([[0.00092013, 0.01618579], + [0.01618579, 0.55845701]]), scale=0.03125, shift=array([4.53171043, 0.97299769])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, 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old_indices_discarded=array([ 7, 10, 13, 14, 16, 17, 18, 19, 21, 22]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.50047281, 0.97387764]), radius=0.03125, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 20, 23, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=1.6184027642815897, linear_terms=array([0.0045845 , 0.03080401]), square_terms=array([[0.00058656, 0.00613405], + [0.00613405, 0.27515297]]), scale=0.03125, shift=array([4.50047281, 0.97387764])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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n_evals_acceptance=1), State(trustregion=Region(center=array([4.50047281, 0.97387764]), radius=0.015625, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([26, 28, 29, 30, 31]), model=ScalarModel(intercept=1.605617847169665, linear_terms=array([ 0.00128156, -0.01188114]), square_terms=array([[0.0002254 , 0.0042768 ], + [0.0042768 , 0.16410452]]), scale=0.015625, shift=array([4.50047281, 0.97387764])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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0.5]), upper=array([20. , 1.1]))), model_indices=array([20, 25, 26, 27, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=1.6062136335670387, linear_terms=array([0.00290969, 0.01676498]), square_terms=array([[0.00098405, 0.01747478], + [0.01747478, 0.58451439]]), scale=0.03125, shift=array([4.48488283, 0.97540147])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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model=ScalarModel(intercept=1.600137490679293, linear_terms=array([ 0.00511285, -0.03923899]), square_terms=array([[0.00379447, 0.07073241], + [0.07073241, 2.64617384]]), scale=0.0625, shift=array([4.45362123, 0.97543964])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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-0.00037724]), square_terms=array([[0.0117151 , 0.21696693], + [0.21696693, 8.44873327]]), scale=array([0.11077837, 0.11077837]), shift=array([4.39116976, 0.97803159])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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square_terms=array([[ 0.04649406, 0.64815023], + [ 0.64815023, 20.13386188]]), scale=array([0.22155673, 0.17033768]), shift=array([4.28039139, 0.92966232])), vector_model=VectorModel(intercepts=array([ 0.06627514, 0.16052249, 0.18679036, 0.20889914, 0.25790787, + 0.27002423, 0.29431582, -0.27833955, -0.43778171, -0.45868004, + -0.54906886, -0.73404126, -0.27030478, -0.09133963, -0.04732048, + -0.05732014, -0.01646955]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], 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old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.70720331, 0.97232932]), radius=0.029420020676152805, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 15, 16]), model=ScalarModel(intercept=1.6372268956710594, linear_terms=array([ 0.1685441 , -0.13872507]), square_terms=array([[ 0.05020446, -0.12669357], + [-0.12669357, 0.64471625]]), scale=0.029420020676152805, shift=array([4.70720331, 0.97232932])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.67717913, 0.97269526]), radius=0.014710010338076403, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 16, 17]), model=ScalarModel(intercept=1.6319414624307902, linear_terms=array([0.00310345, 0.01871238]), square_terms=array([[0.0001745 , 0.00326021], + [0.00326021, 0.12782284]]), scale=0.014710010338076403, shift=array([4.67717913, 0.97269526])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=18, candidate_x=array([4.66242041, 0.97095279]), index=18, x=array([4.66242041, 0.97095279]), fval=1.6292672840092994, rho=0.6756001269705089, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.014861219314677702, relative_step_length=1.010279324971642, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.66242041, 0.97095279]), radius=0.029420020676152805, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 15, 16, 17, 18]), model=ScalarModel(intercept=1.4810088090029692, linear_terms=array([ 0.11958572, -0.03732605]), square_terms=array([[ 0.05065772, -0.12829538], + [-0.12829538, 0.65103621]]), scale=0.029420020676152805, shift=array([4.66242041, 0.97095279])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=19, candidate_x=array([4.63314395, 0.96734331]), index=18, x=array([4.66242041, 0.97095279]), fval=1.6292672840092994, rho=-0.020590492456450688, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 15, 16, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.66242041, 0.97095279]), radius=0.014710010338076403, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 16, 17, 18, 19]), model=ScalarModel(intercept=1.6292156586595015, linear_terms=array([0.00188502, 0.00233158]), square_terms=array([[0.00018003, 0.0034169 ], + [0.0034169 , 0.12685944]]), scale=0.014710010338076403, shift=array([4.66242041, 0.97095279])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=20, candidate_x=array([4.64770842, 0.97107699]), index=20, x=array([4.64770842, 0.97107699]), fval=1.6269015883981965, rho=1.3143613594368355, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.014712511658342373, relative_step_length=1.0001700420467752, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.64770842, 0.97107699]), radius=0.029420020676152805, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 15, 16, 17, 18, 19, 20]), model=ScalarModel(intercept=1.5063160859816729, linear_terms=array([ 0.05041148, -0.04272255]), square_terms=array([[ 0.0075054 , -0.03307161], + [-0.03307161, 0.58165163]]), scale=0.029420020676152805, shift=array([4.64770842, 0.97107699])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=21, candidate_x=array([4.61821743, 0.9715283 ]), index=21, x=array([4.61821743, 0.9715283 ]), fval=1.6215771152258682, rho=0.11367207027105729, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 15, 16, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.029494447103161476, relative_step_length=1.002529788399129, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.61821743, 0.9715283 ]), radius=0.05884004135230561, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 15, 16, 17, 18, 19, 20, 21]), model=ScalarModel(intercept=1.517081598562027, linear_terms=array([ 0.03194748, -0.0915037 ]), square_terms=array([[0.00517109, 0.00423992], + [0.00423992, 2.21259895]]), scale=0.05884004135230561, shift=array([4.61821743, 0.9715283 ])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=22, candidate_x=array([4.55938558, 0.97404376]), index=22, x=array([4.55938558, 0.97404376]), fval=1.6122243121836672, rho=0.2975814223142011, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 15, 16, 17, 18, 19, 20, 21]), old_indices_discarded=array([ 9, 14]), step_length=0.058885598606691694, relative_step_length=1.000774255988593, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.55938558, 0.97404376]), radius=0.11768008270461122, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 15, 16, 17, 18, 19, 20, 21, 22]), model=ScalarModel(intercept=1.6244940972117774, linear_terms=array([0.00768524, 0.3968281 ]), square_terms=array([[ 0.01794061, -0.11175552], + [-0.11175552, 4.76976273]]), scale=0.11768008270461122, shift=array([4.55938558, 0.97404376])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=23, candidate_x=array([4.44236235, 0.96151592]), index=22, x=array([4.55938558, 0.97404376]), fval=1.6122243121836672, rho=-2.222167750707154, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 15, 16, 17, 18, 19, 20, 21, 22]), old_indices_discarded=array([ 0, 1, 3, 4, 7, 8, 9, 10, 11, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.55938558, 0.97404376]), radius=0.05884004135230561, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([15, 16, 17, 18, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=1.6256896704319683, linear_terms=array([0.0043886 , 0.10549552]), square_terms=array([[0.00419989, 0.0564693 ], + [0.0564693 , 1.33147903]]), scale=0.05884004135230561, shift=array([4.55938558, 0.97404376])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=24, candidate_x=array([4.61778209, 0.96683279]), index=22, x=array([4.55938558, 0.97404376]), fval=1.6122243121836672, rho=-5.466651138741908, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([15, 16, 17, 18, 19, 20, 21, 22, 23]), old_indices_discarded=array([ 0, 3, 9, 10, 12, 14]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.55938558, 0.97404376]), radius=0.029420020676152805, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([15, 16, 18, 19, 20, 21, 22, 23, 24]), model=ScalarModel(intercept=1.6269177150442447, linear_terms=array([0.00261458, 0.05266846]), square_terms=array([[0.00103242, 0.01385775], + [0.01385775, 0.33190979]]), scale=0.029420020676152805, shift=array([4.55938558, 0.97404376])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=25, candidate_x=array([4.53016803, 0.97059817]), index=22, x=array([4.55938558, 0.97404376]), fval=1.6122243121836672, rho=-0.5597649420514624, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([15, 16, 18, 19, 20, 21, 22, 23, 24]), old_indices_discarded=array([14, 17]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.55938558, 0.97404376]), radius=0.014710010338076403, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([21, 22, 24, 25]), model=ScalarModel(intercept=1.6125186187197176, linear_terms=array([0.00192027, 0.00012471]), square_terms=array([[0.00019263, 0.00355747], + [0.00355747, 0.13532065]]), scale=0.014710010338076403, shift=array([4.55938558, 0.97404376])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=26, candidate_x=array([4.54468017, 0.97441185]), index=26, x=array([4.54468017, 0.97441185]), fval=1.6100467129384297, rho=1.1664111683806726, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([21, 22, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.01471002222619643, relative_step_length=1.0000008081653073, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.54468017, 0.97441185]), radius=0.029420020676152805, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 19, 20, 21, 22, 23, 24, 25, 26]), model=ScalarModel(intercept=1.610617175628848, linear_terms=array([0.00467212, 0.01317766]), square_terms=array([[0.00085989, 0.01539591], + [0.01539591, 0.54391644]]), scale=0.029420020676152805, shift=array([4.54468017, 0.97441185])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=27, candidate_x=array([4.51525197, 0.97453122]), index=27, x=array([4.51525197, 0.97453122]), fval=1.6064550393910895, rho=0.8455422416631787, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([16, 19, 20, 21, 22, 23, 24, 25, 26]), old_indices_discarded=array([14, 15, 17, 18]), step_length=0.029428443747579514, relative_step_length=1.000286304062102, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.51525197, 0.97453122]), radius=0.05884004135230561, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 19, 21, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=1.6171036489097632, linear_terms=array([0.00432157, 0.08549957]), square_terms=array([[0.00404165, 0.04083728], + [0.04083728, 0.9748026 ]]), scale=0.05884004135230561, shift=array([4.51525197, 0.97453122])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=28, candidate_x=array([4.45646967, 0.97192498]), index=27, x=array([4.51525197, 0.97453122]), fval=1.6064550393910895, rho=-0.3653445002297127, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 19, 21, 22, 23, 24, 25, 26, 27]), old_indices_discarded=array([ 0, 3, 7, 10, 12, 13, 15, 16, 17, 18, 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.51525197, 0.97453122]), radius=0.029420020676152805, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 21, 22, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=1.6158549160636237, linear_terms=array([0.0016874, 0.045421 ]), square_terms=array([[0.00106973, 0.01073937], + [0.01073937, 0.24481613]]), scale=0.029420020676152805, shift=array([4.51525197, 0.97453122])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=29, candidate_x=array([4.54388801, 0.96778475]), index=27, x=array([4.51525197, 0.97453122]), fval=1.6064550393910895, rho=-3.9976405851055894, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 21, 22, 23, 24, 25, 26, 27, 28]), old_indices_discarded=array([15, 16, 19, 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.51525197, 0.97453122]), radius=0.014710010338076403, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=1.6066876339598914, linear_terms=array([ 0.00156986, -0.00517551]), square_terms=array([[0.00019907, 0.00364165], + [0.00364165, 0.13928521]]), scale=0.014710010338076403, shift=array([4.51525197, 0.97453122])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=30, candidate_x=array([4.5005611 , 0.97545133]), index=30, x=array([4.5005611 , 0.97545133]), fval=1.6045259070805813, rho=1.104079170574446, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([22, 25, 26, 27, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=0.014719649538465622, relative_step_length=1.0006552816869387, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.5005611 , 0.97545133]), radius=0.029420020676152805, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 22, 23, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=1.6151275211840224, linear_terms=array([-0.00091945, 0.04982336]), square_terms=array([[0.0018448 , 0.01496444], + [0.01496444, 0.24558318]]), scale=0.029420020676152805, shift=array([4.5005611 , 0.97545133])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=31, candidate_x=array([4.52948592, 0.96781471]), index=30, x=array([4.5005611 , 0.97545133]), fval=1.6045259070805813, rho=-2.139770334550352, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 22, 23, 25, 26, 27, 28, 29, 30]), old_indices_discarded=array([15, 19, 21, 24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.5005611 , 0.97545133]), radius=0.014710010338076403, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([22, 23, 25, 26, 27, 28, 29, 30, 31]), model=ScalarModel(intercept=1.6052861266606393, linear_terms=array([0.00202734, 0.00619617]), square_terms=array([[0.0002099 , 0.00380383], + [0.00380383, 0.14225083]]), scale=0.014710010338076403, shift=array([4.5005611 , 0.97545133])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=32, candidate_x=array([4.48583943, 0.97520725]), index=32, x=array([4.48583943, 0.97520725]), fval=1.6029216364692263, rho=0.8252881334307689, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([22, 23, 25, 26, 27, 28, 29, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.014723693213830385, relative_step_length=1.0009301744485226, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.48583943, 0.97520725]), radius=0.029420020676152805, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 25, 26, 27, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=1.603281247362616, linear_terms=array([0.00373974, 0.00024847]), square_terms=array([[0.00085108, 0.01532586], + [0.01532586, 0.57103593]]), scale=0.029420020676152805, shift=array([4.48583943, 0.97520725])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=33, candidate_x=array([4.45642954, 0.97597933]), index=33, x=array([4.45642954, 0.97597933]), fval=1.599716096343115, rho=0.9127111762327604, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([23, 25, 26, 27, 28, 29, 30, 31, 32]), old_indices_discarded=array([14, 21, 22, 24]), step_length=0.0294200258837129, relative_step_length=1.0000001770073568, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.45642954, 0.97597933]), radius=0.05884004135230561, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 25, 27, 28, 29, 30, 31, 32, 33]), model=ScalarModel(intercept=1.5997771172632202, linear_terms=array([0.00705164, 0.0023645 ]), square_terms=array([[0.00344304, 0.06165063], + [0.06165063, 2.29724063]]), scale=0.05884004135230561, shift=array([4.45642954, 0.97597933])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=34, candidate_x=array([4.39760896, 0.97749389]), index=34, x=array([4.39760896, 0.97749389]), fval=1.5939391812466719, rho=0.9480590071850733, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([23, 25, 27, 28, 29, 30, 31, 32, 33]), old_indices_discarded=array([ 0, 3, 7, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, 26]), step_length=0.05884007156424952, relative_step_length=1.000000513458917, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.39760896, 0.97749389]), radius=0.11768008270461122, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([23, 25, 27, 28, 30, 31, 32, 33, 34]), model=ScalarModel(intercept=1.593889235787229, linear_terms=array([0.01040724, 0.00815608]), square_terms=array([[0.01329919, 0.24024205], + [0.24024205, 9.25060511]]), scale=0.11768008270461122, shift=array([4.39760896, 0.97749389])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=35, candidate_x=array([4.27996586, 0.98044438]), index=35, x=array([4.27996586, 0.98044438]), fval=1.5886714368056303, rho=0.7899999841070257, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([23, 25, 27, 28, 30, 31, 32, 33, 34]), old_indices_discarded=array([ 0, 1, 3, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, + 22, 24, 26, 29]), step_length=0.11768009368678081, relative_step_length=1.0000000933222457, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.27996586, 0.98044438]), radius=0.23536016540922244, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 13, 23, 28, 30, 32, 34, 35]), model=ScalarModel(intercept=1.462227461968244, linear_terms=array([ 0.42418134, -1.67220361]), square_terms=array([[ 0.54111536, -2.08375437], + [-2.08375437, 13.44006276]]), scale=array([0.20858252, 0.16406907]), shift=array([4.27996586, 0.93593093])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=36, candidate_x=array([4.12220384, 0.93710467]), index=35, x=array([4.27996586, 0.98044438]), fval=1.5886714368056303, rho=-3.916267884563185, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 13, 23, 28, 30, 32, 34, 35]), old_indices_discarded=array([ 0, 1, 2, 4, 5, 6, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, + 20, 21, 22, 24, 25, 26, 27, 29, 31, 33]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.27996586, 0.98044438]), radius=0.11768008270461122, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 13, 23, 33, 34, 35, 36]), model=ScalarModel(intercept=1.4622568288955424, linear_terms=array([0.02225047, 0.92573591]), square_terms=array([[ 0.05426403, -0.13596618], + [-0.13596618, 7.70370864]]), scale=0.11768008270461122, shift=array([4.27996586, 0.98044438])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=37, candidate_x=array([4.1634327 , 0.96405462]), index=35, x=array([4.27996586, 0.98044438]), fval=1.5886714368056303, rho=-2.181712710002738, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 10, 13, 23, 33, 34, 35, 36]), old_indices_discarded=array([ 0, 1, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, 26, + 27, 28, 29, 30, 31, 32]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.27996586, 0.98044438]), radius=0.05884004135230561, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 13, 23, 34, 35, 36, 37]), model=ScalarModel(intercept=1.4660466698117092, linear_terms=array([0.01297109, 0.40170174]), square_terms=array([[0.01156038, 0.07971153], + [0.07971153, 1.91612895]]), scale=0.05884004135230561, shift=array([4.27996586, 0.98044438])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=38, candidate_x=array([4.33690661, 0.96561531]), index=35, x=array([4.27996586, 0.98044438]), fval=1.5886714368056303, rho=-1.6144313618454553, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 10, 13, 23, 34, 35, 36, 37]), old_indices_discarded=array([14, 22, 25, 26, 27, 28, 29, 30, 31, 32, 33]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.27996586, 0.98044438]), radius=0.029420020676152805, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 34, 35, 37, 38]), model=ScalarModel(intercept=1.470665765480307, linear_terms=array([0.00298517, 0.10703439]), square_terms=array([[0.00144857, 0.00998505], + [0.00998505, 0.6595069 ]]), scale=0.029420020676152805, shift=array([4.27996586, 0.98044438])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.4707203308184449, shift=array([4.70720331, 0.97232932])), candidate_index=39, candidate_x=array([4.24902077, 0.97613818]), index=35, x=array([4.27996586, 0.98044438]), fval=1.5886714368056303, rho=-1.3905549963491703, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 10, 34, 35, 37, 38]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.27996586, 0.98044438]), radius=0.014710010338076403, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([35, 38, 39]), model=ScalarModel(intercept=1.588671436805631, linear_terms=array([-0.00081796, -0.00402187]), square_terms=array([[0.00023222, 0.00443072], + [0.00443072, 0.1618569 ]]), scale=0.014710010338076403, shift=array([4.27996586, 0.98044438])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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40]), model=ScalarModel(intercept=1.5886714368056296, linear_terms=array([-1.01760419e-05, -4.87679307e-03]), square_terms=array([[5.26567689e-05, 9.93842843e-04], + [9.93842843e-04, 4.23791006e-02]]), scale=0.007355005169038201, shift=array([4.27996586, 0.98044438])), vector_model=VectorModel(intercepts=array([ 0.07234168, 0.18160918, 0.2222667 , 0.25651309, 0.32507299, + 0.35207246, 0.40774796, -0.16115678, -0.34789505, -0.37798502, + -0.47569841, -0.68890744, -0.27909687, -0.10567612, -0.06095636, + -0.0660279 , 0.02915204]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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4.279965862398321, 'DiscFac': 0.9804443756996974}, {'CRRA': 4.122203844999166, 'DiscFac': 0.937104673702021}, {'CRRA': 4.163432700992639, 'DiscFac': 0.9640546179784433}, {'CRRA': 4.336906613655518, 'DiscFac': 0.9656153058500048}, {'CRRA': 4.249020769576481, 'DiscFac': 0.9761381800429344}, {'CRRA': 4.294680071351096, 'DiscFac': 0.9804072400803372}, {'CRRA': 4.272642056859294, 'DiscFac': 0.9814607088251204}], 'criterion': [1.6380478126416131, 4.03723195367052, 3.5440740950812013, 3.2759678353900803, 6.781083894625252, 3.8713442516298198, 3.996890584141768, 7.627891908022631, 1.7551707114922444, 7.144112642232848, 6.6404717126627215, 3.935564366818633, 7.291412101525699, 3.862333195265191, 3.197857033577577, 2.6761443032128733, 1.6565559654451956, 1.631941462430788, 1.6292672840092992, 1.6313284776792012, 1.6269015883981968, 1.6215771152258682, 1.6122243121836672, 1.6696000135319509, 1.6306703543399446, 1.614669208950377, 1.6100467129384297, 1.6064550393910892, 1.6076695381563089, 1.6233520305796163, 1.6045259070805813, 1.6226940047077765, 1.6029216364692263, 1.599716096343115, 1.5939391812466719, 1.5886714368056305, 2.4007693475753995, 1.7378906646063588, 1.6562535862817716, 1.601747168932624, 1.5887715770723967, 1.5881921698252237], 'runtime': [0.0, 1.6408778029999667, 1.879227850999996, 2.101594065000427, 2.313481502000286, 2.55059718099983, 2.782204840999839, 3.0194256559998394, 3.235137909000514, 3.449739153000337, 3.6794994500005487, 3.8966444010002306, 4.28813861800063, 5.764485786000478, 7.000034329999835, 8.24761119699997, 9.516164548999768, 10.76685041500059, 12.019717905000107, 13.265578907999952, 14.518897724000453, 15.760440533999827, 16.995802637999986, 18.22653636799987, 19.587672147000376, 20.82062521500029, 22.057747867000217, 23.28416549299982, 24.62205672500022, 25.949533355999847, 27.21613684300064, 28.504664697999942, 29.771321367999917, 31.040138887000467, 32.30792920600015, 33.793730317000154, 35.13760062700021, 36.411630069999774, 37.71618604300056, 39.015112086000045, 40.31985881299988, 41.661046635000275], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]}, 'multistart_info': {...}}], 'exploration_sample': array([[ 5. , 0.95 ], + [ 5.825 , 0.95 ], + [ 7.596875, 0.93125 ], + [ 4.64375 , 0.6875 ], + [ 9.36875 , 0.8375 ], + [ 8.1875 , 0.725 ], + [10.55 , 0.8 ], + [11.73125 , 0.7625 ], + [ 7.00625 , 0.6125 ], + [15.275 , 0.65 ], + [12.9125 , 0.575 ], + [17.046875, 0.63125 ], + [ 3.4625 , 0.875 ], + [16.45625 , 0.9125 ], + [14.09375 , 0.9875 ], + [18.81875 , 0.5375 ], + [12.321875, 1.08125 ], + [17.6375 , 1.025 ], + [ 2.871875, 0.78125 ], + [ 2.28125 , 1.0625 ]]), 'exploration_results': array([ 1.74175066, 2.04247528, 2.9717037 , 3.76994253, 3.80031518, + 4.27735638, 4.32695188, 4.78264529, 5.25346612, 5.79528191, + 5.94010747, 6.02403907, 6.09730665, 6.10855726, 6.12270544, + 6.45799199, 7.01428788, 7.3683214 , 8.72764877, 13.61935456])}}" diff --git a/content/tables/min/WealthPortfolio_estimate_results.csv b/content/tables/min/WealthPortfolio_estimate_results.csv new file mode 100644 index 0000000..84e4e27 --- /dev/null +++ b/content/tables/min/WealthPortfolio_estimate_results.csv @@ -0,0 +1,7307 @@ +CRRA,10.219768718617258 +DiscFac,0.8035952003147214 +time_to_estimate,134.71642017364502 +params,"{'CRRA': 10.219768718617258, 'DiscFac': 0.8035952003147214}" +criterion,1.4324246742584341 +start_criterion,18.424123431628693 +start_params,"{'CRRA': 5.0, 'DiscFac': 0.95}" +algorithm,multistart_tranquilo_ls +direction,minimize +n_free,2 +message,Absolute params change smaller than tolerance. +success, +n_criterion_evaluations, +n_derivative_evaluations, +n_iterations, +history,"{'params': [{'CRRA': 10.796287222045608, 'DiscFac': 0.7512189549688916}, {'CRRA': 9.83949117893577, 'DiscFac': 0.540401632080349}, {'CRRA': 11.753083265155446, 'DiscFac': 0.7276940506351226}, {'CRRA': 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3.0210618639998756, 3.2580944800001816, 3.4653453969999646, 3.728331842999978, 3.975551980000091, 4.155800905999968, 5.559656017000179, 6.802116995000233, 8.06469720899986, 9.325519093999901, 10.57778590199996, 11.829665229000057, 13.080631366000034, 14.326657801000238, 15.71577292200027, 16.999908634999883, 18.272807574000126, 19.582853569000235, 20.83235119500023, 22.094798552000157, 23.35142596600008, 24.604288662000272, 25.87174642700029, 27.11416403300018, 28.383461697000257, 29.77518187900023, 31.048289716, 32.33646260100022, 33.61736069900007, 34.86628536700027, 36.13524358199993, 37.38796032500022], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]}" +convergence_report,"{'one_step': {'relative_criterion_change': 0.09400888657621548, 'relative_params_change': 0.07336751265176697, 'absolute_criterion_change': 0.13466064873133354, 'absolute_params_change': 0.19765619590330294}, 'five_steps': {'relative_criterion_change': 0.09400888657621548, 'relative_params_change': 0.07336751265176697, 'absolute_criterion_change': 0.13466064873133354, 'absolute_params_change': 0.19765619590330294}}" +multistart_info,"{'start_parameters': [{'CRRA': 10.549999999999999, 'DiscFac': 0.8}, {'CRRA': 10.796287222045608, 'DiscFac': 0.7512189549688916}], 'local_optima': [Minimize with 2 free parameters terminated., Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute params change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 0.4931 0.7235 +relative_params_change 0.2452 0.2452 +absolute_criterion_change 0.5564 0.8164 +absolute_params_change 1.918 1.918 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 10.549999999999999, 'DiscFac': 0.8}, {'CRRA': 11.73125, 'DiscFac': 0.7625000000000001}, {'CRRA': 9.368749999999999, 'DiscFac': 0.8375}, {'CRRA': 7.596874999999999, 'DiscFac': 0.9312500000000001}, {'CRRA': 12.9125, 'DiscFac': 0.575}, {'CRRA': 15.274999999999999, 'DiscFac': 0.65}, {'CRRA': 17.046875, 'DiscFac': 0.6312500000000001}, {'CRRA': 16.45625, 'DiscFac': 0.9125000000000001}, {'CRRA': 18.81875, 'DiscFac': 0.5375}, {'CRRA': 8.1875, 'DiscFac': 0.7250000000000001}, {'CRRA': 14.093749999999998, 'DiscFac': 0.9875}, {'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 17.6375, 'DiscFac': 1.0250000000000001}, {'CRRA': 7.00625, 'DiscFac': 0.6125}, {'CRRA': 5.0, 'DiscFac': 0.95}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 2.871875, 'DiscFac': 0.78125}, {'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 2.28125, 'DiscFac': 1.0625}], 'exploration_results': array([ 1.74531296, 2.10648864, 2.17581064, 2.66803924, + 2.70500778, 2.87740953, 3.0406658 , 3.37839016, + 3.53882151, 3.99992851, 4.37946561, 4.47403011, + 4.90296965, 6.27675034, 11.07528266, 21.17347996, + 25.72759101, 28.93912422, 124.53209372])}" +algorithm_output,"{'states': [State(trustregion=Region(center=array([10.79628722, 0.75121895]), radius=1.0796287222045609, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=1.944688071402379, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=1.0796287222045609, shift=array([10.79628722, 0.75121895])), vector_model=VectorModel(intercepts=array([ 0.06389705, 0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + -0.62576956, -0.79590416, -0.14927533, 0.0536062 , 0.10945384, + 0.08712913, 0.080573 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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State(trustregion=Region(center=array([10.79628722, 0.75121895]), radius=1.0796287222045609, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=1.1166657271561815, linear_terms=array([0.15864049, 0.42371729]), square_terms=array([[0.12859729, 0.39267237], + [0.39267237, 6.44727637]]), scale=array([0.95679604, 0.3 ]), shift=array([10.79628722, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.06389705, 0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + -0.62576956, -0.79590416, -0.14927533, 0.0536062 , 0.10945384, + 0.08712913, 0.080573 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + 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model=ScalarModel(intercept=1.1802551636376832, linear_terms=array([-0.4094047 , -1.11949307]), square_terms=array([[0.49537366, 1.3045479 ], + [1.3045479 , 6.88548901]]), scale=array([0.47839802, 0.3 ]), shift=array([9.83949118, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.06389705, 0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + -0.62576956, -0.79590416, -0.14927533, 0.0536062 , 0.10945384, + 0.08712913, 0.080573 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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linear_terms=array([-0.0322572 , -0.30854104]), square_terms=array([[0.67079418, 1.5479022 ], + [1.5479022 , 7.21799651]]), scale=array([0.95679604, 0.3 ]), shift=array([10.21976966, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.06389705, 0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + -0.62576956, -0.79590416, -0.14927533, 0.0536062 , 0.10945384, + 0.08712913, 0.080573 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], 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0.8790954 ], + [0.8790954 , 6.71082357]]), scale=array([0.47839802, 0.3 ]), shift=array([10.21976966, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.06389705, 0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + -0.62576956, -0.79590416, -0.14927533, 0.0536062 , 0.10945384, + 0.08712913, 0.080573 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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shift=array([10.21976966, 0.80359487])), vector_model=VectorModel(intercepts=array([ 0.06389705, 0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + -0.62576956, -0.79590416, -0.14927533, 0.0536062 , 0.10945384, + 0.08712913, 0.080573 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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x=array([10.21976966, 0.80359487]), fval=1.466665976719959, rho=-0.5278763601515147, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([10, 15, 16, 17, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.21976966, 0.80359487]), radius=0.03373839756889253, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([15, 16, 17, 20, 21]), model=ScalarModel(intercept=1.6512778708853884, linear_terms=array([-0.03985835, 0.03411712]), square_terms=array([[0.00599018, 0.00998315], + [0.00998315, 0.03869545]]), scale=0.03373839756889253, shift=array([10.21976966, 0.80359487])), vector_model=VectorModel(intercepts=array([ 0.06389705, 0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + -0.62576956, -0.79590416, -0.14927533, 0.0536062 , 0.10945384, + 0.08712913, 0.080573 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.0796287222045609, shift=array([10.79628722, 0.75121895])), candidate_index=22, candidate_x=array([10.24904785, 0.78650626]), index=16, x=array([10.21976966, 0.80359487]), fval=1.466665976719959, rho=-4.546783753534983, accepted=False, 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dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.21976966, 0.80359487]), radius=0.008434599392223132, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 22, 23]), model=ScalarModel(intercept=1.4666659767199597, linear_terms=array([0.34812163, 0.50552679]), square_terms=array([[0.78561087, 1.27785522], + [1.27785522, 2.09150806]]), scale=0.008434599392223132, shift=array([10.21976966, 0.80359487])), vector_model=VectorModel(intercepts=array([ 0.06389705, 0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + -0.62576956, -0.79590416, -0.14927533, 0.0536062 , 0.10945384, + 0.08712913, 0.080573 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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State(trustregion=Region(center=array([10.21976966, 0.80359487]), radius=0.004217299696111566, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 23, 24]), model=ScalarModel(intercept=1.4666659767199592, linear_terms=array([0.55084332, 2.09635603]), square_terms=array([[0.23949622, 0.71845366], + [0.71845366, 3.39566675]]), scale=0.004217299696111566, shift=array([10.21976966, 0.80359487])), vector_model=VectorModel(intercepts=array([ 0.06389705, 0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + -0.62576956, -0.79590416, -0.14927533, 0.0536062 , 0.10945384, + 0.08712913, 0.080573 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 24, 25]), model=ScalarModel(intercept=1.4666659767199588, linear_terms=array([-0.04544986, 0.1439351 ]), square_terms=array([[ 0.03369853, -0.00374743], + [-0.00374743, 0.02333965]]), scale=0.002108649848055783, shift=array([10.21976966, 0.80359487])), vector_model=VectorModel(intercepts=array([ 0.06389705, 0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + -0.62576956, -0.79590416, -0.14927533, 0.0536062 , 0.10945384, + 0.08712913, 0.080573 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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linear_terms=array([-0.07066043, 0.18003202]), square_terms=array([[ 0.01861073, -0.05858304], + [-0.05858304, 0.21397908]]), scale=0.0010543249240278915, shift=array([10.21976966, 0.80359487])), vector_model=VectorModel(intercepts=array([ 0.06389705, 0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + -0.62576956, -0.79590416, -0.14927533, 0.0536062 , 0.10945384, + 0.08712913, 0.080573 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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scale=0.0005271624620139457, shift=array([10.21976966, 0.80359487])), vector_model=VectorModel(intercepts=array([ 0.06389705, 0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + -0.62576956, -0.79590416, -0.14927533, 0.0536062 , 0.10945384, + 0.08712913, 0.080573 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + -0.62576956, -0.79590416, -0.14927533, 0.0536062 , 0.10945384, + 0.08712913, 0.080573 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.0796287222045609, shift=array([10.79628722, 0.75121895])), candidate_index=29, candidate_x=array([10.21973477, 0.80385614]), index=16, x=array([10.21976966, 0.80359487]), fval=1.466665976719959, rho=-1.385618786964724, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([16, 27, 28]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.21976966, 0.80359487]), radius=0.00013179061550348643, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 28, 29]), model=ScalarModel(intercept=1.466665976719959, linear_terms=array([-0.0761264 , 0.13860902]), square_terms=array([[ 0.01125216, -0.016678 ], + [-0.016678 , 0.03393693]]), scale=0.00013179061550348643, shift=array([10.21976966, 0.80359487])), vector_model=VectorModel(intercepts=array([ 0.06389705, 0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + 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fval=1.466665976719959, rho=-0.9313463730632265, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([16, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.21976966, 0.80359487]), radius=6.589530775174322e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 29, 30]), model=ScalarModel(intercept=1.466665976719959, linear_terms=array([1.88031026, 0.32552717]), square_terms=array([[27.70522767, 4.07358483], + [ 4.07358483, 0.60357257]]), scale=6.589530775174322e-05, shift=array([10.21976966, 0.80359487])), vector_model=VectorModel(intercepts=array([ 0.06389705, 0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + -0.62576956, -0.79590416, -0.14927533, 0.0536062 , 0.10945384, + 0.08712913, 0.080573 ]), linear_terms=array([[0., 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29, 30]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.21976966, 0.80359487]), radius=3.294765387587161e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 30, 31]), model=ScalarModel(intercept=1.4666659767199595, linear_terms=array([-1.6196538 , -0.30795927]), square_terms=array([[2.28612575, 0.4440688 ], + [0.4440688 , 0.09657711]]), scale=3.294765387587161e-05, shift=array([10.21976966, 0.80359487])), vector_model=VectorModel(intercepts=array([ 0.06389705, 0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + -0.62576956, -0.79590416, -0.14927533, 0.0536062 , 0.10945384, + 0.08712913, 0.080573 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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n_evals_acceptance=1), State(trustregion=Region(center=array([10.21976966, 0.80359487]), radius=1.6473826937935804e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 31, 32]), model=ScalarModel(intercept=1.4666659767199592, linear_terms=array([ 0.11595344, -0.08086352]), square_terms=array([[ 0.01665732, -0.00335936], + [-0.00335936, 0.00953888]]), scale=1.6473826937935804e-05, shift=array([10.21976966, 0.80359487])), vector_model=VectorModel(intercepts=array([ 0.06389705, 0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + -0.62576956, -0.79590416, -0.14927533, 0.0536062 , 0.10945384, + 0.08712913, 0.080573 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 32, 33]), model=ScalarModel(intercept=1.4666659767199604, linear_terms=array([-0.81016988, -1.16337389]), square_terms=array([[0.82069046, 1.14256831], + [1.14256831, 1.59421614]]), scale=8.236913468967902e-06, shift=array([10.21976966, 0.80359487])), vector_model=VectorModel(intercepts=array([ 0.06389705, 0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + -0.62576956, -0.79590416, -0.14927533, 0.0536062 , 0.10945384, + 0.08712913, 0.080573 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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model=ScalarModel(intercept=1.46666597671996, linear_terms=array([0.01519349, 0.21926015]), square_terms=array([[0.01579161, 0.02024966], + [0.02024966, 0.07482116]]), scale=4.118456734483951e-06, shift=array([10.21976966, 0.80359487])), vector_model=VectorModel(intercepts=array([ 0.06389705, 0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + -0.62576956, -0.79590416, -0.14927533, 0.0536062 , 0.10945384, + 0.08712913, 0.080573 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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x=array([10.21976872, 0.8035952 ]), fval=1.4324246742584341, rho=0.16897984080632025, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([16, 35, 36, 37]), old_indices_discarded=array([], dtype=int64), step_length=1.000000000616104e-06, relative_step_length=1.000000000616104, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 39 entries., 'multistart_info': {'start_parameters': [array([10.55, 0.8 ]), array([10.79628722, 0.75121895])], 'local_optima': [{'solution_x': array([10.40901296, 0.74654619]), 'solution_criterion': 1.5670853229897677, 'states': [State(trustregion=Region(center=array([10.55, 0.8 ]), radius=1.055, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=1.6868651526889202, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=1.055, shift=array([10.55, 0.8 ])), vector_model=VectorModel(intercepts=array([ 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 3, 5, 7, 11, 12, 13, 14, 15]), model=ScalarModel(intercept=1.4849181995959484, linear_terms=array([-0.06535588, 0.79069515]), square_terms=array([[ 0.03517026, 0.64745615], + [ 0.64745615, 14.1194396 ]]), scale=array([0.23374235, 0.23374235]), shift=array([10.40901296, 0.74654619])), vector_model=VectorModel(intercepts=array([ 0.08924907, 0.13653644, 0.11708387, 0.18796095, 0.2782384 , + 0.2501 , 0.18052122, -0.28781532, -0.4052013 , -0.43977086, + -0.63052455, -0.74213611, -0.06760469, 0.05962485, 0.09935991, + 0.1539211 , 0.09068669]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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14, 15, 16]), model=ScalarModel(intercept=1.5308731644099367, linear_terms=array([-0.07931731, 0.33477336]), square_terms=array([[ 0.19786976, -0.83656028], + [-0.83656028, 4.04156084]]), scale=0.131875, shift=array([10.40901296, 0.74654619])), vector_model=VectorModel(intercepts=array([ 0.08924907, 0.13653644, 0.11708387, 0.18796095, 0.2782384 , + 0.2501 , 0.18052122, -0.28781532, -0.4052013 , -0.43977086, + -0.63052455, -0.74213611, -0.06760469, 0.05962485, 0.09935991, + 0.1539211 , 0.09068669]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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0.11708387, 0.18796095, 0.2782384 , + 0.2501 , 0.18052122, -0.28781532, -0.4052013 , -0.43977086, + -0.63052455, -0.74213611, -0.06760469, 0.05962485, 0.09935991, + 0.1539211 , 0.09068669]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.055, shift=array([10.55, 0.8 ])), candidate_index=19, 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0.05962485, 0.09935991, + 0.1539211 , 0.09068669]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.055, shift=array([10.55, 0.8 ])), candidate_index=20, candidate_x=array([10.40403559, 0.73083122]), index=14, x=array([10.40901296, 0.74654619]), fval=1.5670853229897677, rho=-2.1118049412822777, accepted=False, 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.40901296, 0.74654619]), radius=0.00412109375, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 20, 21]), model=ScalarModel(intercept=1.5670853229897683, linear_terms=array([ 0.39417543, -0.22137413]), square_terms=array([[ 0.09471189, -0.04987683], + [-0.04987683, 0.02932098]]), scale=0.00412109375, shift=array([10.40901296, 0.74654619])), vector_model=VectorModel(intercepts=array([ 0.08924907, 0.13653644, 0.11708387, 0.18796095, 0.2782384 , + 0.2501 , 0.18052122, -0.28781532, -0.4052013 , -0.43977086, + -0.63052455, -0.74213611, -0.06760469, 0.05962485, 0.09935991, + 0.1539211 , 0.09068669]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + 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0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 21, 22]), model=ScalarModel(intercept=1.5670853229897677, linear_terms=array([0.02911534, 0.84280919]), square_terms=array([[0.00313936, 0.00795999], + [0.00795999, 0.43720739]]), scale=0.002060546875, shift=array([10.40901296, 0.74654619])), vector_model=VectorModel(intercepts=array([ 0.08924907, 0.13653644, 0.11708387, 0.18796095, 0.2782384 , + 0.2501 , 0.18052122, -0.28781532, -0.4052013 , -0.43977086, + -0.63052455, -0.74213611, -0.06760469, 0.05962485, 0.09935991, + 0.1539211 , 0.09068669]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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square_terms=array([[0.00693663, 0.01229075], + [0.01229075, 0.03504717]]), scale=0.0010302734375, shift=array([10.40901296, 0.74654619])), vector_model=VectorModel(intercepts=array([ 0.08924907, 0.13653644, 0.11708387, 0.18796095, 0.2782384 , + 0.2501 , 0.18052122, -0.28781532, -0.4052013 , -0.43977086, + -0.63052455, -0.74213611, -0.06760469, 0.05962485, 0.09935991, + 0.1539211 , 0.09068669]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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vector_model=VectorModel(intercepts=array([ 0.08924907, 0.13653644, 0.11708387, 0.18796095, 0.2782384 , + 0.2501 , 0.18052122, -0.28781532, -0.4052013 , -0.43977086, + -0.63052455, -0.74213611, -0.06760469, 0.05962485, 0.09935991, + 0.1539211 , 0.09068669]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.055, shift=array([10.55, 0.8 ])), candidate_index=25, candidate_x=array([10.40852437, 0.74670942]), index=14, x=array([10.40901296, 0.74654619]), fval=1.5670853229897677, rho=-0.6361517377084711, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.40901296, 0.74654619]), radius=0.000257568359375, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 24, 25]), model=ScalarModel(intercept=1.5670853229897677, linear_terms=array([-0.03060636, 0.08032196]), square_terms=array([[0.01876797, 0.00333634], + [0.00333634, 0.01072747]]), scale=0.000257568359375, shift=array([10.40901296, 0.74654619])), vector_model=VectorModel(intercepts=array([ 0.08924907, 0.13653644, 0.11708387, 0.18796095, 0.2782384 , + 0.2501 , 0.18052122, -0.28781532, -0.4052013 , -0.43977086, + -0.63052455, -0.74213611, -0.06760469, 0.05962485, 0.09935991, + 0.1539211 , 0.09068669]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.055, shift=array([10.55, 0.8 ])), candidate_index=26, candidate_x=array([10.40911054, 0.74628852]), index=14, x=array([10.40901296, 0.74654619]), fval=1.5670853229897677, rho=-6.353147964522871, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.40901296, 0.74654619]), radius=0.0001287841796875, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 25, 26]), model=ScalarModel(intercept=1.567085322989769, linear_terms=array([-0.10737809, -0.23545226]), square_terms=array([[0.01593444, 0.03453719], + [0.03453719, 0.10371827]]), scale=0.0001287841796875, shift=array([10.40901296, 0.74654619])), vector_model=VectorModel(intercepts=array([ 0.08924907, 0.13653644, 0.11708387, 0.18796095, 0.2782384 , + 0.2501 , 0.18052122, -0.28781532, -0.4052013 , -0.43977086, + -0.63052455, -0.74213611, -0.06760469, 0.05962485, 0.09935991, + 0.1539211 , 0.09068669]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.055, shift=array([10.55, 0.8 ])), candidate_index=27, candidate_x=array([10.40901578, 0.74667495]), index=14, x=array([10.40901296, 0.74654619]), fval=1.5670853229897677, rho=-4.300108244331453, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.40901296, 0.74654619]), radius=6.439208984375e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 26, 27]), model=ScalarModel(intercept=1.5670853229897677, linear_terms=array([0.98091845, 0.27409435]), square_terms=array([[1.23510289, 0.32351996], + [0.32351996, 0.08786033]]), scale=6.439208984375e-05, shift=array([10.40901296, 0.74654619])), vector_model=VectorModel(intercepts=array([ 0.08924907, 0.13653644, 0.11708387, 0.18796095, 0.2782384 , + 0.2501 , 0.18052122, -0.28781532, -0.4052013 , -0.43977086, + -0.63052455, -0.74213611, -0.06760469, 0.05962485, 0.09935991, + 0.1539211 , 0.09068669]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.055, shift=array([10.55, 0.8 ])), candidate_index=28, candidate_x=array([10.40897716, 0.74648983]), index=14, x=array([10.40901296, 0.74654619]), fval=1.5670853229897677, rho=-0.7382017964627403, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.40901296, 0.74654619]), radius=3.2196044921875e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 27, 28]), model=ScalarModel(intercept=1.567085322989768, linear_terms=array([-0.48972833, 0.15851916]), square_terms=array([[ 0.19760594, -0.06953882], + [-0.06953882, 0.02860858]]), scale=3.2196044921875e-05, shift=array([10.40901296, 0.74654619])), vector_model=VectorModel(intercepts=array([ 0.08924907, 0.13653644, 0.11708387, 0.18796095, 0.2782384 , + 0.2501 , 0.18052122, -0.28781532, -0.4052013 , -0.43977086, + -0.63052455, -0.74213611, -0.06760469, 0.05962485, 0.09935991, + 0.1539211 , 0.09068669]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.055, shift=array([10.55, 0.8 ])), candidate_index=29, candidate_x=array([10.40904513, 0.74654756]), index=14, x=array([10.40901296, 0.74654619]), fval=1.5670853229897677, rho=-1.5783827078362191, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 27, 28]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.40901296, 0.74654619]), radius=1.60980224609375e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 28, 29]), model=ScalarModel(intercept=1.5670853229897699, linear_terms=array([ 0.25155862, -0.23606302]), square_terms=array([[ 0.06869985, -0.05533087], + [-0.05533087, 0.04757193]]), scale=1.60980224609375e-05, shift=array([10.40901296, 0.74654619])), vector_model=VectorModel(intercepts=array([ 0.08924907, 0.13653644, 0.11708387, 0.18796095, 0.2782384 , + 0.2501 , 0.18052122, -0.28781532, -0.4052013 , -0.43977086, + -0.63052455, -0.74213611, -0.06760469, 0.05962485, 0.09935991, + 0.1539211 , 0.09068669]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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square_terms=array([[0.01474709, 0.01446714], + [0.01446714, 0.02746594]]), scale=8.04901123046875e-06, shift=array([10.40901296, 0.74654619])), vector_model=VectorModel(intercepts=array([ 0.08924907, 0.13653644, 0.11708387, 0.18796095, 0.2782384 , + 0.2501 , 0.18052122, -0.28781532, -0.4052013 , -0.43977086, + -0.63052455, -0.74213611, -0.06760469, 0.05962485, 0.09935991, + 0.1539211 , 0.09068669]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.055, shift=array([10.55, 0.8 ])), candidate_index=31, candidate_x=array([10.40900552, 0.74654313]), index=14, x=array([10.40901296, 0.74654619]), fval=1.5670853229897677, rho=-0.772064324491312, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 29, 30]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.40901296, 0.74654619]), radius=4.024505615234375e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 30, 31]), model=ScalarModel(intercept=1.5670853229897679, linear_terms=array([-0.05644122, 0.01923886]), square_terms=array([[ 0.01059559, -0.00332368], + [-0.00332368, 0.00219334]]), scale=4.024505615234375e-06, shift=array([10.40901296, 0.74654619])), vector_model=VectorModel(intercepts=array([ 0.08924907, 0.13653644, 0.11708387, 0.18796095, 0.2782384 , + 0.2501 , 0.18052122, -0.28781532, -0.4052013 , -0.43977086, + -0.63052455, -0.74213611, -0.06760469, 0.05962485, 0.09935991, + 0.1539211 , 0.09068669]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.055, shift=array([10.55, 0.8 ])), candidate_index=32, candidate_x=array([10.40901694, 0.74654561]), index=14, x=array([10.40901296, 0.74654619]), fval=1.5670853229897677, rho=-8.593574734365282, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.40901296, 0.74654619]), radius=2.0122528076171874e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 31, 32]), model=ScalarModel(intercept=1.56708532298977, linear_terms=array([ 0.14595958, -0.41318107]), square_terms=array([[ 0.01262979, -0.03230522], + [-0.03230522, 0.09453929]]), scale=2.0122528076171874e-06, shift=array([10.40901296, 0.74654619])), vector_model=VectorModel(intercepts=array([ 0.08924907, 0.13653644, 0.11708387, 0.18796095, 0.2782384 , + 0.2501 , 0.18052122, -0.28781532, -0.4052013 , -0.43977086, + -0.63052455, -0.74213611, -0.06760469, 0.05962485, 0.09935991, + 0.1539211 , 0.09068669]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.055, shift=array([10.55, 0.8 ])), candidate_index=33, candidate_x=array([10.40901275, 0.74654819]), index=14, x=array([10.40901296, 0.74654619]), fval=1.5670853229897677, rho=-1.78884728049783, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 31, 32]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.40901296, 0.74654619]), radius=1.0061264038085937e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 32, 33]), model=ScalarModel(intercept=1.5670853229897699, linear_terms=array([0.1437609 , 0.27985127]), square_terms=array([[0.01182457, 0.02571025], + [0.02571025, 0.07867304]]), scale=1.0061264038085937e-06, shift=array([10.40901296, 0.74654619])), vector_model=VectorModel(intercepts=array([ 0.08924907, 0.13653644, 0.11708387, 0.18796095, 0.2782384 , + 0.2501 , 0.18052122, -0.28781532, -0.4052013 , -0.43977086, + -0.63052455, -0.74213611, -0.06760469, 0.05962485, 0.09935991, + 0.1539211 , 0.09068669]), linear_terms=array([[0., 0.], 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old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.40901296, 0.74654619]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 32, 33, 34]), model=ScalarModel(intercept=1.96805461169286, linear_terms=array([0.00985 , 0.0380479]), square_terms=array([[0.00233627, 0.00164559], + [0.00164559, 0.02090977]]), scale=1e-06, shift=array([10.40901296, 0.74654619])), vector_model=VectorModel(intercepts=array([ 0.08924907, 0.13653644, 0.11708387, 0.18796095, 0.2782384 , + 0.2501 , 0.18052122, -0.28781532, -0.4052013 , -0.43977086, + -0.63052455, -0.74213611, -0.06760469, 0.05962485, 0.09935991, + 0.1539211 , 0.09068669]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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0.74654619]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 32, 33, 34, 35]), model=ScalarModel(intercept=1.982165461005107, linear_terms=array([0.00665481, 0.03054179]), square_terms=array([[ 0.00131356, -0.00026886], + [-0.00026886, 0.01738689]]), scale=1e-06, shift=array([10.40901296, 0.74654619])), vector_model=VectorModel(intercepts=array([ 0.08924907, 0.13653644, 0.11708387, 0.18796095, 0.2782384 , + 0.2501 , 0.18052122, -0.28781532, -0.4052013 , -0.43977086, + -0.63052455, -0.74213611, -0.06760469, 0.05962485, 0.09935991, + 0.1539211 , 0.09068669]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.055, shift=array([10.55, 0.8 ])), candidate_index=36, candidate_x=array([10.4090125, 0.7465453]), index=14, x=array([10.40901296, 0.74654619]), fval=1.5670853229897677, rho=-26.756224181270007, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 32, 33, 34, 35]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.40901296, 0.74654619]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=2.012301373023884, linear_terms=array([-0.00925313, 0.0204173 ]), square_terms=array([[ 0.00370906, -0.002292 ], + [-0.002292 , 0.00686131]]), scale=1e-06, shift=array([10.40901296, 0.74654619])), vector_model=VectorModel(intercepts=array([ 0.08924907, 0.13653644, 0.11708387, 0.18796095, 0.2782384 , + 0.2501 , 0.18052122, -0.28781532, -0.4052013 , -0.43977086, + -0.63052455, -0.74213611, -0.06760469, 0.05962485, 0.09935991, + 0.1539211 , 0.09068669]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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0.00360263]]), scale=1e-06, shift=array([10.40901296, 0.74654619])), vector_model=VectorModel(intercepts=array([ 0.08924907, 0.13653644, 0.11708387, 0.18796095, 0.2782384 , + 0.2501 , 0.18052122, -0.28781532, -0.4052013 , -0.43977086, + -0.63052455, -0.74213611, -0.06760469, 0.05962485, 0.09935991, + 0.1539211 , 0.09068669]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.055, shift=array([10.55, 0.8 ])), candidate_index=38, candidate_x=array([10.40901395, 0.74654606]), index=14, x=array([10.40901296, 0.74654619]), fval=1.5670853229897677, rho=-48.45300102560295, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 32, 33, 34, 35, 36, 37]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.40901296, 0.74654619]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 32, 33, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=2.0567741681984857, linear_terms=array([-0.00879561, 0.01102369]), square_terms=array([[ 0.00418854, -0.0023533 ], + [-0.0023533 , 0.00350692]]), scale=1e-06, shift=array([10.40901296, 0.74654619])), vector_model=VectorModel(intercepts=array([ 0.08924907, 0.13653644, 0.11708387, 0.18796095, 0.2782384 , + 0.2501 , 0.18052122, -0.28781532, -0.4052013 , -0.43977086, + -0.63052455, -0.74213611, -0.06760469, 0.05962485, 0.09935991, + 0.1539211 , 0.09068669]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.055, shift=array([10.55, 0.8 ])), candidate_index=39, candidate_x=array([10.40901324, 0.74654523]), index=14, x=array([10.40901296, 0.74654619]), fval=1.5670853229897677, rho=-28.66323392285164, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 32, 33, 34, 35, 36, 37, 38]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.40901296, 0.74654619]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 32, 33, 34, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=2.0391623638797762, linear_terms=array([-0.00455657, 0.02579054]), square_terms=array([[ 0.00406815, -0.00257527], + [-0.00257527, 0.00352134]]), scale=1e-06, shift=array([10.40901296, 0.74654619])), vector_model=VectorModel(intercepts=array([ 0.08924907, 0.13653644, 0.11708387, 0.18796095, 0.2782384 , + 0.2501 , 0.18052122, -0.28781532, -0.4052013 , -0.43977086, + -0.63052455, -0.74213611, -0.06760469, 0.05962485, 0.09935991, + 0.1539211 , 0.09068669]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.055, shift=array([10.55, 0.8 ])), candidate_index=40, candidate_x=array([10.40901303, 0.7465452 ]), index=14, x=array([10.40901296, 0.74654619]), fval=1.5670853229897677, rho=-28.336961624262585, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 32, 33, 34, 35, 36, 37, 38, 39]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.40901296, 0.74654619]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 33, 34, 35, 36, 37, 38, 39, 40]), model=ScalarModel(intercept=2.0520819972922597, linear_terms=array([0.03292117, 0.01335152]), square_terms=array([[ 0.07134015, -0.01126546], + [-0.01126546, 0.0051156 ]]), scale=1e-06, shift=array([10.40901296, 0.74654619])), vector_model=VectorModel(intercepts=array([ 0.08924907, 0.13653644, 0.11708387, 0.18796095, 0.2782384 , + 0.2501 , 0.18052122, -0.28781532, -0.4052013 , -0.43977086, + -0.63052455, -0.74213611, -0.06760469, 0.05962485, 0.09935991, + 0.1539211 , 0.09068669]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.40901296, 0.74654619]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 34, 35, 36, 37, 38, 39, 40, 41]), model=ScalarModel(intercept=1.7695420783172529, linear_terms=array([ 0.10859855, -0.34577159]), square_terms=array([[ 0.1407482 , -0.041069 ], + [-0.041069 , 0.06233794]]), scale=1e-06, shift=array([10.40901296, 0.74654619])), vector_model=VectorModel(intercepts=array([ 0.08924907, 0.13653644, 0.11708387, 0.18796095, 0.2782384 , + 0.2501 , 0.18052122, -0.28781532, -0.4052013 , -0.43977086, + -0.63052455, -0.74213611, -0.06760469, 0.05962485, 0.09935991, + 0.1539211 , 0.09068669]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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x=array([10.40901296, 0.74654619]), fval=1.5670853229897677, rho=-0.7730861760920995, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 34, 35, 36, 37, 38, 39, 40, 41]), old_indices_discarded=array([32, 33, 42, 43, 44, 45, 46]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.40901296, 0.74654619]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 34, 35, 36, 37, 38, 39, 40, 41]), model=ScalarModel(intercept=1.7695420783172529, linear_terms=array([ 0.10859855, -0.34577159]), square_terms=array([[ 0.1407482 , -0.041069 ], + [-0.041069 , 0.06233794]]), scale=1e-06, shift=array([10.40901296, 0.74654619])), vector_model=VectorModel(intercepts=array([ 0.08924907, 0.13653644, 0.11708387, 0.18796095, 0.2782384 , + 0.2501 , 0.18052122, -0.28781532, -0.4052013 , -0.43977086, + -0.63052455, -0.74213611, -0.06760469, 0.05962485, 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State(trustregion=Region(center=array([10.40901296, 0.74654619]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([14, 34, 35, 36, 37, 38, 39, 40, 41]), model=ScalarModel(intercept=1.7695420783172529, linear_terms=array([ 0.10859855, -0.34577159]), square_terms=array([[ 0.1407482 , -0.041069 ], + [-0.041069 , 0.06233794]]), scale=1e-06, shift=array([10.40901296, 0.74654619])), vector_model=VectorModel(intercepts=array([ 0.08924907, 0.13653644, 0.11708387, 0.18796095, 0.2782384 , + 0.2501 , 0.18052122, -0.28781532, -0.4052013 , -0.43977086, + -0.63052455, -0.74213611, -0.06760469, 0.05962485, 0.09935991, + 0.1539211 , 0.09068669]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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scale=1e-06, shift=array([10.40901296, 0.74654619])), vector_model=VectorModel(intercepts=array([ 0.08924907, 0.13653644, 0.11708387, 0.18796095, 0.2782384 , + 0.2501 , 0.18052122, -0.28781532, -0.4052013 , -0.43977086, + -0.63052455, -0.74213611, -0.06760469, 0.05962485, 0.09935991, + 0.1539211 , 0.09068669]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], 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vector_model=VectorModel(intercepts=array([ 0.08924907, 0.13653644, 0.11708387, 0.18796095, 0.2782384 , + 0.2501 , 0.18052122, -0.28781532, -0.4052013 , -0.43977086, + -0.63052455, -0.74213611, -0.06760469, 0.05962485, 0.09935991, + 0.1539211 , 0.09068669]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.055, 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old_indices_discarded=array([ 0, 2, 4, 5, 6, 8, 9, 12, 14]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.21976966, 0.80359487]), radius=0.2699071805511402, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 1, 3, 7, 10, 11, 13, 15, 16, 17]), model=ScalarModel(intercept=1.0970721967169264, linear_terms=array([-0.01342485, -0.55739364]), square_terms=array([[0.0542281 , 0.44622537], + [0.44622537, 5.43203036]]), scale=0.2699071805511402, shift=array([10.21976966, 0.80359487])), vector_model=VectorModel(intercepts=array([ 0.06389705, 0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + -0.62576956, -0.79590416, -0.14927533, 0.0536062 , 0.10945384, + 0.08712913, 0.080573 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.0796287222045609, shift=array([10.79628722, 0.75121895])), candidate_index=19, candidate_x=array([9.9537993 , 0.85300258]), index=16, x=array([10.21976966, 0.80359487]), fval=1.466665976719959, rho=-6.872342914984419, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 1, 3, 7, 10, 11, 13, 15, 16, 17]), old_indices_discarded=array([ 0, 12, 14, 18]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.21976966, 0.80359487]), radius=0.1349535902755701, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 11, 13, 15, 16, 17, 19]), model=ScalarModel(intercept=1.0951311953171219, linear_terms=array([-0.07434461, -0.27707976]), square_terms=array([[0.12562853, 0.47234431], + [0.47234431, 2.1286064 ]]), scale=0.1349535902755701, shift=array([10.21976966, 0.80359487])), vector_model=VectorModel(intercepts=array([ 0.06389705, 0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + -0.62576956, -0.79590416, -0.14927533, 0.0536062 , 0.10945384, + 0.08712913, 0.080573 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.0796287222045609, shift=array([10.79628722, 0.75121895])), candidate_index=20, candidate_x=array([10.35412853, 0.79093913]), index=16, x=array([10.21976966, 0.80359487]), fval=1.466665976719959, rho=-8.377479158857502, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 10, 11, 13, 15, 16, 17, 19]), old_indices_discarded=array([ 0, 1, 12, 18]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.21976966, 0.80359487]), radius=0.06747679513778505, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([10, 15, 16, 17, 19, 20]), model=ScalarModel(intercept=1.674917371962548, linear_terms=array([-0.17510758, -0.97660578]), square_terms=array([[0.02516315, 0.13418318], + [0.13418318, 0.97261011]]), scale=0.06747679513778505, shift=array([10.21976966, 0.80359487])), vector_model=VectorModel(intercepts=array([ 0.06389705, 0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + -0.62576956, -0.79590416, -0.14927533, 0.0536062 , 0.10945384, + 0.08712913, 0.080573 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.0796287222045609, shift=array([10.79628722, 0.75121895])), candidate_index=21, candidate_x=array([10.25753497, 0.86122387]), index=16, x=array([10.21976966, 0.80359487]), fval=1.466665976719959, rho=-0.5278763601515147, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([10, 15, 16, 17, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.21976966, 0.80359487]), radius=0.03373839756889253, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([15, 16, 17, 20, 21]), model=ScalarModel(intercept=1.6512778708853884, linear_terms=array([-0.03985835, 0.03411712]), square_terms=array([[0.00599018, 0.00998315], + [0.00998315, 0.03869545]]), scale=0.03373839756889253, shift=array([10.21976966, 0.80359487])), vector_model=VectorModel(intercepts=array([ 0.06389705, 0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + -0.62576956, -0.79590416, -0.14927533, 0.0536062 , 0.10945384, + 0.08712913, 0.080573 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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model=ScalarModel(intercept=1.4666659767199604, linear_terms=array([ 0.09249895, -0.02335073]), square_terms=array([[ 0.01468019, -0.0030654 ], + [-0.0030654 , 0.02196767]]), scale=0.016869198784446263, shift=array([10.21976966, 0.80359487])), vector_model=VectorModel(intercepts=array([ 0.06389705, 0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + -0.62576956, -0.79590416, -0.14927533, 0.0536062 , 0.10945384, + 0.08712913, 0.080573 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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1.27785522], + [1.27785522, 2.09150806]]), scale=0.008434599392223132, shift=array([10.21976966, 0.80359487])), vector_model=VectorModel(intercepts=array([ 0.06389705, 0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + -0.62576956, -0.79590416, -0.14927533, 0.0536062 , 0.10945384, + 0.08712913, 0.080573 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.0796287222045609, shift=array([10.79628722, 0.75121895])), candidate_index=24, candidate_x=array([10.21165115, 0.80647574]), index=16, x=array([10.21976966, 0.80359487]), fval=1.466665976719959, rho=-6.862307716483873, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([16, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.21976966, 0.80359487]), radius=0.004217299696111566, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 23, 24]), model=ScalarModel(intercept=1.4666659767199592, linear_terms=array([0.55084332, 2.09635603]), square_terms=array([[0.23949622, 0.71845366], + [0.71845366, 3.39566675]]), scale=0.004217299696111566, shift=array([10.21976966, 0.80359487])), vector_model=VectorModel(intercepts=array([ 0.06389705, 0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + -0.62576956, -0.79590416, -0.14927533, 0.0536062 , 0.10945384, + 0.08712913, 0.080573 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.0796287222045609, shift=array([10.79628722, 0.75121895])), candidate_index=25, candidate_x=array([10.2158192 , 0.80184252]), index=16, x=array([10.21976966, 0.80359487]), fval=1.466665976719959, rho=-0.037924939882139255, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([16, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.21976966, 0.80359487]), radius=0.002108649848055783, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 24, 25]), model=ScalarModel(intercept=1.4666659767199588, linear_terms=array([-0.04544986, 0.1439351 ]), square_terms=array([[ 0.03369853, -0.00374743], + [-0.00374743, 0.02333965]]), scale=0.002108649848055783, shift=array([10.21976966, 0.80359487])), vector_model=VectorModel(intercepts=array([ 0.06389705, 0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + -0.62576956, -0.79590416, -0.14927533, 0.0536062 , 0.10945384, + 0.08712913, 0.080573 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.0796287222045609, shift=array([10.79628722, 0.75121895])), candidate_index=26, candidate_x=array([10.22033497, 0.80152254]), index=16, x=array([10.21976966, 0.80359487]), fval=1.466665976719959, rho=-0.6136103055014882, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([16, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.21976966, 0.80359487]), radius=0.0010543249240278915, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 25, 26]), model=ScalarModel(intercept=1.466665976719959, linear_terms=array([-0.07066043, 0.18003202]), square_terms=array([[ 0.01861073, -0.05858304], + [-0.05858304, 0.21397908]]), scale=0.0010543249240278915, shift=array([10.21976966, 0.80359487])), vector_model=VectorModel(intercepts=array([ 0.06389705, 0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + -0.62576956, -0.79590416, -0.14927533, 0.0536062 , 0.10945384, + 0.08712913, 0.080573 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=1.0796287222045609, shift=array([10.79628722, 0.75121895])), candidate_index=27, candidate_x=array([10.22070105, 0.80303109]), index=16, x=array([10.21976966, 0.80359487]), fval=1.466665976719959, rho=-5.736976120905478, accepted=False, 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([10.21976966, 0.80359487]), radius=0.00026358123100697286, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 27, 28]), model=ScalarModel(intercept=1.4666659767199586, linear_terms=array([-0.11867755, -0.3838503 ]), square_terms=array([[0.03057504, 0.07332091], + [0.07332091, 0.21686367]]), scale=0.00026358123100697286, shift=array([10.21976966, 0.80359487])), vector_model=VectorModel(intercepts=array([ 0.06389705, 0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + -0.62576956, -0.79590416, -0.14927533, 0.0536062 , 0.10945384, + 0.08712913, 0.080573 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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State(trustregion=Region(center=array([10.21976966, 0.80359487]), radius=0.00013179061550348643, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([16, 28, 29]), model=ScalarModel(intercept=1.466665976719959, linear_terms=array([-0.0761264 , 0.13860902]), square_terms=array([[ 0.01125216, -0.016678 ], + [-0.016678 , 0.03393693]]), scale=0.00013179061550348643, shift=array([10.21976966, 0.80359487])), vector_model=VectorModel(intercepts=array([ 0.06389705, 0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + -0.62576956, -0.79590416, -0.14927533, 0.0536062 , 0.10945384, + 0.08712913, 0.080573 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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linear_terms=array([-1.6196538 , -0.30795927]), square_terms=array([[2.28612575, 0.4440688 ], + [0.4440688 , 0.09657711]]), scale=3.294765387587161e-05, shift=array([10.21976966, 0.80359487])), vector_model=VectorModel(intercepts=array([ 0.06389705, 0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + -0.62576956, -0.79590416, -0.14927533, 0.0536062 , 0.10945384, + 0.08712913, 0.080573 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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scale=1.6473826937935804e-05, shift=array([10.21976966, 0.80359487])), vector_model=VectorModel(intercepts=array([ 0.06389705, 0.12779359, 0.18470431, 0.26320125, 0.26215047, + 0.28488454, 0.21775791, -0.25498137, -0.46507275, -0.51575513, + -0.62576956, -0.79590416, -0.14927533, 0.0536062 , 0.10945384, + 0.08712913, 0.080573 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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least_squares function with 39 entries., 'history': {'params': [{'CRRA': 10.796287222045608, 'DiscFac': 0.7512189549688916}, {'CRRA': 9.83949117893577, 'DiscFac': 0.540401632080349}, {'CRRA': 11.753083265155446, 'DiscFac': 0.7276940506351226}, {'CRRA': 9.83949117893577, 'DiscFac': 0.855439124212817}, {'CRRA': 11.753083265155446, 'DiscFac': 0.953923601537897}, {'CRRA': 11.727032790520497, 'DiscFac': 0.5}, {'CRRA': 11.709718520536603, 'DiscFac': 0.5}, {'CRRA': 9.83949117893577, 'DiscFac': 1.0362179395826956}, {'CRRA': 11.537716692481002, 'DiscFac': 1.1}, {'CRRA': 11.736268238617884, 'DiscFac': 1.1}, {'CRRA': 10.079195559091445, 'DiscFac': 1.1}, {'CRRA': 10.462366881566767, 'DiscFac': 0.5}, {'CRRA': 10.702528806860887, 'DiscFac': 1.1}, {'CRRA': 9.83949117893577, 'DiscFac': 0.7985554402937353}, {'CRRA': 9.161006721240817, 'DiscFac': 0.8038620148386444}, {'CRRA': 10.07007058449546, 'DiscFac': 0.8203867919740195}, {'CRRA': 10.219769663854269, 'DiscFac': 0.8035948739299863}, {'CRRA': 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23, 24, 25, 26, 27]}, 'multistart_info': {...}}], 'exploration_sample': array([[10.55 , 0.8 ], + [11.73125 , 0.7625 ], + [ 9.36875 , 0.8375 ], + [ 7.596875, 0.93125 ], + [12.9125 , 0.575 ], + [15.275 , 0.65 ], + [17.046875, 0.63125 ], + [16.45625 , 0.9125 ], + [18.81875 , 0.5375 ], + [ 8.1875 , 0.725 ], + [14.09375 , 0.9875 ], + [12.321875, 1.08125 ], + [17.6375 , 1.025 ], + [ 7.00625 , 0.6125 ], + [ 5. , 0.95 ], + [ 4.64375 , 0.6875 ], + [ 2.871875, 0.78125 ], + [ 3.4625 , 0.875 ], + [ 2.28125 , 1.0625 ]]), 'exploration_results': array([ 1.74531296, 2.10648864, 2.17581064, 2.66803924, + 2.70500778, 2.87740953, 3.0406658 , 3.37839016, + 3.53882151, 3.99992851, 4.37946561, 4.47403011, + 4.90296965, 6.27675034, 11.07528266, 21.17347996, + 25.72759101, 28.93912422, 124.53209372])}}" diff --git a/content/tables/msm/IndShockSub(Labor)Market_estimate_results.csv b/content/tables/msm/IndShockSub(Labor)Market_estimate_results.csv new file mode 100644 index 0000000..9c11bab --- /dev/null +++ b/content/tables/msm/IndShockSub(Labor)Market_estimate_results.csv @@ -0,0 +1,97 @@ +CRRA,10.120536495469759 +DiscFac,1.072981697525726 +time_to_estimate,531.695109128952 +_params,"{'CRRA': 10.120536495469759, 'DiscFac': 1.072981697525726}" +_internal_estimates,"InternalParams(values=array([10.1205365, 1.0729817]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([10.1205365, 1.0729817]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2019.14915854), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(30,35]': {'(25,30]': array(0.), '(30,35]': 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'(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.55717516)}}" +_converter,"Converter(params_to_internal=._params_to_internal at 0x7fa41c900700>, params_from_internal=._params_from_internal at 0x7fa41c901fc0>, derivative_to_internal=._derivative_to_internal at 0x7fa41c901510>, func_to_internal=._func_to_internal at 0x7fa41c900ee0>, has_transforming_constraints=False)" +_internal_moments_cov,"[[ 4.95258112e-04 2.66601431e-05 2.79286974e-05 7.46706608e-06 + -3.57660861e-05 2.67259515e-05 1.10371975e-04 2.29478720e-04 + -1.74583374e-04 9.18609805e-05 -9.28899552e-04 -1.50543778e-04] + [ 2.66601431e-05 1.99863137e-03 8.85890234e-05 9.17921130e-05 + -9.23337837e-05 -2.83576297e-04 1.48966314e-04 1.80715547e-04 + -3.10963397e-04 3.47758778e-04 5.28959433e-04 5.19875724e-04] + [ 2.79286974e-05 8.85890234e-05 1.92743661e-03 4.84543237e-05 + 8.64878494e-05 -1.21376982e-04 -2.12383559e-04 -3.48906145e-05 + 4.13284621e-04 3.32697621e-04 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-1.42343182e-02] + [-1.74583374e-04 -3.10963397e-04 4.13284621e-04 6.17674185e-05 + 3.40008787e-04 -6.38169315e-04 -1.09391854e-03 2.56350149e-03 + 5.98616882e-02 -9.63153128e-04 7.46109287e-03 1.60977374e-02] + [ 9.18609805e-05 3.47758778e-04 3.32697621e-04 -3.77973815e-04 + -3.99570271e-04 -4.22552263e-04 1.90605756e-03 6.21389768e-04 + -9.63153128e-04 1.26839727e-01 -5.50753961e-03 1.82934775e-03] + [-9.28899552e-04 5.28959433e-04 -4.47332935e-04 -1.25740378e-03 + -8.07415962e-04 3.24001952e-03 -6.99390334e-04 -2.22168627e-03 + 7.46109287e-03 -5.50753961e-03 5.26149129e-01 -2.79455237e-02] + [-1.50543778e-04 5.19875724e-04 5.68592373e-04 -1.67437857e-03 + -6.44898143e-03 -3.14657898e-03 7.40307679e-03 -1.42343182e-02 + 1.60977374e-02 1.82934775e-03 -2.79455237e-02 1.79476773e+00]]" +_internal_weights,"[[2.01914916e+03 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 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array(0.03382452), 'DiscFac': array(0.52284106)}, '(30,35]': {'CRRA': array(0.01947175), 'DiscFac': array(4.82486194)}, '(35,40]': {'CRRA': array(-0.03328735), 'DiscFac': array(12.33201988)}, '(40,45]': {'CRRA': array(-0.0688371), 'DiscFac': array(18.02229324)}, '(45,50]': {'CRRA': array(-0.13090244), 'DiscFac': array(30.38902444)}, '(50,55]': {'CRRA': array(-0.17304766), 'DiscFac': array(37.87414937)}, '(55,60]': {'CRRA': array(-0.23838127), 'DiscFac': array(52.80409826)}, '(70,75]': {'CRRA': array(-0.28459189), 'DiscFac': array(77.29779196)}, '(75,80]': {'CRRA': array(-0.29310427), 'DiscFac': array(68.59224542)}, '(80,85]': {'CRRA': array(-0.13568029), 'DiscFac': array(73.25662082)}, '(85,90]': {'CRRA': array(-0.11484201), 'DiscFac': array(26.46595524)}, '(90,95]': {'CRRA': array(-0.04664125), 'DiscFac': array(15.70106134)}}" +_no_jacobian_reason, +_cache,{} diff --git a/content/tables/msm/IndShockSub(Stock)(Labor)Market_estimate_results.csv b/content/tables/msm/IndShockSub(Stock)(Labor)Market_estimate_results.csv new file mode 100644 index 0000000..2472700 --- /dev/null +++ b/content/tables/msm/IndShockSub(Stock)(Labor)Market_estimate_results.csv @@ -0,0 +1,97 @@ +CRRA,10.637291414951322 +DiscFac,1.0751692278351839 +time_to_estimate,516.5506954193115 +_params,"{'CRRA': 10.637291414951322, 'DiscFac': 1.0751692278351839}" +_internal_estimates,"InternalParams(values=array([10.63729141, 1.07516923]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([10.63729141, 1.07516923]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2019.14915854), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), 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1.80715547e-04 -3.48906145e-05 -7.30430953e-04 + -9.90302591e-04 9.67901647e-04 2.57065708e-04 6.07248017e-02 + 2.56350149e-03 6.21389768e-04 -2.22168627e-03 -1.42343182e-02] + [-1.74583374e-04 -3.10963397e-04 4.13284621e-04 6.17674185e-05 + 3.40008787e-04 -6.38169315e-04 -1.09391854e-03 2.56350149e-03 + 5.98616882e-02 -9.63153128e-04 7.46109287e-03 1.60977374e-02] + [ 9.18609805e-05 3.47758778e-04 3.32697621e-04 -3.77973815e-04 + -3.99570271e-04 -4.22552263e-04 1.90605756e-03 6.21389768e-04 + -9.63153128e-04 1.26839727e-01 -5.50753961e-03 1.82934775e-03] + [-9.28899552e-04 5.28959433e-04 -4.47332935e-04 -1.25740378e-03 + -8.07415962e-04 3.24001952e-03 -6.99390334e-04 -2.22168627e-03 + 7.46109287e-03 -5.50753961e-03 5.26149129e-01 -2.79455237e-02] + [-1.50543778e-04 5.19875724e-04 5.68592373e-04 -1.67437857e-03 + -6.44898143e-03 -3.14657898e-03 7.40307679e-03 -1.42343182e-02 + 1.60977374e-02 1.82934775e-03 -2.79455237e-02 1.79476773e+00]]" +_internal_weights,"[[2.01914916e+03 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0.00000000e+00 1.16405624e+02 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 6.98762089e+01 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.64677360e+01 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 1.67051754e+01 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 7.88396527e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 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'(80,85]': 14.358754447244063, '(85,90]': 15.71527224435591, '(90,95]': 17.99554082928189}" +_has_constraints,False +_jacobian,"{'(25,30]': {'CRRA': array(0.03852375), 'DiscFac': array(0.9797486)}, '(30,35]': {'CRRA': array(0.01587168), 'DiscFac': array(5.66025615)}, '(35,40]': {'CRRA': array(-0.02574771), 'DiscFac': array(13.79178499)}, '(40,45]': {'CRRA': array(-0.05402906), 'DiscFac': array(22.21231674)}, '(45,50]': {'CRRA': array(-0.09710035), 'DiscFac': array(30.06294819)}, '(50,55]': {'CRRA': array(-0.12775565), 'DiscFac': array(39.41808128)}, '(55,60]': {'CRRA': array(-0.14610295), 'DiscFac': array(49.70397145)}, '(70,75]': {'CRRA': array(-0.17672986), 'DiscFac': array(77.17066173)}, '(75,80]': {'CRRA': array(-0.07860524), 'DiscFac': array(67.18776461)}, '(80,85]': {'CRRA': array(-0.0231848), 'DiscFac': array(59.96696888)}, '(85,90]': {'CRRA': array(-0.08614828), 'DiscFac': array(24.29070912)}, '(90,95]': {'CRRA': array(-0.03203695), 'DiscFac': array(16.37311659)}}" +_no_jacobian_reason, +_cache,{} diff --git a/content/tables/msm/IndShockSub(Stock)Market_estimate_results.csv b/content/tables/msm/IndShockSub(Stock)Market_estimate_results.csv new file mode 100644 index 0000000..7c8248a --- /dev/null +++ b/content/tables/msm/IndShockSub(Stock)Market_estimate_results.csv @@ -0,0 +1,97 @@ +CRRA,1.6899139270900547 +DiscFac,0.9795091538349516 +time_to_estimate,515.8025386333466 +_params,"{'CRRA': 1.6899139270900547, 'DiscFac': 0.9795091538349516}" +_internal_estimates,"InternalParams(values=array([1.68991393, 0.97950915]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([1.68991393, 0.97950915]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2019.14915854), '(30,35]': array(0.), '(35,40]': array(0.), 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14.358754447244063, '(85,90]': 15.71527224435591, '(90,95]': 17.99554082928189}" +_has_constraints,False +_jacobian,"{'(25,30]': {'CRRA': array(0.25091248), 'DiscFac': array(12.18909018)}, '(30,35]': {'CRRA': array(0.75488419), 'DiscFac': array(42.79812654)}, '(35,40]': {'CRRA': array(0.96405127), 'DiscFac': array(62.05600204)}, '(40,45]': {'CRRA': array(1.3439964), 'DiscFac': array(92.13445197)}, '(45,50]': {'CRRA': array(2.02520225), 'DiscFac': array(142.30890094)}, '(50,55]': {'CRRA': array(2.439634), 'DiscFac': array(172.36113605)}, '(55,60]': {'CRRA': array(3.41365907), 'DiscFac': array(230.75908878)}, '(70,75]': {'CRRA': array(3.19943685), 'DiscFac': array(203.86511346)}, '(75,80]': {'CRRA': array(1.08118883), 'DiscFac': array(74.62108544)}, '(80,85]': {'CRRA': array(1.99009032), 'DiscFac': array(110.88288514)}, '(85,90]': {'CRRA': array(0.95896302), 'DiscFac': array(57.33195063)}, '(90,95]': {'CRRA': array(0.85359993), 'DiscFac': array(51.8164894)}}" +_no_jacobian_reason, +_cache,{} diff --git a/content/tables/msm/PortfolioSub(Labor)Market_estimate_results.csv b/content/tables/msm/PortfolioSub(Labor)Market_estimate_results.csv new file mode 100644 index 0000000..ba56efd --- /dev/null +++ b/content/tables/msm/PortfolioSub(Labor)Market_estimate_results.csv @@ -0,0 +1,97 @@ +CRRA,16.035365501911656 +DiscFac,1.0173759669425746 +time_to_estimate,719.5181894302368 +_params,"{'CRRA': 16.035365501911656, 'DiscFac': 1.0173759669425746}" +_internal_estimates,"InternalParams(values=array([16.0353655 , 1.01737597]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([16.0353655 , 1.01737597]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2158.28167807), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(30,35]': {'(25,30]': array(0.), '(30,35]': array(514.72491632), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(35,40]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(567.49891065), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(40,45]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(605.53346513), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(45,50]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(279.92614085), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(50,55]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(130.94785271), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(55,60]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(61.05068109), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(70,75]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), 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array(0.), '(85,90]': array(1.90282463), '(90,95]': array(0.)}, '(90,95]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.56618589)}}" +_converter,"Converter(params_to_internal=._params_to_internal at 0x7fe67e87a950>, params_from_internal=._params_from_internal at 0x7fe67e87a680>, derivative_to_internal=._derivative_to_internal at 0x7fe67e87add0>, func_to_internal=._func_to_internal at 0x7fe67e87b400>, has_transforming_constraints=False)" +_internal_moments_cov,"[[ 4.63331552e-04 -8.29455113e-06 2.62343723e-06 1.27222785e-05 + -6.70714064e-05 3.51434321e-05 4.95550858e-05 -1.59011871e-04 + 6.46025432e-05 -7.75378886e-06 -9.90751500e-04 1.05939616e-03] + [-8.29455113e-06 1.94278530e-03 6.21427749e-05 1.33977556e-04 + 4.05035336e-05 3.24097529e-05 -9.09560608e-06 -3.48865507e-04 + -4.45091936e-04 -9.40023292e-04 4.15494682e-04 -4.44444208e-03] + [ 2.62343723e-06 6.21427749e-05 1.76211792e-03 -9.07845887e-05 + -3.11748414e-05 -7.20625366e-05 1.23099100e-04 1.21619646e-04 + 2.42370531e-04 -4.53895101e-04 4.77856997e-04 -2.15951955e-03] + [ 1.27222785e-05 1.33977556e-04 -9.07845887e-05 1.65143639e-03 + -2.53506647e-04 -1.19096295e-04 5.24085619e-05 -2.00215176e-06 + -8.96273332e-05 4.13045513e-05 3.57275514e-04 -2.29200628e-05] + [-6.70714064e-05 4.05035336e-05 -3.11748414e-05 -2.53506647e-04 + 3.57237090e-03 1.96792266e-04 1.02791978e-04 -6.06222612e-04 + -8.16268084e-04 1.18356570e-03 -1.21385564e-03 -1.39621761e-03] + [ 3.51434321e-05 3.24097529e-05 -7.20625366e-05 -1.19096295e-04 + 1.96792266e-04 7.63662771e-03 1.87644341e-04 -6.91727595e-04 + -8.41433602e-04 7.97137838e-04 -3.67034802e-03 7.47381063e-05] + [ 4.95550858e-05 -9.09560608e-06 1.23099100e-04 5.24085619e-05 + 1.02791978e-04 1.87644341e-04 1.63798336e-02 4.49609135e-04 + 3.74504692e-04 -4.32072461e-04 -1.26483119e-03 2.21734482e-03] + [-1.59011871e-04 -3.48865507e-04 1.21619646e-04 -2.00215176e-06 + -6.06222612e-04 -6.91727595e-04 4.49609135e-04 6.10812312e-02 + -7.03144448e-04 9.30021499e-04 1.02943264e-02 1.34912486e-02] + [ 6.46025432e-05 -4.45091936e-04 2.42370531e-04 -8.96273332e-05 + -8.16268084e-04 -8.41433602e-04 3.74504692e-04 -7.03144448e-04 + 5.99210025e-02 1.09220053e-03 -1.79592505e-03 -1.15555069e-02] + [-7.75378886e-06 -9.40023292e-04 -4.53895101e-04 4.13045513e-05 + 1.18356570e-03 7.97137838e-04 -4.32072461e-04 9.30021499e-04 + 1.09220053e-03 1.22663712e-01 -1.08608497e-02 1.60128201e-02] + [-9.90751500e-04 4.15494682e-04 4.77856997e-04 3.57275514e-04 + -1.21385564e-03 -3.67034802e-03 -1.26483119e-03 1.02943264e-02 + -1.79592505e-03 -1.08608497e-02 5.25534506e-01 -2.55742896e-02] + [ 1.05939616e-03 -4.44444208e-03 -2.15951955e-03 -2.29200628e-05 + -1.39621761e-03 7.47381063e-05 2.21734482e-03 1.34912486e-02 + -1.15555069e-02 1.60128201e-02 -2.55742896e-02 1.76620438e+00]]" +_internal_weights,"[[2.15828168e+03 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 5.14724916e+02 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 5.67498911e+02 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 6.05533465e+02 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 2.79926141e+02 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 1.30947853e+02 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 6.10506811e+01 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.63716412e+01 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 1.66886394e+01 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 8.15237029e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 1.90282463e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.66185892e-01]]" +_internal_jacobian,"[[-6.75789971e+04 -5.66144143e+05] + [ 6.29166713e+04 -3.30480144e+05] + [ 7.12398340e+05 7.42092247e+06] + [ 1.05772970e+06 2.21302009e+07] + [ 1.41059020e+06 5.65295699e+07] + [ 2.64517035e+06 7.57868010e+07] + [ 5.39494381e+06 1.07998281e+08] + [ 5.73687811e+06 7.39125144e+07] + [ 4.25925073e+06 6.80562913e+07] + [ 2.53921040e+06 7.53441428e+07] + [ 3.58479523e+05 5.30664799e+07] + [ 8.23300559e+05 1.08868225e+07]]" +_empirical_moments,"{'(25,30]': 0.5574998121910544, '(30,35]': 0.9675607353906764, '(35,40]': 2.168068545096722, '(40,45]': 3.234950933121561, '(45,50]': 4.688547995398578, '(50,55]': 6.142846523177507, '(55,60]': 8.209944114318054, '(70,75]': 13.905111816750804, '(75,80]': 14.562181663837016, '(80,85]': 14.358754447244063, '(85,90]': 15.71527224435591, '(90,95]': 17.99554082928189}" +_has_constraints,False +_jacobian,"{'(25,30]': {'CRRA': array(-67578.99709662), 'DiscFac': array(-566144.14307097)}, '(30,35]': {'CRRA': array(62916.67127415), 'DiscFac': array(-330480.14385649)}, '(35,40]': {'CRRA': array(712398.34009049), 'DiscFac': array(7420922.46546151)}, '(40,45]': {'CRRA': array(1057729.70216717), 'DiscFac': array(22130200.88671936)}, '(45,50]': {'CRRA': array(1410590.19535983), 'DiscFac': array(56529569.89179237)}, '(50,55]': {'CRRA': array(2645170.35358017), 'DiscFac': array(75786801.04619929)}, '(55,60]': {'CRRA': array(5394943.80771548), 'DiscFac': array(1.07998281e+08)}, '(70,75]': {'CRRA': array(5736878.11204669), 'DiscFac': array(73912514.35359986)}, '(75,80]': {'CRRA': array(4259250.72813986), 'DiscFac': array(68056291.28537746)}, '(80,85]': {'CRRA': array(2539210.40317956), 'DiscFac': array(75344142.78431329)}, '(85,90]': {'CRRA': array(358479.52323137), 'DiscFac': array(53066479.86708646)}, '(90,95]': {'CRRA': array(823300.55903608), 'DiscFac': array(10886822.51310813)}}" +_no_jacobian_reason, +_cache,{} diff --git a/content/tables/msm/PortfolioSub(Stock)(Labor)Market_estimate_results.csv b/content/tables/msm/PortfolioSub(Stock)(Labor)Market_estimate_results.csv new file mode 100644 index 0000000..55b19d7 --- /dev/null +++ b/content/tables/msm/PortfolioSub(Stock)(Labor)Market_estimate_results.csv @@ -0,0 +1,97 @@ +CRRA,10.415127453154128 +DiscFac,1.0468359228606232 +time_to_estimate,581.8424572944641 +_params,"{'CRRA': 10.415127453154128, 'DiscFac': 1.0468359228606232}" +_internal_estimates,"InternalParams(values=array([10.41512745, 1.04683592]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([10.41512745, 1.04683592]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2158.28167807), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(30,35]': {'(25,30]': array(0.), '(30,35]': array(514.72491632), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(35,40]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(567.49891065), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(40,45]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(605.53346513), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(45,50]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(279.92614085), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(50,55]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(130.94785271), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(55,60]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(61.05068109), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(70,75]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(16.37164118), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(75,80]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(16.68863935), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(80,85]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(8.15237029), '(85,90]': array(0.), '(90,95]': array(0.)}, '(85,90]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(1.90282463), '(90,95]': array(0.)}, '(90,95]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.56618589)}}" +_converter,"Converter(params_to_internal=._params_to_internal at 0x7f9853e03370>, params_from_internal=._params_from_internal at 0x7f9853e03250>, derivative_to_internal=._derivative_to_internal at 0x7f9853e03760>, func_to_internal=._func_to_internal at 0x7f98462eacb0>, has_transforming_constraints=False)" +_internal_moments_cov,"[[ 4.63331552e-04 -8.29455113e-06 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-3.67034802e-03 -1.26483119e-03 1.02943264e-02 + -1.79592505e-03 -1.08608497e-02 5.25534506e-01 -2.55742896e-02] + [ 1.05939616e-03 -4.44444208e-03 -2.15951955e-03 -2.29200628e-05 + -1.39621761e-03 7.47381063e-05 2.21734482e-03 1.34912486e-02 + -1.15555069e-02 1.60128201e-02 -2.55742896e-02 1.76620438e+00]]" +_internal_weights,"[[2.15828168e+03 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 5.14724916e+02 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 5.67498911e+02 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 6.05533465e+02 + 0.00000000e+00 0.00000000e+00 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0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 8.15237029e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 1.90282463e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.66185892e-01]]" +_internal_jacobian,"[[ 2.45508000e-02 9.19173253e-01] + [ 2.52863906e-02 4.55307829e+00] + [-1.58254440e-02 1.40770057e+01] + [-2.52771170e-02 2.25056670e+01] + [-6.77132098e-02 2.79471917e+01] + [-8.78590871e-02 4.04845393e+01] + [-1.57792415e-01 5.13961890e+01] + [-6.86590492e-02 6.36205535e+01] + [-2.50102178e-02 6.22827743e+01] + [-1.89529692e-02 6.27339073e+01] + [-1.37068248e-01 4.19839510e+01] + [-1.33125465e-01 2.67369271e+01]]" +_empirical_moments,"{'(25,30]': 0.5574998121910544, '(30,35]': 0.9675607353906764, '(35,40]': 2.168068545096722, '(40,45]': 3.234950933121561, '(45,50]': 4.688547995398578, '(50,55]': 6.142846523177507, '(55,60]': 8.209944114318054, '(70,75]': 13.905111816750804, '(75,80]': 14.562181663837016, '(80,85]': 14.358754447244063, '(85,90]': 15.71527224435591, '(90,95]': 17.99554082928189}" +_has_constraints,False +_jacobian,"{'(25,30]': {'CRRA': array(0.0245508), 'DiscFac': array(0.91917325)}, '(30,35]': {'CRRA': array(0.02528639), 'DiscFac': array(4.55307829)}, '(35,40]': {'CRRA': array(-0.01582544), 'DiscFac': array(14.07700567)}, '(40,45]': {'CRRA': array(-0.02527712), 'DiscFac': array(22.50566702)}, '(45,50]': {'CRRA': array(-0.06771321), 'DiscFac': array(27.94719174)}, '(50,55]': {'CRRA': array(-0.08785909), 'DiscFac': array(40.48453928)}, '(55,60]': {'CRRA': array(-0.15779242), 'DiscFac': array(51.39618903)}, '(70,75]': {'CRRA': array(-0.06865905), 'DiscFac': array(63.62055351)}, '(75,80]': {'CRRA': array(-0.02501022), 'DiscFac': array(62.28277428)}, '(80,85]': {'CRRA': array(-0.01895297), 'DiscFac': array(62.73390727)}, '(85,90]': {'CRRA': array(-0.13706825), 'DiscFac': array(41.98395098)}, '(90,95]': {'CRRA': array(-0.13312547), 'DiscFac': array(26.73692707)}}" +_no_jacobian_reason, +_cache,{} diff --git a/content/tables/msm/PortfolioSub(Stock)Market_estimate_results.csv b/content/tables/msm/PortfolioSub(Stock)Market_estimate_results.csv new file mode 100644 index 0000000..5525f0d --- /dev/null +++ b/content/tables/msm/PortfolioSub(Stock)Market_estimate_results.csv @@ -0,0 +1,97 @@ +CRRA,2.0278733469356074 +DiscFac,0.947507160328444 +time_to_estimate,485.0004813671112 +_params,"{'CRRA': 2.0278733469356074, 'DiscFac': 0.947507160328444}" +_internal_estimates,"InternalParams(values=array([2.02787335, 0.94750716]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([2.02787335, 0.94750716]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2158.28167807), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(30,35]': {'(25,30]': array(0.), '(30,35]': array(514.72491632), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(35,40]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(567.49891065), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': 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has_transforming_constraints=False)" +_internal_moments_cov,"[[ 4.63331552e-04 -8.29455113e-06 2.62343723e-06 1.27222785e-05 + -6.70714064e-05 3.51434321e-05 4.95550858e-05 -1.59011871e-04 + 6.46025432e-05 -7.75378886e-06 -9.90751500e-04 1.05939616e-03] + [-8.29455113e-06 1.94278530e-03 6.21427749e-05 1.33977556e-04 + 4.05035336e-05 3.24097529e-05 -9.09560608e-06 -3.48865507e-04 + -4.45091936e-04 -9.40023292e-04 4.15494682e-04 -4.44444208e-03] + [ 2.62343723e-06 6.21427749e-05 1.76211792e-03 -9.07845887e-05 + -3.11748414e-05 -7.20625366e-05 1.23099100e-04 1.21619646e-04 + 2.42370531e-04 -4.53895101e-04 4.77856997e-04 -2.15951955e-03] + [ 1.27222785e-05 1.33977556e-04 -9.07845887e-05 1.65143639e-03 + -2.53506647e-04 -1.19096295e-04 5.24085619e-05 -2.00215176e-06 + -8.96273332e-05 4.13045513e-05 3.57275514e-04 -2.29200628e-05] + [-6.70714064e-05 4.05035336e-05 -3.11748414e-05 -2.53506647e-04 + 3.57237090e-03 1.96792266e-04 1.02791978e-04 -6.06222612e-04 + -8.16268084e-04 1.18356570e-03 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soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([7.83791147, 0.73864328]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2158.28167807), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(30,35]': {'(25,30]': array(0.), '(30,35]': array(514.72491632), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(35,40]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(567.49891065), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': 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array(5094018.64085281), 'DiscFac': array(83682445.48172842)}, '(55,60]': {'CRRA': array(9603537.16310164), 'DiscFac': array(1.12967147e+08)}, '(70,75]': {'CRRA': array(3097308.99225454), 'DiscFac': array(12455211.88175488)}, '(75,80]': {'CRRA': array(1300192.55714479), 'DiscFac': array(13442431.61321701)}, '(80,85]': {'CRRA': array(640281.29624133), 'DiscFac': array(30937694.18392828)}, '(85,90]': {'CRRA': array(882875.62144124), 'DiscFac': array(36937310.30523588)}, '(90,95]': {'CRRA': array(1481416.26824558), 'DiscFac': array(16141341.69088265)}}" +_no_jacobian_reason, +_cache,{} diff --git a/content/tables/msm/WarmGlowPortfolioSub(Labor)Market_estimate_results.csv b/content/tables/msm/WarmGlowPortfolioSub(Labor)Market_estimate_results.csv new file mode 100644 index 0000000..a8039a0 --- /dev/null +++ b/content/tables/msm/WarmGlowPortfolioSub(Labor)Market_estimate_results.csv @@ -0,0 +1,97 @@ +CRRA,16.108511883223933 +DiscFac,1.0084067716800273 +time_to_estimate,715.5001871585846 +_params,"{'CRRA': 16.108511883223933, 'DiscFac': 1.0084067716800273}" +_internal_estimates,"InternalParams(values=array([16.10851188, 1.00840677]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([16.10851188, 1.00840677]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2158.28167807), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(30,35]': {'(25,30]': array(0.), '(30,35]': array(514.72491632), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': 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+_converter,"Converter(params_to_internal=._params_to_internal at 0x7f68b6537490>, params_from_internal=._params_from_internal at 0x7f68b65370a0>, derivative_to_internal=._derivative_to_internal at 0x7f68b6537370>, func_to_internal=._func_to_internal at 0x7f68b6536320>, has_transforming_constraints=False)" +_internal_moments_cov,"[[ 4.63331552e-04 -8.29455113e-06 2.62343723e-06 1.27222785e-05 + -6.70714064e-05 3.51434321e-05 4.95550858e-05 -1.59011871e-04 + 6.46025432e-05 -7.75378886e-06 -9.90751500e-04 1.05939616e-03] + [-8.29455113e-06 1.94278530e-03 6.21427749e-05 1.33977556e-04 + 4.05035336e-05 3.24097529e-05 -9.09560608e-06 -3.48865507e-04 + -4.45091936e-04 -9.40023292e-04 4.15494682e-04 -4.44444208e-03] + [ 2.62343723e-06 6.21427749e-05 1.76211792e-03 -9.07845887e-05 + -3.11748414e-05 -7.20625366e-05 1.23099100e-04 1.21619646e-04 + 2.42370531e-04 -4.53895101e-04 4.77856997e-04 -2.15951955e-03] + [ 1.27222785e-05 1.33977556e-04 -9.07845887e-05 1.65143639e-03 + -2.53506647e-04 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3.74504692e-04 -7.03144448e-04 + 5.99210025e-02 1.09220053e-03 -1.79592505e-03 -1.15555069e-02] + [-7.75378886e-06 -9.40023292e-04 -4.53895101e-04 4.13045513e-05 + 1.18356570e-03 7.97137838e-04 -4.32072461e-04 9.30021499e-04 + 1.09220053e-03 1.22663712e-01 -1.08608497e-02 1.60128201e-02] + [-9.90751500e-04 4.15494682e-04 4.77856997e-04 3.57275514e-04 + -1.21385564e-03 -3.67034802e-03 -1.26483119e-03 1.02943264e-02 + -1.79592505e-03 -1.08608497e-02 5.25534506e-01 -2.55742896e-02] + [ 1.05939616e-03 -4.44444208e-03 -2.15951955e-03 -2.29200628e-05 + -1.39621761e-03 7.47381063e-05 2.21734482e-03 1.34912486e-02 + -1.15555069e-02 1.60128201e-02 -2.55742896e-02 1.76620438e+00]]" +_internal_weights,"[[2.15828168e+03 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 5.14724916e+02 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 5.67498911e+02 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 6.05533465e+02 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 2.79926141e+02 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 1.30947853e+02 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 6.10506811e+01 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.63716412e+01 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 1.66886394e+01 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 8.15237029e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 1.90282463e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.66185892e-01]]" +_internal_jacobian,"[[-6.66945202e+04 -5.65512534e+05] + [ 6.03851628e+04 -3.47902728e+05] + [ 6.70099918e+05 7.03193003e+06] + [ 9.90796713e+05 2.13007843e+07] + [ 1.32534381e+06 5.49819123e+07] + [ 2.52322927e+06 7.36830733e+07] + [ 5.17062111e+06 1.04985588e+08] + [ 5.42306783e+06 7.05251398e+07] + [ 3.97954327e+06 6.46908411e+07] + [ 2.34747254e+06 7.21476499e+07] + [ 3.61285478e+05 5.17026380e+07] + [ 7.73627749e+05 9.96169823e+06]]" +_empirical_moments,"{'(25,30]': 0.5574998121910544, '(30,35]': 0.9675607353906764, '(35,40]': 2.168068545096722, '(40,45]': 3.234950933121561, '(45,50]': 4.688547995398578, '(50,55]': 6.142846523177507, '(55,60]': 8.209944114318054, '(70,75]': 13.905111816750804, '(75,80]': 14.562181663837016, '(80,85]': 14.358754447244063, '(85,90]': 15.71527224435591, '(90,95]': 17.99554082928189}" +_has_constraints,False +_jacobian,"{'(25,30]': {'CRRA': array(-66694.52024247), 'DiscFac': array(-565512.5335048)}, '(30,35]': {'CRRA': array(60385.16275463), 'DiscFac': array(-347902.72760634)}, '(35,40]': {'CRRA': array(670099.91772833), 'DiscFac': array(7031930.02942947)}, '(40,45]': {'CRRA': array(990796.71302322), 'DiscFac': array(21300784.27947865)}, '(45,50]': {'CRRA': array(1325343.81337981), 'DiscFac': array(54981912.31926441)}, '(50,55]': {'CRRA': array(2523229.267605), 'DiscFac': array(73683073.26230405)}, '(55,60]': {'CRRA': array(5170621.11464229), 'DiscFac': array(1.04985588e+08)}, '(70,75]': {'CRRA': array(5423067.83271693), 'DiscFac': array(70525139.75333104)}, '(75,80]': {'CRRA': array(3979543.27149752), 'DiscFac': array(64690841.07529136)}, '(80,85]': {'CRRA': array(2347472.53970107), 'DiscFac': array(72147649.90878029)}, '(85,90]': {'CRRA': array(361285.47766543), 'DiscFac': array(51702637.98911797)}, '(90,95]': {'CRRA': array(773627.74936255), 'DiscFac': array(9961698.22756919)}}" +_no_jacobian_reason, +_cache,{} diff --git a/content/tables/msm/WarmGlowPortfolioSub(Stock)(Labor)Market_estimate_results.csv b/content/tables/msm/WarmGlowPortfolioSub(Stock)(Labor)Market_estimate_results.csv new file mode 100644 index 0000000..4a7c91c --- /dev/null +++ b/content/tables/msm/WarmGlowPortfolioSub(Stock)(Labor)Market_estimate_results.csv @@ -0,0 +1,97 @@ +CRRA,10.469331918692177 +DiscFac,1.046944960051022 +time_to_estimate,585.425628900528 +_params,"{'CRRA': 10.469331918692177, 'DiscFac': 1.046944960051022}" +_internal_estimates,"InternalParams(values=array([10.46933192, 1.04694496]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([10.46933192, 1.04694496]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2158.28167807), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': 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'(90,95]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.56618589)}}" +_converter,"Converter(params_to_internal=._params_to_internal at 0x7f3b77f90550>, params_from_internal=._params_from_internal at 0x7f3b77f91000>, derivative_to_internal=._derivative_to_internal at 0x7f3b77f930a0>, func_to_internal=._func_to_internal at 0x7f3b77f93010>, has_transforming_constraints=False)" +_internal_moments_cov,"[[ 4.63331552e-04 -8.29455113e-06 2.62343723e-06 1.27222785e-05 + -6.70714064e-05 3.51434321e-05 4.95550858e-05 -1.59011871e-04 + 6.46025432e-05 -7.75378886e-06 -9.90751500e-04 1.05939616e-03] + [-8.29455113e-06 1.94278530e-03 6.21427749e-05 1.33977556e-04 + 4.05035336e-05 3.24097529e-05 -9.09560608e-06 -3.48865507e-04 + -4.45091936e-04 -9.40023292e-04 4.15494682e-04 -4.44444208e-03] + [ 2.62343723e-06 6.21427749e-05 1.76211792e-03 -9.07845887e-05 + -3.11748414e-05 -7.20625366e-05 1.23099100e-04 1.21619646e-04 + 2.42370531e-04 -4.53895101e-04 4.77856997e-04 -2.15951955e-03] + [ 1.27222785e-05 1.33977556e-04 -9.07845887e-05 1.65143639e-03 + -2.53506647e-04 -1.19096295e-04 5.24085619e-05 -2.00215176e-06 + -8.96273332e-05 4.13045513e-05 3.57275514e-04 -2.29200628e-05] + [-6.70714064e-05 4.05035336e-05 -3.11748414e-05 -2.53506647e-04 + 3.57237090e-03 1.96792266e-04 1.02791978e-04 -6.06222612e-04 + -8.16268084e-04 1.18356570e-03 -1.21385564e-03 -1.39621761e-03] + [ 3.51434321e-05 3.24097529e-05 -7.20625366e-05 -1.19096295e-04 + 1.96792266e-04 7.63662771e-03 1.87644341e-04 -6.91727595e-04 + -8.41433602e-04 7.97137838e-04 -3.67034802e-03 7.47381063e-05] + [ 4.95550858e-05 -9.09560608e-06 1.23099100e-04 5.24085619e-05 + 1.02791978e-04 1.87644341e-04 1.63798336e-02 4.49609135e-04 + 3.74504692e-04 -4.32072461e-04 -1.26483119e-03 2.21734482e-03] + [-1.59011871e-04 -3.48865507e-04 1.21619646e-04 -2.00215176e-06 + -6.06222612e-04 -6.91727595e-04 4.49609135e-04 6.10812312e-02 + -7.03144448e-04 9.30021499e-04 1.02943264e-02 1.34912486e-02] + [ 6.46025432e-05 -4.45091936e-04 2.42370531e-04 -8.96273332e-05 + -8.16268084e-04 -8.41433602e-04 3.74504692e-04 -7.03144448e-04 + 5.99210025e-02 1.09220053e-03 -1.79592505e-03 -1.15555069e-02] + [-7.75378886e-06 -9.40023292e-04 -4.53895101e-04 4.13045513e-05 + 1.18356570e-03 7.97137838e-04 -4.32072461e-04 9.30021499e-04 + 1.09220053e-03 1.22663712e-01 -1.08608497e-02 1.60128201e-02] + [-9.90751500e-04 4.15494682e-04 4.77856997e-04 3.57275514e-04 + -1.21385564e-03 -3.67034802e-03 -1.26483119e-03 1.02943264e-02 + -1.79592505e-03 -1.08608497e-02 5.25534506e-01 -2.55742896e-02] + [ 1.05939616e-03 -4.44444208e-03 -2.15951955e-03 -2.29200628e-05 + -1.39621761e-03 7.47381063e-05 2.21734482e-03 1.34912486e-02 + -1.15555069e-02 1.60128201e-02 -2.55742896e-02 1.76620438e+00]]" +_internal_weights,"[[2.15828168e+03 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 5.14724916e+02 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 5.67498911e+02 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 6.05533465e+02 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 2.79926141e+02 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 1.30947853e+02 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 6.10506811e+01 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.63716412e+01 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 1.66886394e+01 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 8.15237029e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 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13.905111816750804, '(75,80]': 14.562181663837016, '(80,85]': 14.358754447244063, '(85,90]': 15.71527224435591, '(90,95]': 17.99554082928189}" +_has_constraints,False +_jacobian,"{'(25,30]': {'CRRA': array(0.02976886), 'DiscFac': array(0.82782956)}, '(30,35]': {'CRRA': array(0.01464313), 'DiscFac': array(5.91119689)}, '(35,40]': {'CRRA': array(-0.013383), 'DiscFac': array(13.53000756)}, '(40,45]': {'CRRA': array(-0.02211385), 'DiscFac': array(21.46905464)}, '(45,50]': {'CRRA': array(-0.10169598), 'DiscFac': array(30.25781359)}, '(50,55]': {'CRRA': array(-0.05545297), 'DiscFac': array(38.63905612)}, '(55,60]': {'CRRA': array(-0.14495094), 'DiscFac': array(52.36208621)}, '(70,75]': {'CRRA': array(-0.10239363), 'DiscFac': array(72.41542198)}, '(75,80]': {'CRRA': array(-0.04038518), 'DiscFac': array(62.84502787)}, '(80,85]': {'CRRA': array(-0.01452171), 'DiscFac': array(59.59306501)}, '(85,90]': {'CRRA': array(-0.1141468), 'DiscFac': array(41.94055651)}, '(90,95]': {'CRRA': array(-0.10374346), 'DiscFac': array(25.17865248)}}" +_no_jacobian_reason, +_cache,{} diff --git a/content/tables/msm/WarmGlowPortfolioSub(Stock)Market_estimate_results.csv b/content/tables/msm/WarmGlowPortfolioSub(Stock)Market_estimate_results.csv new file mode 100644 index 0000000..3bf08da --- /dev/null +++ b/content/tables/msm/WarmGlowPortfolioSub(Stock)Market_estimate_results.csv @@ -0,0 +1,97 @@ +CRRA,2.0278733469356074 +DiscFac,0.947507160328444 +time_to_estimate,482.8548994064331 +_params,"{'CRRA': 2.0278733469356074, 'DiscFac': 0.947507160328444}" +_internal_estimates,"InternalParams(values=array([2.02787335, 0.94750716]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([2.02787335, 0.94750716]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': 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array(76.58658385)}, '(85,90]': {'CRRA': array(0.88942688), 'DiscFac': array(68.97191388)}, '(90,95]': {'CRRA': array(0.), 'DiscFac': array(0.)}}" +_no_jacobian_reason, +_cache,{} diff --git a/content/tables/msm/WarmGlowPortfolio_estimate_results.csv b/content/tables/msm/WarmGlowPortfolio_estimate_results.csv new file mode 100644 index 0000000..a6e337c --- /dev/null +++ b/content/tables/msm/WarmGlowPortfolio_estimate_results.csv @@ -0,0 +1,97 @@ +CRRA,8.0340535845575 +DiscFac,0.7317513481626051 +time_to_estimate,586.0827894210815 +_params,"{'CRRA': 8.0340535845575, 'DiscFac': 0.7317513481626051}" +_internal_estimates,"InternalParams(values=array([8.03405358, 0.73175135]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([8.03405358, 0.73175135]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2158.28167807), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(30,35]': {'(25,30]': array(0.), '(30,35]': array(514.72491632), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(35,40]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(567.49891065), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(40,45]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': 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+_internal_estimates,"InternalParams(values=array([5.37701581, 1.04362009]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([5.37701581, 1.04362009]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2019.14915854), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(30,35]': {'(25,30]': array(0.), '(30,35]': array(500.34239184), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(35,40]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(518.82380586), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(40,45]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(591.66555413), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(45,50]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(294.07884176), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(50,55]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), 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params_from_internal=._params_from_internal at 0x7f63b97f9cf0>, derivative_to_internal=._derivative_to_internal at 0x7f63b97f9d80>, func_to_internal=._func_to_internal at 0x7f63b97f9e10>, has_transforming_constraints=False)" +_internal_moments_cov,"[[ 4.95258112e-04 2.66601431e-05 2.79286974e-05 7.46706608e-06 + -3.57660861e-05 2.67259515e-05 1.10371975e-04 2.29478720e-04 + -1.74583374e-04 9.18609805e-05 -9.28899552e-04 -1.50543778e-04] + [ 2.66601431e-05 1.99863137e-03 8.85890234e-05 9.17921130e-05 + -9.23337837e-05 -2.83576297e-04 1.48966314e-04 1.80715547e-04 + -3.10963397e-04 3.47758778e-04 5.28959433e-04 5.19875724e-04] + [ 2.79286974e-05 8.85890234e-05 1.92743661e-03 4.84543237e-05 + 8.64878494e-05 -1.21376982e-04 -2.12383559e-04 -3.48906145e-05 + 4.13284621e-04 3.32697621e-04 -4.47332935e-04 5.68592373e-04] + [ 7.46706608e-06 9.17921130e-05 4.84543237e-05 1.69014402e-03 + -1.64277002e-05 -4.40250202e-05 -1.69566236e-04 -7.30430953e-04 + 6.17674185e-05 -3.77973815e-04 -1.25740378e-03 -1.67437857e-03] + [-3.57660861e-05 -9.23337837e-05 8.64878494e-05 -1.64277002e-05 + 3.40044865e-03 2.31358468e-04 -1.98388371e-04 -9.90302591e-04 + 3.40008787e-04 -3.99570271e-04 -8.07415962e-04 -6.44898143e-03] + [ 2.67259515e-05 -2.83576297e-04 -1.21376982e-04 -4.40250202e-05 + 2.31358468e-04 8.59065022e-03 -5.28504370e-04 9.67901647e-04 + -6.38169315e-04 -4.22552263e-04 3.24001952e-03 -3.14657898e-03] + [ 1.10371975e-04 1.48966314e-04 -2.12383559e-04 -1.69566236e-04 + -1.98388371e-04 -5.28504370e-04 1.43110225e-02 2.57065708e-04 + -1.09391854e-03 1.90605756e-03 -6.99390334e-04 7.40307679e-03] + [ 2.29478720e-04 1.80715547e-04 -3.48906145e-05 -7.30430953e-04 + -9.90302591e-04 9.67901647e-04 2.57065708e-04 6.07248017e-02 + 2.56350149e-03 6.21389768e-04 -2.22168627e-03 -1.42343182e-02] + [-1.74583374e-04 -3.10963397e-04 4.13284621e-04 6.17674185e-05 + 3.40008787e-04 -6.38169315e-04 -1.09391854e-03 2.56350149e-03 + 5.98616882e-02 -9.63153128e-04 7.46109287e-03 1.60977374e-02] + [ 9.18609805e-05 3.47758778e-04 3.32697621e-04 -3.77973815e-04 + -3.99570271e-04 -4.22552263e-04 1.90605756e-03 6.21389768e-04 + -9.63153128e-04 1.26839727e-01 -5.50753961e-03 1.82934775e-03] + [-9.28899552e-04 5.28959433e-04 -4.47332935e-04 -1.25740378e-03 + -8.07415962e-04 3.24001952e-03 -6.99390334e-04 -2.22168627e-03 + 7.46109287e-03 -5.50753961e-03 5.26149129e-01 -2.79455237e-02] + [-1.50543778e-04 5.19875724e-04 5.68592373e-04 -1.67437857e-03 + -6.44898143e-03 -3.14657898e-03 7.40307679e-03 -1.42343182e-02 + 1.60977374e-02 1.82934775e-03 -2.79455237e-02 1.79476773e+00]]" +_internal_weights,"[[2.01914916e+03 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 5.00342392e+02 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 5.18823806e+02 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 5.91665554e+02 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 2.94078842e+02 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 1.16405624e+02 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 6.98762089e+01 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.64677360e+01 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 1.67051754e+01 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 7.88396527e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 1.90060184e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.57175161e-01]]" +_internal_jacobian,"[[ 1.45639294e-02 4.52339405e+00] + [-6.62111793e-02 1.45768684e+01] + [-1.95336387e-01 3.07011541e+01] + [-2.79855117e-01 4.46815547e+01] + [-3.65778202e-01 5.98648820e+01] + [-4.56818085e-01 7.75674437e+01] + [-4.96739603e-01 9.16333181e+01] + [-5.04330460e-01 1.41250137e+02] + [-4.46535591e-01 1.17491151e+02] + [-2.73181204e-01 9.55011238e+01] + [-3.70252107e-01 5.92618519e+01] + [-2.18445157e-01 3.15404650e+01]]" +_empirical_moments,"{'(25,30]': 0.5574998121910544, '(30,35]': 0.9675607353906764, '(35,40]': 2.168068545096722, '(40,45]': 3.234950933121561, '(45,50]': 4.688547995398578, '(50,55]': 6.142846523177507, '(55,60]': 8.209944114318054, '(70,75]': 13.905111816750804, '(75,80]': 14.562181663837016, '(80,85]': 14.358754447244063, '(85,90]': 15.71527224435591, '(90,95]': 17.99554082928189}" +_has_constraints,False +_jacobian,"{'(25,30]': {'CRRA': array(0.01456393), 'DiscFac': array(4.52339405)}, '(30,35]': {'CRRA': array(-0.06621118), 'DiscFac': array(14.57686844)}, '(35,40]': {'CRRA': array(-0.19533639), 'DiscFac': array(30.70115407)}, '(40,45]': {'CRRA': array(-0.27985512), 'DiscFac': array(44.68155469)}, '(45,50]': {'CRRA': array(-0.3657782), 'DiscFac': array(59.86488197)}, '(50,55]': {'CRRA': array(-0.45681808), 'DiscFac': array(77.5674437)}, '(55,60]': {'CRRA': array(-0.4967396), 'DiscFac': array(91.63331811)}, '(70,75]': {'CRRA': array(-0.50433046), 'DiscFac': array(141.25013734)}, '(75,80]': {'CRRA': array(-0.44653559), 'DiscFac': array(117.49115137)}, '(80,85]': {'CRRA': array(-0.2731812), 'DiscFac': array(95.50112381)}, '(85,90]': {'CRRA': array(-0.37025211), 'DiscFac': array(59.26185186)}, '(90,95]': {'CRRA': array(-0.21844516), 'DiscFac': array(31.540465)}}" +_no_jacobian_reason, +_cache,{} diff --git a/content/tables/msm/WarmGlowSub(Stock)Market_estimate_results.csv b/content/tables/msm/WarmGlowSub(Stock)Market_estimate_results.csv new file mode 100644 index 0000000..b3fb863 --- /dev/null +++ b/content/tables/msm/WarmGlowSub(Stock)Market_estimate_results.csv @@ -0,0 +1,97 @@ +CRRA,1.6065655986698697 +DiscFac,0.9790270061791481 +time_to_estimate,472.92487931251526 +_params,"{'CRRA': 1.6065655986698697, 'DiscFac': 0.9790270061791481}" +_internal_estimates,"InternalParams(values=array([1.6065656 , 0.97902701]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([1.6065656 , 0.97902701]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2019.14915854), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(30,35]': {'(25,30]': array(0.), '(30,35]': array(500.34239184), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(35,40]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(518.82380586), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(40,45]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(591.66555413), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(45,50]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(294.07884176), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(50,55]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(116.40562412), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(55,60]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(69.87620894), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(70,75]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(16.46773596), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(75,80]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), 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+_internal_jacobian,"[[2.51339302e-01 1.25161824e+01] + [6.91024677e-01 4.11977382e+01] + [1.06112960e+00 7.01364273e+01] + [2.01196744e+00 1.18062163e+02] + [1.95906455e+00 1.29991022e+02] + [2.89635545e+00 1.86078870e+02] + [4.08859681e+00 2.57726092e+02] + [2.96738665e+00 1.85505053e+02] + [1.39566959e+00 8.49439513e+01] + [1.78923564e+00 9.93353854e+01] + [9.79240590e-01 5.10985338e+01] + [1.00519672e+00 6.03845002e+01]]" +_empirical_moments,"{'(25,30]': 0.5574998121910544, '(30,35]': 0.9675607353906764, '(35,40]': 2.168068545096722, '(40,45]': 3.234950933121561, '(45,50]': 4.688547995398578, '(50,55]': 6.142846523177507, '(55,60]': 8.209944114318054, '(70,75]': 13.905111816750804, '(75,80]': 14.562181663837016, '(80,85]': 14.358754447244063, '(85,90]': 15.71527224435591, '(90,95]': 17.99554082928189}" +_has_constraints,False +_jacobian,"{'(25,30]': {'CRRA': array(0.2513393), 'DiscFac': array(12.51618244)}, '(30,35]': {'CRRA': array(0.69102468), 'DiscFac': array(41.19773815)}, 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+CRRA,1.7007109953786266 +DiscFac,0.9696762813125013 +time_to_estimate,519.7153921127319 +_params,"{'CRRA': 1.7007109953786266, 'DiscFac': 0.9696762813125013}" +_internal_estimates,"InternalParams(values=array([1.700711 , 0.96967628]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([1.700711 , 0.96967628]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2019.14915854), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(30,35]': {'(25,30]': array(0.), '(30,35]': array(500.34239184), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': 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array(0.), '(90,95]': array(0.55717516)}}" +_converter,"Converter(params_to_internal=._params_to_internal at 0x7f80642a1090>, params_from_internal=._params_from_internal at 0x7f8075f8b2e0>, derivative_to_internal=._derivative_to_internal at 0x7f80757f16c0>, func_to_internal=._func_to_internal at 0x7f80757f17e0>, has_transforming_constraints=False)" +_internal_moments_cov,"[[ 4.95258112e-04 2.66601431e-05 2.79286974e-05 7.46706608e-06 + -3.57660861e-05 2.67259515e-05 1.10371975e-04 2.29478720e-04 + -1.74583374e-04 9.18609805e-05 -9.28899552e-04 -1.50543778e-04] + [ 2.66601431e-05 1.99863137e-03 8.85890234e-05 9.17921130e-05 + -9.23337837e-05 -2.83576297e-04 1.48966314e-04 1.80715547e-04 + -3.10963397e-04 3.47758778e-04 5.28959433e-04 5.19875724e-04] + [ 2.79286974e-05 8.85890234e-05 1.92743661e-03 4.84543237e-05 + 8.64878494e-05 -1.21376982e-04 -2.12383559e-04 -3.48906145e-05 + 4.13284621e-04 3.32697621e-04 -4.47332935e-04 5.68592373e-04] + [ 7.46706608e-06 9.17921130e-05 4.84543237e-05 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+_jacobian,"{'(25,30]': {'CRRA': array(-68752.51579378), 'DiscFac': array(-574393.6108013)}, '(30,35]': {'CRRA': array(65248.21682618), 'DiscFac': array(-332957.69632734)}, '(35,40]': {'CRRA': array(737672.82315978), 'DiscFac': array(7573809.23941928)}, '(40,45]': {'CRRA': array(1098699.08952011), 'DiscFac': array(22614574.62081938)}, '(45,50]': {'CRRA': array(1467051.20099021), 'DiscFac': array(57643628.72897539)}, '(50,55]': {'CRRA': array(2746397.87222362), 'DiscFac': array(77320072.38929991)}, '(55,60]': {'CRRA': array(5594483.26115322), 'DiscFac': array(1.10187439e+08)}, '(70,75]': {'CRRA': array(5963233.74826386), 'DiscFac': array(75656548.91590396)}, '(75,80]': {'CRRA': array(4435123.21199968), 'DiscFac': array(69778435.57071303)}, '(80,85]': {'CRRA': array(2653892.76373984), 'DiscFac': array(77178812.33691593)}, '(85,90]': {'CRRA': array(366575.86700859), 'DiscFac': array(54283037.56225435)}, '(90,95]': {'CRRA': array(852088.40803855), 'DiscFac': array(11260155.96864601)}}" +_no_jacobian_reason, +_cache,{} diff --git a/content/tables/msm/WealthPortfolioSub(Stock)(Labor)Market_estimate_results.csv b/content/tables/msm/WealthPortfolioSub(Stock)(Labor)Market_estimate_results.csv new file mode 100644 index 0000000..93b052a --- /dev/null +++ b/content/tables/msm/WealthPortfolioSub(Stock)(Labor)Market_estimate_results.csv @@ -0,0 +1,97 @@ +CRRA,10.516758257492437 +DiscFac,1.0471170203613893 +time_to_estimate,609.2074275016785 +_params,"{'CRRA': 10.516758257492437, 'DiscFac': 1.0471170203613893}" +_internal_estimates,"InternalParams(values=array([10.51675826, 1.04711702]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([10.51675826, 1.04711702]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2158.28167807), 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'(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(45,50]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(279.92614085), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(50,55]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(130.94785271), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(55,60]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(61.05068109), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(70,75]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(16.37164118), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(75,80]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(16.68863935), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(80,85]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(8.15237029), '(85,90]': array(0.), '(90,95]': array(0.)}, '(85,90]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(1.90282463), '(90,95]': array(0.)}, '(90,95]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.56618589)}}" +_converter,"Converter(params_to_internal=._params_to_internal at 0x7f999bba6d40>, params_from_internal=._params_from_internal at 0x7f999bba7be0>, derivative_to_internal=._derivative_to_internal at 0x7f999ba23130>, func_to_internal=._func_to_internal at 0x7f999ba21510>, has_transforming_constraints=False)" +_internal_moments_cov,"[[ 4.63331552e-04 -8.29455113e-06 2.62343723e-06 1.27222785e-05 + -6.70714064e-05 3.51434321e-05 4.95550858e-05 -1.59011871e-04 + 6.46025432e-05 -7.75378886e-06 -9.90751500e-04 1.05939616e-03] + [-8.29455113e-06 1.94278530e-03 6.21427749e-05 1.33977556e-04 + 4.05035336e-05 3.24097529e-05 -9.09560608e-06 -3.48865507e-04 + -4.45091936e-04 -9.40023292e-04 4.15494682e-04 -4.44444208e-03] + [ 2.62343723e-06 6.21427749e-05 1.76211792e-03 -9.07845887e-05 + -3.11748414e-05 -7.20625366e-05 1.23099100e-04 1.21619646e-04 + 2.42370531e-04 -4.53895101e-04 4.77856997e-04 -2.15951955e-03] + [ 1.27222785e-05 1.33977556e-04 -9.07845887e-05 1.65143639e-03 + -2.53506647e-04 -1.19096295e-04 5.24085619e-05 -2.00215176e-06 + -8.96273332e-05 4.13045513e-05 3.57275514e-04 -2.29200628e-05] + [-6.70714064e-05 4.05035336e-05 -3.11748414e-05 -2.53506647e-04 + 3.57237090e-03 1.96792266e-04 1.02791978e-04 -6.06222612e-04 + -8.16268084e-04 1.18356570e-03 -1.21385564e-03 -1.39621761e-03] + [ 3.51434321e-05 3.24097529e-05 -7.20625366e-05 -1.19096295e-04 + 1.96792266e-04 7.63662771e-03 1.87644341e-04 -6.91727595e-04 + -8.41433602e-04 7.97137838e-04 -3.67034802e-03 7.47381063e-05] + [ 4.95550858e-05 -9.09560608e-06 1.23099100e-04 5.24085619e-05 + 1.02791978e-04 1.87644341e-04 1.63798336e-02 4.49609135e-04 + 3.74504692e-04 -4.32072461e-04 -1.26483119e-03 2.21734482e-03] + [-1.59011871e-04 -3.48865507e-04 1.21619646e-04 -2.00215176e-06 + -6.06222612e-04 -6.91727595e-04 4.49609135e-04 6.10812312e-02 + -7.03144448e-04 9.30021499e-04 1.02943264e-02 1.34912486e-02] + [ 6.46025432e-05 -4.45091936e-04 2.42370531e-04 -8.96273332e-05 + -8.16268084e-04 -8.41433602e-04 3.74504692e-04 -7.03144448e-04 + 5.99210025e-02 1.09220053e-03 -1.79592505e-03 -1.15555069e-02] + [-7.75378886e-06 -9.40023292e-04 -4.53895101e-04 4.13045513e-05 + 1.18356570e-03 7.97137838e-04 -4.32072461e-04 9.30021499e-04 + 1.09220053e-03 1.22663712e-01 -1.08608497e-02 1.60128201e-02] + [-9.90751500e-04 4.15494682e-04 4.77856997e-04 3.57275514e-04 + -1.21385564e-03 -3.67034802e-03 -1.26483119e-03 1.02943264e-02 + -1.79592505e-03 -1.08608497e-02 5.25534506e-01 -2.55742896e-02] + [ 1.05939616e-03 -4.44444208e-03 -2.15951955e-03 -2.29200628e-05 + -1.39621761e-03 7.47381063e-05 2.21734482e-03 1.34912486e-02 + -1.15555069e-02 1.60128201e-02 -2.55742896e-02 1.76620438e+00]]" +_internal_weights,"[[2.15828168e+03 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 5.14724916e+02 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 5.67498911e+02 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 6.05533465e+02 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 2.79926141e+02 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 1.30947853e+02 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 6.10506811e+01 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.63716412e+01 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 1.66886394e+01 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 8.15237029e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 1.90282463e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.66185892e-01]]" +_internal_jacobian,"[[ 2.75382775e-02 8.02546379e-01] + [ 3.01312663e-02 3.97638493e+00] + [ 2.89575380e-03 1.48684968e+01] + [-6.82632685e-02 2.69599504e+01] + [-7.04058092e-02 3.19420206e+01] + [-4.45996580e-02 3.56904723e+01] + [-1.10115830e-01 4.44026696e+01] + [-2.61974131e-02 6.57280058e+01] + [-1.52268572e-02 6.40434361e+01] + [-4.87298427e-02 6.10973217e+01] + [-9.14214649e-02 4.47224244e+01] + [-1.25031612e-01 2.38541005e+01]]" +_empirical_moments,"{'(25,30]': 0.5574998121910544, '(30,35]': 0.9675607353906764, '(35,40]': 2.168068545096722, '(40,45]': 3.234950933121561, '(45,50]': 4.688547995398578, 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array(-0.09142146), 'DiscFac': array(44.72242437)}, '(90,95]': {'CRRA': array(-0.12503161), 'DiscFac': array(23.85410053)}}" +_no_jacobian_reason, +_cache,{} diff --git a/content/tables/msm/WealthPortfolioSub(Stock)Market_estimate_results.csv b/content/tables/msm/WealthPortfolioSub(Stock)Market_estimate_results.csv new file mode 100644 index 0000000..f1aa708 --- /dev/null +++ b/content/tables/msm/WealthPortfolioSub(Stock)Market_estimate_results.csv @@ -0,0 +1,97 @@ +CRRA,6.628337898636361 +DiscFac,0.7815105267511713 +time_to_estimate,584.865697145462 +_params,"{'CRRA': 6.628337898636361, 'DiscFac': 0.7815105267511713}" +_internal_estimates,"InternalParams(values=array([6.6283379 , 0.78151053]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([6.6283379 , 0.78151053]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2158.28167807), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(30,35]': {'(25,30]': array(0.), '(30,35]': array(514.72491632), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(35,40]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(567.49891065), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(40,45]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), 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{'CRRA': array(0.43692766), 'DiscFac': array(9.2612203)}, '(90,95]': {'CRRA': array(0.46235413), 'DiscFac': array(7.5390286)}}" +_no_jacobian_reason, +_cache,{} diff --git a/content/tables/msm/WealthPortfolio_estimate_results.csv b/content/tables/msm/WealthPortfolio_estimate_results.csv new file mode 100644 index 0000000..c323762 --- /dev/null +++ b/content/tables/msm/WealthPortfolio_estimate_results.csv @@ -0,0 +1,97 @@ +CRRA,4.570611372168379 +DiscFac,0.8612819679031366 +time_to_estimate,586.6631124019623 +_params,"{'CRRA': 4.570611372168379, 'DiscFac': 0.8612819679031366}" +_internal_estimates,"InternalParams(values=array([4.57061137, 0.86128197]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([4.57061137, 0.86128197]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2158.28167807), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(30,35]': {'(25,30]': array(0.), '(30,35]': array(514.72491632), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(35,40]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(567.49891065), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(40,45]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(605.53346513), '(45,50]': 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array(5493466.22090993), 'DiscFac': array(39414797.73483838)}, '(80,85]': {'CRRA': array(2527103.49691757), 'DiscFac': array(67796062.74984673)}, '(85,90]': {'CRRA': array(2309490.06664654), 'DiscFac': array(73744812.9947672)}, '(90,95]': {'CRRA': array(2857078.85766507), 'DiscFac': array(25874500.62208251)}}" +_no_jacobian_reason, +_cache,{} diff --git a/content/tables/parameters.tex b/content/tables/parameters.tex new file mode 100644 index 0000000..9d52ab1 --- /dev/null +++ b/content/tables/parameters.tex @@ -0,0 +1,9 @@ +\begin{tabular}{lrrlll} +\toprule +Name & criterion & CRRA & WealthShare & BeqFac & BeqShift \\ +\midrule +Portfolio & 0.642000 & 9.252000 & & & \\ +WarmGlowPortfolio & 0.641000 & 9.207000 & & 23.051000 & 45.643000 \\ +WealthPortfolio & 0.242000 & 5.336000 & 0.171000 & & \\ +\bottomrule +\end{tabular} diff --git a/docs/conf.py b/docs/conf.py new file mode 100644 index 0000000..ba2680a --- /dev/null +++ b/docs/conf.py @@ -0,0 +1,64 @@ +from __future__ import annotations + +import importlib.metadata +from typing import Any + +project = "estimark" +copyright = "2024, Alan Lujan" +author = "Alan Lujan" +version = release = importlib.metadata.version("estimark") + +extensions = [ + "myst_parser", + "sphinx.ext.autodoc", + "sphinx.ext.intersphinx", + "sphinx.ext.mathjax", + "sphinx.ext.napoleon", + "sphinx_autodoc_typehints", + "sphinx_copybutton", +] + +source_suffix = [".rst", ".md"] +exclude_patterns = [ + "_build", + "**.ipynb_checkpoints", + "Thumbs.db", + ".DS_Store", + ".env", + ".venv", +] + +html_theme = "furo" + +html_theme_options: dict[str, Any] = { + "footer_icons": [ + { + "name": "GitHub", + "url": "https://github.com/econ-ark/EstimatingMicroDSOPs", + "html": """ + + + + """, + "class": "", + }, + ], + "source_repository": "https://github.com/econ-ark/EstimatingMicroDSOPs", + "source_branch": "main", + "source_directory": "docs/", +} + +myst_enable_extensions = [ + "colon_fence", +] + +intersphinx_mapping = { + "python": ("https://docs.python.org/3", None), +} + +nitpick_ignore = [ + ("py:class", "_io.StringIO"), + ("py:class", "_io.BytesIO"), +] + +always_document_param_types = True diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 0000000..06be69e --- /dev/null +++ b/docs/index.md @@ -0,0 +1,17 @@ +# estimark + +```{toctree} +:maxdepth: 2 +:hidden: + +``` + +```{include} ../README.md +:start-after: +``` + +## Indices and tables + +- {ref}`genindex` +- {ref}`modindex` +- {ref}`search` diff --git a/environment.yml b/environment.yml index 8c99ff8..4bd9a17 100644 --- a/environment.yml +++ b/environment.yml @@ -3,6 +3,22 @@ channels: - conda-forge - defaults dependencies: - - python + - python=3.12 + - estimagic=0.4.7 + - jupyter + - statsmodels + - jupyterlab + - black + - ruff + - nbqa + - dask + - openpyxl - nodejs - mystmd + - pip + - pip: + - git+https://github.com/econ-ark/HARK@master + - DFO-LS + - tranquilo + - black[jupyter] + - -e . diff --git a/noxfile.py b/noxfile.py new file mode 100644 index 0000000..8c927ef --- /dev/null +++ b/noxfile.py @@ -0,0 +1,107 @@ +from __future__ import annotations + +import argparse +import shutil +from pathlib import Path + +import nox + +DIR = Path(__file__).parent.resolve() + +nox.needs_version = ">=2024.3.2" +nox.options.sessions = ["lint", "pylint", "tests"] +nox.options.default_venv_backend = "uv|virtualenv" + + +@nox.session +def lint(session: nox.Session) -> None: + """ + Run the linter. + """ + session.install("pre-commit") + session.run( + "pre-commit", "run", "--all-files", "--show-diff-on-failure", *session.posargs + ) + + +@nox.session +def pylint(session: nox.Session) -> None: + """ + Run PyLint. + """ + # This needs to be installed into the package environment, and is slower + # than a pre-commit check + session.install(".", "pylint>=3.2") + session.run("pylint", "estimark", *session.posargs) + + +@nox.session +def tests(session: nox.Session) -> None: + """ + Run the unit and regular tests. + """ + session.install(".[test]") + session.run("pytest", *session.posargs) + + +@nox.session(reuse_venv=True) +def docs(session: nox.Session) -> None: + """ + Build the docs. Pass --non-interactive to avoid serving. First positional argument is the target directory. + """ + + parser = argparse.ArgumentParser() + parser.add_argument( + "-b", dest="builder", default="html", help="Build target (default: html)" + ) + parser.add_argument("output", nargs="?", help="Output directory") + args, posargs = parser.parse_known_args(session.posargs) + serve = args.builder == "html" and session.interactive + + session.install("-e.[docs]", "sphinx-autobuild") + + shared_args = ( + "-n", # nitpicky mode + "-T", # full tracebacks + f"-b={args.builder}", + "docs", + args.output or f"docs/_build/{args.builder}", + *posargs, + ) + + if serve: + session.run("sphinx-autobuild", "--open-browser", *shared_args) + else: + session.run("sphinx-build", "--keep-going", *shared_args) + + +@nox.session +def build_api_docs(session: nox.Session) -> None: + """ + Build (regenerate) API docs. + """ + + session.install("sphinx") + session.run( + "sphinx-apidoc", + "-o", + "docs/api/", + "--module-first", + "--no-toc", + "--force", + "src/estimark", + ) + + +@nox.session +def build(session: nox.Session) -> None: + """ + Build an SDist and wheel. + """ + + build_path = DIR.joinpath("build") + if build_path.exists(): + shutil.rmtree(build_path) + + session.install("build") + session.run("python", "-m", "build") diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..2cce21e --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,157 @@ +[build-system] +requires = ["hatchling", "hatch-vcs"] +build-backend = "hatchling.build" + + +[project] +name = "estimark" +authors = [ + { name = "Alan Lujan", email = "alanlujan91@gmail.com" }, +] +description = "Estimating Microeconomic Dynamic Stochastic Optimization Problems" +readme = "README.md" +license.file = "LICENSE" +requires-python = ">=3.8" +classifiers = [ + "Development Status :: 1 - Planning", + "Intended Audience :: Science/Research", + "Intended Audience :: Developers", + "License :: OSI Approved :: MIT License", + "Operating System :: OS Independent", + "Programming Language :: Python", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3 :: Only", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Topic :: Scientific/Engineering", + "Typing :: Typed", +] +dynamic = ["version"] +dependencies = [] + +[project.optional-dependencies] +test = [ + "pytest >=6", + "pytest-cov >=3", +] +dev = [ + "pytest >=6", + "pytest-cov >=3", +] +docs = [ + "sphinx>=7.0", + "myst_parser>=0.13", + "sphinx_copybutton", + "sphinx_autodoc_typehints", + "furo>=2023.08.17", +] + +[project.urls] +Homepage = "https://github.com/econ-ark/EstimatingMicroDSOPs" +"Bug Tracker" = "https://github.com/econ-ark/EstimatingMicroDSOPs/issues" +Discussions = "https://github.com/econ-ark/EstimatingMicroDSOPs/discussions" +Changelog = "https://github.com/econ-ark/EstimatingMicroDSOPs/releases" + + +[tool.hatch] +version.source = "vcs" +build.hooks.vcs.version-file = "src/estimark/_version.py" + +[tool.hatch.envs.default] +features = ["test"] +scripts.test = "pytest {args}" + + +[tool.pytest.ini_options] +minversion = "6.0" +addopts = ["-ra", "--showlocals", "--strict-markers", "--strict-config"] +xfail_strict = true +filterwarnings = [ + "error", +] +log_cli_level = "INFO" +testpaths = [ + "tests", +] + + +[tool.coverage] +run.source = ["estimark"] +report.exclude_also = [ + '\.\.\.', + 'if typing.TYPE_CHECKING:', +] + +[tool.mypy] +files = ["src", "tests"] +python_version = "3.8" +warn_unused_configs = true +strict = true +enable_error_code = ["ignore-without-code", "redundant-expr", "truthy-bool"] +warn_unreachable = true +disallow_untyped_defs = false +disallow_incomplete_defs = false + +[[tool.mypy.overrides]] +module = "estimark.*" +disallow_untyped_defs = true +disallow_incomplete_defs = true + + +[tool.ruff] + +[tool.ruff.lint] +extend-select = [ + "B", # flake8-bugbear + "I", # isort + "ARG", # flake8-unused-arguments + "C4", # flake8-comprehensions + "EM", # flake8-errmsg + "ICN", # flake8-import-conventions + "G", # flake8-logging-format + "PGH", # pygrep-hooks + "PIE", # flake8-pie + "PL", # pylint + "PT", # flake8-pytest-style + "PTH", # flake8-use-pathlib + "RET", # flake8-return + "RUF", # Ruff-specific + "SIM", # flake8-simplify + "T20", # flake8-print + "UP", # pyupgrade + "YTT", # flake8-2020 + "EXE", # flake8-executable + "NPY", # NumPy specific rules + "PD", # pandas-vet +] +ignore = [ + "PLR09", # Too many <...> + "PLR2004", # Magic value used in comparison + "ISC001", # Conflicts with formatter +] +isort.required-imports = ["from __future__ import annotations"] +# Uncomment if using a _compat.typing backport +# typing-modules = ["estimark._compat.typing"] + +[tool.ruff.lint.per-file-ignores] +"tests/**" = ["T20"] +"noxfile.py" = ["T20"] + + +[tool.pylint] +py-version = "3.8" +ignore-paths = [".*/_version.py"] +reports.output-format = "colorized" +similarities.ignore-imports = "yes" +messages_control.disable = [ + "design", + "fixme", + "line-too-long", + "missing-module-docstring", + "missing-function-docstring", + "wrong-import-position", +] diff --git a/reproduce.sh b/reproduce.sh new file mode 100644 index 0000000..6a8f990 --- /dev/null +++ b/reproduce.sh @@ -0,0 +1,22 @@ +#!/bin/bash + +# Check if conda is available +if ! command -v conda >/dev/null 2>&1; then + echo "Conda is not available. Please install Anaconda or Miniconda." + exit 1 +fi + +# Check if the environment exists +if conda env list | grep -q 'estimatingmicrodsops'; then + echo "Environment 'estimatingmicrodsops' already exists. Updating it..." + conda env update -q -f environment.yml +else + echo "Creating environment using conda..." + conda env create -q -f environment.yml +fi + +# Activate the environment +conda activate estimatingmicrodsops + +# Execute script to reproduce figures +ipython src/run_all.py diff --git a/src/README.md b/src/README.md new file mode 100644 index 0000000..04a6889 --- /dev/null +++ b/src/README.md @@ -0,0 +1,9 @@ +# Description + +1. The "Stata" directory is a clone of the corresponding directory in the + original online version of the `SolvingMicoDSOPs` project, available at + http://econ.jhu.edu/people/ccarroll/Topics/EstimatingMicroDSOPs.zip + - That directory is being added here to clarify the origin of the SCFdata.txt + file +1. Original Matlab and Mathematica code to solve the model here is also + available in that zip archive diff --git a/src/data/Cagetti2003.csv b/src/data/Cagetti2003.csv new file mode 100644 index 0000000..3d5af6f --- /dev/null +++ b/src/data/Cagetti2003.csv @@ -0,0 +1,65 @@ +1.064914 +1.057997 +1.051422 +1.045179 +1.039259 +1.033653 +1.028352 +1.023348 +1.018632 +1.014198 +1.010037 +1.006143 +1.002509 +0.9991282 +0.9959943 +0.9931012 +0.9904431 +0.9880143 +0.9858095 +0.9838233 +0.9820506 +0.9804866 +0.9791264 +0.9779656 +0.9769995 +0.9762239 +0.9756346 +0.9752274 +0.9749984 +0.9749437 +0.9750595 +0.9753422 +0.9757881 +0.9763936 +0.9771553 +0.9780698 +0.9791338 +0.9803439 +0.981697 +0.9902111 +0.9902111 +0.9902111 +0.9902111 +0.9902111 +0.9902111 +0.9902111 +0.9902111 +0.9902111 +0.9902111 +0.9902111 +0.9902111 +0.9902111 +0.9902111 +0.9902111 +0.9902111 +0.9902111 +0.9902111 +0.9902111 +0.9902111 +0.9902111 +0.9902111 +0.9902111 +0.9902111 +0.9902111 +0.9902111 diff --git a/src/data/S&P Target Date glidepath.xlsx b/src/data/S&P Target Date glidepath.xlsx new file mode 100644 index 0000000..7c3ea4f Binary files /dev/null and b/src/data/S&P Target Date glidepath.xlsx differ diff --git a/src/data/SCFdata.csv b/src/data/SCFdata.csv new file mode 100644 index 0000000..9dbb1a4 --- /dev/null +++ b/src/data/SCFdata.csv @@ -0,0 +1,232521 @@ +wave,age,age_group,education,networth,norminc,wealth_income_ratio,weight,monetary_year +1995,75,"(70,75]",NoHS,181.91076514816453,37.660216567282355,4.83031649122807,9277.705170483694,2019 +1995,75,"(70,75]",NoHS,137.58949137549757,37.660216567282355,3.653443976608187,9351.1564893363,2019 +1995,75,"(70,75]",NoHS,126.57691287041133,37.660216567282355,3.361024561403509,9482.938263724354,2019 +1995,75,"(70,75]",NoHS,171.24656346749228,37.660216567282355,4.547147602339182,9727.445111562256,2019 +1995,75,"(70,75]",NoHS,121.73834586466165,37.660216567282355,3.2325450292397657,9463.613960123936,2019 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+1995,77,"(75,80]",NoHS,13797.270588235295,2596.572826481047,5.3136466836301945,21.344317469959833,2019 +1995,77,"(75,80]",NoHS,14488.605042016807,2279.4341606513003,6.356228792270532,23.937492986433583,2019 +1995,38,"(35,40]",College,563.2091994692613,154.60509959200127,3.6428888888888875,4070.924340307075,2019 +1995,38,"(35,40]",College,563.2091994692613,154.60509959200127,3.6428888888888875,4238.871600486153,2019 +1995,38,"(35,40]",College,563.2091994692613,154.60509959200127,3.6428888888888875,4180.966729161804,2019 +1995,38,"(35,40]",College,563.2091994692613,154.60509959200127,3.6428888888888875,3969.6025119607352,2019 +1995,38,"(35,40]",College,563.2091994692613,154.60509959200127,3.6428888888888875,4210.007337393427,2019 +1995,45,"(40,45]",College,32.999026979212736,89.1952497646161,0.36996395061728393,6230.970033642894,2019 +1995,45,"(40,45]",College,32.999026979212736,89.1952497646161,0.36996395061728393,6144.311061047608,2019 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+1995,44,"(40,45]",HS,23.302538699690402,59.46349984307739,0.39187970370370373,4999.200762057217,2019 +1995,44,"(40,45]",HS,17.67044670499779,15.262298293056533,1.157784126984127,5088.543632750934,2019 +1995,44,"(40,45]",HS,6.948182220256523,25.76751659866687,0.2696488888888889,5006.5527059475135,2019 +1995,44,"(40,45]",HS,6.909473684210527,27.749633260102783,0.24899333333333337,5025.00819274159,2019 +1995,44,"(40,45]",HS,6.696576735957541,21.803283275795042,0.3071361616161617,5030.872523626694,2019 +1995,38,"(35,40]",HS,69.34634232640425,91.177366426052,0.760565314009662,7493.441899179239,2019 +1995,38,"(35,40]",HS,165.9628482972136,91.177366426052,1.820219806763285,7457.34324918692,2019 +1995,38,"(35,40]",HS,113.49342768686422,91.177366426052,1.2447543961352658,7458.792856509809,2019 +1995,38,"(35,40]",HS,63.48199911543565,91.177366426052,0.6962473429951691,7580.221708429957,2019 +1995,38,"(35,40]",HS,58.488597965501995,91.177366426052,0.6414815458937199,7618.25842989238,2019 +1995,78,"(75,80]",NoHS,2061.229544449359,1048.5397138995982,1.9658097038437303,3241.8417232242314,2019 +1995,78,"(75,80]",NoHS,2061.229544449359,1048.5397138995982,1.9658097038437303,2759.148342839793,2019 +1995,78,"(75,80]",NoHS,2061.229544449359,1048.5397138995982,1.9658097038437303,2863.097533333103,2019 +1995,78,"(75,80]",NoHS,2061.229544449359,1048.5397138995982,1.9658097038437303,2776.100255949881,2019 +1995,78,"(75,80]",NoHS,2061.229544449359,1048.5397138995982,1.9658097038437303,2872.027078055848,2019 +1995,52,"(50,55]",HS,-14.43828394515701,59.46349984307739,-0.2428091851851852,6924.40738403535,2019 +1995,52,"(50,55]",HS,-14.43828394515701,59.46349984307739,-0.2428091851851852,6949.8243228730835,2019 +1995,52,"(50,55]",HS,-14.43828394515701,59.46349984307739,-0.2428091851851852,6959.514862450749,2019 +1995,52,"(50,55]",HS,-14.43828394515701,59.46349984307739,-0.2428091851851852,6943.8368827028335,2019 +1995,52,"(50,55]",HS,-14.43828394515701,59.46349984307739,-0.2428091851851852,6944.1672126191315,2019 +1995,32,"(30,35]",HS,12.193188854489165,27.749633260102783,0.43940000000000007,4742.310882712975,2019 +1995,32,"(30,35]",HS,10.993224237063247,27.749633260102783,0.39615746031746035,4669.200132877133,2019 +1995,32,"(30,35]",HS,10.064219371959311,27.749633260102783,0.36267936507936516,4680.341138959759,2019 +1995,32,"(30,35]",HS,10.490013268465281,27.749633260102783,0.3780234920634921,4650.4048645025,2019 +1995,32,"(30,35]",HS,10.160990712074303,27.749633260102783,0.3661666666666667,4669.557717423927,2019 +1995,54,"(50,55]",NoHS,744.539336576736,186.31896617497586,3.99604695035461,8509.461707605318,2019 +1995,54,"(50,55]",NoHS,724.7592746572313,186.31896617497586,3.889884586288416,8624.406913773299,2019 +1995,54,"(50,55]",NoHS,735.0170367094206,186.31896617497586,3.944939432624113,8501.061800142383,2019 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+1995,55,"(50,55]",College,3111.9727554179567,212.08648277364273,14.673131048805814,492.11639763061555,2019 +1995,21,"(20,25]",HS,40.27623175586024,35.67809990584644,1.1288782716049384,4819.587240894411,2019 +1995,21,"(20,25]",HS,40.27623175586024,35.67809990584644,1.1288782716049384,4799.099576851141,2019 +1995,21,"(20,25]",HS,40.27623175586024,35.67809990584644,1.1288782716049384,4785.967471653864,2019 +1995,21,"(20,25]",HS,40.27623175586024,35.67809990584644,1.1288782716049384,4755.8470137188215,2019 +1995,21,"(20,25]",HS,40.27623175586024,35.67809990584644,1.1288782716049384,4766.051742918955,2019 +1995,50,"(45,50]",HS,76.0622733303848,29.731749921538697,2.5582844444444452,6981.160975710676,2019 +1995,50,"(45,50]",HS,76.0622733303848,29.731749921538697,2.5582844444444452,6852.23697822933,2019 +1995,50,"(45,50]",HS,45.09544449358691,29.731749921538697,1.516743703703704,6909.275842406541,2019 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+1995,25,"(20,25]",HS,0.4838567005749669,23.785399937230956,0.020342592592592596,5559.5585625509875,2019 +1995,25,"(20,25]",HS,0.4838567005749669,23.785399937230956,0.020342592592592596,5522.435537062017,2019 +1995,25,"(20,25]",HS,0.2903140203449801,23.785399937230956,0.012205555555555558,5528.287588701409,2019 +1995,88,"(85,90]",HS,101.02927908005309,31.713866582974614,3.1856500000000003,7961.355754168617,2019 +1995,88,"(85,90]",HS,124.25440070765148,59.46349984307739,2.0895911111111114,7902.7239562649165,2019 +1995,88,"(85,90]",HS,50.708182220256525,59.46349984307739,0.8527614814814816,8175.149947768063,2019 +1995,88,"(85,90]",HS,85.54586466165414,87.21313310318017,0.9808828282828285,8141.718688798661,2019 +1995,88,"(85,90]",HS,44.90190181335692,59.46349984307739,0.7551170370370371,8004.727359343235,2019 +1995,27,"(25,30]",College,15.193100398053959,21.803283275795042,0.6968262626262628,8188.92980106929,2019 +1995,27,"(25,30]",College,15.193100398053959,21.803283275795042,0.6968262626262628,8144.2058262090095,2019 +1995,27,"(25,30]",College,15.77372843874392,21.803283275795042,0.7234565656565658,8229.609717622083,2019 +1995,27,"(25,30]",College,15.193100398053959,21.803283275795042,0.6968262626262628,8174.657870659003,2019 +1995,27,"(25,30]",College,15.193100398053959,21.803283275795042,0.6968262626262628,8183.32044710239,2019 +1995,57,"(55,60]",College,4547.22720919947,136.76604963907803,33.24821636070853,1946.846346312655,2019 +1995,57,"(55,60]",College,3720.76125608138,33.69598324441053,110.42150718954248,1742.7376726015293,2019 +1995,57,"(55,60]",College,3532.1461724900487,87.21313310318017,40.50016375757576,1741.491720002914,2019 +1995,57,"(55,60]",College,4268.719292348518,136.76604963907803,31.21183439613526,1758.4790691307094,2019 +1995,57,"(55,60]",College,3054.2147810703227,55.499266520205566,55.03162424603175,1755.7460873428959,2019 +1995,45,"(40,45]",HS,227.99327731092438,188.30108283641175,1.2107911111111112,8483.709336893771,2019 +1995,45,"(40,45]",HS,227.99327731092438,188.30108283641175,1.2107911111111112,8405.28955343685,2019 +1995,45,"(40,45]",HS,227.99327731092438,188.30108283641175,1.2107911111111112,8448.919527845143,2019 +1995,45,"(40,45]",HS,227.99327731092438,188.30108283641175,1.2107911111111112,8857.10480629922,2019 +1995,45,"(40,45]",HS,227.99327731092438,188.30108283641175,1.2107911111111112,8579.767517773593,2019 +1995,29,"(25,30]",HS,-3.870853604599735,25.76751659866687,-0.15022222222222226,7703.614372482216,2019 +1995,29,"(25,30]",HS,-3.870853604599735,25.76751659866687,-0.15022222222222226,7769.576390507655,2019 +1995,29,"(25,30]",HS,-3.870853604599735,25.76751659866687,-0.15022222222222226,7788.681698231536,2019 +1995,29,"(25,30]",HS,-3.870853604599735,25.76751659866687,-0.15022222222222226,7885.633885916715,2019 +1995,29,"(25,30]",HS,-3.870853604599735,25.76751659866687,-0.15022222222222226,7805.203712959383,2019 +1995,33,"(30,35]",NoHS,24.19283502874834,25.76751659866687,0.938888888888889,6611.530868581921,2019 +1995,33,"(30,35]",NoHS,29.999115435647944,25.76751659866687,1.1642222222222225,6709.50230474464,2019 +1995,33,"(30,35]",NoHS,24.19283502874834,25.76751659866687,0.938888888888889,6640.555467119525,2019 +1995,33,"(30,35]",NoHS,24.19283502874834,25.76751659866687,0.938888888888889,6752.967896862212,2019 +1995,33,"(30,35]",NoHS,24.19283502874834,25.76751659866687,0.938888888888889,6687.079406736064,2019 +1995,58,"(55,60]",College,51270.26887218046,1090.1641637897524,47.0298608,33.256112451152106,2019 +1995,58,"(55,60]",College,50668.58338788147,1090.1641637897524,46.47793889292929,34.20219418135996,2019 +1995,58,"(55,60]",College,50672.4735957541,1090.1641637897524,46.481507353535356,34.18563392382753,2019 +1995,58,"(55,60]",College,51657.23810703229,1090.1641637897524,47.384824985858586,32.510805420774574,2019 +1995,58,"(55,60]",College,51457.8891463954,1090.1641637897524,47.201963571717165,32.40059001795,2019 +1995,47,"(45,50]",College,528.3328084918178,301.28173253825884,1.753617134502924,846.6333596208157,2019 +1995,47,"(45,50]",College,620.7300840336135,303.2638491996948,2.04683177923021,865.0936895285089,2019 +1995,47,"(45,50]",College,623.0138876603272,394.44121562574674,1.5794847571189279,849.9269530482885,2019 +1995,47,"(45,50]",College,595.1824502432553,394.44121562574674,1.5089256058068121,828.2498398541742,2019 +1995,47,"(45,50]",College,596.5372490048651,370.6558156885158,1.6094101960784313,840.7172779312068,2019 +1995,60,"(55,60]",College,12576.790446705,396.42333228718263,31.72565644444445,22.912149894566873,2019 +1995,60,"(55,60]",College,12677.432640424591,396.42333228718263,31.979532000000003,20.120435579797295,2019 +1995,60,"(55,60]",College,12600.789739053516,396.42333228718263,31.786196000000004,20.973505920242754,2019 +1995,60,"(55,60]",College,12488.534984520125,396.42333228718263,31.503027111111116,20.498943767727734,2019 +1995,60,"(55,60]",College,12524.050066342326,396.42333228718263,31.59261588888889,21.266240005160498,2019 +1995,59,"(55,60]",College,10497.619495798319,979.1656307493413,10.720984444444442,25.713727335780288,2019 +1995,59,"(55,60]",College,10519.083379035825,987.0940973950849,10.656616635430611,22.562484295780024,2019 +1995,59,"(55,60]",College,10567.372277753206,979.1656307493413,10.79222140350877,23.550849279301794,2019 +1995,59,"(55,60]",College,10683.943034055728,1280.4473632876,8.343914275885794,23.009157385376763,2019 +1995,59,"(55,60]",College,10598.281043785935,1280.4473632876,8.277014227726179,23.915111099708973,2019 +1995,60,"(55,60]",NoHS,2634.1352321981426,346.87041575128484,7.5940037333333335,663.8123505084354,2019 +1995,60,"(55,60]",NoHS,2638.0060858027423,346.87041575128484,7.6051630984126986,593.6867018239094,2019 +1995,60,"(55,60]",NoHS,2634.1352321981426,346.87041575128484,7.5940037333333335,595.7324669811644,2019 +1995,60,"(55,60]",NoHS,2643.812366209642,346.87041575128484,7.621902146031746,596.612532124066,2019 +1995,60,"(55,60]",NoHS,2643.812366209642,346.87041575128484,7.621902146031746,597.1757912179758,2019 +1995,47,"(45,50]",HS,288.8430959752322,51.53503319733374,5.604791111111112,4167.361897726578,2019 +1995,47,"(45,50]",HS,275.2951083591331,51.53503319733374,5.341902222222222,4151.776461489871,2019 +1995,47,"(45,50]",HS,308.1973639982309,51.53503319733374,5.9803466666666685,4250.773400584716,2019 +1995,47,"(45,50]",HS,279.16596196373285,51.53503319733374,5.417013333333334,4313.198158836817,2019 +1995,47,"(45,50]",HS,329.4870588235294,51.53503319733374,6.393457777777779,4225.794326409483,2019 +1995,43,"(40,45]",HS,0,1.9821166614359134,0,8268.346201841223,2019 +1995,43,"(40,45]",HS,0,1.9821166614359134,0,8325.974901932323,2019 +1995,43,"(40,45]",HS,0,1.9821166614359134,0,8324.69081464424,2019 +1995,43,"(40,45]",HS,0,1.9821166614359134,0,8308.297477062108,2019 +1995,43,"(40,45]",HS,0,1.9821166614359134,0,8328.274278733332,2019 +1995,41,"(40,45]",HS,0,39.642333228718265,0,5775.92517814123,2019 +1995,41,"(40,45]",HS,0,39.642333228718265,0,5879.149225521737,2019 +1995,41,"(40,45]",HS,0,39.642333228718265,0,5784.419391485603,2019 +1995,41,"(40,45]",HS,0,39.642333228718265,0,5805.742302071189,2019 +1995,41,"(40,45]",HS,0,39.642333228718265,0,5812.517772396228,2019 +1995,64,"(60,65]",HS,562.3382574082265,59.46349984307739,9.456864444444445,3569.064291358902,2019 +1995,64,"(60,65]",HS,383.69836355594873,83.24889978030835,4.609050264550265,6358.060626983193,2019 +1995,64,"(60,65]",HS,488.59849624060155,37.660216567282355,12.973863157894737,3668.171387332922,2019 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+1995,53,"(50,55]",College,2330.8344980097304,156.58721625343713,14.885215752461324,457.97780081675467,2019 +1995,53,"(50,55]",College,2326.9636444051303,156.58721625343713,14.860495639943741,440.81119912418035,2019 +1995,42,"(40,45]",HS,76.23646174259177,83.24889978030835,0.9157653968253969,6560.7350328257,2019 +1995,42,"(40,45]",HS,188.49121627598407,33.69598324441053,5.593877908496731,6385.746047547368,2019 +1995,42,"(40,45]",HS,105.26786377708977,55.499266520205566,1.8967433333333332,6527.392089844989,2019 +1995,42,"(40,45]",HS,180.7495090667846,37.660216567282355,4.799481403508771,6498.577669890476,2019 +1995,42,"(40,45]",HS,138.17011941618753,45.588683213026,3.0307986473429955,6440.848191146308,2019 +1995,61,"(60,65]",College,95566.94337019019,6778.838982110824,14.09783351137102,229.55644387083765,2019 +1995,61,"(60,65]",College,87456.92444051304,7393.295147155956,11.829221301161752,203.52311590468244,2019 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+1995,39,"(35,40]",NoHS,0.0019354268022998672,11.892699968615478,1.6274074074074075e-4,4765.905467518223,2019 +1995,35,"(30,35]",HS,12.193188854489165,53.517149858769656,0.22783703703703706,6966.708571979632,2019 +1995,35,"(30,35]",HS,11.806103494029191,53.517149858769656,0.22060411522633747,6961.30713356865,2019 +1995,35,"(30,35]",HS,13.160902255639098,53.517149858769656,0.245919341563786,6983.761329144155,2019 +1995,35,"(30,35]",HS,13.547987616099071,53.517149858769656,0.2531522633744856,6853.282210965184,2019 +1995,35,"(30,35]",HS,12.96735957540911,53.517149858769656,0.24230288065843622,6969.015521023216,2019 +1995,40,"(35,40]",HS,58.062804068996016,71.35619981169287,0.8137037037037037,8475.57471567228,2019 +1995,40,"(35,40]",HS,62.320743034055724,71.35619981169287,0.8733753086419753,8463.497941887825,2019 +1995,40,"(35,40]",HS,64.74002653693057,71.35619981169287,0.9072796296296298,8558.293045108167,2019 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+1995,47,"(45,50]",HS,25.450862450243253,53.517149858769656,0.4755646090534979,5169.564903530292,2019 +1995,47,"(45,50]",HS,12.677045555064131,53.517149858769656,0.23687818930041155,5150.120731995399,2019 +1995,47,"(45,50]",HS,76.15904467049978,53.517149858769656,1.4230773662551441,5156.716098660969,2019 +1995,47,"(45,50]",HS,16.160813799203893,53.517149858769656,0.30197448559670786,5251.484662527624,2019 +1995,47,"(45,50]",HS,33.579655019902695,53.517149858769656,0.6274559670781893,5223.070197696627,2019 +1995,42,"(40,45]",HS,191.80079610791685,79.28466645743653,2.419141111111111,11043.45019356344,2019 +1995,42,"(40,45]",HS,191.80079610791685,79.28466645743653,2.419141111111111,11122.912958084817,2019 +1995,42,"(40,45]",HS,191.80079610791685,79.28466645743653,2.419141111111111,10887.513339256804,2019 +1995,42,"(40,45]",HS,191.80079610791685,79.28466645743653,2.419141111111111,11192.208330142557,2019 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+1995,51,"(50,55]",NoHS,12.193188854489165,27.749633260102783,0.43940000000000007,5415.180990007244,2019 +1995,51,"(50,55]",NoHS,12.193188854489165,27.749633260102783,0.43940000000000007,5418.000648197023,2019 +1995,32,"(30,35]",NoHS,1.3160902255639098,23.785399937230956,0.05533185185185186,6627.653515186372,2019 +1995,32,"(30,35]",NoHS,1.3160902255639098,23.785399937230956,0.05533185185185186,6566.6831510205175,2019 +1995,32,"(30,35]",NoHS,1.3160902255639098,23.785399937230956,0.05533185185185186,6630.863434780603,2019 +1995,32,"(30,35]",NoHS,1.3160902255639098,23.785399937230956,0.05533185185185186,6591.160823882936,2019 +1995,32,"(30,35]",NoHS,1.3160902255639098,23.785399937230956,0.05533185185185186,6601.594737314937,2019 +1995,27,"(25,30]",NoHS,3.2902255639097744,8.523101644174426,0.38603617571059434,6005.215180919561,2019 +1995,27,"(25,30]",NoHS,3.2902255639097744,9.910583307179566,0.3319911111111111,5972.417599391028,2019 +1995,27,"(25,30]",NoHS,3.2902255639097744,8.523101644174426,0.38603617571059434,6035.0471196924955,2019 +1995,27,"(25,30]",NoHS,3.2902255639097744,8.523101644174426,0.38603617571059434,5994.749098630067,2019 +1995,27,"(25,30]",NoHS,3.2902255639097744,9.117736642605202,0.3608599033816425,6001.101654681641,2019 +1995,67,"(65,70]",HS,8.45781512605042,59.46349984307739,0.14223540740740742,6932.839618563134,2019 +1995,67,"(65,70]",HS,9.251340114993365,59.46349984307739,0.15558014814814813,6863.8102498490725,2019 +1995,67,"(65,70]",HS,8.18685537372844,59.46349984307739,0.1376786666666667,6792.21791266473,2019 +1995,67,"(65,70]",HS,8.806191950464395,59.46349984307739,0.14809407407407407,7033.5043816762645,2019 +1995,67,"(65,70]",HS,9.03844316674038,59.46349984307739,0.15199985185185186,6832.808399582353,2019 +1995,35,"(30,35]",College,1152.5466607695712,128.8375829933344,8.945733333333333,701.2947968887518,2019 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+1995,68,"(65,70]",HS,679.9734984520124,138.74816630051396,4.900774666666666,6458.196785846356,2019 +1995,68,"(65,70]",HS,684.4056258292791,138.74816630051396,4.932718349206348,6123.358457551207,2019 +1995,68,"(65,70]",HS,751.1391419725785,138.74816630051396,5.413686984126983,6542.147020201644,2019 +1995,32,"(30,35]",HS,76.15904467049978,41.624449890154175,1.8296708994708997,4473.01559099352,2019 +1995,32,"(30,35]",HS,76.15904467049978,41.624449890154175,1.8296708994708997,4405.271625118612,2019 +1995,32,"(30,35]",HS,76.15904467049978,41.624449890154175,1.8296708994708997,4432.531056208014,2019 +1995,32,"(30,35]",HS,76.15904467049978,41.624449890154175,1.8296708994708997,4377.607772904763,2019 +1995,32,"(30,35]",HS,76.15904467049978,41.624449890154175,1.8296708994708997,4427.6958639532495,2019 +1995,44,"(40,45]",HS,6.115948695267581,33.69598324441053,0.1815037908496732,6664.411374436129,2019 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+1995,38,"(35,40]",NoHS,130.68001769128705,79.28466645743653,1.6482382222222223,4933.478416316537,2019 +1995,38,"(35,40]",NoHS,139.19589562140646,79.28466645743653,1.7556471111111112,5032.993203553747,2019 +1995,42,"(40,45]",College,1632.2809022556391,168.47991622205262,9.688281777777778,2933.387859658297,2019 +1995,42,"(40,45]",College,1613.2750110570544,158.56933291487306,10.173940833333333,2513.136171272982,2019 +1995,42,"(40,45]",College,1929.6785846970367,190.28319949784765,10.141087546296298,2585.3189303829286,2019 +1995,42,"(40,45]",College,1488.304502432552,154.60509959200127,9.62649037037037,2511.965714070228,2019 +1995,42,"(40,45]",College,1800.5856169836356,174.42626620636034,10.322904090909093,2597.587231815348,2019 +1995,66,"(65,70]",College,654.5032817337462,87.21313310318017,7.504641313131316,7468.72686064229,2019 +1995,66,"(65,70]",College,656.825793896506,73.3383164731288,8.956106786786785,7567.848016139035,2019 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+1995,72,"(70,75]",College,1516.9875276426362,132.8018163162062,11.422942620232172,3235.302222845653,2019 +1995,59,"(55,60]",HS,356.1204670499779,231.90764938800186,1.5356132839506174,9907.519441101655,2019 +1995,59,"(55,60]",HS,356.1204670499779,231.90764938800186,1.5356132839506174,9762.015146987233,2019 +1995,59,"(55,60]",HS,356.1204670499779,231.90764938800186,1.5356132839506174,9919.838648100002,2019 +1995,59,"(55,60]",HS,356.1204670499779,231.90764938800186,1.5356132839506174,9902.52053263903,2019 +1995,59,"(55,60]",HS,356.1204670499779,231.90764938800186,1.5356132839506174,9772.555379312686,2019 +1995,44,"(40,45]",College,740.6878372401593,198.21166614359132,3.7368528888888894,742.368319698571,2019 +1995,44,"(40,45]",College,740.6878372401593,198.21166614359132,3.7368528888888894,723.2780936771694,2019 +1995,44,"(40,45]",College,740.6878372401593,198.21166614359132,3.7368528888888894,739.9043736338573,2019 +1995,44,"(40,45]",College,740.6878372401593,198.21166614359132,3.7368528888888894,691.3112522770805,2019 +1995,44,"(40,45]",College,740.6878372401593,198.21166614359132,3.7368528888888894,746.2251284576398,2019 +1995,35,"(30,35]",HS,5.419195046439628,17.83904995292322,0.30378271604938273,4941.187663579424,2019 +1995,35,"(30,35]",HS,5.419195046439628,17.83904995292322,0.30378271604938273,4931.115835728289,2019 +1995,35,"(30,35]",HS,5.419195046439628,17.83904995292322,0.30378271604938273,4943.958348174308,2019 +1995,35,"(30,35]",HS,5.419195046439628,17.83904995292322,0.30378271604938273,4856.148398154924,2019 +1995,35,"(30,35]",HS,5.419195046439628,17.83904995292322,0.30378271604938273,4938.613751767832,2019 +1995,46,"(45,50]",College,222.57408226448476,178.3904995292322,1.2476790123456791,6447.767986257734,2019 +1995,46,"(45,50]",College,222.57408226448476,178.3904995292322,1.2476790123456791,6393.7224913204645,2019 +1995,46,"(45,50]",College,222.57408226448476,178.3904995292322,1.2476790123456791,6433.122417208442,2019 +1995,46,"(45,50]",College,222.57408226448476,178.3904995292322,1.2476790123456791,6507.408984819075,2019 +1995,46,"(45,50]",College,222.57408226448476,178.3904995292322,1.2476790123456791,6492.498586783655,2019 +1995,31,"(30,35]",College,6.928827952233525,89.1952497646161,0.07768158024691359,4641.634444483834,2019 +1995,31,"(30,35]",College,6.928827952233525,89.1952497646161,0.07768158024691359,4583.818259806442,2019 +1995,31,"(30,35]",College,6.928827952233525,89.1952497646161,0.07768158024691359,4645.001704118705,2019 +1995,31,"(30,35]",College,6.928827952233525,89.1952497646161,0.07768158024691359,4590.337621070145,2019 +1995,31,"(30,35]",College,6.928827952233525,89.1952497646161,0.07768158024691359,4634.033773655466,2019 +1995,56,"(55,60]",HS,3855.370190181336,626.3488650137485,6.155308016877639,168.8397178311953,2019 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+1995,29,"(25,30]",HS,0.9677134011499338,31.713866582974614,0.030513888888888892,4922.895377242667,2019 +1995,59,"(55,60]",College,2363.852879256966,475.70799874461915,4.969125777777778,187.15845390970588,2019 +1995,59,"(55,60]",College,2363.349668288368,475.70799874461915,4.968067962962963,155.329574508333,2019 +1995,59,"(55,60]",College,2363.891587793012,475.70799874461915,4.969207148148149,155.59500202933998,2019 +1995,59,"(55,60]",College,2364.1238390092876,475.70799874461915,4.969695370370371,162.0612975216458,2019 +1995,59,"(55,60]",College,2363.872233524989,475.70799874461915,4.9691664629629635,157.88061482535488,2019 +1995,24,"(20,25]",HS,26.37986731534719,61.44561650451331,0.4293205734767025,4669.763637504649,2019 +1995,24,"(20,25]",HS,26.360513047324194,39.642333228718265,0.6649586666666667,4629.913167451394,2019 +1995,24,"(20,25]",HS,26.37986731534719,69.37408315025698,0.38025536507936497,4661.853638190432,2019 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+1998,49,"(45,50]",College,3556.594,412.11659887005646,8.630067339562368,186.76590916532825,2019 +1998,19,"(15,20]",HS,18.561533333333333,10.164310734463278,1.8261477652781999,3523.1406205515123,2019 +1998,19,"(15,20]",HS,18.23333333333333,35.11307344632768,0.5192747755676816,3473.255691391937,2019 +1998,19,"(15,20]",HS,18.415666666666667,7.207420338983052,2.5550981905496952,3539.6989884740287,2019 +1998,19,"(15,20]",HS,18.78033333333333,13.306006779661017,1.4114176885916014,3559.9422271648973,2019 +1998,19,"(15,20]",HS,18.68916666666667,10.533922033898305,1.774188816522913,3607.017663970815,2019 +1998,42,"(40,45]",HS,-24.068,33.265016949152546,-0.7235228539576365,5323.541997959207,2019 +1998,42,"(40,45]",HS,-24.068,33.265016949152546,-0.7235228539576365,5345.500076315095,2019 +1998,42,"(40,45]",HS,-24.068,33.265016949152546,-0.7235228539576365,5368.4281220918765,2019 +1998,42,"(40,45]",HS,-24.068,33.265016949152546,-0.7235228539576365,5340.016485893555,2019 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+1998,61,"(60,65]",College,260707.49333333335,12455.900790960452,20.930440737175353,1.7637393134810686,2019 +1998,61,"(60,65]",College,283169.1366666667,11753.6393220339,24.092038976883114,1.7547858162094887,2019 +1998,61,"(60,65]",College,276415.51,13509.292994350282,20.46113813029295,1.6450475810565979,2019 +1998,63,"(60,65]",NoHS,0.9116666666666666,33.265016949152546,0.027406168710516533,6125.598470780922,2019 +1998,63,"(60,65]",NoHS,0.9116666666666666,33.265016949152546,0.027406168710516533,6121.197345240161,2019 +1998,63,"(60,65]",NoHS,0.9116666666666666,33.265016949152546,0.027406168710516533,6307.788268087036,2019 +1998,63,"(60,65]",NoHS,0.9116666666666666,33.265016949152546,0.027406168710516533,6085.637040037103,2019 +1998,63,"(60,65]",NoHS,0.9116666666666666,33.265016949152546,0.027406168710516533,6288.349454833145,2019 +1998,55,"(50,55]",HS,-17.194033333333334,59.13780790960452,-0.2907451923076923,5324.665625908461,2019 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+1998,24,"(20,25]",HS,-1.9145,25.872790960451983,-0.07399665551839464,4889.6590997826115,2019 +1998,24,"(20,25]",HS,-1.9145,24.024734463276836,-0.07968870594288655,4852.286565373811,2019 +1998,24,"(20,25]",HS,-3.7378333333333336,25.872790960451983,-0.14446966077400858,4886.397102421453,2019 +1998,24,"(20,25]",HS,-3.7378333333333336,24.024734463276836,-0.1555827116027785,4890.8717112391905,2019 +1998,24,"(20,25]",HS,-1.9145,25.872790960451983,-0.07399665551839464,4836.473497855682,2019 +1998,56,"(55,60]",NoHS,124.71600000000001,86.85865536723163,1.435849996442041,5302.376837344687,2019 +1998,56,"(55,60]",NoHS,95.72500000000001,86.85865536723163,1.1020778481463034,5262.795603126098,2019 +1998,56,"(55,60]",NoHS,99.37166666666667,85.0105988700565,1.1689326741311619,5438.247910638387,2019 +1998,56,"(55,60]",NoHS,102.836,86.85865536723163,1.1839464882943145,5309.242983302038,2019 +1998,56,"(55,60]",NoHS,110.31166666666667,86.85865536723163,1.2700135202447878,5334.202115556072,2019 +1998,41,"(40,45]",HS,86.51716666666667,49.89752542372881,1.7338969404186797,7002.483708420163,2019 +1998,41,"(40,45]",HS,97.45716666666668,49.89752542372881,1.9531462901028123,7138.210990594196,2019 +1998,41,"(40,45]",HS,97.27483333333333,49.89752542372881,1.949492134274743,7478.602280180979,2019 +1998,41,"(40,45]",HS,91.80483333333333,49.89752542372881,1.839867459432677,7024.182022308514,2019 +1998,41,"(40,45]",HS,88.3405,49.89752542372881,1.7704384986993686,7308.544068451311,2019 +1998,51,"(50,55]",HS,24368.12066666667,2125.2649717514123,11.46592118656391,14.635923813578808,2019 +1998,51,"(50,55]",HS,17854.08,2143.7455367231637,8.328451159035868,15.731066752257544,2019 +1998,51,"(50,55]",HS,20618.253333333334,2125.2649717514123,9.701497746110224,16.275653375010755,2019 +1998,51,"(50,55]",HS,21674.69266666667,2125.2649717514123,10.198583684746257,14.828356112193319,2019 +1998,51,"(50,55]",HS,23692.02866666667,2125.2649717514123,11.147799912752655,15.680390977537717,2019 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+1998,66,"(65,70]",NoHS,51.38153333333334,46.201412429378536,1.1121204013377926,8754.962146983144,2019 +1998,52,"(50,55]",College,4462.426,641.275604519774,6.958671074571338,184.42826699004786,2019 +1998,52,"(50,55]",College,3804.0203333333334,641.275604519774,5.931958593968368,185.53712073516473,2019 +1998,52,"(50,55]",College,4415.693966666667,641.275604519774,6.885797519107881,172.3483856761194,2019 +1998,52,"(50,55]",College,4643.8476666666675,641.275604519774,7.241578556764625,188.78345131410256,2019 +1998,52,"(50,55]",College,3977.4193333333337,641.275604519774,6.202355594537027,180.52794782762228,2019 +1998,52,"(50,55]",HS,641.4486666666667,17.55653672316384,36.53617320894209,5533.247383220714,2019 +1998,52,"(50,55]",HS,641.2663333333334,17.55653672316384,36.52578771343074,5302.544073047052,2019 +1998,52,"(50,55]",HS,641.2663333333334,17.55653672316384,36.52578771343074,4941.204165755663,2019 +1998,52,"(50,55]",HS,641.4486666666667,17.55653672316384,36.53617320894209,5406.981344037345,2019 +1998,52,"(50,55]",HS,639.6253333333334,17.55653672316384,36.432318253828555,4932.342710680521,2019 +1998,51,"(50,55]",College,12793.236,1108.8338983050849,11.537558528428093,182.33691989144364,2019 +1998,51,"(50,55]",College,12796.153333333334,1108.8338983050849,11.540189520624303,180.98444902747238,2019 +1998,51,"(50,55]",College,12787.766,1108.8338983050849,11.5326254180602,169.76309155991544,2019 +1998,51,"(50,55]",College,12801.623333333335,1108.8338983050849,11.545122630992196,185.3697193082039,2019 +1998,51,"(50,55]",College,12794.694666666666,1108.8338983050849,11.538874024526198,179.299402800348,2019 +1998,33,"(30,35]",HS,8.952566666666668,55.441694915254246,0.16147714604236343,4963.265068715965,2019 +1998,33,"(30,35]",HS,8.952566666666668,55.441694915254246,0.16147714604236343,4959.197355514536,2019 +1998,33,"(30,35]",HS,9.1349,55.441694915254246,0.1647658862876254,4965.241891159401,2019 +1998,33,"(30,35]",HS,9.1349,55.441694915254246,0.1647658862876254,4973.725277521789,2019 +1998,33,"(30,35]",HS,9.226066666666668,55.441694915254246,0.16641025641025642,4924.793303918951,2019 +1998,33,"(30,35]",HS,111.98913333333333,40.65724293785311,2.754469443599878,8855.78775811832,2019 +1998,33,"(30,35]",HS,60.37056666666667,177.41342372881357,0.34028184225195096,8908.90667503904,2019 +1998,33,"(30,35]",HS,194.14853333333332,138.6042372881356,1.4007402452619842,8991.48968870925,2019 +1998,33,"(30,35]",HS,268.97813333333335,179.26148022598866,1.500479260766128,8948.61051127487,2019 +1998,33,"(30,35]",HS,257.7099333333333,40.65724293785311,6.3385983581635745,8959.472399157445,2019 +1998,24,"(20,25]",HS,3.48439,92.40282485875707,0.037708695652173906,4519.890724576453,2019 +1998,24,"(20,25]",HS,5.581223333333334,92.40282485875707,0.060401003344481605,4531.37675843525,2019 +1998,24,"(20,25]",HS,3.557323333333333,92.40282485875707,0.03849799331103678,4538.742366160141,2019 +1998,24,"(20,25]",HS,5.654156666666667,92.40282485875707,0.06119030100334448,4556.20035611359,2019 +1998,24,"(20,25]",HS,3.64849,92.40282485875707,0.039484615384615375,4507.598099412168,2019 +1998,49,"(45,50]",HS,96.08966666666667,53.593638418079095,1.7929304578479992,5591.279710315321,2019 +1998,49,"(45,50]",HS,132.374,77.61837288135592,1.7054467271858578,5566.014772001896,2019 +1998,49,"(45,50]",HS,152.97766666666666,68.37809039548021,2.2372322154930853,5544.849703159203,2019 +1998,49,"(45,50]",HS,119.97533333333332,62.833920903954805,1.9094038953373988,5582.040310129707,2019 +1998,49,"(45,50]",HS,145.86666666666665,77.61837288135592,1.8792801401497052,5610.985985146491,2019 +1998,95,"(90,95]",NoHS,103.29183333333333,11.827561581920904,8.733146948160535,4865.347059727807,2019 +1998,95,"(90,95]",NoHS,113.02843333333334,12.012367231638418,9.409338821713405,4948.177618274218,2019 +1998,95,"(90,95]",NoHS,100.5386,12.012367231638418,8.369590944172884,5008.200430721334,2019 +1998,95,"(90,95]",NoHS,110.85866666666668,11.827561581920904,9.372909698996656,5065.592204600509,2019 +1998,95,"(90,95]",NoHS,130.58713333333333,12.381978531073447,10.546548195477461,5033.684955633605,2019 +1998,41,"(40,45]",HS,84.60266666666668,92.40282485875707,0.9155852842809364,2862.0423856391167,2019 +1998,41,"(40,45]",HS,84.785,92.40282485875707,0.9175585284280935,2944.4420857573,2019 +1998,41,"(40,45]",HS,84.60266666666668,92.40282485875707,0.9155852842809364,2784.9227706132797,2019 +1998,41,"(40,45]",HS,84.785,92.40282485875707,0.9175585284280935,2781.732790621125,2019 +1998,41,"(40,45]",HS,84.60266666666668,92.40282485875707,0.9155852842809364,2877.871529283582,2019 +1998,42,"(40,45]",HS,26.000733333333333,36.96112994350283,0.7034615384615382,11364.942809840073,2019 +1998,42,"(40,45]",HS,25.399033333333332,36.96112994350283,0.6871822742474915,11604.459751634478,2019 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+1998,38,"(35,40]",College,67.02573333333333,109.03533333333333,0.6147157190635452,1164.665237643595,2019 +1998,79,"(75,80]",HS,25.709,14.599646327683615,1.760933068032683,5122.590558547633,2019 +1998,79,"(75,80]",HS,25.891333333333332,14.599646327683615,1.7734219550400068,5159.582604262398,2019 +1998,79,"(75,80]",HS,26.62066666666667,14.599646327683615,1.823377503069303,5165.231858608974,2019 +1998,79,"(75,80]",HS,26.256,14.599646327683615,1.798399729054655,5112.81959226238,2019 +1998,79,"(75,80]",HS,26.164833333333334,14.599646327683615,1.7921552855509928,5165.453776790842,2019 +1998,36,"(35,40]",HS,498.4993333333333,55.441694915254246,8.991415830546263,5613.293713119658,2019 +1998,36,"(35,40]",HS,576.7203333333334,55.441694915254246,10.402285395763657,5370.317362986463,2019 +1998,36,"(35,40]",HS,496.676,55.441694915254246,8.958528428093643,5014.988619618716,2019 +1998,36,"(35,40]",HS,510.898,55.441694915254246,9.21505016722408,5480.975115262682,2019 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+1998,50,"(45,50]",College,19271.101733333333,2273.109491525424,8.477858987954427,13.739997953806727,2019 +1998,32,"(30,35]",NoHS,34.71626666666667,73.92225988700567,0.4696321070234113,8868.852183699666,2019 +1998,32,"(30,35]",NoHS,36.77663333333333,70.22614689265536,0.523688611160007,8980.844698497454,2019 +1998,32,"(30,35]",NoHS,26.985333333333333,79.46642937853107,0.3395815509061212,9109.887732935771,2019 +1998,32,"(30,35]",NoHS,22.335833333333333,59.13780790960452,0.377691262541806,9065.46514445607,2019 +1998,32,"(30,35]",NoHS,20.530733333333334,60.98586440677967,0.336647410560454,9159.775380332714,2019 +1998,59,"(55,60]",HS,792.5118333333334,168.17314124293785,4.712475651438862,6741.640125075498,2019 +1998,59,"(55,60]",HS,770.8141666666667,140.45229378531073,5.4880852842809364,6427.320494090182,2019 +1998,59,"(55,60]",HS,1004.7478333333333,145.99646327683615,6.882001185385886,6016.6121023037485,2019 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+1998,45,"(40,45]",HS,14.805466666666668,72.07420338983052,0.20541977531944086,4789.82416136691,2019 +1998,45,"(40,45]",HS,12.982133333333334,72.07420338983052,0.18012177343281022,4790.649443881636,2019 +1998,20,"(15,20]",HS,-4.284833333333333,22.176677966101696,-0.19321348940914157,4889.6590997826115,2019 +1998,20,"(15,20]",HS,-4.284833333333333,24.024734463276836,-0.17835091330074607,4852.286565373811,2019 +1998,20,"(15,20]",HS,-7.9315,24.024734463276836,-0.33013892462052996,4886.397102421453,2019 +1998,20,"(15,20]",HS,-7.9315,22.176677966101696,-0.3576505016722408,4890.8717112391905,2019 +1998,20,"(15,20]",HS,-6.108166666666667,22.176677966101696,-0.2754319955406912,4836.473497855682,2019 +1998,67,"(65,70]",College,19755.81666666667,109.03533333333333,181.1872909698997,221.0179552196265,2019 +1998,67,"(65,70]",College,20846.170000000002,197.7420451977401,105.42102960022505,220.95350677744145,2019 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+1998,58,"(55,60]",HS,3408.357,77.61837288135592,43.911729574773055,18.276295415364523,2019 +1998,58,"(55,60]",HS,3406.5336666666667,77.61837288135592,43.888238573021184,18.964756336956746,2019 +1998,58,"(55,60]",HS,3406.5336666666667,77.61837288135592,43.888238573021184,19.865685399598963,2019 +1998,42,"(40,45]",College,3788.7043333333336,868.5865536723164,4.3619191631680065,210.4318284884508,2019 +1998,42,"(40,45]",College,3730.357666666667,866.7384971751412,4.303902132909271,209.38568558777993,2019 +1998,42,"(40,45]",College,3553.4943333333335,866.7384971751412,4.099845968437792,201.77189031955086,2019 +1998,42,"(40,45]",College,3588.1376666666665,868.5865536723164,4.13100761403259,220.22539405255057,2019 +1998,42,"(40,45]",College,3610.0176666666666,866.7384971751412,4.1650597941967185,206.02250552194423,2019 +1998,25,"(20,25]",College,-27.532333333333334,60.98586440677967,-0.4514543427586905,4615.175054734371,2019 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+1998,37,"(35,40]",College,48.22716666666666,35.11307344632768,1.373481781376518,5802.5001302645705,2019 +1998,64,"(60,65]",College,4782.6033333333335,578.4416836158192,8.268082105420625,210.4318284884508,2019 +1998,64,"(60,65]",College,5025.1066666666675,578.4416836158192,8.68731768301153,209.38568558777993,2019 +1998,64,"(60,65]",College,5076.16,578.4416836158192,8.775577804609615,201.77189031955086,2019 +1998,64,"(60,65]",College,4738.843333333333,578.4416836158192,8.192430572622266,220.22539405255057,2019 +1998,64,"(60,65]",College,5577.576666666667,578.4416836158192,9.642418284590809,206.02250552194423,2019 +1998,83,"(80,85]",NoHS,627.8101333333333,17.741342372881356,35.38684503901895,7818.181633544305,2019 +1998,83,"(80,85]",NoHS,629.4329,17.741342372881356,35.4783131270903,7497.530008109044,2019 +1998,83,"(80,85]",NoHS,642.1962333333333,17.741342372881356,36.19772505574136,6998.4320412563175,2019 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+2001,33,"(30,35]",NoHS,92.843519510329,191.1196212322074,0.48578748174435504,1440.7621599713277,2019 +2001,47,"(45,50]",HS,43.15733741392502,60.2629436317771,0.7161505033280159,9230.276934236308,2019 +2001,47,"(45,50]",HS,43.15733741392502,60.2629436317771,0.7161505033280159,9461.463386495996,2019 +2001,47,"(45,50]",HS,43.15733741392502,60.2629436317771,0.7161505033280159,9526.528523272274,2019 +2001,47,"(45,50]",HS,43.15733741392502,60.2629436317771,0.7161505033280159,9308.201502651724,2019 +2001,47,"(45,50]",HS,43.15733741392502,60.2629436317771,0.7161505033280159,9341.918192557248,2019 +2001,50,"(45,50]",College,22201.269380260135,1205.258872635542,18.42033266406293,313.2379130398481,2019 +2001,50,"(45,50]",College,22204.450099464426,1205.258872635542,18.422971698113212,306.9161349652556,2019 +2001,50,"(45,50]",College,21448.275960214232,1205.258872635542,17.795576076791903,316.60850175098983,2019 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+2001,29,"(25,30]",NoHS,39.82762662586075,30.992371010628222,1.2850784024301547,8214.64201335267,2019 +2001,29,"(25,30]",NoHS,39.66022035195103,30.992371010628222,1.2796768707482995,8423.28851161206,2019 +2001,29,"(25,30]",NoHS,39.60999846977812,30.992371010628222,1.278056411243743,8206.031411620148,2019 +2001,29,"(25,30]",NoHS,39.89458913542464,30.992371010628222,1.2872390151028967,8213.4373676008,2019 +2001,46,"(45,50]",HS,586.6585462892119,53.37575007385973,10.991106363421812,4754.997232404943,2019 +2001,46,"(45,50]",HS,586.6585462892119,55.097548463339066,10.647634289564882,4312.874892108386,2019 +2001,46,"(45,50]",HS,586.5078806426932,53.37575007385973,10.988283627510649,4047.017505464283,2019 +2001,46,"(45,50]",HS,586.5078806426932,53.37575007385973,10.988283627510649,4513.602916948848,2019 +2001,46,"(45,50]",HS,586.4911400153022,53.37575007385973,10.987969990187187,4342.206558637334,2019 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+2001,48,"(45,50]",College,5180.38714613619,983.1468803927065,5.269189425762043,2990.3188104891906,2019 +2001,48,"(45,50]",College,1858.5444529456772,328.86349239055505,5.65141615274975,1683.9475103706488,2019 +2001,48,"(45,50]",College,3187.917674062739,256.54796003242257,12.426205508162488,3024.64120391865,2019 +2001,48,"(45,50]",College,3083.623565416986,516.5395168438037,5.969772814786293,2934.80257284236,2019 +2001,48,"(45,50]",College,2617.0622800306046,984.8686787821858,2.6572702903565437,1728.0090429010131,2019 +2001,42,"(40,45]",College,5.357000765110941,29.27057262114888,0.1830166028675621,4264.988676763724,2019 +2001,42,"(40,45]",College,5.357000765110941,29.27057262114888,0.1830166028675621,4271.592612073406,2019 +2001,42,"(40,45]",College,5.357000765110941,29.27057262114888,0.1830166028675621,4290.599998071853,2019 +2001,42,"(40,45]",College,5.357000765110941,29.27057262114888,0.1830166028675621,4254.825509387189,2019 +2001,42,"(40,45]",College,5.524407039020658,29.27057262114888,0.18873587170717343,4297.736712350248,2019 +2001,79,"(75,80]",HS,202.39418515684773,24.105177452710844,8.39629517575224,7929.189264347791,2019 +2001,79,"(75,80]",HS,202.39418515684773,24.105177452710844,8.39629517575224,8221.25553698984,2019 +2001,79,"(75,80]",HS,202.39418515684773,24.105177452710844,8.39629517575224,8391.661767141044,2019 +2001,79,"(75,80]",HS,202.39418515684773,24.105177452710844,8.39629517575224,8160.570743583294,2019 +2001,79,"(75,80]",HS,202.39418515684773,24.105177452710844,8.39629517575224,8281.429154793106,2019 +2001,34,"(30,35]",HS,-139.6084621270084,49.93215329490103,-2.7959631803274334,4655.397984941879,2019 +2001,34,"(30,35]",HS,-150.4982402448355,46.488556515942335,-3.2373179879918426,4663.198998192739,2019 +2001,34,"(30,35]",HS,-157.1861208875287,56.819346852818406,-2.7664190032788416,4679.511829040546,2019 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+2001,28,"(25,30]",College,158.18218821729153,41.323161347504296,3.8279304646386865,5588.851214055233,2019 +2001,28,"(25,30]",College,158.34959449120123,41.323161347504296,3.8319816134000773,5528.212451740592,2019 +2001,20,"(15,20]",HS,19.837643458301454,25.826975842190187,0.7680978051597998,5868.551777503491,2019 +2001,20,"(15,20]",HS,19.837643458301454,25.826975842190187,0.7680978051597998,5801.5174198070235,2019 +2001,20,"(15,20]",HS,19.837643458301454,25.826975842190187,0.7680978051597998,5791.619296478081,2019 +2001,20,"(15,20]",HS,19.837643458301454,25.826975842190187,0.7680978051597998,5766.609911339383,2019 +2001,20,"(15,20]",HS,19.837643458301454,25.826975842190187,0.7680978051597998,5804.326441573603,2019 +2001,54,"(50,55]",College,464.82026013771997,123.96948404251289,3.74947321695974,9065.84072776332,2019 +2001,54,"(50,55]",College,381.1171231828616,123.96948404251289,3.074281756727849,9569.703578491326,2019 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+2001,29,"(25,30]",NoHS,8.537719969395562,51.653951684380374,0.16528686946476703,7018.857202551279,2019 +2001,29,"(25,30]",NoHS,8.537719969395562,51.653951684380374,0.16528686946476703,7141.366783533158,2019 +2001,29,"(25,30]",NoHS,8.370313695485846,51.653951684380374,0.16204595045565398,7114.519801823985,2019 +2001,62,"(60,65]",College,857.1201224177505,215.22479868491826,3.982441278398151,566.849495594937,2019 +2001,62,"(60,65]",College,857.2038255547055,215.22479868491826,3.982830188679245,561.6814596042875,2019 +2001,62,"(60,65]",College,857.2373068094874,213.5030002954389,4.015106605636777,540.6956422688152,2019 +2001,62,"(60,65]",College,857.1703442999235,213.5030002954389,4.014792968313314,561.0082290226909,2019 +2001,62,"(60,65]",College,856.9527161438408,215.22479868491826,3.9816634578359635,591.9400672946446,2019 +2001,61,"(60,65]",College,55734.90558530987,6095.166298756884,9.144115657135895,9.610553906013468,2019 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+2001,25,"(20,25]",HS,9.341270084162202,25.826975842190187,0.3616865614170196,4607.264503049364,2019 +2001,25,"(20,25]",HS,9.743045141545524,51.653951684380374,0.1886214863303812,4593.482411023969,2019 +2001,66,"(65,70]",College,299.6572302983933,103.30790336876075,2.9006225131562062,5499.349533344605,2019 +2001,66,"(65,70]",College,313.04973221117064,103.30790336876075,3.030259273520729,5575.372792012115,2019 +2001,66,"(65,70]",College,278.73144605967866,103.30790336876075,2.6980650750866384,5697.318021654779,2019 +2001,66,"(65,70]",College,319.7459831675593,103.30790336876075,3.0950776537029907,5484.660291834503,2019 +2001,66,"(65,70]",College,278.73144605967866,103.30790336876075,2.6980650750866384,5573.2355263019,2019 +2001,74,"(70,75]",HS,121.62065799540933,13.774387115834767,8.829478725452445,9580.931596131453,2019 +2001,75,"(70,75]",HS,173.76771231828616,13.774387115834767,12.615277242972661,9340.562168340775,2019 +2001,75,"(70,75]",HS,162.7523794950268,13.774387115834767,11.81558047747401,9414.822943868456,2019 +2001,74,"(70,75]",HS,70.17671002295333,13.774387115834767,5.094724682325761,8983.130532696474,2019 +2001,77,"(75,80]",HS,193.45469013006885,13.774387115834767,14.044522525991528,9396.06665877755,2019 +2001,25,"(20,25]",HS,57.75516449885233,113.63869370563681,0.5082350264290966,6280.755712682699,2019 +2001,25,"(20,25]",HS,59.429227237949505,113.63869370563681,0.5229664764705196,6362.255905874395,2019 +2001,25,"(20,25]",HS,57.75516449885233,113.63869370563681,0.5082350264290966,6413.044929842004,2019 +2001,25,"(20,25]",HS,57.75516449885233,113.63869370563681,0.5082350264290966,6277.847165355109,2019 +2001,25,"(20,25]",HS,59.429227237949505,113.63869370563681,0.5229664764705196,6355.693345296554,2019 +2001,41,"(40,45]",NoHS,-102.15130833970926,36.157766179066265,-2.8251553990868583,6372.438405412605,2019 +2001,41,"(40,45]",NoHS,-102.1847895944912,36.157766179066265,-2.826081375946605,6460.609368329824,2019 +2001,41,"(40,45]",NoHS,-102.00064269319051,37.87956456854561,-2.692761753071726,6777.298550064971,2019 +2001,41,"(40,45]",NoHS,-102.16804896710023,32.71416940010757,-3.123051901992177,6577.065833525606,2019 +2001,41,"(40,45]",NoHS,-102.01738332058149,30.992371010628222,-3.291693406922518,6435.619245022264,2019 +2001,31,"(30,35]",HS,21.428003060443764,55.097548463339066,0.3889102810935695,5603.270285643522,2019 +2001,31,"(30,35]",HS,21.428003060443764,82.64632269500859,0.25927352072904636,5561.663118790429,2019 +2001,31,"(30,35]",HS,21.428003060443764,43.04495973698364,0.49780515979976897,5567.788244024629,2019 +2001,31,"(30,35]",HS,21.428003060443764,49.93215329490103,0.42914237913773184,5604.353692329548,2019 +2001,31,"(30,35]",HS,21.428003060443764,53.37575007385973,0.40145577403207167,5551.981955258985,2019 +2001,30,"(25,30]",HS,8.671644988523337,25.826975842190187,0.335759209344115,7113.168883797645,2019 +2001,30,"(25,30]",HS,8.654904361132365,25.826975842190187,0.33511102554229244,7144.006874120127,2019 +2001,30,"(25,30]",HS,8.671644988523337,25.826975842190187,0.335759209344115,7047.665328972449,2019 +2001,30,"(25,30]",HS,8.671644988523337,25.826975842190187,0.335759209344115,7160.414583098749,2019 +2001,30,"(25,30]",HS,8.671644988523337,25.826975842190187,0.335759209344115,7144.8270936625095,2019 +2001,68,"(65,70]",NoHS,56.918133129303754,18.939782284272805,3.00521580845031,7944.990004875146,2019 +2001,68,"(65,70]",NoHS,45.199693955623566,17.21798389479346,2.625144397381594,8258.18849105603,2019 +2001,68,"(65,70]",NoHS,45.199693955623566,17.21798389479346,2.625144397381594,8585.154322782586,2019 +2001,68,"(65,70]",NoHS,45.199693955623566,17.21798389479346,2.625144397381594,7965.002772432606,2019 +2001,68,"(65,70]",NoHS,45.199693955623566,17.21798389479346,2.625144397381594,8298.776797457258,2019 +2001,22,"(20,25]",HS,-21.84651874521806,49.93215329490103,-0.43752406623026574,5627.620076785774,2019 +2001,22,"(20,25]",HS,-21.84651874521806,49.93215329490103,-0.43752406623026574,5633.340113867499,2019 +2001,22,"(20,25]",HS,-21.84651874521806,49.93215329490103,-0.43752406623026574,5629.520789122079,2019 +2001,22,"(20,25]",HS,-21.84651874521806,49.93215329490103,-0.43752406623026574,5579.797072416471,2019 +2001,22,"(20,25]",HS,-21.84651874521806,49.93215329490103,-0.43752406623026574,5607.501146157858,2019 +2001,34,"(30,35]",HS,26.048416220351953,84.36812108448795,0.3087471415212215,7923.332294659925,2019 +2001,34,"(30,35]",HS,29.547207345065036,82.64632269500859,0.35751387819278657,7944.16675936324,2019 +2001,34,"(30,35]",HS,24.206947207345063,82.64632269500859,0.29289805544859454,8012.72180316232,2019 +2001,34,"(30,35]",HS,25.194644223412393,82.64632269500859,0.304848944294699,7891.887004690539,2019 +2001,34,"(30,35]",HS,27.370925784238718,82.64632269500859,0.33118141124374284,7938.832753123379,2019 +2001,42,"(40,45]",HS,19530.452945677123,542.3664926859939,36.0096967807788,230.84596413888525,2019 +2001,42,"(40,45]",HS,4984.354399387911,401.17902474868754,12.424264709527831,230.5749335033823,2019 +2001,42,"(40,45]",HS,10376.343075745983,259.9915568113812,39.910307869242914,235.68928410458275,2019 +2001,42,"(40,45]",HS,8487.498087222648,495.87793617005156,17.11610351687845,231.71488299586844,2019 +2001,42,"(40,45]",HS,4085.884927314461,480.3817506647374,8.505495726389563,234.06497481304714,2019 +2001,24,"(20,25]",HS,-4.519969395562356,24.105177452710844,-0.18751031409868524,6375.031340165568,2019 +2001,24,"(20,25]",HS,-3.682938026013772,25.826975842190187,-0.1426004364009755,6333.1005875667925,2019 +2001,24,"(20,25]",HS,-8.03550114766641,24.105177452710844,-0.33335166950877376,6225.685995910138,2019 +2001,24,"(20,25]",HS,-6.863657230298394,24.105177452710844,-0.28473788437207764,6275.677735324943,2019 +2001,24,"(20,25]",HS,-4.35256312165264,25.826975842190187,-0.16852778847388014,6301.978853646423,2019 +2001,57,"(55,60]",HS,9630.882938026012,144.63106471626506,66.58931092652693,184.93501837162862,2019 +2001,57,"(55,60]",HS,4453.006885998469,144.63106471626506,30.788730586574246,182.1910018669292,2019 +2001,57,"(55,60]",HS,6045.0405508798785,144.63106471626506,41.79628050681189,187.5846359142148,2019 +2001,57,"(55,60]",HS,4744.293802601377,144.63106471626506,32.80273025652309,183.42498355210063,2019 +2001,57,"(55,60]",HS,6867.005355776588,144.63106471626506,47.479463483506606,184.4947035631073,2019 +2001,82,"(80,85]",NoHS,0.4017750573833206,11.363869370563684,0.03535548009941541,8200.59047120997,2019 +2001,82,"(80,85]",NoHS,0.28459066564651875,11.363869370563684,0.025043465070419244,8200.888665446937,2019 +2001,82,"(80,85]",NoHS,0.3515531752104055,11.363869370563684,0.03093604508698848,8232.757315303685,2019 +2001,82,"(80,85]",NoHS,0.03348125478194339,11.536049209511617,0.0029023155305490265,8260.251221641187,2019 +2001,82,"(80,85]",NoHS,0.2678500382555471,11.536049209511617,0.023218524244392212,8251.346348069459,2019 +2001,34,"(30,35]",HS,-33.81606732976282,48.21035490542169,-0.7014274712580449,5886.150831354704,2019 +2001,34,"(30,35]",HS,-35.49013006885999,46.488556515942335,-0.7634164777021921,5976.2626685702535,2019 +2001,34,"(30,35]",HS,-33.81606732976282,46.488556515942335,-0.7274062664898244,6038.487056957043,2019 +2001,34,"(30,35]",HS,-37.33159908186688,46.488556515942335,-0.8030277100357964,5900.57466188182,2019 +2001,34,"(30,35]",HS,-35.6575363427697,46.488556515942335,-0.7670174988234287,5953.916027564437,2019 +2001,59,"(55,60]",HS,266.92930374904364,67.15013718969449,3.9751118154083116,517.3304623670867,2019 +2001,59,"(55,60]",HS,266.7618974751339,67.15013718969449,3.972618800785916,556.8286224022262,2019 +2001,59,"(55,60]",HS,266.92930374904364,67.15013718969449,3.9751118154083116,546.2338825660626,2019 +2001,59,"(55,60]",HS,266.7618974751339,67.15013718969449,3.972618800785916,540.5291555979459,2019 +2001,59,"(55,60]",HS,266.92930374904364,67.15013718969449,3.9751118154083116,528.6915745577573,2019 +2001,51,"(50,55]",HS,170.92180566182097,18.939782284272805,9.024486295375782,5159.075247906576,2019 +2001,51,"(50,55]",HS,601.3233358837031,18.939782284272805,31.749221129275032,4447.387338468869,2019 +2001,51,"(50,55]",HS,228.67697016067328,18.939782284272805,12.07389645395036,5163.237984409311,2019 +2001,51,"(50,55]",HS,296.8113236419281,20.661580673752148,14.365373507893725,5133.116236187705,2019 +2001,51,"(50,55]",HS,80.52241775057384,24.105177452710844,3.340461521535838,5245.154008888978,2019 +2001,25,"(20,25]",College,166.5190206579954,80.92452430552926,2.0577077478924126,5189.895952440313,2019 +2001,25,"(20,25]",College,157.86411629686305,75.75912913709122,2.0837636083592956,5134.111672612564,2019 +2001,25,"(20,25]",College,152.84192807957155,82.64632269500859,1.849349409575151,5149.172905102377,2019 +2001,25,"(20,25]",College,144.4716143840857,79.20272591604991,1.8240737640421223,5190.642175265151,2019 +2001,25,"(20,25]",College,144.4716143840857,84.36812108448795,1.7123957784885229,5134.323961544471,2019 +2001,37,"(35,40]",HS,48.715225707727626,53.37575007385973,0.9126846112760381,6250.466158385736,2019 +2001,37,"(35,40]",HS,48.5478194338179,53.37575007385973,0.9095482380414124,6416.227104893651,2019 +2001,37,"(35,40]",HS,48.5478194338179,53.37575007385973,0.9095482380414124,6480.352179170274,2019 +2001,37,"(35,40]",HS,47.04116296863045,53.37575007385973,0.8813208789297824,6326.132955180933,2019 +2001,37,"(35,40]",HS,47.04116296863045,53.37575007385973,0.8813208789297824,6430.003788382213,2019 +2001,39,"(35,40]",HS,61.94032134659526,84.36812108448795,0.7341673673705138,5413.865648127081,2019 +2001,39,"(35,40]",HS,46.87375669472074,86.08991947396729,0.5444743935309974,5403.96967766938,2019 +2001,39,"(35,40]",HS,50.22188217291507,86.08991947396729,0.5833654216403543,5435.4139686387925,2019 +2001,39,"(35,40]",HS,65.28844682478959,86.08991947396729,0.7583750481324605,5406.89462782134,2019 +2001,39,"(35,40]",HS,41.851568477429225,86.08991947396729,0.48613785136696186,5478.675128057963,2019 +2001,55,"(50,55]",NoHS,1482.7173680183628,206.6158067375215,7.176204915928636,2024.7545019335835,2019 +2001,55,"(50,55]",NoHS,1504.6475899005356,206.6158067375215,7.282345013477089,2012.3232807734396,2019 +2001,55,"(50,55]",NoHS,1453.2538638102524,206.6158067375215,7.03360447952766,2113.8969313923944,2019 +2001,55,"(50,55]",NoHS,1465.6419280795717,206.6158067375215,7.093561481196253,2068.271159021934,2019 +2001,55,"(50,55]",NoHS,1466.9811782708491,206.6158067375215,7.100043319214477,2065.4544710706405,2019 +2001,58,"(55,60]",NoHS,98.43488905891354,36.157766179066265,2.7223719676549862,7228.343826416754,2019 +2001,58,"(55,60]",NoHS,202.7289977046672,36.157766179066265,5.606789885765627,7509.440201143984,2019 +2001,58,"(55,60]",NoHS,96.8612700841622,36.157766179066265,2.6788510552468963,7808.614696382256,2019 +2001,58,"(55,60]",NoHS,117.68661055853099,36.157766179066265,3.2548086620092778,7417.247562400604,2019 +2001,58,"(55,60]",NoHS,124.04804896710023,36.157766179066265,3.4307442653611306,7453.794213167223,2019 +2001,40,"(35,40]",College,79547.35990818669,1945.6321801116608,40.88509674198615,2.1257090517232013,2019 +2001,40,"(35,40]",College,269191.8832440704,1945.6321801116608,138.35702657252583,2.168847389551151,2019 +2001,40,"(35,40]",College,123869.67758224944,1945.6321801116608,63.665516457041996,1.9139833519487623,2019 +2001,40,"(35,40]",College,78230.79326702372,1945.6321801116608,40.208418665512625,2.4909727322479034,2019 +2001,40,"(35,40]",College,224583.0497322112,1945.6321801116608,115.42934580744972,1.9791266809042838,2019 +2001,72,"(70,75]",HS,1877.628768171385,117.08229048459552,16.036829826270132,1829.2084804051806,2019 +2001,72,"(70,75]",HS,4267.520734506504,204.89400834804215,20.827943037053096,3092.1984589872027,2019 +2001,72,"(70,75]",HS,3033.0836113236423,118.80408887407486,25.53012813000508,1889.4923795086029,2019 +2001,72,"(70,75]",HS,2158.0175363427697,161.84904861105852,13.333520059970997,1832.0525459357546,2019 +2001,72,"(70,75]",HS,974.4719204284621,259.9915568113812,3.748090639479583,960.0710670272654,2019 +2001,56,"(55,60]",College,19639.76924254017,2582.6975842190186,7.604362726222565,209.41371697501842,2019 +2001,56,"(55,60]",College,19631.398928844683,2582.6975842190186,7.601121807213452,196.4381247756557,2019 +2001,56,"(55,60]",College,19632.21921958684,2582.6975842190186,7.601439417276345,209.75370225208076,2019 +2001,56,"(55,60]",College,19631.38218821729,2582.6975842190186,7.601115325375432,206.44987499851882,2019 +2001,56,"(55,60]",College,19631.398928844683,2582.6975842190186,7.601121807213452,199.0858788589583,2019 +2001,41,"(40,45]",HS,1045.8372149961745,146.35286310574438,7.145997644340756,6595.061001145788,2019 +2001,41,"(40,45]",HS,1039.8105891354246,146.35286310574438,7.104818908695554,6005.03059916003,2019 +2001,41,"(40,45]",HS,1043.6609334353482,146.35286310574438,7.131127545357767,5539.873355176067,2019 +2001,41,"(40,45]",HS,1050.373925019128,146.35286310574438,7.17699608145145,6238.2615104440465,2019 +2001,41,"(40,45]",HS,1071.8019280795716,146.35286310574438,7.323409363745499,6046.652898262486,2019 +2001,54,"(50,55]",HS,-0.31807192042846216,1.239694840425129,-0.2565727548881188,4603.785627499502,2019 +2001,54,"(50,55]",HS,-0.31807192042846216,1.239694840425129,-0.2565727548881188,4588.382547790634,2019 +2001,54,"(50,55]",HS,-0.31807192042846216,1.239694840425129,-0.2565727548881188,4589.757626718254,2019 +2001,54,"(50,55]",HS,-0.31807192042846216,1.239694840425129,-0.2565727548881188,4572.065410575858,2019 +2001,54,"(50,55]",HS,-0.31807192042846216,1.239694840425129,-0.2565727548881188,4615.000576381625,2019 +2001,47,"(45,50]",HS,79.51798010711553,103.30790336876075,0.7697182646643563,4681.332674142651,2019 +2001,47,"(45,50]",HS,79.51798010711553,103.30790336876075,0.7697182646643563,4771.802457023456,2019 +2001,47,"(45,50]",HS,79.51798010711553,103.30790336876075,0.7697182646643563,4778.637235239243,2019 +2001,47,"(45,50]",HS,79.51798010711553,103.30790336876075,0.7697182646643563,4713.7696181987285,2019 +2001,47,"(45,50]",HS,79.51798010711553,103.30790336876075,0.7697182646643563,4731.01255779997,2019 +2001,46,"(45,50]",HS,21.344299923488908,60.2629436317771,0.3541861488530723,8878.571986121311,2019 +2001,46,"(45,50]",HS,19.502830910482018,60.2629436317771,0.3236289124814346,9288.261326698377,2019 +2001,46,"(45,50]",HS,19.70371843917368,60.2629436317771,0.3269624291765224,9338.760106640435,2019 +2001,46,"(45,50]",HS,25.362050497322112,60.2629436317771,0.42085648275482707,9104.4657719801,2019 +2001,46,"(45,50]",HS,28.040550879877582,60.2629436317771,0.46530337202266353,9138.887916685871,2019 +2001,41,"(40,45]",HS,595.464116296863,89.53351625292598,6.650739758893398,7344.4097161717145,2019 +2001,41,"(40,45]",HS,595.464116296863,89.53351625292598,6.650739758893398,6678.68960318386,2019 +2001,41,"(40,45]",HS,595.2967100229533,89.53351625292598,6.648869997926602,6239.627352753966,2019 +2001,41,"(40,45]",HS,595.2967100229533,89.53351625292598,6.648869997926602,6983.715451294936,2019 +2001,41,"(40,45]",HS,595.2967100229533,89.53351625292598,6.648869997926602,6714.878957002444,2019 +2001,37,"(35,40]",College,7513.695791889824,2823.7493587461267,2.660893314988214,838.2426195454639,2019 +2001,37,"(35,40]",College,7571.618362662586,3185.32702053679,2.3770301491325747,830.5169778583602,2019 +2001,37,"(35,40]",College,10034.66687069625,2255.555890217943,4.448866425440981,850.3077674520167,2019 +2001,37,"(35,40]",College,9312.643611323641,3374.7248433795176,2.7595267891523187,836.9052989994561,2019 +2001,37,"(35,40]",College,8283.764651874522,1997.2861317960408,4.1475102239985135,839.9155336431419,2019 +2001,73,"(70,75]",HS,646.1882172915073,84.36812108448795,7.659151454189685,5602.620256358584,2019 +2001,73,"(70,75]",HS,646.1882172915073,70.59373396865318,9.15362003061694,5147.6963586334105,2019 +2001,73,"(70,75]",HS,646.1882172915073,79.20272591604991,8.158661331636837,4704.768729689224,2019 +2001,73,"(70,75]",HS,646.1882172915073,79.20272591604991,8.158661331636837,5276.953267554594,2019 +2001,73,"(70,75]",HS,646.1882172915073,77.48092752657055,8.339964916784325,5111.37853757086,2019 +2001,25,"(20,25]",HS,-17.661361897475135,22.383379063231494,-0.7890391279879152,6857.899695585392,2019 +2001,25,"(20,25]",HS,-17.661361897475135,22.383379063231494,-0.7890391279879152,6825.0501192165175,2019 +2001,25,"(20,25]",HS,-17.661361897475135,22.383379063231494,-0.7890391279879152,6716.927857597084,2019 +2001,25,"(20,25]",HS,-21.009487375669472,22.383379063231494,-0.9386200053315957,6834.16745851397,2019 +2001,25,"(20,25]",HS,-19.335424636572306,22.383379063231494,-0.8638295666597555,6808.475350221865,2019 +2001,36,"(35,40]",HS,665.2390512624331,120.5258872635542,5.519470267891524,383.87870550397076,2019 +2001,36,"(35,40]",HS,665.4064575363428,120.5258872635542,5.520859233181144,380.0995672904181,2019 +2001,36,"(35,40]",HS,665.0716449885233,120.5258872635542,5.518081302601903,366.39752365860045,2019 +2001,36,"(35,40]",HS,665.0549043611325,120.5258872635542,5.517942406072942,379.83279118179513,2019 +2001,36,"(35,40]",HS,665.3897169089518,120.5258872635542,5.5207203366521815,401.00316033870854,2019 +2001,36,"(35,40]",HS,4.017750573833205,17.21798389479346,0.23334616865614166,6933.364427463454,2019 +2001,36,"(35,40]",HS,4.185156847742923,17.21798389479346,0.24306892568348093,6954.611257798652,2019 +2001,36,"(35,40]",HS,4.185156847742923,17.21798389479346,0.24306892568348093,6888.18817089857,2019 +2001,36,"(35,40]",HS,4.185156847742923,17.21798389479346,0.24306892568348093,6937.023027727126,2019 +2001,36,"(35,40]",HS,4.185156847742923,17.21798389479346,0.24306892568348093,7012.709213545556,2019 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+2001,78,"(75,80]",NoHS,169.74996174445295,12.913487921095093,13.14516750096265,9670.59289071288,2019 +2001,78,"(75,80]",NoHS,169.74996174445295,12.913487921095093,13.14516750096265,9601.443465705697,2019 +2001,78,"(75,80]",NoHS,169.74996174445295,12.913487921095093,13.14516750096265,9507.004347862445,2019 +2001,43,"(40,45]",NoHS,65.23822494261668,92.97711303188467,0.7016589654729817,8669.046244450892,2019 +2001,43,"(40,45]",NoHS,58.592195868400914,89.53351625292598,0.6544163383786025,8695.611955319091,2019 +2001,43,"(40,45]",NoHS,55.24407039020658,89.53351625292598,0.6170211190426824,8612.560672199583,2019 +2001,43,"(40,45]",NoHS,60.53410864575364,89.53351625292598,0.6761055655934363,8673.620729927252,2019 +2001,43,"(40,45]",NoHS,54.05548584544759,91.25531464240532,0.5923543856844982,8768.254013925292,2019 +2001,43,"(40,45]",College,23361.545524100995,490.7125410016135,47.60739449702424,542.4855986778236,2019 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+2001,37,"(35,40]",HS,365.2302677888294,80.92452430552926,4.513221065222556,8414.499163449053,2019 +2001,71,"(70,75]",College,1771.660596786534,153.24005666366176,11.561341305655185,851.9272879471112,2019 +2001,71,"(70,75]",College,1883.7056159143076,199.7286131796041,9.431325767131836,821.2234381997605,2019 +2001,71,"(70,75]",College,2996.404896710023,354.6904682327453,8.447943108366262,878.9647095940143,2019 +2001,71,"(70,75]",College,2470.9166029074217,132.5784759899096,18.63738879526327,851.3994909349979,2019 +2001,71,"(70,75]",College,1999.1657230298395,290.98392782200943,6.8703647586085905,837.3098149453699,2019 +2001,42,"(40,45]",HS,7314.097291507269,51.653951684380374,141.5980201514568,1358.7590490127375,2019 +2001,42,"(40,45]",HS,7317.579342004591,51.653951684380374,141.66543126684638,1358.5689359130422,2019 +2001,42,"(40,45]",HS,7317.529120122418,51.653951684380374,141.66445899114365,1368.369126537344,2019 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+2001,27,"(25,30]",College,471.75087987758224,430.4495973698365,1.0959491721216787,4.621997277952129,2019 +2001,27,"(25,30]",College,471.75087987758224,430.4495973698365,1.0959491721216787,6.1711844133439655,2019 +2001,27,"(25,30]",College,471.75087987758224,430.4495973698365,1.0959491721216787,4.558412919664695,2019 +2001,21,"(20,25]",HS,-16.90635960214231,8.781171786344663,-1.9252965337078227,6112.540948449552,2019 +2001,21,"(20,25]",HS,-16.571547054322878,5.509754846333906,-3.007674119175973,6121.034227799557,2019 +2001,21,"(20,25]",HS,-9.473521040550882,8.60899194739673,-1.100421640354255,6032.216408252175,2019 +2001,21,"(20,25]",HS,-7.799458301453711,5.337575007385973,-1.4612362900120486,6048.438744845071,2019 +2001,21,"(20,25]",HS,-18.245609793420044,6.542833880021514,-2.788640232657114,6088.757255350656,2019 +2001,21,"(20,25]",HS,225.66365723029838,51.653951684380374,4.368758824284431,7216.745920665581,2019 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+2001,31,"(30,35]",HS,17.996174445294567,36.157766179066265,0.4977125621137943,7015.068189344092,2019 +2001,82,"(80,85]",NoHS,136.68722264728387,37.87956456854561,3.6084686876465852,9613.061850971419,2019 +2001,82,"(80,85]",NoHS,135.1805661820964,37.87956456854561,3.568693772534743,9549.633524860521,2019 +2001,82,"(80,85]",NoHS,135.1805661820964,37.87956456854561,3.568693772534743,9632.441857016898,2019 +2001,82,"(80,85]",NoHS,135.01315990818668,37.87956456854561,3.5642743375223156,9660.773589872528,2019 +2001,82,"(80,85]",NoHS,136.68722264728387,37.87956456854561,3.6084686876465852,9613.29661373133,2019 +2001,42,"(40,45]",HS,64.61882172915072,79.20272591604991,0.8158661331636837,7371.368393581148,2019 +2001,42,"(40,45]",HS,64.61882172915072,53.37575007385973,1.2106400685654661,7628.398547590841,2019 +2001,42,"(40,45]",HS,64.95363427697016,84.36812108448795,0.7698836176750253,7827.715162346562,2019 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+2001,46,"(45,50]",College,6.110328997704667,75.75912913709122,0.0806546889767914,5539.737963129745,2019 +2001,46,"(45,50]",College,6.110328997704667,75.75912913709122,0.0806546889767914,5627.407860821344,2019 +2001,46,"(45,50]",College,5.94292272379495,75.75912913709122,0.07844497147057793,5638.996425710762,2019 +2001,46,"(45,50]",College,6.110328997704667,75.75912913709122,0.0806546889767914,5598.59041717024,2019 +2001,46,"(45,50]",College,6.110328997704667,75.75912913709122,0.0806546889767914,5610.752932227908,2019 +2001,70,"(65,70]",NoHS,419.0179035960214,258.2697584219018,1.6224040559620077,9642.122971518058,2019 +2001,70,"(65,70]",NoHS,420.3571537872992,258.2697584219018,1.6275895263765887,10634.479574013445,2019 +2001,70,"(65,70]",NoHS,417.00902830910485,258.2697584219018,1.6146258503401365,10512.67298445733,2019 +2001,70,"(65,70]",NoHS,416.92532517215,258.2697584219018,1.6143017584392252,10126.003723797507,2019 +2001,70,"(65,70]",NoHS,418.85049732211166,258.2697584219018,1.621755872160185,10390.749499820617,2019 +2001,29,"(25,30]",HS,94.58454475899006,98.14250820032271,0.9637469684994158,6751.249397711321,2019 +2001,29,"(25,30]",HS,101.61560826319817,110.19509692667813,0.9221427368117059,6769.001861201296,2019 +2001,29,"(25,30]",HS,61.27069625095639,122.24768565303354,0.5012012777473467,6827.41569277448,2019 +2001,29,"(25,30]",HS,79.01576128538639,92.97711303188467,0.8498409846118742,6724.4557473794075,2019 +2001,29,"(25,30]",HS,68.97138485080337,101.5861049792814,0.6789450669938587,6764.456903969284,2019 +2001,62,"(60,65]",College,7343.9458301453715,645.6743960547547,11.374070080862534,284.6504344729279,2019 +2001,62,"(60,65]",College,8164.236572302984,645.6743960547547,12.644510332434862,284.80317035657504,2019 +2001,62,"(60,65]",College,7494.611476664117,645.6743960547547,11.607416249518677,290.8654916977788,2019 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+2001,42,"(40,45]",College,48.46411629686305,75.75912913709122,0.6397132180487975,5429.254591428315,2019 +2001,42,"(40,45]",College,41.88504973221117,137.74387115834767,0.3040792260300346,5383.986142485395,2019 +2001,42,"(40,45]",College,54.992960979342,139.46566954782702,0.3943118127754246,5438.285272167864,2019 +2001,34,"(30,35]",NoHS,20.08875286916603,12.569128243199225,1.5982614291516555,7204.750636135328,2019 +2001,34,"(30,35]",NoHS,20.08875286916603,20.661580673752148,0.9722757027339238,7203.402906593957,2019 +2001,34,"(30,35]",NoHS,18.41469013006886,25.826975842190187,0.7130021820048774,7090.9272950264885,2019 +2001,34,"(30,35]",NoHS,18.41469013006886,14.807466149522373,1.2436084569852512,7208.151935915717,2019 +2001,34,"(30,35]",NoHS,18.41469013006886,29.27057262114888,0.6291195723572447,7188.471784285049,2019 +2001,33,"(30,35]",HS,18.515133894414692,87.81171786344665,0.21085037788700386,4624.506573645321,2019 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+2001,47,"(45,50]",HS,-0.7868094873756695,41.323161347504296,-0.019040399178539343,6308.424722983507,2019 +2001,47,"(45,50]",HS,-0.7868094873756695,41.323161347504296,-0.019040399178539343,6335.841074380918,2019 +2001,47,"(45,50]",HS,0.8537719969395563,41.323161347504296,0.020660858683095883,6323.150506532877,2019 +2001,47,"(45,50]",HS,-0.7868094873756695,41.323161347504296,-0.019040399178539343,6276.993591679486,2019 +2001,47,"(45,50]",HS,0.8537719969395563,41.323161347504296,0.020660858683095883,6330.842708911263,2019 +2001,58,"(55,60]",HS,10450.504055087988,182.51062928481065,57.25970096119616,1683.9141473156938,2019 +2001,58,"(55,60]",HS,10890.949961744454,182.51062928481065,59.67296263468008,1691.2959722515832,2019 +2001,58,"(55,60]",HS,10539.999449120123,182.51062928481065,57.750058122216494,1743.51522669641,2019 +2001,58,"(55,60]",HS,10042.534965570008,182.51062928481065,55.02438408613713,1670.9283739477385,2019 +2001,58,"(55,60]",HS,10468.416526396328,182.51062928481065,57.357845772698546,1658.1710160838097,2019 +2001,26,"(25,30]",HS,-19.653496557000768,75.75912913709122,-0.2594208352294606,5831.205194275129,2019 +2001,26,"(25,30]",HS,-17.98780413159908,75.75912913709122,-0.23743414604263657,5840.976498283121,2019 +2001,26,"(25,30]",HS,-19.653496557000768,75.75912913709122,-0.2594208352294606,5861.409437482035,2019 +2001,26,"(25,30]",HS,-19.9883091048202,75.75912913709122,-0.2638402702418875,5891.4588538375265,2019 +2001,26,"(25,30]",HS,-17.979433817903594,75.75912913709122,-0.2373236601673259,5845.674920462143,2019 +2001,86,"(85,90]",College,2519.12960979342,125.69128243199225,20.04219832156176,4678.5890941019325,2019 +2001,86,"(85,90]",College,2445.4708492731447,117.08229048459552,20.886769802260527,4730.993104029374,2019 +2001,86,"(85,90]",College,2428.730221882173,98.14250820032271,24.74697525484871,6008.558910903018,2019 +2001,86,"(85,90]",College,2467.233664881408,129.1348792109509,19.10586574252343,4939.3264180698525,2019 +2001,86,"(85,90]",College,2423.7080336648814,122.24768565303354,19.826207921382746,5057.423526966439,2019 +2001,37,"(35,40]",HS,3.348125478194338,51.653951684380374,0.06481838018226158,6025.022336887691,2019 +2001,37,"(35,40]",HS,3.348125478194338,51.653951684380374,0.06481838018226158,6034.351519393842,2019 +2001,37,"(35,40]",HS,3.348125478194338,51.653951684380374,0.06481838018226158,6061.202686861276,2019 +2001,37,"(35,40]",HS,3.348125478194338,51.653951684380374,0.06481838018226158,6010.665133364326,2019 +2001,37,"(35,40]",HS,3.348125478194338,51.653951684380374,0.06481838018226158,6071.284510330955,2019 +2001,35,"(30,35]",NoHS,35.82494261667942,58.54114524229776,0.6119617658384109,8836.857311758355,2019 +2001,35,"(30,35]",NoHS,31.807192042846214,58.54114524229776,0.5433305397630751,9154.38534238395,2019 +2001,35,"(30,35]",NoHS,29.965723029839328,58.54114524229776,0.5118745611452128,9268.013132471418,2019 +2001,35,"(30,35]",NoHS,33.146442234123946,58.54114524229776,0.5662076151215203,9034.503917276521,2019 +2001,35,"(30,35]",NoHS,30.97016067329763,58.54114524229776,0.5290323676640468,9197.430731630266,2019 +2001,30,"(25,30]",HS,15.066564651874522,61.984742021256444,0.24306892568348096,6133.54497985755,2019 +2001,30,"(25,30]",HS,15.066564651874522,61.984742021256444,0.24306892568348096,6143.822912231411,2019 +2001,30,"(25,30]",HS,15.066564651874522,61.984742021256444,0.24306892568348096,6165.315270581322,2019 +2001,30,"(25,30]",HS,13.392501912777352,61.984742021256444,0.21606126727420527,6196.92270689926,2019 +2001,30,"(25,30]",HS,13.225095638867636,61.984742021256444,0.21336050143327773,6148.7649409903515,2019 +2001,63,"(60,65]",HS,415.1005967865341,43.04495973698364,9.643419329996151,5224.329894781024,2019 +2001,63,"(60,65]",HS,407.5505738332058,46.488556515942335,8.766685919650879,5516.7165467522045,2019 +2001,63,"(60,65]",HS,408.3876052027544,25.826975842190187,15.812443845462713,5544.476515989755,2019 +2001,63,"(60,65]",HS,427.72302983932667,43.04495973698364,9.9366576819407,5377.178085126726,2019 +2001,63,"(60,65]",HS,425.2119357306809,27.548774231669533,15.434876780901039,5457.078032552729,2019 +2001,23,"(20,25]",HS,3.6661973986228005,30.992371010628222,0.1182935438326274,5677.783171267427,2019 +2001,23,"(20,25]",HS,3.8336036725325173,30.992371010628222,0.12369507551448253,5625.501644855165,2019 +2001,23,"(20,25]",HS,3.682938026013772,30.992371010628222,0.11883369700081291,5625.294631895662,2019 +2001,23,"(20,25]",HS,3.8336036725325173,30.992371010628222,0.12369507551448253,5609.789812555645,2019 +2001,23,"(20,25]",HS,3.8336036725325173,30.992371010628222,0.12369507551448253,5601.716863894086,2019 +2001,21,"(20,25]",HS,18.933649579188984,5.165395168438037,3.6654793993068933,4092.6446929661674,2019 +2001,21,"(20,25]",HS,18.933649579188984,5.165395168438037,3.6654793993068933,4129.594263482718,2019 +2001,21,"(20,25]",HS,18.933649579188984,5.165395168438037,3.6654793993068933,4172.26266144734,2019 +2001,21,"(20,25]",HS,18.933649579188984,5.337575007385973,3.5472381283615086,4106.085500425955,2019 +2001,21,"(20,25]",HS,18.933649579188984,5.165395168438037,3.6654793993068933,4096.084629985762,2019 +2001,84,"(80,85]",HS,459.0280030604438,22.383379063231494,20.507538283818608,7822.618966259741,2019 +2001,84,"(80,85]",HS,486.83418515684775,24.105177452710844,20.196249793718025,8110.759794736034,2019 +2001,84,"(80,85]",HS,505.7343534812548,87.81171786344665,5.759303721488595,8278.875722292883,2019 +2001,84,"(80,85]",HS,516.4483550114767,37.87956456854561,13.633957013337067,8050.890620216507,2019 +2001,84,"(80,85]",HS,456.2992807957154,24.105177452710844,18.929513449584682,8170.124663980963,2019 +2001,71,"(70,75]",NoHS,92.0734506503443,15.66836534426205,5.876391609930307,8178.066267233871,2019 +2001,71,"(70,75]",NoHS,99.60673297628156,15.496185505314111,6.4278227014076075,8235.464307359889,2019 +2001,71,"(70,75]",NoHS,93.32899770466717,15.66836534426205,5.956524222792993,8093.400684892751,2019 +2001,71,"(70,75]",NoHS,92.0734506503443,15.496185505314111,5.941684850040645,8081.273399575744,2019 +2001,71,"(70,75]",NoHS,92.0734506503443,15.66836534426205,5.876391609930307,8153.582311002729,2019 +2001,49,"(45,50]",College,68978.08110175976,909.1095496450945,75.87433343834962,12.752621228742626,2019 +2001,49,"(45,50]",College,115233.439020658,1194.9280822986661,96.43545978012759,13.775491741973905,2019 +2001,49,"(45,50]",College,22347.063504208112,852.2902027922762,26.22001688039424,13.284137936944528,2019 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+2001,32,"(30,35]",College,72.3195103289977,120.5258872635542,0.6000330051157929,10101.941248409214,2019 +2001,32,"(30,35]",College,95.78986993114002,87.81171786344665,1.0908552100085316,9888.9753501964,2019 +2001,32,"(30,35]",College,80.72330527926549,94.69891142136402,0.8524206251969055,9939.832863691934,2019 +2001,53,"(50,55]",College,21657.349655700076,996.9212675085412,21.724232756940886,209.41371697501842,2019 +2001,53,"(50,55]",College,24697.447589900534,654.2833880021514,37.747324848509415,194.79556708313498,2019 +2001,53,"(50,55]",College,16797.043305279265,445.9457828751505,37.6661108823219,209.75370225208076,2019 +2001,53,"(50,55]",College,31216.24789594491,513.0959200648451,60.839010164025076,213.1017896887116,2019 +2001,53,"(50,55]",College,16134.784085692427,420.1188070329604,38.405288731638656,199.0858788589583,2019 +2001,29,"(25,30]",HS,0,29.27057262114888,0,7203.125694089834,2019 +2001,29,"(25,30]",HS,0,29.27057262114888,0,7182.706770328492,2019 +2001,29,"(25,30]",HS,0,30.992371010628222,0,7192.281123344762,2019 +2001,29,"(25,30]",HS,0,29.27057262114888,0,7232.949750859416,2019 +2001,29,"(25,30]",HS,0,29.27057262114888,0,7172.760746317852,2019 +2001,24,"(20,25]",HS,-1.456434583014537,18.939782284272805,-0.07689816921622851,4424.125985261159,2019 +2001,24,"(20,25]",HS,-1.422953328232594,18.939782284272805,-0.07513039521125775,4373.590784238125,2019 +2001,24,"(20,25]",HS,-1.4899158377964805,18.939782284272805,-0.07866594322119928,4366.128884559099,2019 +2001,24,"(20,25]",HS,-1.4899158377964805,18.939782284272805,-0.07866594322119928,4347.275055733452,2019 +2001,24,"(20,25]",HS,-1.4899158377964805,18.939782284272805,-0.07866594322119928,4375.708421887384,2019 +2001,31,"(30,35]",College,102.78745218056618,1101.9509692667814,0.09327770023103579,5823.326964377731,2019 +2001,31,"(30,35]",College,102.78745218056618,1101.9509692667814,0.09327770023103579,5813.556030989789,2019 +2001,31,"(30,35]",College,102.78745218056618,1101.9509692667814,0.09327770023103579,5844.5704375521555,2019 +2001,31,"(30,35]",College,106.18579954093344,1101.9509692667814,0.09636163722564497,5883.20763686751,2019 +2001,31,"(30,35]",College,104.52847742922724,1101.9509692667814,0.09485764824797843,5828.618825072999,2019 +2001,50,"(45,50]",College,24.809609793420044,80.92452430552926,0.3065771471173776,4405.060143438147,2019 +2001,50,"(45,50]",College,75.33282325937262,80.92452430552926,0.9309022685750334,4490.190780907043,2019 +2001,50,"(45,50]",College,52.39816373374139,80.92452430552926,0.6474942445866343,4496.622199309308,2019 +2001,50,"(45,50]",College,29.39654169854629,80.92452430552926,0.36325875191505747,4435.5827957216725,2019 +2001,50,"(45,50]",College,48.53107880642693,80.92452430552926,0.5997079281331159,4451.808129676826,2019 +2001,44,"(40,45]",HS,666.6117827084927,120.5258872635542,5.530859783266406,11278.96182332654,2019 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+2001,73,"(70,75]",NoHS,735.4157612853865,56.819346852818406,12.943052006394327,6723.334789464499,2019 +2001,73,"(70,75]",NoHS,735.0809487375669,56.819346852818406,12.937159426377756,6181.1852172814815,2019 +2001,73,"(70,75]",NoHS,735.2483550114766,56.819346852818406,12.940105716386041,6907.4497264078655,2019 +2001,73,"(70,75]",NoHS,735.2483550114766,56.819346852818406,12.940105716386041,6695.217422999024,2019 +2001,53,"(50,55]",HS,2819.1216526396324,139.46566954782702,20.213731893875647,3994.902283203081,2019 +2001,53,"(50,55]",HS,3023.3573068094875,141.18746793730637,21.413779501676416,3914.1405178858076,2019 +2001,53,"(50,55]",HS,3071.9051262433054,161.84904861105852,18.980062920390957,4196.308675812829,2019 +2001,53,"(50,55]",HS,2896.1285386381023,154.9618550531411,18.689299619218758,4016.1489271804494,2019 +2001,53,"(50,55]",HS,2539.5531752104057,154.9618550531411,16.388247122748474,4003.014605513725,2019 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+2001,54,"(50,55]",College,6119.871155317521,254.82616164294322,24.0158668005703,1162.3988768827019,2019 +2001,54,"(50,55]",College,6290.458148431523,313.3673068852409,20.073753739521937,1115.1990386035545,2019 +2001,54,"(50,55]",College,3635.0598316755927,225.5555890217943,16.11602641920948,1104.5132487635835,2019 +2001,48,"(45,50]",HS,1340.1207039020658,172.17983894793457,7.783261455525607,11278.96182332654,2019 +2001,48,"(45,50]",HS,1054.2577505738332,172.17983894793457,6.123003465537158,11042.086600875853,2019 +2001,48,"(45,50]",HS,2102.3047283856163,172.17983894793457,12.20993550250289,13377.496463922676,2019 +2001,48,"(45,50]",HS,2277.863687834736,172.17983894793457,13.229561031959955,11305.465226834665,2019 +2001,48,"(45,50]",HS,1585.437857689365,172.17983894793457,9.208034270311899,11291.18149259581,2019 +2001,38,"(35,40]",NoHS,247.74454475899006,41.323161347504296,5.995295051983058,6693.2723952291435,2019 +2001,80,"(75,80]",HS,287.43657230298396,39.60136295802496,7.258249485191944,9172.972740464988,2019 +2001,45,"(40,45]",HS,15.953817903596022,17.21798389479346,0.9265787447054293,6907.699814629636,2019 +2001,24,"(20,25]",HS,8.286610558530986,36.157766179066265,0.229179272787282,8548.946171382056,2019 +2001,52,"(50,55]",NoHS,287.0347972456006,32.71416940010757,8.774020631092556,6811.150669636454,2019 +2001,30,"(25,30]",NoHS,-10.8814078041316,41.323161347504296,-0.2633246694904377,6496.087821714218,2019 +2001,30,"(25,30]",NoHS,-11.551032899770467,41.323161347504296,-0.27952926453600313,6535.048529418144,2019 +2001,30,"(25,30]",NoHS,-11.718439173680185,41.323161347504296,-0.2835804132973945,6579.401590542936,2019 +2001,30,"(25,30]",NoHS,-11.132517214996176,41.323161347504296,-0.26940139263252477,6470.627509061301,2019 +2001,30,"(25,30]",NoHS,-12.053251721499617,41.323161347504296,-0.29168271082017716,6519.0232103658145,2019 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+2001,46,"(45,50]",NoHS,13.05768936495792,46.488556515942335,0.2808796474564669,6143.165848553301,2019 +2001,46,"(45,50]",NoHS,13.225095638867636,74.03733074761188,0.17862739654879062,6186.169127237896,2019 +2001,71,"(70,75]",HS,488.9937260902831,77.48092752657055,6.311149617079536,3175.3059204510296,2019 +2001,71,"(70,75]",HS,505.56694720734504,77.48092752657055,6.525050271681,3589.1933794819793,2019 +2001,71,"(70,75]",HS,534.3608263198164,77.48092752657055,6.896675651392633,3502.9887000806075,2019 +2001,71,"(70,75]",HS,488.9937260902831,77.48092752657055,6.311149617079536,3409.825926796224,2019 +2001,71,"(70,75]",HS,488.82631981637337,77.48092752657055,6.3089890044067944,3378.7427476814046,2019 +2001,76,"(75,80]",College,1185.9395256312166,46.488556515942335,25.51035382706542,8211.66585702667,2019 +2001,76,"(75,80]",College,1182.0557000765111,36.157766179066265,32.6916130333535,7416.573409226953,2019 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+2001,41,"(40,45]",NoHS,224.4750726855394,34.43596778958692,6.518622448979591,7804.166074383259,2019 +2001,41,"(40,45]",NoHS,224.87684774292273,34.43596778958692,6.530289757412398,7618.442767593866,2019 +2001,41,"(40,45]",NoHS,225.0777352716144,34.43596778958692,6.536123411628802,7743.532455650163,2019 +2001,23,"(20,25]",HS,-4.35256312165264,15.496185505314111,-0.2808796474564669,8491.876456041313,2019 +2001,23,"(20,25]",HS,-4.35256312165264,15.496185505314111,-0.2808796474564669,8513.374859289657,2019 +2001,23,"(20,25]",HS,-4.35256312165264,15.496185505314111,-0.2808796474564669,8389.018518604036,2019 +2001,23,"(20,25]",HS,-4.35256312165264,15.496185505314111,-0.2808796474564669,8444.278298366267,2019 +2001,23,"(20,25]",HS,-4.35256312165264,15.496185505314111,-0.2808796474564669,8493.137343713746,2019 +2001,53,"(50,55]",College,780528.152869166,23192.62430628679,33.65415412078181,22.186381816816397,2019 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+2001,77,"(75,80]",College,17343.457383320583,1170.822904845955,14.813049276314302,11.096688211252678,2019 +2001,77,"(75,80]",College,30262.199540933434,1582.3327199315188,19.125054522188698,11.208984887044869,2019 +2001,77,"(75,80]",College,15776.702065799542,950.4327109925989,16.599493981349717,10.445347271925723,2019 +2001,81,"(80,85]",NoHS,187.41132364192808,49.93215329490103,3.753319480036647,7151.521231837697,2019 +2001,81,"(80,85]",NoHS,187.21043611323643,48.21035490542169,3.88319970845481,7389.482317875745,2019 +2001,81,"(80,85]",NoHS,187.26065799540933,48.21035490542169,3.8842414324220247,7547.37028953594,2019 +2001,81,"(80,85]",NoHS,187.31087987758227,49.93215329490103,3.7513078751344393,7387.216172891205,2019 +2001,81,"(80,85]",NoHS,187.39458301453712,49.93215329490103,3.752984212552946,7485.525009143933,2019 +2001,24,"(20,25]",HS,91.73863810252487,86.08991947396729,1.0656141701963806,6493.220024103065,2019 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+2001,75,"(70,75]",NoHS,2269.3594491201225,91.25531464240532,24.868244200492587,3476.8115730430036,2019 +2001,75,"(70,75]",NoHS,2269.3594491201225,91.25531464240532,24.868244200492587,3515.754695536766,2019 +2001,75,"(70,75]",NoHS,2269.3594491201225,91.25531464240532,24.868244200492587,4465.15535742903,2019 +2001,75,"(70,75]",NoHS,2269.3594491201225,91.25531464240532,24.868244200492587,3670.573950388506,2019 +2001,75,"(70,75]",NoHS,2269.3594491201225,91.25531464240532,24.868244200492587,3758.335749233418,2019 +2001,43,"(40,45]",HS,21.7628156082632,129.1348792109509,0.16852778847388017,4150.1734223431695,2019 +2001,43,"(40,45]",HS,21.7628156082632,129.1348792109509,0.16852778847388017,4181.6452225723615,2019 +2001,43,"(40,45]",HS,21.7628156082632,129.1348792109509,0.16852778847388017,4243.299916167811,2019 +2001,43,"(40,45]",HS,21.7628156082632,129.1348792109509,0.16852778847388017,4165.611124073518,2019 +2001,43,"(40,45]",HS,21.7628156082632,129.1348792109509,0.16852778847388017,4167.797781498556,2019 +2001,49,"(45,50]",NoHS,1545.9969395562357,49.93215329490103,30.96195211981995,291.0107675206736,2019 +2001,49,"(45,50]",NoHS,1137.5256312165266,49.93215329490103,22.781425517506943,292.4046915671419,2019 +2001,49,"(45,50]",NoHS,1628.0260137719968,49.93215329490103,32.60476278995658,598.3194637687418,2019 +2001,49,"(45,50]",NoHS,1505.8194338179035,48.21035490542169,31.234356950327292,292.58451333200617,2019 +2001,49,"(45,50]",NoHS,1527.4148431522572,55.097548463339066,27.722010974201,309.4071342538417,2019 +2001,35,"(30,35]",HS,14.489013006885997,60.2629436317771,0.24042989163320314,4753.443231276478,2019 +2001,35,"(30,35]",HS,14.656419280795715,60.2629436317771,0.24320782221244294,4755.383361830469,2019 +2001,35,"(30,35]",HS,14.656419280795715,60.2629436317771,0.24320782221244294,4791.051827467316,2019 +2001,35,"(30,35]",HS,14.497383320581484,60.2629436317771,0.24056878816216515,4773.389751337259,2019 +2001,35,"(30,35]",HS,14.472272379495028,60.2629436317771,0.2401520985752792,4798.4154620845975,2019 +2001,76,"(75,80]",HS,292.123947972456,48.21035490542169,6.059361075966774,8898.301505361665,2019 +2001,76,"(75,80]",HS,290.9018821729151,49.93215329490103,5.8259430642784125,9226.06436570877,2019 +2001,76,"(75,80]",HS,290.4498852333588,48.21035490542169,6.024636943726277,9417.297789924702,2019 +2001,76,"(75,80]",HS,291.2869166029074,48.21035490542169,6.041999009846525,9157.962625351774,2019 +2001,76,"(75,80]",HS,290.8851415455241,48.21035490542169,6.033665218108807,9293.592454146437,2019 +2001,25,"(20,25]",College,172.42846212700843,111.91689531615746,1.5406830366399102,6411.261688170629,2019 +2001,25,"(20,25]",College,172.42846212700843,111.91689531615746,1.5406830366399102,6494.455349903613,2019 +2001,25,"(20,25]",College,172.42846212700843,111.91689531615746,1.5406830366399102,6546.299704060806,2019 +2001,25,"(20,25]",College,172.59586840091814,111.91689531615746,1.542178845413347,6408.292704993657,2019 +2001,25,"(20,25]",College,172.42846212700843,111.91689531615746,1.5406830366399102,6487.756427809881,2019 +2001,24,"(20,25]",NoHS,21.29407804131599,58.54114524229776,0.3637454981992797,6787.6962163332555,2019 +2001,24,"(20,25]",NoHS,21.277337413925018,58.54114524229776,0.36345953475729914,6786.859796205681,2019 +2001,24,"(20,25]",NoHS,21.46148431522571,58.54114524229776,0.3666051326190854,6802.858937740796,2019 +2001,24,"(20,25]",NoHS,21.46148431522571,58.54114524229776,0.3666051326190854,6774.398565557492,2019 +2001,24,"(20,25]",NoHS,21.46148431522571,58.54114524229776,0.3666051326190854,6775.394860304875,2019 +2001,56,"(55,60]",HS,0,43.04495973698364,0,6713.741802684456,2019 +2001,56,"(55,60]",HS,0,43.04495973698364,0,6966.771458326586,2019 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+2001,27,"(25,30]",NoHS,-160.82720734506503,56.819346852818406,-2.830500810959032,4818.683496723124,2019 +2001,27,"(25,30]",NoHS,-160.6765416985463,55.097548463339066,-2.9162194358875624,4805.023825196032,2019 +2001,27,"(25,30]",NoHS,-160.6765416985463,55.097548463339066,-2.9162194358875624,4811.4287914330025,2019 +2001,27,"(25,30]",NoHS,-160.843947972456,55.097548463339066,-2.919257797458606,4838.634931178701,2019 +2001,27,"(25,30]",NoHS,-160.6765416985463,55.097548463339066,-2.9162194358875624,4798.37022177531,2019 +2001,46,"(45,50]",HS,444.3297322111706,189.39782284272803,2.3460128819967094,1121.8860339548692,2019 +2001,46,"(45,50]",HS,442.65566947207344,189.39782284272803,2.3371740119718556,1113.5753115413188,2019 +2001,46,"(45,50]",HS,460.90295332823257,189.39782284272803,2.4335176952427626,1070.4657039159174,2019 +2001,46,"(45,50]",HS,432.61129303749044,189.39782284272803,2.2841407918227326,1112.977659258073,2019 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+2001,46,"(45,50]",NoHS,111.8273909716909,68.87193557917384,1.6237004235656527,5668.862027583518,2019 +2001,58,"(55,60]",HS,901.4827850038256,168.7362421689759,5.3425557747165096,6520.027268588544,2019 +2001,58,"(55,60]",HS,903.1568477429228,168.7362421689759,5.352476955356652,5915.059948357258,2019 +2001,58,"(55,60]",HS,903.1568477429228,168.7362421689759,5.352476955356652,5622.161573456584,2019 +2001,58,"(55,60]",HS,903.1568477429228,168.7362421689759,5.352476955356652,6226.336254255484,2019 +2001,58,"(55,60]",HS,901.3153787299159,168.7362421689759,5.341563656652495,5950.033687185914,2019 +2001,40,"(35,40]",NoHS,255.12716143840856,292.70572621148875,0.8716165711567647,342.6502683816799,2019 +2001,40,"(35,40]",NoHS,253.4530986993114,292.70572621148875,0.8658973023171533,359.0655533987132,2019 +2001,40,"(35,40]",NoHS,253.4530986993114,292.70572621148875,0.8658973023171533,352.27907715432315,2019 +2001,40,"(35,40]",NoHS,253.4530986993114,292.70572621148875,0.8658973023171533,349.49554082221476,2019 +2001,40,"(35,40]",NoHS,255.12716143840856,292.70572621148875,0.8716165711567647,343.480678873285,2019 +2001,85,"(80,85]",HS,2.0088752869166027,14.979645988470308,0.1341069934805412,5711.377179197456,2019 +2001,85,"(80,85]",HS,2.0088752869166027,14.979645988470308,0.1341069934805412,5709.1377703124845,2019 +2001,85,"(80,85]",HS,2.0088752869166027,14.979645988470308,0.1341069934805412,5735.423214868679,2019 +2001,85,"(80,85]",HS,2.0088752869166027,14.979645988470308,0.1341069934805412,5751.421977329264,2019 +2001,85,"(80,85]",HS,2.0088752869166027,15.151825827418245,0.13258305037280776,5748.40008855611,2019 +2001,35,"(30,35]",HS,64.56859984697782,144.63106471626506,0.44643659350532666,10029.075686463502,2019 +2001,35,"(30,35]",HS,66.24266258607499,144.63106471626506,0.4580113042521591,10369.82065225311,2019 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+2001,34,"(30,35]",NoHS,215.3179495026779,172.17983894793457,1.2505410088563729,6977.310046064709,2019 +2001,34,"(30,35]",NoHS,227.17031369548585,172.17983894793457,1.3193781286099346,6787.619428192114,2019 +2001,34,"(30,35]",NoHS,227.086610558531,172.17983894793457,1.3188919907585677,6804.536692824731,2019 +2001,34,"(30,35]",NoHS,0,11.019509692667812,0,4919.447760697695,2019 +2001,34,"(30,35]",NoHS,0,11.019509692667812,0,4900.512359799171,2019 +2001,34,"(30,35]",NoHS,0,10.847329853719879,0,4910.079624128745,2019 +2001,34,"(30,35]",NoHS,0,10.847329853719879,0,4937.43280546607,2019 +2001,34,"(30,35]",NoHS,0,11.019509692667812,0,4899.818377068024,2019 +2001,58,"(55,60]",College,1568.261973986228,215.22479868491826,7.286623026569117,8600.80792861763,2019 +2001,58,"(55,60]",College,1568.4293802601376,215.22479868491826,7.287400847131304,7811.740176547641,2019 +2001,58,"(55,60]",College,1568.261973986228,215.22479868491826,7.286623026569117,7308.01060632702,2019 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+2001,52,"(50,55]",College,504.06029074215763,136.02207276886833,3.705724229027651,9636.801106672629,2019 +2001,21,"(20,25]",HS,66.81184391736802,75.75912913709122,0.881898256729793,7564.305316041345,2019 +2001,21,"(20,25]",HS,66.67791889824024,68.87193557917384,0.9681435309973044,7665.656464556303,2019 +2001,21,"(20,25]",HS,66.62769701606733,44.76675812646299,1.4883297295696218,7727.366104318431,2019 +2001,21,"(20,25]",HS,66.47703136954858,60.2629436317771,1.1031162330161175,7518.443585568733,2019 +2001,21,"(20,25]",HS,66.5942157612854,56.819346852818406,1.1720341652956212,7647.252477720669,2019 +2001,39,"(35,40]",College,-152.0551185921959,61.984742021256444,-2.453105613314509,9835.946007741752,2019 +2001,39,"(35,40]",College,-154.44902830910482,70.59373396865318,-2.187857471566629,10085.062013432278,2019 +2001,39,"(35,40]",College,-172.093649579189,61.984742021256444,-2.776387284473538,10181.103007174494,2019 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+2001,55,"(50,55]",NoHS,0.016740627390971693,11.019509692667812,0.0015191807855217563,4856.030359243418,2019 +2001,55,"(50,55]",NoHS,0.016740627390971693,11.019509692667812,0.0015191807855217563,4871.415419259663,2019 +2001,55,"(50,55]",NoHS,0.016740627390971693,11.019509692667812,0.0015191807855217563,4908.471492598917,2019 +2001,38,"(35,40]",HS,32.409854628921195,144.63106471626506,0.22408640005867575,5690.599168339237,2019 +2001,38,"(35,40]",HS,45.86931905126244,142.9092663267857,0.32096812355312665,5622.309423176926,2019 +2001,38,"(35,40]",HS,51.72853863810253,144.63106471626506,0.35765856207712193,5661.293129693086,2019 +2001,38,"(35,40]",HS,61.10328997704667,144.63106471626506,0.4224769422593835,5656.847824993926,2019 +2001,38,"(35,40]",HS,38.00122417750574,144.63106471626506,0.26274593395309603,5668.861058060249,2019 +2001,27,"(25,30]",HS,8.688385615914306,43.04495973698364,0.20184443588756257,6300.618877380329,2019 +2001,27,"(25,30]",HS,8.604682478959448,43.04495973698364,0.19989988448209473,6338.4072474228215,2019 +2001,27,"(25,30]",HS,8.772088752869166,43.04495973698364,0.20378898729303044,6381.425713592309,2019 +2001,27,"(25,30]",HS,8.604682478959448,43.04495973698364,0.19989988448209473,6275.924672048198,2019 +2001,27,"(25,30]",HS,8.437276205049733,43.04495973698364,0.19601078167115904,6322.864134320245,2019 +2001,72,"(70,75]",College,0,27.548774231669533,0,8308.53920416493,2019 +2001,72,"(70,75]",College,0,25.826975842190187,0,8531.173521769611,2019 +2001,72,"(70,75]",College,0,27.548774231669533,0,8668.543687060817,2019 +2001,72,"(70,75]",College,0,25.826975842190187,0,8465.736244142352,2019 +2001,72,"(70,75]",College,0,25.826975842190187,0,8302.016722872311,2019 +2001,68,"(65,70]",HS,0,14.63528631057444,0,6324.944412423695,2019 +2001,68,"(65,70]",HS,0,14.63528631057444,0,6288.002317045398,2019 +2001,68,"(65,70]",HS,0,14.63528631057444,0,6287.907924366221,2019 +2001,68,"(65,70]",HS,0,14.63528631057444,0,6299.8495204673,2019 +2001,68,"(65,70]",HS,0,14.63528631057444,0,6303.7534025016075,2019 +2001,45,"(40,45]",NoHS,13.258576893649579,20.661580673752148,0.6417019638043897,5307.054352889779,2019 +2001,45,"(40,45]",HS,12.254139250191278,14.63528631057444,0.8373009581190967,5330.118758571581,2019 +2001,45,"(40,45]",HS,8.135944912012242,18.939782284272805,0.4295690832078972,5319.442633184333,2019 +2001,45,"(40,45]",HS,8.922754399387912,60.2629436317771,0.14806369987348042,5280.6124550265085,2019 +2001,45,"(40,45]",College,5.407222647283857,29.27057262114888,0.18473238351944551,5580.443141660158,2019 +2001,50,"(45,50]",College,485.9804131599082,172.17983894793457,2.8225163650365808,6166.030698041842,2019 +2001,50,"(45,50]",College,485.9804131599082,172.17983894793457,2.8225163650365808,5600.307050171477,2019 +2001,50,"(45,50]",College,485.9804131599082,172.17983894793457,2.8225163650365808,5231.782146823012,2019 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+2004,39,"(35,40]",HS,48.711086535008974,64.52825618551145,0.7548799458483753,4647.828977576074,2019 +2004,39,"(35,40]",HS,49.96810628366248,64.52825618551145,0.7743600902527075,4590.4229228581735,2019 +2004,76,"(75,80]",HS,1786.5393177737883,574.3014800510518,3.1108039589502297,237.41283090716033,2019 +2004,76,"(75,80]",HS,1797.5382405745063,574.3014800510518,3.129955786314039,242.83538571055314,2019 +2004,76,"(75,80]",HS,1810.1084380610414,574.3014800510518,3.151843589015536,236.0649872843409,2019 +2004,76,"(75,80]",HS,1770.8265709156194,574.3014800510518,3.0834442055733584,117.00299442641028,2019 +2004,76,"(75,80]",HS,1832.1062836624776,574.3014800510518,3.190147243743155,247.71998204478737,2019 +2004,63,"(60,65]",HS,520.2490484739677,161.69167793684528,3.217537569726813,665.4162647811534,2019 +2004,63,"(60,65]",HS,575.40078994614,161.69167793684528,3.5586295923707603,668.0069529882035,2019 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+2004,52,"(50,55]",HS,12.177378815080791,19.358476855653432,0.6290463297232252,4105.1881477333,2019 +2004,52,"(50,55]",HS,12.177378815080791,20.97168326029122,0.5806581505137463,4129.860393479493,2019 +2004,52,"(50,55]",HS,12.177378815080791,19.358476855653432,0.6290463297232252,4035.9053880871943,2019 +2004,84,"(80,85]",HS,477.98175942549375,65.09287842713468,7.343073020815159,10697.271806893072,2019 +2004,84,"(80,85]",HS,829.7901615798922,65.09287842713468,12.747787187023292,9143.436997546056,2019 +2004,84,"(80,85]",HS,817.377091561939,65.09287842713468,12.5570893669561,8142.841056102536,2019 +2004,84,"(80,85]",HS,506.26470377019746,65.09287842713468,7.77757438299357,10444.501102966471,2019 +2004,84,"(80,85]",HS,770.2388509874327,65.09287842713468,11.83292042999208,8508.318292201471,2019 +2004,27,"(25,30]",HS,-5.672301615798923,20.97168326029122,-0.2704743126909192,8506.25499528136,2019 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+2016,59,"(55,60]",HS,518.3857948139797,109.93892253597035,4.715216256957328,6041.043771179487,2019 +2016,59,"(55,60]",HS,518.2624577226607,109.93892253597035,4.714094387755101,6143.369863728725,2019 +2016,30,"(25,30]",HS,25.777452085682075,99.94447503270031,0.25791772959183673,5564.78100441461,2019 +2016,30,"(25,30]",HS,25.53077790304397,99.94447503270031,0.2554496173469388,5578.42580664794,2019 +2016,30,"(25,30]",HS,25.53077790304397,99.94447503270031,0.2554496173469388,5551.164957028887,2019 +2016,30,"(25,30]",HS,25.654114994363024,99.94447503270031,0.25668367346938775,5580.714156309629,2019 +2016,30,"(25,30]",HS,25.777452085682075,99.94447503270031,0.25791772959183673,5592.148038036246,2019 +2016,42,"(40,45]",HS,-15.565140924464488,19.988895006540066,-0.7786894132653059,3852.0106475531347,2019 +2016,42,"(40,45]",HS,-15.762480270574972,19.988895006540066,-0.7885618622448978,3852.043235576121,2019 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+2016,28,"(25,30]",NoHS,7.646899661781285,52.47084939216765,0.14573615160349856,4856.088969211966,2019 +2016,29,"(25,30]",College,126.42051860202932,68.71182658498145,1.8398654916512063,3754.123302342944,2019 +2016,29,"(25,30]",College,146.1544532130778,68.71182658498145,2.1270640074211506,3738.766037388078,2019 +2016,29,"(25,30]",College,149.85456595264938,68.71182658498145,2.180913729128015,3746.0302469337157,2019 +2016,29,"(25,30]",College,132.58737316798198,68.71182658498145,1.929615027829314,3746.8226804496676,2019 +2016,29,"(25,30]",College,127.65388951521984,68.71182658498145,1.8578153988868276,3766.1061347877558,2019 +2016,56,"(55,60]",HS,116.10953776775648,31.232648447718848,3.7175693877551015,4591.595961693184,2019 +2016,56,"(55,60]",HS,115.98620067643742,31.232648447718848,3.7136204081632647,4655.232067976907,2019 +2016,56,"(55,60]",HS,116.10953776775648,31.232648447718848,3.7175693877551015,4600.444843351535,2019 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+2016,61,"(60,65]",NoHS,137.13851183765502,24.986118758175078,5.488588010204081,5147.3527712469595,2019 +2016,61,"(60,65]",NoHS,143.44103720405863,24.986118758175078,5.740829081632653,5115.086749019161,2019 +2016,61,"(60,65]",NoHS,150.5822547914318,23.736812820266326,6.343827873254565,5073.178326088355,2019 +2016,61,"(60,65]",NoHS,180.17698985343856,24.986118758175078,7.211083545918367,5141.350502818403,2019 +2016,33,"(30,35]",HS,0.37001127395715894,14.991671254905045,0.02468112244897959,3753.787744770132,2019 +2016,33,"(30,35]",HS,0.37001127395715894,14.991671254905045,0.02468112244897959,3725.422721770016,2019 +2016,33,"(30,35]",HS,0.37001127395715894,13.742365316996294,0.026924860853432275,3729.2549157217936,2019 +2016,33,"(30,35]",HS,0.37001127395715894,14.991671254905045,0.02468112244897959,3730.562918877101,2019 +2016,33,"(30,35]",HS,0.37001127395715894,14.991671254905045,0.02468112244897959,3747.244374966687,2019 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+------------ + +This example script allows the user to create all of the figures and tables +modules for EstimatingMicroDSOPs.StructuralEstimation. + +This is example is kept as simple and minimal as possible to illustrate the +format of a "replication archive." + +The file structure is as follows: + +./EstimatingMicroDSOPs/ + calibration/ # Directory that contain the necessary code and data to parameterize the model + code/ # The main estimation code, in this case StructuralEstimation.py + figures/ # Any figures created by the main code + tables/ # Any tables created by the main code + +Because computational modeling can be very memory- and time-intensive, this file +also allows the user to choose whether to run files based on there resouce +requirements. Files are categorized as one of the following three: + +- low_resource: low RAM needed and runs quickly, say less than 1-5 minutes +- medium_resource: moderate RAM needed and runs moderately quickly, say 5-10+ mintues +- high_resource: high RAM needed (and potentially parallel computing required), and high time to run, perhaps even hours, days, or longer. + +The designation is purposefully vague and left up the to researcher to specify +more clearly below. Using time taken on an example machine is entirely reasonable +here. + +Finally, this code may serve as example code for efforts that fall outside +the HARK package structure for one reason or another. Therefore this script will +attempt to import the necessary MicroDSOP sub-modules as though they are part of +the HARK package; if that fails, this script reverts to manaully updating the +Python PATH with the locations of the MicroDSOP directory structure so it can +still run. +""" + +from __future__ import annotations + +from estimark.estimation import estimate +from estimark.options import ( + all_replications, + high_resource, + low_resource, + medium_resource, +) + + +# Ask the user which replication to run, and run it: +def run_replication(): + which_model = input( + """Which model would you like to run? + + [1] IndShockConsumerType + + 2 PortfolioConsumerType + + 3 BequestWarmGlowConsumerType + + 4 BequestWarmGlowPortfolioType + + 5 WealthPortfolioConsumerType \n\n""", + ) + + which_replication = input( + """Which replication would you like to run? (See documentation in do_all.py for details.) Please enter the option number to run that option; default is in brackets: + + [1] low-resource: ~90 sec; output ./tables/estimate_results.csv + + 2 medium-resource: ~7 min; output ./figures/SMMcontour.pdf + ./figures/SMMcontour.png + 3 high-resource: ~30 min; output ./tables/bootstrap_results.csv + + 4 all: ~40 min; output: all above. + + q quit: exit without executing.\n\n""", + ) + + subjective_markets = input( + """Would you like to add subjective stock or labor market beliefs to the model?: + + [1] No + + 2 Subjective Stock Market Beliefs + + 3 Subjective Labor Market Beliefs + + 4 Both\n\n""", + ) + + replication_specs = {} + + if which_model == "1" or which_model == "": + agent_name = "IndShock" + elif which_model == "2": + agent_name = "Portfolio" + elif which_model == "3": + agent_name = "WarmGlow" + elif which_model == "4": + agent_name = "WarmGlowPortfolio" + elif which_model == "5": + agent_name = "WealthPortfolio" + else: + print("Invalid model choice.") + return + + if which_replication == "q": + return + + if which_replication == "1" or which_replication == "": + print("Running low-resource replication...") + replication_specs.update(**low_resource) + + elif which_replication == "2": + print("Running medium-resource replication...") + replication_specs.update(**medium_resource) + + elif which_replication == "3": + print("Running high-resource replication...") + replication_specs.update(**high_resource) + + elif which_replication == "4": + print("Running all replications...") + replication_specs.update(**all_replications) + + else: + print("Invalid replication choice.") + return + + if int(subjective_markets) > 1: + agent_name += "Sub" + + if subjective_markets == "2" or subjective_markets == "4": + agent_name += "(Stock)" + print("Adding subjective stock market beliefs...") + + if subjective_markets == "3" or subjective_markets == "4": + agent_name += "(Labor)" + print("Adding subjective labor market beliefs...") + + agent_name += "Market" + + replication_specs["agent_name"] = agent_name + replication_specs["save_dir"] = "content/tables/min" + + estimate(**replication_specs) + + +if __name__ == "__main__": + run_replication() diff --git a/src/estimark/__init__.py b/src/estimark/__init__.py new file mode 100644 index 0000000..44f3336 --- /dev/null +++ b/src/estimark/__init__.py @@ -0,0 +1,11 @@ +""" +Copyright (c) 2024 Alan Lujan. All rights reserved. + +estimark: Estimating Microeconomic Dynamic Stochastic Optimization Problems +""" + +from __future__ import annotations + +from ._version import version as __version__ + +__all__ = ["__version__"] diff --git a/src/estimark/_version.pyi b/src/estimark/_version.pyi new file mode 100644 index 0000000..91744f9 --- /dev/null +++ b/src/estimark/_version.pyi @@ -0,0 +1,4 @@ +from __future__ import annotations + +version: str +version_tuple: tuple[int, int, int] | tuple[int, int, int, str, str] diff --git a/src/estimark/agents.py b/src/estimark/agents.py new file mode 100644 index 0000000..77d71c2 --- /dev/null +++ b/src/estimark/agents.py @@ -0,0 +1,107 @@ +"""Demonstrates an example estimation of microeconomic dynamic stochastic optimization +problem, as described in Section 9 of Chris Carroll's EstimatingMicroDSOPs.pdf notes. +The estimation attempts to match the age-conditional wealth profile of simulated +consumers to the median wealth holdings of seven age groups in the 2004 SCF by +varying only two parameters: the coefficient of relative risk aversion and a scaling +factor for an age-varying sequence of discount factors. The estimation uses a +consumption-saving model with idiosyncratic shocks to permanent and transitory +income as defined in ConsIndShockModel. +""" + +from __future__ import annotations + +import numpy as np +from HARK.ConsumptionSaving.ConsBequestModel import ( + BequestWarmGlowConsumerType, + BequestWarmGlowPortfolioType, +) +from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType +from HARK.ConsumptionSaving.ConsPortfolioModel import PortfolioConsumerType +from HARK.ConsumptionSaving.ConsWealthPortfolioModel import WealthPortfolioConsumerType +from HARK.core import AgentType + +# ===================================================== +# Define objects and functions used for the estimation +# ===================================================== + + +class TempConsumerType(AgentType): + def check_restrictions(self): + return None + + def sim_birth(self, which_agents): + """Alternate method for simulating initial states for simulated agents, drawing from a finite + distribution. Used to overwrite IndShockConsumerType.simBirth, which uses lognormal distributions. + + Parameters + ---------- + which_agents : np.array(Bool) + Boolean array of size self.AgentCount indicating which agents should be "born". + + Returns + ------- + None + + """ + # Get and store states for newly born agents + # Take directly from pre-specified distribution + self.state_now["aNrm"][which_agents] = self.aNrmInit[which_agents] + # No variation in permanent income needed + self.state_now["pLvl"][which_agents] = 1.0 + # How many periods since each agent was born + self.t_age[which_agents] = 0 + # Which period of the cycle each agents is currently in + self.t_cycle[which_agents] = 0 + + def sim_death(self): + return np.zeros(self.AgentCount, dtype=bool) + + +### Overwrite sim_one_period to not have death or look up of agent ages + + +class IndShkLifeCycleConsumerType(TempConsumerType, IndShockConsumerType): + """A very lightly edited version of IndShockConsumerType. Uses an alternate method of making new + consumers and specifies DiscFac as being age-dependent. Called "temp" because only used here. + """ + + +class PortfolioLifeCycleConsumerType(TempConsumerType, PortfolioConsumerType): + """A very lightly edited version of PortfolioConsumerType. Uses an alternate method of making new + consumers and specifies DiscFac as being age-dependent. Called "temp" because only used here. + """ + + def post_solve(self): + for solution in self.solution: + solution.cFunc = solution.cFuncAdj + + +class BequestWarmGlowLifeCycleConsumerType( + TempConsumerType, + BequestWarmGlowConsumerType, +): + """A very lightly edited version of BequestWarmGlowConsumerType. Uses an alternate method of making new + consumers and specifies DiscFac as being age-dependent. Called "temp" because only used here. + """ + + +class BequestWarmGlowLifeCyclePortfolioType( + TempConsumerType, + BequestWarmGlowPortfolioType, +): + """A very lightly edited version of BequestWarmGlowPortfolioType. Uses an alternate method of making new + consumers and specifies DiscFac as being age-dependent. Called "temp" because only used here. + """ + + def post_solve(self): + for solution in self.solution: + solution.cFunc = solution.cFuncAdj + + +class WealthPortfolioLifeCycleConsumerType( + TempConsumerType, + WealthPortfolioConsumerType, +): + """A very lightly edited version of WealthPortfolioConsumerType. Uses an alternate method of making new + consumers and specifies DiscFac as being age-dependent. Called "temp" because only used here. + """ diff --git a/src/estimark/content/tables/min/PortfolioDiscFac_estimate_results.csv b/src/estimark/content/tables/min/PortfolioDiscFac_estimate_results.csv new file mode 100644 index 0000000..9ead805 --- /dev/null +++ b/src/estimark/content/tables/min/PortfolioDiscFac_estimate_results.csv @@ -0,0 +1,15418 @@ +CRRA,6.614528749695077 + +DiscFac,1.0357967813213964 + +time_to_estimate,207.89406180381775 + +params,"{'CRRA': 6.614528749695077, 'DiscFac': 1.0357967813213964}" + +criterion,0.5414098309112865 + +start_criterion,0.6339659358362199 + +start_params,"{'CRRA': 9.252106996349742, 'DiscFac': 1.0}" + +algorithm,multistart_tranquilo_ls + +direction,minimize + +n_free,2 + +message,Absolute criterion change smaller than tolerance. + +success, + +n_criterion_evaluations, + +n_derivative_evaluations, + +n_iterations, + +history,"{'params': [{'CRRA': 9.474902352534894, 'DiscFac': 0.9999364724331312}, {'CRRA': 8.635210994449684, 'DiscFac': 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78.78975840006024, 79.93701329967007, 81.08435950009152, 82.23052149964496, 83.39726469991729, 84.69916970003396, 85.85893659992144], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71]}" + +convergence_report,"{'one_step': {'relative_criterion_change': 1.525257377346415e-05, 'relative_params_change': 0.004758913549058271, 'absolute_criterion_change': 8.257893387653148e-06, 'absolute_params_change': 0.031397306790084616}, 'five_steps': {'relative_criterion_change': 1.525257377346415e-05, 'relative_params_change': 0.004758913549058271, 'absolute_criterion_change': 8.257893387653148e-06, 'absolute_params_change': 0.031397306790084616}}" + +multistart_info,"{'start_parameters': [{'CRRA': 9.252106996349742, 'DiscFac': 1.0}, {'CRRA': 9.474902352534894, 'DiscFac': 0.9999364724331312}], 'local_optima': [Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 1.183e-07* 3.672e-05 +relative_params_change 8.471e-07* 0.001055 +absolute_criterion_change 6.406e-08* 1.988e-05 +absolute_params_change 1e-06* 0.006933 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 6.004e-07* 0.03035 +relative_params_change 4.702e-06* 0.1395 +absolute_criterion_change 3.251e-07* 0.01643 +absolute_params_change 2.892e-05 0.9212 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 9.252106996349742, 'DiscFac': 1.0}, {'CRRA': 16.45625, 'DiscFac': 0.9125000000000001}, {'CRRA': 14.093749999999998, 'DiscFac': 0.9875}, {'CRRA': 17.046875, 'DiscFac': 0.6312500000000001}, {'CRRA': 7.596874999999999, 'DiscFac': 0.9312500000000001}, {'CRRA': 18.81875, 'DiscFac': 0.5375}, {'CRRA': 15.274999999999999, 'DiscFac': 0.65}, {'CRRA': 11.73125, 'DiscFac': 0.7625000000000001}, {'CRRA': 10.549999999999999, 'DiscFac': 0.8}, {'CRRA': 9.368749999999999, 'DiscFac': 0.8375}, {'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 12.9125, 'DiscFac': 0.575}, {'CRRA': 17.6375, 'DiscFac': 1.0250000000000001}, {'CRRA': 8.1875, 'DiscFac': 0.7250000000000001}, {'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 7.00625, 'DiscFac': 0.6125}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 2.871875, 'DiscFac': 0.78125}, {'CRRA': 2.28125, 'DiscFac': 1.0625}], 'exploration_results': array([0.64235819, 0.99955613, 1.1121001 , 1.75705837, 1.77123339, + 1.82132756, 1.94396028, 2.07018913, 2.11467658, 2.15218408, + 2.24402179, 2.54668134, 2.78807842, 2.90740886, 3.01539999, + 3.32546076, 3.5830362 , 4.07905962, 4.08578359, 6.94508729])}" + +algorithm_output,"{'states': [State(trustregion=Region(center=array([9.47490235, 0.99993647]), radius=0.9474902352534894, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=0.6448272261874601, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + 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model=ScalarModel(intercept=0.6108530298545867, linear_terms=array([-0.10139668, -0.14696738]), square_terms=array([[0.07792252, 0.32866092], + [0.32866092, 1.49842941]]), scale=0.05921813970334309, shift=array([9.47490235, 0.99993647])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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scale=0.014804534925835772, shift=array([9.50661188, 1.0031676 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=19, candidate_x=array([9.49185035, 1.0059291 ]), index=19, x=array([9.49185035, 1.0059291 ]), fval=0.6370780738825652, rho=1.4693827516516844, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.015017609941676606, relative_step_length=1.0143925504521585, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.49185035, 1.0059291 ]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 16, 17, 18, 19]), model=ScalarModel(intercept=0.6412257536348447, linear_terms=array([-0.06905576, -0.03758833]), square_terms=array([[0.03588925, 0.1094543 ], + [0.1094543 , 0.37040144]]), scale=0.029609069851671544, shift=array([9.49185035, 1.0059291 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=20, candidate_x=array([9.52125862, 1.00094573]), index=19, x=array([9.49185035, 1.0059291 ]), fval=0.6370780738825652, rho=-0.11096998055275568, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 12, 16, 17, 18, 19]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.49185035, 1.0059291 ]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18, 19, 20]), model=ScalarModel(intercept=0.6375966987395302, linear_terms=array([ 0.00059101, -0.00033053]), square_terms=array([[2.18308578e-05, 1.28615347e-03], + [1.28615347e-03, 8.40653356e-02]]), scale=0.014804534925835772, shift=array([9.49185035, 1.0059291 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=21, candidate_x=array([9.47704841, 1.00621177]), index=21, x=array([9.47704841, 1.00621177]), fval=0.6363070785814076, rho=1.2946325204884988, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int32), step_length=0.01480464158988585, relative_step_length=1.0000072048227528, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.47704841, 1.00621177]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 16, 17, 18, 19, 20, 21]), model=ScalarModel(intercept=0.6740948994884155, linear_terms=array([-0.0775299 , -0.08137508]), square_terms=array([[0.03091198, 0.10157856], + [0.10157856, 0.37039842]]), scale=0.029609069851671544, shift=array([9.47704841, 1.00621177])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=22, candidate_x=array([9.50732214, 1.00462907]), index=21, x=array([9.47704841, 1.00621177]), fval=0.6363070785814076, rho=-0.029474268266789803, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 12, 16, 17, 18, 19, 20, 21]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.47704841, 1.00621177]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18, 19, 20, 21, 22]), model=ScalarModel(intercept=0.6367076097989959, linear_terms=array([ 6.38911855e-04, -5.64861763e-05]), square_terms=array([[1.99591697e-05, 1.23030124e-03], + [1.23030124e-03, 8.44345370e-02]]), scale=0.014804534925835772, shift=array([9.47704841, 1.00621177])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=23, candidate_x=array([9.46224557, 1.00643568]), index=23, x=array([9.46224557, 1.00643568]), fval=0.6355934835508369, rho=1.1173242341721383, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 19, 20, 21, 22]), old_indices_discarded=array([], dtype=int32), step_length=0.014804537200668226, relative_step_length=1.0000001536578127, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.46224557, 1.00643568]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 17, 18, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=0.6376967017750665, linear_terms=array([-0.00887642, -0.07715602]), square_terms=array([[0.00091426, 0.02162778], + [0.02162778, 0.54194842]]), scale=0.029609069851671544, shift=array([9.46224557, 1.00643568])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=24, candidate_x=array([9.49199772, 1.00943198]), index=23, x=array([9.46224557, 1.00643568]), fval=0.6355934835508369, rho=-0.23439049876600507, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 12, 17, 18, 19, 20, 21, 22, 23]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.46224557, 1.00643568]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18, 19, 20, 21, 22, 23, 24]), model=ScalarModel(intercept=0.6357439312845821, linear_terms=array([ 0.00057539, -0.00082847]), square_terms=array([[2.06319310e-05, 1.26644672e-03], + [1.26644672e-03, 8.60694145e-02]]), scale=0.014804534925835772, shift=array([9.46224557, 1.00643568])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=25, candidate_x=array([9.44744469, 1.00679353]), index=25, x=array([9.44744469, 1.00679353]), fval=0.6347642471305107, rho=1.4044689675006616, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 19, 20, 21, 22, 23, 24]), old_indices_discarded=array([], dtype=int32), step_length=0.014805197302692493, relative_step_length=1.000044741483609, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.44744469, 1.00679353]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 18, 19, 20, 21, 22, 23, 24, 25]), model=ScalarModel(intercept=0.6347153364849043, linear_terms=array([ 0.00136442, -0.00288838]), square_terms=array([[7.21853077e-05, 4.81168593e-03], + [4.81168593e-03, 3.57552054e-01]]), scale=0.029609069851671544, shift=array([9.44744469, 1.00679353])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=26, candidate_x=array([9.41784149, 1.00742862]), index=26, x=array([9.41784149, 1.00742862]), fval=0.6332046797989737, rho=1.1053204142647344, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 19, 20, 21, 22, 23, 24, 25]), old_indices_discarded=array([17]), step_length=0.02961001570090617, relative_step_length=1.0000319445777717, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.41784149, 1.00742862]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 18, 19, 21, 22, 23, 24, 25, 26]), model=ScalarModel(intercept=0.6332385782456339, linear_terms=array([0.0028514 , 0.00022792]), square_terms=array([[2.79552380e-04, 1.89198753e-02], + [1.89198753e-02, 1.43266898e+00]]), scale=0.05921813970334309, shift=array([9.41784149, 1.00742862])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=27, candidate_x=array([9.35862837, 1.00819965]), index=27, x=array([9.35862837, 1.00819965]), fval=0.6303511715372276, rho=1.0071263421242136, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 19, 21, 22, 23, 24, 25, 26]), old_indices_discarded=array([12, 15, 16, 17, 20]), step_length=0.0592181407159667, relative_step_length=1.0000000170998888, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.35862837, 1.00819965]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 18, 19, 21, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.639178807035496, linear_terms=array([ 0.00132575, -0.26376211]), square_terms=array([[8.65219778e-04, 5.51863712e-02], + [5.51863712e-02, 3.95321986e+00]]), scale=array([0.10496142, 0.09838088]), shift=array([9.35862837, 1.00161912])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=28, candidate_x=array([9.25366695, 1.00955655]), index=28, x=array([9.25366695, 1.00955655]), fval=0.6252147257628975, rho=1.035476052619481, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 19, 21, 23, 24, 25, 26, 27]), old_indices_discarded=array([11, 12, 13, 14, 15, 16, 17, 20, 22]), step_length=0.10497019014652463, relative_step_length=0.8863009769673553, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25366695, 1.00955655]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 19, 21, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=1.3525263472005808, linear_terms=array([-0.05604803, -3.65995549]), square_terms=array([[3.37929225e-03, 1.66025609e-01], + [1.66025609e-01, 9.20973251e+00]]), scale=array([0.20992284, 0.15018314]), shift=array([9.25366695, 0.94981686])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=29, candidate_x=array([9.04374411, 1.01220715]), index=29, x=array([9.04374411, 1.01220715]), fval=0.6151402665456015, rho=1.0345307568988933, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 19, 21, 23, 24, 25, 26, 27, 28]), old_indices_discarded=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, + 18, 20, 22]), step_length=0.20993957271621416, relative_step_length=0.8862975676368734, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.04374411, 1.01220715]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 13, 22, 25, 27, 28, 29]), model=ScalarModel(intercept=2.887557528467335, linear_terms=array([ 0.32145309, -7.82327945]), square_terms=array([[ 0.01909841, -0.39706853], + [-0.39706853, 13.16541827]]), scale=array([0.41984568, 0.25381927]), shift=array([9.04374411, 0.84618073])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=30, candidate_x=array([8.62389843, 0.98935244]), index=29, x=array([9.04374411, 1.01220715]), fval=0.6151402665456015, rho=-1.1829304881995348, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 10, 13, 22, 25, 27, 28, 29]), old_indices_discarded=array([ 0, 1, 2, 4, 5, 6, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, + 21, 23, 24, 26]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.04374411, 1.01220715]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 13, 23, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=1.1785163391833393, linear_terms=array([ 0.04245163, -3.2666068 ]), square_terms=array([[ 1.11423119e-03, -2.87009785e-02], + [-2.87009785e-02, 8.89866124e+00]]), scale=array([0.20992284, 0.14885785]), shift=array([9.04374411, 0.95114215])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=31, candidate_x=array([8.83382127, 1.00530622]), index=29, x=array([9.04374411, 1.01220715]), fval=0.6151402665456015, rho=-0.21904622845287416, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 13, 23, 25, 26, 27, 28, 29]), old_indices_discarded=array([ 0, 1, 3, 6, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, + 30]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.04374411, 1.01220715]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 13, 25, 26, 27, 28, 29, 31]), model=ScalarModel(intercept=0.588971346798013, linear_terms=array([ 0.01505103, -0.13000888]), square_terms=array([[ 2.31746838e-04, -3.20175653e-03], + [-3.20175653e-03, 3.75543886e+00]]), scale=array([0.10496142, 0.09637714]), shift=array([9.04374411, 1.00362286])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=32, candidate_x=array([8.93878269, 1.00687716]), index=29, x=array([9.04374411, 1.01220715]), fval=0.6151402665456015, rho=-0.19293832377303985, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 13, 25, 26, 27, 28, 29, 31]), old_indices_discarded=array([ 0, 3, 12, 16, 17, 18, 19, 20, 21, 22, 23, 24, 30]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.04374411, 1.01220715]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 28, 29, 31, 32]), model=ScalarModel(intercept=0.6214336044267341, linear_terms=array([0.00147887, 0.10259686]), square_terms=array([[4.10977860e-04, 1.78962346e-02], + [1.78962346e-02, 8.79816604e-01]]), scale=0.05921813970334309, shift=array([9.04374411, 1.01220715])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=33, candidate_x=array([9.10280287, 1.00410548]), index=29, x=array([9.04374411, 1.01220715]), fval=0.6151402665456015, rho=-2.08297420159985, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 28, 29, 31, 32]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.04374411, 1.01220715]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([29, 32, 33]), model=ScalarModel(intercept=0.6151402665456009, linear_terms=array([0.00149709, 0.00482735]), square_terms=array([[7.65543339e-05, 5.14393571e-03], + [5.14393571e-03, 3.91745261e-01]]), scale=0.029609069851671544, shift=array([9.04374411, 1.01220715])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 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radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 28, 29, 31, 32, 33, 34]), model=ScalarModel(intercept=0.620881773286023, linear_terms=array([0.00197631, 0.09173778]), square_terms=array([[3.44046695e-04, 1.64408522e-02], + [1.64408522e-02, 8.92929811e-01]]), scale=0.05921813970334309, shift=array([9.0141328 , 1.01223102])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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1.1]))), model_indices=array([29, 32, 33, 34, 35]), model=ScalarModel(intercept=0.6137867229839677, linear_terms=array([0.00142032, 0.00043433]), square_terms=array([[7.69832064e-05, 5.16913372e-03], + [5.16913372e-03, 3.92730596e-01]]), scale=0.029609069851671544, shift=array([9.0141328 , 1.01223102])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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model=ScalarModel(intercept=0.619467823965291, linear_terms=array([0.00209097, 0.09020298]), square_terms=array([[3.29303090e-04, 1.61351934e-02], + [1.61351934e-02, 9.00329160e-01]]), scale=0.05921813970334309, shift=array([8.98452585, 1.01258669])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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linear_terms=array([0.00140759, 0.00037964]), square_terms=array([[7.68743253e-05, 5.17639009e-03], + [5.17639009e-03, 3.94127433e-01]]), scale=0.029609069851671544, shift=array([8.98452585, 1.01258669])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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square_terms=array([[3.04730334e-04, 2.06301143e-02], + [2.06301143e-02, 1.58200616e+00]]), scale=0.05921813970334309, shift=array([8.95491894, 1.01294575])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], 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+ [6.38199381e-02, 2.30738160e+00]]), scale=array([0.10496142, 0.09565189]), shift=array([8.89570505, 1.00434811])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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1.60156668e+00]]), scale=0.05921813970334309, shift=array([8.89570505, 1.01365764])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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scale=array([0.10496142, 0.09531582]), shift=array([8.83649067, 1.00468418])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=42, candidate_x=array([8.73152925, 1.01043262]), index=41, x=array([8.83649067, 1.01432978]), fval=0.6057905110013826, rho=-0.2539961161624663, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 32, 35, 36, 37, 38, 39, 41]), old_indices_discarded=array([ 1, 3, 7, 10, 26, 27, 28, 29, 30, 33, 34, 40]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.83649067, 1.01432978]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 31, 32, 35, 37, 38, 39, 41, 42]), model=ScalarModel(intercept=0.6128960194558581, linear_terms=array([0.00323829, 0.08375858]), square_terms=array([[2.19797272e-04, 1.29854569e-02], + [1.29854569e-02, 9.21144727e-01]]), scale=0.05921813970334309, shift=array([8.83649067, 1.01432978])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=43, candidate_x=array([8.77720289, 1.00979089]), index=41, x=array([8.83649067, 1.01432978]), fval=0.6057905110013826, rho=-0.636748464236313, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 32, 35, 37, 38, 39, 41, 42]), old_indices_discarded=array([ 3, 7, 10, 29, 30, 33, 34, 36, 40]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.83649067, 1.01432978]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 31, 32, 37, 38, 39, 41, 42, 43]), model=ScalarModel(intercept=0.6129958444671166, linear_terms=array([0.00125322, 0.0420077 ]), square_terms=array([[7.02244693e-05, 3.72273429e-03], + [3.72273429e-03, 2.29436197e-01]]), scale=0.029609069851671544, shift=array([8.83649067, 1.01432978])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=44, candidate_x=array([8.80679818, 1.00940247]), index=41, x=array([8.83649067, 1.01432978]), fval=0.6057905110013826, rho=-1.1780504359812205, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 32, 37, 38, 39, 41, 42, 43]), old_indices_discarded=array([35]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.83649067, 1.01432978]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 39, 41, 43, 44]), model=ScalarModel(intercept=0.6059146038962105, linear_terms=array([0.00063063, 0.00133693]), square_terms=array([[1.93356047e-05, 1.31514692e-03], + [1.31514692e-03, 1.01583452e-01]]), scale=0.014804534925835772, shift=array([8.83649067, 1.01432978])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=45, candidate_x=array([8.82168485, 1.01432664]), index=45, x=array([8.82168485, 1.01432664]), fval=0.6051564916805549, rho=1.020928649746183, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 39, 41, 43, 44]), old_indices_discarded=array([], dtype=int32), step_length=0.014805816548052337, relative_step_length=1.0000865695695937, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.82168485, 1.01432664]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 31, 37, 39, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.6115555266177718, linear_terms=array([0.00124126, 0.04101205]), square_terms=array([[7.04241015e-05, 3.73186729e-03], + [3.73186729e-03, 2.30210452e-01]]), scale=0.029609069851671544, shift=array([8.82168485, 1.01432664])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=46, candidate_x=array([8.79199479, 1.00954484]), index=45, x=array([8.82168485, 1.01432664]), fval=0.6051564916805549, rho=-1.230884779808124, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 37, 39, 41, 42, 43, 44, 45]), old_indices_discarded=array([32, 35, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.82168485, 1.01432664]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 41, 43, 44, 45, 46]), model=ScalarModel(intercept=0.6052045803357664, linear_terms=array([5.75893275e-04, 2.08251016e-05]), square_terms=array([[2.14888603e-05, 1.39552818e-03], + [1.39552818e-03, 1.02157333e-01]]), scale=0.014804534925835772, shift=array([8.82168485, 1.01432664])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=47, candidate_x=array([8.80688164, 1.01452473]), index=47, x=array([8.80688164, 1.01452473]), fval=0.6045010959198394, rho=1.1411150863578703, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 41, 43, 44, 45, 46]), old_indices_discarded=array([], dtype=int32), step_length=0.014804535502910653, relative_step_length=1.0000000389796022, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.80688164, 1.01452473]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 31, 41, 42, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=0.6105358682194937, linear_terms=array([0.00053509, 0.04163391]), square_terms=array([[1.09771507e-04, 4.72352439e-03], + [4.72352439e-03, 2.30297392e-01]]), scale=0.029609069851671544, shift=array([8.80688164, 1.01452473])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=48, candidate_x=array([8.8363726 , 1.00857494]), index=47, x=array([8.80688164, 1.01452473]), fval=0.6045010959198394, rho=-2.17105462493869, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 41, 42, 43, 44, 45, 46, 47]), old_indices_discarded=array([32, 37, 39]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.80688164, 1.01452473]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 41, 43, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=0.6045817009488239, linear_terms=array([ 0.00060537, -0.00012916]), square_terms=array([[2.04142540e-05, 1.36233150e-03], + [1.36233150e-03, 1.02744088e-01]]), scale=0.014804534925835772, shift=array([8.80688164, 1.01452473])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + 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44, 45, 46, 47, 48]), old_indices_discarded=array([], dtype=int32), step_length=0.01480454508906136, relative_step_length=1.0000006864940802, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.79207864, 1.01473837]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 41, 43, 44, 45, 46, 47, 48, 49]), model=ScalarModel(intercept=0.6039320017441308, linear_terms=array([1.22941931e-03, 2.77206681e-05]), square_terms=array([[7.95912011e-05, 5.38269834e-03], + [5.38269834e-03, 4.12173721e-01]]), scale=0.029609069851671544, shift=array([8.79207864, 1.01473837])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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old_indices_discarded=array([13, 32, 37, 39, 42]), step_length=0.02960907001757359, relative_step_length=1.000000005603082, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.76247205, 1.01512188]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 42, 43, 44, 45, 46, 47, 49, 50]), model=ScalarModel(intercept=0.6026578745853444, linear_terms=array([0.0024301, 0.0002779]), square_terms=array([[3.16909370e-04, 2.15042159e-02], + [2.15042159e-02, 1.65484964e+00]]), scale=0.05921813970334309, shift=array([8.76247205, 1.01512188])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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13, 29, 30, 32, 34, 35, 36, 37, 38, 39, 40, 41, 48]), step_length=0.05921814073463714, relative_step_length=1.0000000174151713, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.70325877, 1.0158803 ]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 13, 30, 42, 44, 45, 50, 51]), model=ScalarModel(intercept=0.5745601532643059, linear_terms=array([ 0.01933147, -0.20828263]), square_terms=array([[ 3.96552248e-04, -7.70697193e-03], + [-7.70697193e-03, 3.60952666e+00]]), scale=array([0.10496142, 0.09454056]), shift=array([8.70325877, 1.00545944])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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old_indices_discarded=array([ 1, 3, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 46, + 47, 48, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.70325877, 1.0158803 ]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 42, 43, 46, 49, 50, 51]), model=ScalarModel(intercept=0.5848648139367352, linear_terms=array([ 0.01259792, -0.01987223]), square_terms=array([[ 2.31944576e-04, -1.32546665e-02], + [-1.32546665e-02, 2.24249333e+00]]), scale=0.05921813970334309, shift=array([8.70325877, 1.0158803 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + 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old_indices_discarded=array([ 3, 13, 31, 32, 35, 36, 37, 38, 39, 41, 44, 45, 47, 48, 52]), step_length=0.05922045545199665, relative_step_length=1.0000391053934683, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.64403857, 1.01605409]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 42, 43, 50, 51, 52, 53]), model=ScalarModel(intercept=0.6233574060676437, linear_terms=array([ 0.01619734, -0.70631774]), square_terms=array([[2.97413525e-04, 7.75950577e-04], + [7.75950577e-04, 5.79975163e+00]]), scale=array([0.10496142, 0.09445367]), shift=array([8.64403857, 1.00554633])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 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43, 50, 51, 52, 53]), old_indices_discarded=array([ 1, 3, 13, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 44, 45, + 46, 47, 48, 49]), step_length=0.10496625831674383, relative_step_length=0.8862677791178409, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.53907715, 1.01706193]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 30, 42, 50, 51, 52, 53, 54]), model=ScalarModel(intercept=0.7023662250726452, linear_terms=array([ 0.06492856, -1.1147206 ]), square_terms=array([[ 3.47923203e-03, -8.06332216e-02], + [-8.06332216e-02, 3.61802648e+00]]), scale=array([0.20992284, 0.14643046]), shift=array([8.53907715, 0.95356954])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=55, candidate_x=array([8.32915431, 0.99542161]), index=54, x=array([8.53907715, 1.01706193]), fval=0.5930898839617496, rho=-1.7530958161469754, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 30, 42, 50, 51, 52, 53, 54]), old_indices_discarded=array([ 0, 1, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, + 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 44, + 45, 46, 47, 48, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.53907715, 1.01706193]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 42, 51, 52, 53, 54, 55]), model=ScalarModel(intercept=0.615086708073995, linear_terms=array([ 0.00978289, -0.72244786]), square_terms=array([[4.17284358e-04, 3.04748646e-02], + [3.04748646e-02, 5.74255506e+00]]), scale=array([0.10496142, 0.09394975]), shift=array([8.53907715, 1.00605025])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=56, candidate_x=array([8.43411573, 1.01836827]), index=56, x=array([8.43411573, 1.01836827]), fval=0.5885914443329772, rho=0.3283224223143453, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 30, 42, 51, 52, 53, 54, 55]), old_indices_discarded=array([ 1, 3, 13, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 44, + 45, 46, 47, 48, 49, 50]), step_length=0.10496954879325875, relative_step_length=0.8862955617916246, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.43411573, 1.01836827]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 51, 52, 53, 54, 55, 56]), model=ScalarModel(intercept=1.9169446392811247, linear_terms=array([-0.05516787, -6.07713486]), square_terms=array([[2.74937852e-03, 1.66261876e-01], + [1.66261876e-01, 1.36583309e+01]]), scale=array([0.20992284, 0.14577728]), shift=array([8.43411573, 0.95422272])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=57, candidate_x=array([8.22419289, 1.02085937]), index=57, x=array([8.22419289, 1.02085937]), fval=0.5799642053547169, rho=0.46355807804061855, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 30, 51, 52, 53, 54, 55, 56]), old_indices_discarded=array([ 0, 1, 3, 11, 12, 13, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, + 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, + 45, 46, 47, 48, 49, 50]), step_length=0.2099376195491761, relative_step_length=0.8862893219918404, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.22419289, 1.02085937]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 30, 52, 53, 54, 55, 56, 57]), model=ScalarModel(intercept=3.341466430284669, linear_terms=array([-1.05103033, -7.77084409]), square_terms=array([[ 0.18279549, 1.34509322], + [ 1.34509322, 11.10009818]]), scale=array([0.41984568, 0.24949316]), shift=array([8.22419289, 0.85050684])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=58, candidate_x=array([8.64403857, 0.99493627]), index=57, x=array([8.22419289, 1.02085937]), fval=0.5799642053547169, rho=-1.0936320536105784, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 30, 52, 53, 54, 55, 56, 57]), old_indices_discarded=array([ 0, 1, 2, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, + 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, + 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.22419289, 1.02085937]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 52, 53, 54, 55, 56, 57]), model=ScalarModel(intercept=1.9832433701127465, linear_terms=array([-0.11608732, -6.1156747 ]), square_terms=array([[5.73554046e-03, 2.62479555e-01], + [2.62479555e-01, 1.32048414e+01]]), scale=array([0.20992284, 0.14453174]), shift=array([8.22419289, 0.95546826])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=59, candidate_x=array([8.01427005, 1.02527945]), index=59, x=array([8.01427005, 1.02527945]), fval=0.5734330166298551, rho=1.093147952169101, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 30, 52, 53, 54, 55, 56, 57]), old_indices_discarded=array([ 1, 3, 13, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, + 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 58]), step_length=0.20996936856056853, relative_step_length=0.8864233561389423, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.01427005, 1.02527945]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 52, 54, 55, 56, 57, 59]), model=ScalarModel(intercept=9.981376675556422, linear_terms=array([ -0.67948996, -26.92467299]), square_terms=array([[2.66781078e-02, 9.72846991e-01], + [9.72846991e-01, 3.85178573e+01]]), scale=array([0.41984568, 0.24728311]), shift=array([8.01427005, 0.85271689])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=60, candidate_x=array([7.90516021, 1.02719534]), index=60, x=array([7.90516021, 1.02719534]), fval=0.570654333265082, rho=28.312992905781908, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 30, 52, 54, 55, 56, 57, 59]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, + 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, + 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 58]), step_length=0.1091266633945737, relative_step_length=0.23034889296854485, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.90516021, 1.02719534]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([10, 30, 52, 54, 55, 56, 57, 59, 60]), model=ScalarModel(intercept=6.968668322923406, linear_terms=array([ -0.3193095 , -18.62779191]), square_terms=array([[1.10008541e-02, 4.89131866e-01], + [4.89131866e-01, 2.70983093e+01]]), scale=array([0.41984568, 0.24632517]), shift=array([7.90516021, 0.85367483])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=61, candidate_x=array([7.53569564, 1.02680097]), index=61, x=array([7.53569564, 1.02680097]), fval=0.557843378100565, rho=0.7126019978012866, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([10, 30, 52, 54, 55, 56, 57, 59, 60]), old_indices_discarded=array([ 0, 1, 3, 6, 7, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, + 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, + 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 58]), step_length=0.36946477399899075, relative_step_length=0.7798809111740238, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.53569564, 1.02680097]), radius=0.9474902352534894, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([30, 52, 54, 55, 56, 57, 59, 60, 61]), model=ScalarModel(intercept=12.13842084066151, linear_terms=array([ -0.90389113, -30.58695776]), square_terms=array([[ 0.04679947, 1.22766505], + [ 1.22766505, 40.38597232]]), scale=array([0.83969136, 0.3 ]), shift=array([7.53569564, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=62, candidate_x=array([6.69600428, 1.03632926]), index=62, x=array([6.69600428, 1.03632926]), fval=0.5424834330682993, rho=0.7246036090517496, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([30, 52, 54, 55, 56, 57, 59, 60, 61]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, + 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, + 53, 58]), step_length=0.8397454168164292, relative_step_length=0.88628398011064, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69600428, 1.03632926]), radius=1.8949804705069788, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([52, 54, 55, 56, 57, 59, 60, 61, 62]), model=ScalarModel(intercept=13.436478042611053, linear_terms=array([ -1.77594432, -32.96460288]), square_terms=array([[ 0.1709616 , 2.30049165], + [ 2.30049165, 42.12835475]]), scale=array([1.67938272, 0.3 ]), shift=array([6.69600428, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=63, candidate_x=array([6.58843012, 1.03594893]), index=63, x=array([6.58843012, 1.03594893]), fval=0.5414632317465802, rho=0.501065213354614, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([52, 54, 55, 56, 57, 59, 60, 61, 62]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, + 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, + 51, 53, 58]), step_length=0.10757483992757301, relative_step_length=0.05676831059836341, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58843012, 1.03594893]), radius=1.8949804705069788, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([54, 55, 56, 57, 59, 60, 61, 62, 63]), model=ScalarModel(intercept=13.527389310158597, linear_terms=array([ -1.76412438, -33.07272766]), square_terms=array([[ 0.17044821, 2.26794554], + [ 2.26794554, 42.11033571]]), scale=array([1.67938272, 0.3 ]), shift=array([6.58843012, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=64, candidate_x=array([5.99457255, 1.04132824]), index=63, x=array([6.58843012, 1.03594893]), fval=0.5414632317465802, rho=-2.858177349740313, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([54, 55, 56, 57, 59, 60, 61, 62, 63]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, + 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, + 51, 52, 53, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58843012, 1.03594893]), radius=0.9474902352534894, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([55, 56, 57, 59, 60, 61, 62, 63, 64]), model=ScalarModel(intercept=13.033433276409284, linear_terms=array([ -0.7928235 , -31.91157346]), square_terms=array([[3.91876133e-02, 1.01921230e+00], + [1.01921230e+00, 4.07663306e+01]]), scale=array([0.83969136, 0.3 ]), shift=array([6.58843012, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=65, candidate_x=array([6.28160798, 1.03757835]), index=63, x=array([6.58843012, 1.03594893]), fval=0.5414632317465802, rho=-1.670491030486846, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([55, 56, 57, 59, 60, 61, 62, 63, 64]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, + 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, + 51, 52, 53, 54, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58843012, 1.03594893]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([55, 57, 59, 60, 61, 62, 63, 64, 65]), model=ScalarModel(intercept=7.57645714444444, linear_terms=array([ -0.29574748, -19.22814953]), square_terms=array([[9.87830891e-03, 4.07101559e-01], + [4.07101559e-01, 2.62853990e+01]]), scale=array([0.41984568, 0.24194837]), shift=array([6.58843012, 0.85805163])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=66, candidate_x=array([6.34718361, 1.03719355]), index=63, x=array([6.58843012, 1.03594893]), fval=0.5414632317465802, rho=-1.608088619606695, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([55, 57, 59, 60, 61, 62, 63, 64, 65]), old_indices_discarded=array([ 1, 3, 7, 10, 13, 30, 31, 32, 35, 37, 38, 39, 41, 42, 43, 44, 45, + 46, 47, 48, 49, 50, 51, 52, 53, 54, 56, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58843012, 1.03594893]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([61, 62, 63, 64, 65, 66]), model=ScalarModel(intercept=2.51780763816455, linear_terms=array([-0.09621641, -7.35783746]), square_terms=array([[3.42399524e-03, 1.80214137e-01], + [1.80214137e-01, 1.37071817e+01]]), scale=array([0.20992284, 0.13698695]), shift=array([6.58843012, 0.96301305])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=67, candidate_x=array([6.48488598, 1.03743422]), index=63, x=array([6.58843012, 1.03594893]), fval=0.5414632317465802, rho=-1.0724059676808537, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([61, 62, 63, 64, 65, 66]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58843012, 1.03594893]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([62, 63, 65, 66, 67]), model=ScalarModel(intercept=0.7479032481802277, linear_terms=array([-0.01574272, -1.69550858]), square_terms=array([[8.99601089e-04, 6.45218243e-02], + [6.45218243e-02, 6.96729079e+00]]), scale=array([0.10496142, 0.08450624]), shift=array([6.58843012, 1.01549376])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=68, candidate_x=array([6.60273304, 1.03595193]), index=68, x=array([6.60273304, 1.03595193]), fval=0.5414533040238657, rho=1.1451990693564231, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([62, 63, 65, 66, 67]), old_indices_discarded=array([], dtype=int32), step_length=0.014302919721513424, relative_step_length=0.12076468285870495, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.60273304, 1.03595193]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([62, 63, 65, 66, 67, 68]), model=ScalarModel(intercept=0.7438421645946036, linear_terms=array([-0.01554274, -1.67681543]), square_terms=array([[8.98462125e-04, 6.42713904e-02], + [6.42713904e-02, 6.94910166e+00]]), scale=array([0.10496142, 0.08450474]), shift=array([6.60273304, 1.01549526])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=69, candidate_x=array([6.61449989, 1.0357986 ]), index=69, x=array([6.61449989, 1.0357986 ]), fval=0.5414101559834424, rho=10.755716308584471, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([62, 63, 65, 66, 67, 68]), old_indices_discarded=array([], dtype=int32), step_length=0.011767854290167887, relative_step_length=0.09936021588249544, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([62, 63, 65, 66, 67, 68, 69]), model=ScalarModel(intercept=0.7410229267529151, linear_terms=array([-0.01539784, -1.66026198]), square_terms=array([[8.96829948e-04, 6.39617448e-02], + [6.39617448e-02, 6.90724557e+00]]), scale=array([0.10496142, 0.08458141]), shift=array([6.61449989, 1.01541859])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=70, candidate_x=array([6.62265463, 1.03568817]), index=69, x=array([6.61449989, 1.0357986 ]), fval=0.5414101559834424, rho=-22.25987391656959, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([62, 63, 65, 66, 67, 68, 69]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([62, 63, 66, 67, 68, 69, 70]), model=ScalarModel(intercept=0.5414839920684693, linear_terms=array([4.15483856e-05, 3.93468494e-03]), square_terms=array([[2.84874482e-04, 2.52791748e-02], + [2.52791748e-02, 3.36942768e+00]]), scale=0.05921813970334309, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=71, candidate_x=array([6.60701913, 1.03578557]), index=69, x=array([6.61449989, 1.0357986 ]), fval=0.5414101559834424, rho=-16.79541545743258, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([62, 63, 66, 67, 68, 69, 70]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([62, 63, 67, 68, 69, 70, 71]), model=ScalarModel(intercept=0.5414615742402571, linear_terms=array([8.4752230e-05, 4.0438811e-03]), square_terms=array([[6.98826564e-05, 6.29275999e-03], + [6.29275999e-03, 8.41875305e-01]]), scale=0.029609069851671544, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=72, candidate_x=array([6.58489072, 1.03587769]), index=69, x=array([6.61449989, 1.0357986 ]), fval=0.5414101559834424, rho=-1.588880786010452, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([62, 63, 67, 68, 69, 70, 71]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([63, 68, 69, 70, 71, 72]), model=ScalarModel(intercept=0.5414393947813702, linear_terms=array([-2.11246402e-05, -1.93873062e-03]), square_terms=array([[1.87338726e-05, 1.64576236e-03], + [1.64576236e-03, 2.06670312e-01]]), scale=0.014804534925835772, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=73, candidate_x=array([6.62945645, 1.03581837]), index=69, x=array([6.61449989, 1.0357986 ]), fval=0.5414101559834424, rho=-10.089924952581605, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([63, 68, 69, 70, 71, 72]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.007402267462917886, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([63, 68, 69, 70, 71, 72, 73]), model=ScalarModel(intercept=0.5414619458203902, linear_terms=array([6.9562924e-06, 8.5447900e-04]), square_terms=array([[4.61608585e-06, 4.03102152e-04], + [4.03102152e-04, 5.06614459e-02]]), scale=0.007402267462917886, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=74, candidate_x=array([6.60709792, 1.03573282]), index=69, x=array([6.61449989, 1.0357986 ]), fval=0.5414101559834424, rho=-19.52833863302675, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([63, 68, 69, 70, 71, 72, 73]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.003701133731458943, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([68, 69, 70, 71, 73, 74]), model=ScalarModel(intercept=0.5414709833059852, linear_terms=array([ 2.31365098e-06, -2.97464372e-04]), square_terms=array([[1.14990591e-06, 1.00511445e-04], + [1.00511445e-04, 1.26982949e-02]]), scale=0.003701133731458943, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 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69, 70, 71, 73, 74]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.0018505668657294715, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([69, 70, 71, 74, 75]), model=ScalarModel(intercept=0.5414505447289019, linear_terms=array([-8.39195189e-06, -4.82590678e-04]), square_terms=array([[2.93663647e-07, 2.57520778e-05], + [2.57520778e-05, 3.23949356e-03]]), scale=0.0018505668657294715, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.0009252834328647358, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([69, 75, 76]), model=ScalarModel(intercept=0.5414101559834426, linear_terms=array([6.06884618e-06, 6.19553068e-04]), square_terms=array([[7.09654175e-08, 5.99044034e-06], + [5.99044034e-06, 7.63676033e-04]]), scale=0.0009252834328647358, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.0004626417164323679, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([69, 76, 77]), model=ScalarModel(intercept=0.5414101559834427, linear_terms=array([ 5.92027706e-05, -8.95483752e-05]), square_terms=array([[1.36984529e-08, 3.03717688e-07], + [3.03717688e-07, 2.08099140e-04]]), scale=0.0004626417164323679, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([69, 77, 78]), model=ScalarModel(intercept=0.5414101559834427, linear_terms=array([-3.31705191e-05, 8.65285911e-06]), square_terms=array([[2.22032765e-08, 8.67788726e-07], + [8.67788726e-07, 5.06632311e-05]]), scale=0.00023132085821618394, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.252107, 1. ]), radius=0.05782566872718589, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 15, 16]), model=ScalarModel(intercept=0.6662963313940111, linear_terms=array([-0.15809224, -0.38864171]), square_terms=array([[0.08582577, 0.3626811 ], + [0.3626811 , 1.64746182]]), scale=0.05782566872718589, shift=array([9.252107, 1. ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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n_evals_acceptance=1), State(trustregion=Region(center=array([9.31141951, 1.00056177]), radius=0.028912834363592946, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 16, 17]), model=ScalarModel(intercept=0.5717127918231406, linear_terms=array([-0.03750891, -0.05450237]), square_terms=array([[0.01170099, 0.06888337], + [0.06888337, 0.44451557]]), scale=0.028912834363592946, shift=array([9.31141951, 1.00056177])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 16, 17, 18]), model=ScalarModel(intercept=0.641670882731942, linear_terms=array([ 0.0001693, -0.0200567]), square_terms=array([[1.68433034e-05, 9.19250943e-04], + [9.19250943e-04, 5.69319975e-02]]), scale=0.014456417181796473, shift=array([9.31141951, 1.00056177])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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shift=array([9.28262709, 1.00867427])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=23, candidate_x=array([9.2681721 , 1.00887763]), index=23, x=array([9.2681721 , 1.00887763]), fval=0.626016100574723, rho=0.9614924551145125, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 19, 20, 21, 22]), old_indices_discarded=array([], dtype=int32), step_length=0.014456422294036085, relative_step_length=1.0000003536311624, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.2681721 , 1.00887763]), radius=0.028912834363592946, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 16, 17, 18, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=0.6302545671695479, linear_terms=array([0.00190945, 0.02595137]), square_terms=array([[4.59886870e-05, 3.02614441e-03], + [3.02614441e-03, 2.38414991e-01]]), scale=0.028912834363592946, shift=array([9.2681721 , 1.00887763])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=24, candidate_x=array([9.2392219 , 1.00611615]), index=23, x=array([9.2681721 , 1.00887763]), fval=0.626016100574723, rho=-0.27020250132363843, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 16, 17, 18, 19, 20, 21, 22, 23]), old_indices_discarded=array([12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.2681721 , 1.00887763]), radius=0.014456417181796473, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 19, 20, 21, 22, 23, 24]), model=ScalarModel(intercept=0.6260945388463117, linear_terms=array([ 7.51624753e-04, -5.54521229e-05]), square_terms=array([[1.66698146e-05, 1.13418053e-03], + [1.13418053e-03, 8.70860695e-02]]), scale=0.014456417181796473, shift=array([9.2681721 , 1.00887763])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=25, candidate_x=array([9.25371701, 1.00907341]), index=25, x=array([9.25371701, 1.00907341]), fval=0.6253205234400713, rho=0.9257747492589474, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 19, 20, 21, 22, 23, 24]), old_indices_discarded=array([], dtype=int32), step_length=0.014456419134255051, relative_step_length=1.000000135058262, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25371701, 1.00907341]), radius=0.028912834363592946, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 19, 20, 21, 22, 23, 24, 25]), model=ScalarModel(intercept=0.6253360883435206, linear_terms=array([1.50448575e-03, 8.41583250e-05]), square_terms=array([[6.56507364e-05, 4.50340306e-03], + [4.50340306e-03, 3.49079339e-01]]), scale=0.028912834363592946, shift=array([9.25371701, 1.00907341])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=26, candidate_x=array([9.22480647, 1.00943785]), index=26, x=array([9.22480647, 1.00943785]), fval=0.6238879213243532, rho=0.955377431374471, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 19, 20, 21, 22, 23, 24, 25]), old_indices_discarded=array([12, 16, 18]), step_length=0.028912835624545444, relative_step_length=1.0000000436122063, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.22480647, 1.00943785]), radius=0.05782566872718589, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 19, 21, 22, 23, 24, 25, 26]), model=ScalarModel(intercept=0.6238467663285244, linear_terms=array([0.00300031, 0.00025396]), square_terms=array([[2.60662493e-04, 1.79499400e-02], + [1.79499400e-02, 1.39799232e+00]]), scale=0.05782566872718589, shift=array([9.22480647, 1.00943785])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=27, candidate_x=array([9.16698541, 1.0101682 ]), index=27, x=array([9.16698541, 1.0101682 ]), fval=0.6211424674970292, rho=0.9207546180217053, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 19, 21, 22, 23, 24, 25, 26]), old_indices_discarded=array([12, 15, 16, 18, 20]), step_length=0.05782566998640155, relative_step_length=1.0000000217760674, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.16698541, 1.0101682 ]), radius=0.11565133745437178, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 19, 21, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.6293604631753875, linear_terms=array([ 0.00168088, -0.25402434]), square_terms=array([[7.94437328e-04, 5.20961396e-02], + [5.20961396e-02, 3.88207149e+00]]), scale=array([0.10249333, 0.09616256]), shift=array([9.16698541, 1.00383744])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=28, candidate_x=array([9.06449208, 1.01142033]), index=28, x=array([9.06449208, 1.01142033]), fval=0.6161874288905664, rho=0.9826654333073533, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 19, 21, 22, 23, 24, 25, 26, 27]), old_indices_discarded=array([11, 12, 13, 14, 15, 16, 17, 18, 20]), step_length=0.10250097734076673, relative_step_length=0.8862930563272275, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.06449208, 1.01142033]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 19, 21, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=1.3269180344206313, linear_terms=array([-0.0530022 , -3.58500408]), square_terms=array([[3.16437378e-03, 1.58383379e-01], + [1.58383379e-01, 9.04125674e+00]]), scale=array([0.20498666, 0.14678316]), shift=array([9.06449208, 0.95321684])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=29, candidate_x=array([8.85950542, 1.01399005]), index=29, x=array([8.85950542, 1.01399005]), fval=0.6068057893338722, rho=0.9768016993555535, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 19, 21, 23, 24, 25, 26, 27, 28]), old_indices_discarded=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, + 18, 20, 22]), step_length=0.20500276489564104, relative_step_length=0.8862965591579142, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.85950542, 1.01399005]), radius=0.46260534981748713, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 13, 21, 22, 24, 27, 28, 29]), model=ScalarModel(intercept=5.056588947128265, linear_terms=array([ 0.12014523, -14.39462599]), square_terms=array([[ 4.11394441e-03, -9.76345780e-02], + [-9.76345780e-02, 2.30839568e+01]]), scale=array([0.40997332, 0.24799163]), shift=array([8.85950542, 0.85200837])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=30, candidate_x=array([8.5580007 , 1.00630498]), index=29, x=array([8.85950542, 1.01399005]), fval=0.6068057893338722, rho=-0.3014966051278916, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 13, 21, 22, 24, 27, 28, 29]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, + 20, 23, 25, 26]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.85950542, 1.01399005]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 13, 24, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=0.9805939633807261, linear_terms=array([-0.03836743, -1.98882772]), square_terms=array([[3.07342158e-03, 1.18191343e-01], + [1.18191343e-01, 5.34723779e+00]]), scale=array([0.20498666, 0.1454983 ]), shift=array([8.85950542, 0.9545017 ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=31, candidate_x=array([8.65451876, 1.01183367]), index=31, x=array([8.65451876, 1.01183367]), fval=0.6028983591291701, rho=0.4338323431380853, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 13, 24, 25, 26, 27, 28, 29, 30]), old_indices_discarded=array([ 1, 3, 6, 7, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, + 23]), step_length=0.20499800027263237, relative_step_length=0.8862759600748705, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.65451876, 1.01183367]), radius=0.46260534981748713, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 13, 27, 28, 29, 30, 31]), model=ScalarModel(intercept=2.6746347212609862, linear_terms=array([ 0.41935614, -7.16647929]), square_terms=array([[ 0.03391117, -0.55595491], + [-0.55595491, 12.02901321]]), scale=array([0.40997332, 0.24906982]), shift=array([8.65451876, 0.85093018])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=32, candidate_x=array([8.24454545, 0.98780609]), index=31, x=array([8.65451876, 1.01183367]), fval=0.6028983591291701, rho=-1.992201463503947, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 10, 13, 27, 28, 29, 30, 31]), old_indices_discarded=array([ 0, 1, 2, 4, 5, 6, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, + 21, 22, 23, 24, 25, 26]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.65451876, 1.01183367]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 13, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=0.8053556101878039, linear_terms=array([ 0.0500788 , -1.40901645]), square_terms=array([[ 1.81988958e-03, -5.72982752e-02], + [-5.72982752e-02, 4.04450417e+00]]), scale=array([0.20498666, 0.14657649]), shift=array([8.65451876, 0.95342351])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=33, candidate_x=array([8.44953211, 1.002411 ]), index=31, x=array([8.65451876, 1.01183367]), fval=0.6028983591291701, rho=-1.2944780565240657, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 10, 13, 28, 29, 30, 31, 32]), old_indices_discarded=array([ 0, 1, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, + 26, 27]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.65451876, 1.01183367]), radius=0.11565133745437178, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 13, 28, 29, 30, 31, 33]), model=ScalarModel(intercept=0.5580455956991042, linear_terms=array([0.02153789, 0.02119119]), square_terms=array([[ 0.0023124 , -0.05543391], + [-0.05543391, 1.707328 ]]), scale=array([0.10249333, 0.09532983]), shift=array([8.65451876, 1.00467017])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=34, candidate_x=array([8.55202543, 1.00039176]), index=31, x=array([8.65451876, 1.01183367]), fval=0.6028983591291701, rho=-1.872524866103317, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 10, 13, 28, 29, 30, 31, 33]), old_indices_discarded=array([ 1, 26, 27, 32]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.65451876, 1.01183367]), radius=0.05782566872718589, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 13, 29, 30, 31, 33, 34]), model=ScalarModel(intercept=0.5796812293248299, linear_terms=array([0.01586132, 0.02969879]), square_terms=array([[ 0.00226369, -0.03434719], + [-0.03434719, 0.60957011]]), scale=0.05782566872718589, shift=array([8.65451876, 1.01183367])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=35, candidate_x=array([8.59693373, 1.00593781]), index=31, x=array([8.65451876, 1.01183367]), fval=0.6028983591291701, rho=-1.1577436588415695, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 10, 13, 29, 30, 31, 33, 34]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.65451876, 1.01183367]), radius=0.028912834363592946, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([30, 31, 34, 35]), model=ScalarModel(intercept=0.6034966185125235, linear_terms=array([-0.00034619, -0.03914939]), square_terms=array([[9.39236593e-05, 5.48779016e-03], + [5.48779016e-03, 3.65303262e-01]]), scale=0.028912834363592946, shift=array([8.65451876, 1.01183367])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=36, candidate_x=array([8.62565678, 1.0153636 ]), index=36, x=array([8.62565678, 1.0153636 ]), fval=0.5971978537018936, rho=2.442794438040451, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([30, 31, 34, 35]), old_indices_discarded=array([], dtype=int32), step_length=0.02907704145844469, relative_step_length=1.0056793842065692, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.62565678, 1.0153636 ]), radius=0.05782566872718589, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 13, 30, 31, 33, 34, 35, 36]), model=ScalarModel(intercept=0.5603293659645237, linear_terms=array([-0.00236478, 0.12100806]), square_terms=array([[0.00302626, 0.06759657], + [0.06759657, 1.59874941]]), scale=0.05782566872718589, shift=array([8.62565678, 1.0153636 ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=37, candidate_x=array([8.68324715, 1.00858284]), index=36, x=array([8.62565678, 1.0153636 ]), fval=0.5971978537018936, rho=-1.2581050712790383, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 13, 30, 31, 33, 34, 35, 36]), old_indices_discarded=array([ 3, 29]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.62565678, 1.0153636 ]), radius=0.028912834363592946, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([30, 31, 34, 35, 36, 37]), model=ScalarModel(intercept=0.5981101022806663, linear_terms=array([ 0.00137081, -0.01014437]), square_terms=array([[6.44240543e-05, 4.61221760e-03], + [4.61221760e-03, 3.91431444e-01]]), scale=0.028912834363592946, shift=array([8.62565678, 1.0153636 ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=38, candidate_x=array([8.59675474, 1.01644935]), index=38, x=array([8.59675474, 1.01644935]), fval=0.5956021578624653, rho=0.987318065496306, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([30, 31, 34, 35, 36, 37]), old_indices_discarded=array([], dtype=int32), step_length=0.02892243332840774, relative_step_length=1.0003319966729682, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.59675474, 1.01644935]), radius=0.05782566872718589, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([10, 30, 31, 33, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=0.5967490097134395, linear_terms=array([0.00266649, 0.00222244]), square_terms=array([[2.84220825e-04, 1.95806140e-02], + [1.95806140e-02, 1.56758521e+00]]), scale=0.05782566872718589, shift=array([8.59675474, 1.01644935])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=39, candidate_x=array([8.53893254, 1.01708857]), index=39, x=array([8.53893254, 1.01708857]), fval=0.5930797801438898, rho=0.9626177327032385, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([10, 30, 31, 33, 34, 35, 36, 37, 38]), old_indices_discarded=array([ 3, 7, 13, 29]), step_length=0.05782572833136036, relative_step_length=1.0000010307563367, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.53893254, 1.01708857]), radius=0.11565133745437178, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([10, 30, 31, 33, 34, 35, 36, 38, 39]), model=ScalarModel(intercept=0.6164032682482006, linear_terms=array([-0.0014308, -0.4276386]), square_terms=array([[9.03112375e-04, 5.61729802e-02], + [5.61729802e-02, 4.04976927e+00]]), scale=array([0.10249333, 0.09270238]), shift=array([8.53893254, 1.00729762])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=40, candidate_x=array([8.43643921, 1.01837244]), index=40, x=array([8.43643921, 1.01837244]), fval=0.5886586453047548, rho=0.9960093815093017, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([10, 30, 31, 33, 34, 35, 36, 38, 39]), old_indices_discarded=array([ 1, 3, 7, 13, 28, 29, 32, 37]), step_length=0.10250137012000532, relative_step_length=0.8862964525632524, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.43643921, 1.01837244]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 30, 31, 32, 35, 36, 39, 40]), model=ScalarModel(intercept=0.724464510197967, linear_terms=array([ 0.0588604 , -1.15277065]), square_terms=array([[ 2.63888902e-03, -4.80501605e-02], + [-4.80501605e-02, 3.54022042e+00]]), scale=array([0.20498666, 0.14330711]), shift=array([8.43643921, 0.95669289])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=41, candidate_x=array([8.23145255, 1.00141166]), index=40, x=array([8.43643921, 1.01837244]), fval=0.5886586453047548, rho=-1.288535811487196, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 30, 31, 32, 35, 36, 39, 40]), old_indices_discarded=array([ 0, 1, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, + 26, 27, 28, 29, 33, 34, 37, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.43643921, 1.01837244]), radius=0.11565133745437178, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 33, 34, 35, 38, 39, 40]), model=ScalarModel(intercept=0.6190068223081573, linear_terms=array([-0.00232714, -0.74138835]), square_terms=array([[9.27865058e-04, 6.83995451e-02], + [6.83995451e-02, 5.89689117e+00]]), scale=array([0.10249333, 0.09206044]), shift=array([8.43643921, 1.00793956])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=42, candidate_x=array([8.33394588, 1.02058172]), index=42, x=array([8.33394588, 1.02058172]), fval=0.5842263244969528, rho=0.6656700898189453, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 30, 33, 34, 35, 38, 39, 40]), old_indices_discarded=array([ 1, 3, 13, 29, 31, 32, 36, 37, 41]), step_length=0.10251713719284976, relative_step_length=0.8864327853821501, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.33394588, 1.02058172]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 30, 32, 33, 39, 40, 41, 42]), model=ScalarModel(intercept=0.8040646506922852, linear_terms=array([-0.16272345, -1.28751653]), square_terms=array([[0.03910645, 0.3480799 ], + [0.3480799 , 3.42094897]]), scale=array([0.20498666, 0.14220247]), shift=array([8.33394588, 0.95779753])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=43, candidate_x=array([8.53893254, 0.99684816]), index=42, x=array([8.33394588, 1.02058172]), fval=0.5842263244969528, rho=-2.655032004787166, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 30, 32, 33, 39, 40, 41, 42]), old_indices_discarded=array([ 0, 1, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, + 26, 27, 28, 29, 31, 34, 35, 36, 37, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.33394588, 1.02058172]), radius=0.11565133745437178, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 32, 33, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=0.5905582188830226, linear_terms=array([ 0.00921118, -0.6113136 ]), square_terms=array([[4.08156812e-04, 2.99902373e-02], + [2.99902373e-02, 5.44560063e+00]]), scale=array([0.10249333, 0.09095581]), shift=array([8.33394588, 1.00904419])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=44, candidate_x=array([8.23145255, 1.01975565]), index=44, x=array([8.23145255, 1.01975565]), fval=0.5809782664932625, rho=0.24916298067878628, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 32, 33, 39, 40, 41, 42, 43]), old_indices_discarded=array([ 1, 3, 13, 29, 30, 31, 34, 35, 36, 37, 38]), step_length=0.10249665809363835, relative_step_length=0.8862557091834465, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.23145255, 1.01975565]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 32, 33, 40, 41, 42, 44]), model=ScalarModel(intercept=0.9956483394129002, linear_terms=array([-0.42250664, -1.67357518]), square_terms=array([[0.14817312, 0.69125356], + [0.69125356, 3.60029371]]), scale=array([0.20498666, 0.1426155 ]), shift=array([8.23145255, 0.9573845 ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=45, candidate_x=array([8.43643921, 0.9962964 ]), index=44, x=array([8.23145255, 1.01975565]), fval=0.5809782664932625, rho=-1.1980641129471428, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 10, 32, 33, 40, 41, 42, 44]), old_indices_discarded=array([ 0, 1, 11, 12, 13, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, + 28, 29, 30, 31, 34, 35, 36, 37, 38, 39, 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.23145255, 1.01975565]), radius=0.11565133745437178, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 32, 33, 40, 41, 42, 44, 45]), model=ScalarModel(intercept=0.600332066842147, linear_terms=array([-0.00086165, -0.70849018]), square_terms=array([[8.18384405e-04, 5.99221947e-02], + [5.99221947e-02, 5.42443420e+00]]), scale=array([0.10249333, 0.09136884]), shift=array([8.23145255, 1.00863116])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=46, candidate_x=array([8.12895922, 1.02157425]), index=46, x=array([8.12895922, 1.02157425]), fval=0.5767016574644672, rho=0.6023906618426587, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 32, 33, 40, 41, 42, 44, 45]), old_indices_discarded=array([ 1, 3, 30, 31, 34, 35, 36, 37, 38, 39, 43]), step_length=0.10250946224206313, relative_step_length=0.8863664225457526, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.12895922, 1.02157425]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 32, 40, 41, 42, 44, 45, 46]), model=ScalarModel(intercept=1.8860503514071725, linear_terms=array([-0.11681592, -5.841289 ]), square_terms=array([[5.93972205e-03, 2.64135430e-01], + [2.64135430e-01, 1.28731339e+01]]), scale=array([0.20498666, 0.1417062 ]), shift=array([8.12895922, 0.9582938 ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=47, candidate_x=array([7.92397257, 1.02550171]), index=47, x=array([7.92397257, 1.02550171]), fval=0.5700309839906248, rho=2.144194285592646, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 32, 40, 41, 42, 44, 45, 46]), old_indices_discarded=array([ 0, 1, 3, 13, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, + 36, 37, 38, 39, 43]), step_length=0.2050242792857033, relative_step_length=0.8863895731711363, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.92397257, 1.02550171]), radius=0.46260534981748713, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 32, 40, 41, 42, 44, 46, 47]), model=ScalarModel(intercept=9.701952266205492, linear_terms=array([ -0.61691483, -26.37740675]), square_terms=array([[2.29677817e-02, 8.91869339e-01], + [8.91869339e-01, 3.80689975e+01]]), scale=array([0.40997332, 0.2422358 ]), shift=array([7.92397257, 0.8577642 ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=48, candidate_x=array([7.81188801, 1.02714111]), index=48, x=array([7.81188801, 1.02714111]), fval=0.5665120380760114, rho=16.57798751629955, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 32, 40, 41, 42, 44, 46, 47]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, + 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, + 37, 38, 39, 43, 45]), step_length=0.1120965490592223, relative_step_length=0.24231572138854002, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.81188801, 1.02714111]), radius=0.46260534981748713, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 32, 41, 42, 44, 46, 47, 48]), model=ScalarModel(intercept=9.766219925631294, linear_terms=array([ -0.6871664 , -26.30219402]), square_terms=array([[2.73943250e-02, 9.72602328e-01], + [9.72602328e-01, 3.76072310e+01]]), scale=array([0.40997332, 0.2414161 ]), shift=array([7.81188801, 0.8585839 ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=49, candidate_x=array([8.22186132, 1.02118484]), index=48, x=array([7.81188801, 1.02714111]), fval=0.5665120380760114, rho=-2.3061269690172246, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 32, 41, 42, 44, 46, 47, 48]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, + 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, + 37, 38, 39, 40, 43, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.81188801, 1.02714111]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([10, 32, 41, 42, 44, 46, 47, 48, 49]), model=ScalarModel(intercept=1.4654889570621736, linear_terms=array([-0.05047381, -4.00812911]), square_terms=array([[2.45887025e-03, 1.30332665e-01], + [1.30332665e-01, 8.90254327e+00]]), scale=array([0.20498666, 0.13892277]), shift=array([7.81188801, 0.96107723])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=50, candidate_x=array([7.60690135, 1.02565727]), index=50, x=array([7.60690135, 1.02565727]), fval=0.5602123257733056, rho=0.5841994228455609, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([10, 32, 41, 42, 44, 46, 47, 48, 49]), old_indices_discarded=array([ 1, 3, 7, 13, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 43, 45]), step_length=0.20499202897310145, relative_step_length=0.886250144119507, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.60690135, 1.02565727]), radius=0.46260534981748713, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([32, 41, 42, 44, 46, 47, 48, 49, 50]), model=ScalarModel(intercept=7.123730519114742, linear_terms=array([ -0.32734999, -18.89524377]), square_terms=array([[1.11231711e-02, 4.88743382e-01], + [4.88743382e-01, 2.71922839e+01]]), scale=array([0.40997332, 0.24215803]), shift=array([7.60690135, 0.85784197])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=51, candidate_x=array([7.19692803, 1.03046403]), index=51, x=array([7.19692803, 1.03046403]), fval=0.549027411692029, rho=1.0036558314595654, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([32, 41, 42, 44, 46, 47, 48, 49, 50]), old_indices_discarded=array([ 0, 1, 3, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, + 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, + 40, 43, 45]), step_length=0.4100014945883672, relative_step_length=0.8862878363817586, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.19692803, 1.03046403]), radius=0.9252106996349743, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([32, 41, 44, 46, 47, 48, 49, 50, 51]), model=ScalarModel(intercept=12.73480276563746, linear_terms=array([ -0.87087078, -31.77015852]), square_terms=array([[ 0.042882 , 1.15994563], + [ 1.15994563, 41.41239327]]), scale=array([0.81994663, 0.3 ]), shift=array([7.19692803, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=52, candidate_x=array([6.3769814 , 1.03855253]), index=52, x=array([6.3769814 , 1.03855253]), fval=0.5426853573405204, rho=0.4587040426302159, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([32, 41, 44, 46, 47, 48, 49, 50, 51]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, + 35, 36, 37, 38, 39, 40, 42, 43, 45]), step_length=0.8199865279119269, relative_step_length=0.8862700444725059, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.3769814 , 1.03855253]), radius=1.8504213992699485, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([41, 44, 46, 47, 48, 49, 50, 51, 52]), model=ScalarModel(intercept=14.222023285880177, linear_terms=array([ -1.72856364, -34.55682834]), square_terms=array([[ 0.15979702, 2.19225084], + [ 2.19225084, 43.64084662]]), scale=array([1.63989327, 0.3 ]), shift=array([6.3769814, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=53, candidate_x=array([6.13394911, 1.03978719]), index=52, x=array([6.3769814 , 1.03855253]), fval=0.5426853573405204, rho=-4.2277721907011685, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([41, 44, 46, 47, 48, 49, 50, 51, 52]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, + 34, 35, 36, 37, 38, 39, 40, 42, 43, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.3769814 , 1.03855253]), radius=0.9252106996349743, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([44, 46, 47, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=15.125592672308542, linear_terms=array([ -0.92742527, -36.66916769]), square_terms=array([[4.28554863e-02, 1.16804426e+00], + [1.16804426e+00, 4.61029190e+01]]), scale=array([0.81994663, 0.3 ]), shift=array([6.3769814, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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model=ScalarModel(intercept=10.065029563372587, linear_terms=array([ -0.39719221, -25.86425371]), square_terms=array([[1.16915302e-02, 5.40173955e-01], + [5.40173955e-01, 3.51273033e+01]]), scale=array([0.40997332, 0.23571039]), shift=array([6.3769814 , 0.86428961])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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model=ScalarModel(intercept=2.3920584590081724, linear_terms=array([-0.08950127, -6.81205893]), square_terms=array([[3.19260557e-03, 1.63216758e-01], + [1.63216758e-01, 1.25472782e+01]]), scale=array([0.20498666, 0.13321706]), shift=array([6.3769814 , 0.96678294])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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linear_terms=array([ -0.48580072, -33.75567653]), square_terms=array([[1.32030757e-02, 6.68795751e-01], + [6.68795751e-01, 4.63083580e+01]]), scale=array([0.40997332, 0.23615296]), shift=array([6.54738856, 0.86384704])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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model=ScalarModel(intercept=2.0362311686925794, linear_terms=array([-0.07626161, -5.56401149]), square_terms=array([[2.97339012e-03, 1.42670586e-01], + [1.42670586e-01, 1.03610380e+01]]), scale=array([0.20498666, 0.13365963]), shift=array([6.54738856, 0.96634037])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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linear_terms=array([-0.01427721, -1.59977577]), square_terms=array([[8.28292861e-04, 6.05950908e-02], + [6.05950908e-02, 7.01428649e+00]]), scale=array([0.10249333, 0.08241296]), shift=array([6.54738856, 1.01758704])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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square_terms=array([[3.29232175e-03, 1.89561505e-01], + [1.89561505e-01, 1.70827528e+01]]), scale=array([0.20498666, 0.13397188]), shift=array([6.53909631, 0.96602812])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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6.53773842e+00]]), scale=array([0.10249333, 0.08272521]), shift=array([6.53909631, 1.01727479])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=62, candidate_x=array([6.61897887, 1.03592701]), index=62, x=array([6.61897887, 1.03592701]), fval=0.5415174113025932, rho=0.6103070390488394, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([52, 54, 55, 56, 57, 58, 59, 60, 61]), old_indices_discarded=array([53]), step_length=0.07989035553686327, relative_step_length=0.6907862658171386, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61897887, 1.03592701]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([52, 56, 57, 58, 59, 60, 61, 62, 63]), model=ScalarModel(intercept=4.084195599052262, linear_terms=array([ -0.15194779, -13.11124864]), square_terms=array([[4.33717818e-03, 2.79546070e-01], + [2.79546070e-01, 2.42461864e+01]]), scale=array([0.20498666, 0.13452982]), shift=array([6.61897887, 0.96547018])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=64, candidate_x=array([6.76282269, 1.03712945]), index=62, x=array([6.61897887, 1.03592701]), fval=0.5415174113025932, rho=-0.822486077235511, accepted=False, new_indices=array([63]), old_indices_used=array([52, 56, 57, 58, 59, 60, 61, 62]), old_indices_discarded=array([50, 51, 53, 54, 55]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61897887, 1.03592701]), radius=0.11565133745437178, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([52, 56, 58, 59, 60, 61, 62, 63, 64]), model=ScalarModel(intercept=0.8537489797787152, linear_terms=array([-0.02180749, -2.41772558]), square_terms=array([[1.03255446e-03, 8.41806686e-02], + [8.41806686e-02, 9.32527369e+00]]), scale=array([0.10249333, 0.08328316]), shift=array([6.61897887, 1.01671684])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=65, candidate_x=array([6.61232843, 1.03835811]), index=62, x=array([6.61897887, 1.03592701]), fval=0.5415174113025932, rho=-0.7324310982234294, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([52, 56, 58, 59, 60, 61, 62, 63, 64]), old_indices_discarded=array([51, 53, 54, 55, 57]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61897887, 1.03592701]), radius=0.05782566872718589, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([56, 58, 59, 60, 61, 62, 63, 64, 65]), model=ScalarModel(intercept=0.5447400521949106, linear_terms=array([-0.00137124, -0.18835934]), square_terms=array([[3.31269825e-04, 3.32990005e-02], + [3.32990005e-02, 4.49770488e+00]]), scale=0.05782566872718589, shift=array([6.61897887, 1.03592701])), vector_model=VectorModel(intercepts=array([ 0.04871065, 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candidate_index=66, candidate_x=array([6.56122348, 1.03877707]), index=62, x=array([6.61897887, 1.03592701]), fval=0.5415174113025932, rho=-0.7212604565703155, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([56, 58, 59, 60, 61, 62, 63, 64, 65]), old_indices_discarded=array([52, 57]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61897887, 1.03592701]), radius=0.028912834363592946, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([56, 58, 59, 60, 61, 62, 64, 65, 66]), model=ScalarModel(intercept=0.5416031178062954, linear_terms=array([4.51099211e-05, 1.50726294e-03]), square_terms=array([[6.53193566e-05, 5.88023268e-03], + [5.88023268e-03, 7.83342000e-01]]), scale=0.028912834363592946, shift=array([6.61897887, 1.03592701])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 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x=array([6.59006647, 1.03608841]), fval=0.5414379715211688, rho=3.2219246695022767, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([56, 58, 59, 60, 61, 62, 64, 65, 66]), old_indices_discarded=array([], dtype=int32), step_length=0.028912851176776357, relative_step_length=1.000000581512805, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.59006647, 1.03608841]), radius=0.05782566872718589, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([56, 58, 59, 60, 61, 62, 65, 66, 67]), model=ScalarModel(intercept=0.5415547803952224, linear_terms=array([ 1.01377641e-05, -3.95364543e-05]), square_terms=array([[2.63397868e-04, 2.35619632e-02], + [2.35619632e-02, 3.13951084e+00]]), scale=0.05782566872718589, shift=array([6.59006647, 1.03608841])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, 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fval=0.5414293228153648, rho=13.747183659712503, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([56, 58, 59, 60, 61, 62, 65, 66, 67]), old_indices_discarded=array([52, 55, 57, 63, 64]), step_length=0.0069704007281480235, relative_step_length=0.1205416362936239, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=0.05782566872718589, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([56, 59, 60, 61, 62, 65, 66, 67, 68]), model=ScalarModel(intercept=0.5414796419298218, linear_terms=array([0.00011312, 0.00042998]), square_terms=array([[2.70149950e-04, 2.41187611e-02], + [2.41187611e-02, 3.15020230e+00]]), scale=0.05782566872718589, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=69, candidate_x=array([6.52527224, 1.03657627]), index=68, x=array([6.58309627, 1.03614145]), fval=0.5414293228153648, rho=-2.4940420439925477, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([56, 59, 60, 61, 62, 65, 66, 67, 68]), old_indices_discarded=array([52, 55, 57, 58, 63, 64]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=0.028912834363592946, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([56, 59, 60, 62, 65, 66, 67, 68, 69]), model=ScalarModel(intercept=0.5415011318321497, linear_terms=array([ 1.74351890e-05, -2.75129179e-04]), square_terms=array([[6.75592967e-05, 6.01725598e-03], + [6.01725598e-03, 7.88582554e-01]]), scale=0.028912834363592946, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, 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new_indices=array([], dtype=int32), old_indices_used=array([56, 59, 60, 62, 65, 66, 67, 68, 69]), old_indices_discarded=array([58, 61]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=0.014456417181796473, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([56, 60, 62, 65, 66, 67, 68, 69, 70]), model=ScalarModel(intercept=0.5414799019531137, linear_terms=array([-3.44991235e-06, -9.05048244e-05]), square_terms=array([[1.67238718e-05, 1.49276761e-03], + [1.49276761e-03, 1.97781855e-01]]), scale=0.014456417181796473, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 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66, 67, 68, 69, 70]), old_indices_discarded=array([59]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=0.0072282085908982364, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([56, 62, 65, 66, 67, 68, 70, 71]), model=ScalarModel(intercept=0.5414632259336039, linear_terms=array([2.83264621e-06, 2.05882321e-05]), square_terms=array([[4.16386161e-06, 3.73300224e-04], + [3.73300224e-04, 4.94351827e-02]]), scale=0.0072282085908982364, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=0.0036141042954491182, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([67, 68, 71, 72]), model=ScalarModel(intercept=0.5414388981465452, linear_terms=array([2.71386294e-05, 3.84330426e-03]), square_terms=array([[1.04795962e-06, 9.32698230e-05], + [9.32698230e-05, 1.23060327e-02]]), scale=0.0036141042954491182, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=0.0018070521477245591, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([67, 68, 71, 72, 73]), model=ScalarModel(intercept=0.5414389397529535, linear_terms=array([3.45115217e-07, 6.30331745e-05]), square_terms=array([[2.70925737e-07, 2.39729562e-05], + [2.39729562e-05, 3.08425190e-03]]), scale=0.0018070521477245591, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([68, 73, 74]), model=ScalarModel(intercept=0.541429322815365, linear_terms=array([1.41688629e-05, 6.76977808e-05]), square_terms=array([[6.26837304e-08, 5.47876928e-06], + [5.47876928e-06, 7.68259730e-04]]), scale=0.0009035260738622796, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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model=ScalarModel(intercept=0.5414293228153648, linear_terms=array([-2.29729551e-06, -2.98650390e-04]), square_terms=array([[1.73350246e-08, 1.53237458e-06], + [1.53237458e-06, 2.01506003e-04]]), scale=0.0004517630369311398, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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square_terms=array([[5.97250724e-09, 4.20181109e-07], + [4.20181109e-07, 4.91845731e-05]]), scale=0.0002258815184655699, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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scale=0.00011294075923278494, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=78, candidate_x=array([6.58300196, 1.03607308]), index=68, x=array([6.58309627, 1.03614145]), fval=0.5414293228153648, rho=-0.9288183820802519, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([68, 76, 77]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=5.647037961639247e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([68, 77, 78]), model=ScalarModel(intercept=0.5414293228153648, linear_terms=array([-1.22674426e-05, -2.40256385e-05]), square_terms=array([[1.26112163e-09, 5.06903136e-08], + [5.06903136e-08, 3.10638384e-06]]), scale=5.647037961639247e-05, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=79, candidate_x=array([6.58311861, 1.03619332]), index=68, x=array([6.58309627, 1.03614145]), fval=0.5414293228153648, rho=-0.12812387344724027, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([68, 77, 78]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=2.8235189808196236e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([68, 78, 79]), model=ScalarModel(intercept=0.541429322815365, linear_terms=array([-2.27116547e-05, 1.08555655e-05]), square_terms=array([[4.35976955e-09, 1.69031789e-08], + [1.69031789e-08, 7.57228023e-07]]), scale=2.8235189808196236e-05, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=80, candidate_x=array([6.58312214, 1.03612944]), index=68, x=array([6.58309627, 1.03614145]), fval=0.5414293228153648, rho=-0.35421190384338624, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([68, 78, 79]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=1.4117594904098118e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([68, 79, 80]), model=ScalarModel(intercept=0.541429322815365, linear_terms=array([ 4.26256780e-06, -1.29794683e-06]), square_terms=array([[1.87697604e-10, 7.48543093e-10], + [7.48543093e-10, 1.92469128e-07]]), scale=1.4117594904098118e-05, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=81, candidate_x=array([6.58308246, 1.03614548]), index=81, x=array([6.58308246, 1.03614548]), fval=0.5414254298290705, rho=0.85915882442786, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([68, 79, 80]), old_indices_discarded=array([], dtype=int32), step_length=1.4381793867319074e-05, relative_step_length=1.018714162363751, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58308246, 1.03614548]), radius=2.8235189808196236e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([68, 78, 79, 80, 81]), model=ScalarModel(intercept=0.5414393336189507, linear_terms=array([-6.83138000e-06, -5.43556504e-06]), square_terms=array([[8.38980666e-10, 1.12053013e-08], + [1.12053013e-08, 7.72680051e-07]]), scale=2.8235189808196236e-05, shift=array([6.58308246, 1.03614548])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + 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old_indices_discarded=array([], dtype=int32), step_length=2.8574644124841038e-05, relative_step_length=1.0120223847953826, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58310557, 1.03616229]), radius=5.647037961639247e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([68, 77, 78, 79, 80, 81, 82]), model=ScalarModel(intercept=0.5414289185236869, linear_terms=array([-9.17816477e-06, -1.57462672e-05]), square_terms=array([[1.32415510e-09, 4.69483871e-08], + [4.69483871e-08, 3.08710853e-06]]), scale=5.647037961639247e-05, shift=array([6.58310557, 1.03616229])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58310557, 1.03616229]), radius=2.8235189808196236e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([68, 78, 79, 80, 81, 82, 83]), model=ScalarModel(intercept=0.541436888279065, linear_terms=array([-1.16278729e-05, 4.19591953e-06]), square_terms=array([[9.75111772e-10, 1.22776981e-08], + [1.22776981e-08, 7.67636851e-07]]), scale=2.8235189808196236e-05, shift=array([6.58310557, 1.03616229])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58313299, 1.03615297]), radius=5.647037961639247e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([68, 77, 78, 79, 80, 81, 82, 83, 84]), model=ScalarModel(intercept=0.5414339494417576, linear_terms=array([-6.40143056e-06, -9.29451992e-06]), square_terms=array([[1.23223241e-09, 4.52561651e-08], + [4.52561651e-08, 3.07989367e-06]]), scale=5.647037961639247e-05, shift=array([6.58313299, 1.03615297])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=85, candidate_x=array([6.58317551, 1.03619013]), index=84, x=array([6.58313299, 1.03615297]), fval=0.5414181528663952, rho=-1.3209196314306082, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([68, 77, 78, 79, 80, 81, 82, 83, 84]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58313299, 1.03615297]), radius=2.8235189808196236e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([68, 79, 80, 81, 82, 83, 84, 85]), model=ScalarModel(intercept=0.5414281500618983, linear_terms=array([-1.16204997e-06, 6.18900498e-06]), square_terms=array([[1.57251918e-10, 5.33198393e-09], + [5.33198393e-09, 7.67778577e-07]]), scale=2.8235189808196236e-05, shift=array([6.58313299, 1.03615297])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 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1.03615297])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=91, candidate_x=array([6.58313347, 1.03615385]), index=91, x=array([6.58313347, 1.03615385]), fval=0.5414180888046741, rho=0.09302207306449327, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([84, 89, 90]), old_indices_discarded=array([], dtype=int32), step_length=9.99999999895458e-07, relative_step_length=0.9999999998954582, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 92 entries., 'history': {'params': [{'CRRA': 9.252106996349742, 'DiscFac': 1.0}, {'CRRA': 8.432160362616244, 'DiscFac': 0.5396951369531111}, {'CRRA': 10.07205363008324, 'DiscFac': 0.839644435905758}, {'CRRA': 8.432160362616244, 'DiscFac': 0.8285194430703369}, {'CRRA': 10.07205363008324, 'DiscFac': 1.0999990254130083}, {'CRRA': 10.07205363008324, 'DiscFac': 0.5384287157628791}, {'CRRA': 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[0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=17, candidate_x=array([9.5340187 , 0.99306208]), index=0, x=array([9.47490235, 0.99993647]), fval=0.6448272261874602, rho=-0.3116075291616809, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 12, 15, 16]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.47490235, 0.99993647]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 16, 17]), model=ScalarModel(intercept=0.6954288371659393, linear_terms=array([-0.10174434, -0.15897912]), square_terms=array([[0.03333137, 0.10554599], + [0.10554599, 0.37011472]]), scale=0.029609069851671544, shift=array([9.47490235, 0.99993647])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=18, candidate_x=array([9.50661188, 1.0031676 ]), index=18, x=array([9.50661188, 1.0031676 ]), fval=0.6396317123330025, rho=0.05607187891543219, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 12, 16, 17]), old_indices_discarded=array([], dtype=int32), step_length=0.03187372417953975, relative_step_length=1.0764851560421564, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.50661188, 1.0031676 ]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=0.6396317123330028, linear_terms=array([ 0.00028177, -0.01430195]), square_terms=array([[2.43882216e-05, 1.35799469e-03], + [1.35799469e-03, 8.34202654e-02]]), scale=0.014804534925835772, shift=array([9.50661188, 1.0031676 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=19, candidate_x=array([9.49185035, 1.0059291 ]), index=19, x=array([9.49185035, 1.0059291 ]), fval=0.6370780738825652, rho=1.4693827516516844, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.015017609941676606, relative_step_length=1.0143925504521585, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.49185035, 1.0059291 ]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 16, 17, 18, 19]), model=ScalarModel(intercept=0.6412257536348447, linear_terms=array([-0.06905576, -0.03758833]), square_terms=array([[0.03588925, 0.1094543 ], + [0.1094543 , 0.37040144]]), scale=0.029609069851671544, shift=array([9.49185035, 1.0059291 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=20, candidate_x=array([9.52125862, 1.00094573]), index=19, x=array([9.49185035, 1.0059291 ]), fval=0.6370780738825652, rho=-0.11096998055275568, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 12, 16, 17, 18, 19]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.49185035, 1.0059291 ]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18, 19, 20]), model=ScalarModel(intercept=0.6375966987395302, linear_terms=array([ 0.00059101, -0.00033053]), square_terms=array([[2.18308578e-05, 1.28615347e-03], + [1.28615347e-03, 8.40653356e-02]]), scale=0.014804534925835772, shift=array([9.49185035, 1.0059291 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=21, candidate_x=array([9.47704841, 1.00621177]), index=21, x=array([9.47704841, 1.00621177]), fval=0.6363070785814076, rho=1.2946325204884988, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int32), step_length=0.01480464158988585, relative_step_length=1.0000072048227528, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.47704841, 1.00621177]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 16, 17, 18, 19, 20, 21]), model=ScalarModel(intercept=0.6740948994884155, linear_terms=array([-0.0775299 , -0.08137508]), square_terms=array([[0.03091198, 0.10157856], + [0.10157856, 0.37039842]]), scale=0.029609069851671544, shift=array([9.47704841, 1.00621177])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=22, candidate_x=array([9.50732214, 1.00462907]), index=21, x=array([9.47704841, 1.00621177]), fval=0.6363070785814076, rho=-0.029474268266789803, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 12, 16, 17, 18, 19, 20, 21]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.47704841, 1.00621177]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18, 19, 20, 21, 22]), model=ScalarModel(intercept=0.6367076097989959, linear_terms=array([ 6.38911855e-04, -5.64861763e-05]), square_terms=array([[1.99591697e-05, 1.23030124e-03], + [1.23030124e-03, 8.44345370e-02]]), scale=0.014804534925835772, shift=array([9.47704841, 1.00621177])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=23, candidate_x=array([9.46224557, 1.00643568]), index=23, x=array([9.46224557, 1.00643568]), fval=0.6355934835508369, rho=1.1173242341721383, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 19, 20, 21, 22]), old_indices_discarded=array([], dtype=int32), step_length=0.014804537200668226, relative_step_length=1.0000001536578127, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.46224557, 1.00643568]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 17, 18, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=0.6376967017750665, linear_terms=array([-0.00887642, -0.07715602]), square_terms=array([[0.00091426, 0.02162778], + [0.02162778, 0.54194842]]), scale=0.029609069851671544, shift=array([9.46224557, 1.00643568])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], 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20, 21, 22, 23]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.46224557, 1.00643568]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18, 19, 20, 21, 22, 23, 24]), model=ScalarModel(intercept=0.6357439312845821, linear_terms=array([ 0.00057539, -0.00082847]), square_terms=array([[2.06319310e-05, 1.26644672e-03], + [1.26644672e-03, 8.60694145e-02]]), scale=0.014804534925835772, shift=array([9.46224557, 1.00643568])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=25, candidate_x=array([9.44744469, 1.00679353]), index=25, x=array([9.44744469, 1.00679353]), fval=0.6347642471305107, rho=1.4044689675006616, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 19, 20, 21, 22, 23, 24]), old_indices_discarded=array([], dtype=int32), step_length=0.014805197302692493, relative_step_length=1.000044741483609, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.44744469, 1.00679353]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 18, 19, 20, 21, 22, 23, 24, 25]), model=ScalarModel(intercept=0.6347153364849043, linear_terms=array([ 0.00136442, -0.00288838]), square_terms=array([[7.21853077e-05, 4.81168593e-03], + [4.81168593e-03, 3.57552054e-01]]), scale=0.029609069851671544, shift=array([9.44744469, 1.00679353])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=26, candidate_x=array([9.41784149, 1.00742862]), index=26, x=array([9.41784149, 1.00742862]), fval=0.6332046797989737, rho=1.1053204142647344, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 19, 20, 21, 22, 23, 24, 25]), old_indices_discarded=array([17]), step_length=0.02961001570090617, relative_step_length=1.0000319445777717, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.41784149, 1.00742862]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 18, 19, 21, 22, 23, 24, 25, 26]), model=ScalarModel(intercept=0.6332385782456339, linear_terms=array([0.0028514 , 0.00022792]), square_terms=array([[2.79552380e-04, 1.89198753e-02], + [1.89198753e-02, 1.43266898e+00]]), scale=0.05921813970334309, shift=array([9.41784149, 1.00742862])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=27, candidate_x=array([9.35862837, 1.00819965]), index=27, x=array([9.35862837, 1.00819965]), fval=0.6303511715372276, rho=1.0071263421242136, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 19, 21, 22, 23, 24, 25, 26]), old_indices_discarded=array([12, 15, 16, 17, 20]), step_length=0.0592181407159667, relative_step_length=1.0000000170998888, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.35862837, 1.00819965]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 18, 19, 21, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.639178807035496, linear_terms=array([ 0.00132575, -0.26376211]), square_terms=array([[8.65219778e-04, 5.51863712e-02], + [5.51863712e-02, 3.95321986e+00]]), scale=array([0.10496142, 0.09838088]), shift=array([9.35862837, 1.00161912])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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relative_step_length=0.8863009769673553, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25366695, 1.00955655]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 19, 21, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=1.3525263472005808, linear_terms=array([-0.05604803, -3.65995549]), square_terms=array([[3.37929225e-03, 1.66025609e-01], + [1.66025609e-01, 9.20973251e+00]]), scale=array([0.20992284, 0.15018314]), shift=array([9.25366695, 0.94981686])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=29, candidate_x=array([9.04374411, 1.01220715]), index=29, x=array([9.04374411, 1.01220715]), fval=0.6151402665456015, rho=1.0345307568988933, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 19, 21, 23, 24, 25, 26, 27, 28]), old_indices_discarded=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, + 18, 20, 22]), step_length=0.20993957271621416, relative_step_length=0.8862975676368734, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.04374411, 1.01220715]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 13, 22, 25, 27, 28, 29]), model=ScalarModel(intercept=2.887557528467335, linear_terms=array([ 0.32145309, -7.82327945]), square_terms=array([[ 0.01909841, -0.39706853], + [-0.39706853, 13.16541827]]), scale=array([0.41984568, 0.25381927]), shift=array([9.04374411, 0.84618073])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=30, candidate_x=array([8.62389843, 0.98935244]), index=29, x=array([9.04374411, 1.01220715]), fval=0.6151402665456015, rho=-1.1829304881995348, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 10, 13, 22, 25, 27, 28, 29]), old_indices_discarded=array([ 0, 1, 2, 4, 5, 6, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, + 21, 23, 24, 26]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.04374411, 1.01220715]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 13, 23, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=1.1785163391833393, linear_terms=array([ 0.04245163, -3.2666068 ]), square_terms=array([[ 1.11423119e-03, -2.87009785e-02], + [-2.87009785e-02, 8.89866124e+00]]), scale=array([0.20992284, 0.14885785]), shift=array([9.04374411, 0.95114215])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=31, candidate_x=array([8.83382127, 1.00530622]), index=29, x=array([9.04374411, 1.01220715]), fval=0.6151402665456015, rho=-0.21904622845287416, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 13, 23, 25, 26, 27, 28, 29]), old_indices_discarded=array([ 0, 1, 3, 6, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, + 30]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.04374411, 1.01220715]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 13, 25, 26, 27, 28, 29, 31]), model=ScalarModel(intercept=0.588971346798013, linear_terms=array([ 0.01505103, -0.13000888]), square_terms=array([[ 2.31746838e-04, -3.20175653e-03], + [-3.20175653e-03, 3.75543886e+00]]), scale=array([0.10496142, 0.09637714]), shift=array([9.04374411, 1.00362286])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=32, candidate_x=array([8.93878269, 1.00687716]), index=29, x=array([9.04374411, 1.01220715]), fval=0.6151402665456015, rho=-0.19293832377303985, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 13, 25, 26, 27, 28, 29, 31]), old_indices_discarded=array([ 0, 3, 12, 16, 17, 18, 19, 20, 21, 22, 23, 24, 30]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.04374411, 1.01220715]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 28, 29, 31, 32]), model=ScalarModel(intercept=0.6214336044267341, linear_terms=array([0.00147887, 0.10259686]), square_terms=array([[4.10977860e-04, 1.78962346e-02], + [1.78962346e-02, 8.79816604e-01]]), scale=0.05921813970334309, shift=array([9.04374411, 1.01220715])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=33, candidate_x=array([9.10280287, 1.00410548]), index=29, x=array([9.04374411, 1.01220715]), fval=0.6151402665456015, rho=-2.08297420159985, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 28, 29, 31, 32]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.04374411, 1.01220715]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([29, 32, 33]), model=ScalarModel(intercept=0.6151402665456009, linear_terms=array([0.00149709, 0.00482735]), square_terms=array([[7.65543339e-05, 5.14393571e-03], + [5.14393571e-03, 3.91745261e-01]]), scale=0.029609069851671544, shift=array([9.04374411, 1.01220715])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=34, candidate_x=array([9.0141328 , 1.01223102]), index=34, x=array([9.0141328 , 1.01223102]), fval=0.6138905842835336, rho=0.8565054081815938, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([29, 32, 33]), old_indices_discarded=array([], dtype=int32), step_length=0.02961131833730782, relative_step_length=1.0000759390837854, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.0141328 , 1.01223102]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 28, 29, 31, 32, 33, 34]), model=ScalarModel(intercept=0.620881773286023, linear_terms=array([0.00197631, 0.09173778]), square_terms=array([[3.44046695e-04, 1.64408522e-02], + [1.64408522e-02, 8.92929811e-01]]), scale=0.05921813970334309, shift=array([9.0141328 , 1.01223102])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=35, candidate_x=array([8.95483028, 1.00724033]), index=34, x=array([9.0141328 , 1.01223102]), fval=0.6138905842835336, rho=-0.906107936879546, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 28, 29, 31, 32, 33, 34]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.0141328 , 1.01223102]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([29, 32, 33, 34, 35]), model=ScalarModel(intercept=0.6137867229839677, linear_terms=array([0.00142032, 0.00043433]), square_terms=array([[7.69832064e-05, 5.16913372e-03], + [5.16913372e-03, 3.92730596e-01]]), scale=0.029609069851671544, shift=array([9.0141328 , 1.01223102])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=36, candidate_x=array([8.98452585, 1.01258669]), index=36, x=array([8.98452585, 1.01258669]), fval=0.6125286403569352, rho=0.9657336720601446, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([29, 32, 33, 34, 35]), old_indices_discarded=array([], dtype=int32), step_length=0.029609089398228004, relative_step_length=1.0000006601543567, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.98452585, 1.01258669]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 28, 29, 31, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=0.619467823965291, linear_terms=array([0.00209097, 0.09020298]), square_terms=array([[3.29303090e-04, 1.61351934e-02], + [1.61351934e-02, 9.00329160e-01]]), scale=0.05921813970334309, shift=array([8.98452585, 1.01258669])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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linear_terms=array([0.00140759, 0.00037964]), square_terms=array([[7.68743253e-05, 5.17639009e-03], + [5.17639009e-03, 3.94127433e-01]]), scale=0.029609069851671544, shift=array([8.98452585, 1.01258669])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=38, candidate_x=array([8.95491894, 1.01294575]), index=38, x=array([8.95491894, 1.01294575]), fval=0.6110646963985666, rho=1.04699122192598, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([29, 32, 33, 34, 35, 36, 37]), old_indices_discarded=array([], dtype=int32), step_length=0.02960908484218274, relative_step_length=1.0000005062810575, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.95491894, 1.01294575]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([29, 31, 32, 33, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=0.6110062274424244, linear_terms=array([0.0028986 , 0.00157643]), square_terms=array([[3.04730334e-04, 2.06301143e-02], + [2.06301143e-02, 1.58200616e+00]]), scale=0.05921813970334309, shift=array([8.95491894, 1.01294575])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], 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+ [6.38199381e-02, 2.30738160e+00]]), scale=array([0.10496142, 0.09565189]), shift=array([8.89570505, 1.00434811])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=40, candidate_x=array([9.00066647, 1.00519543]), index=39, x=array([8.89570505, 1.01365764]), fval=0.608454913769554, rho=-1.8130574184159354, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 32, 34, 35, 36, 37, 38, 39]), old_indices_discarded=array([ 0, 1, 3, 7, 10, 21, 23, 25, 26, 27, 28, 29, 30, 33]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.89570505, 1.01365764]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 32, 34, 35, 36, 37, 38, 39, 40]), model=ScalarModel(intercept=0.6082515870760768, linear_terms=array([0.00289756, 0.00212777]), square_terms=array([[2.93803015e-04, 2.03395974e-02], + [2.03395974e-02, 1.60156668e+00]]), scale=0.05921813970334309, shift=array([8.89570505, 1.01365764])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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scale=array([0.10496142, 0.09531582]), shift=array([8.83649067, 1.00468418])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=42, candidate_x=array([8.73152925, 1.01043262]), index=41, x=array([8.83649067, 1.01432978]), fval=0.6057905110013826, rho=-0.2539961161624663, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 32, 35, 36, 37, 38, 39, 41]), old_indices_discarded=array([ 1, 3, 7, 10, 26, 27, 28, 29, 30, 33, 34, 40]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.83649067, 1.01432978]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 31, 32, 35, 37, 38, 39, 41, 42]), model=ScalarModel(intercept=0.6128960194558581, linear_terms=array([0.00323829, 0.08375858]), square_terms=array([[2.19797272e-04, 1.29854569e-02], + [1.29854569e-02, 9.21144727e-01]]), scale=0.05921813970334309, shift=array([8.83649067, 1.01432978])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=43, candidate_x=array([8.77720289, 1.00979089]), index=41, x=array([8.83649067, 1.01432978]), fval=0.6057905110013826, rho=-0.636748464236313, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 32, 35, 37, 38, 39, 41, 42]), old_indices_discarded=array([ 3, 7, 10, 29, 30, 33, 34, 36, 40]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.83649067, 1.01432978]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 31, 32, 37, 38, 39, 41, 42, 43]), model=ScalarModel(intercept=0.6129958444671166, linear_terms=array([0.00125322, 0.0420077 ]), square_terms=array([[7.02244693e-05, 3.72273429e-03], + [3.72273429e-03, 2.29436197e-01]]), scale=0.029609069851671544, shift=array([8.83649067, 1.01432978])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=44, candidate_x=array([8.80679818, 1.00940247]), index=41, x=array([8.83649067, 1.01432978]), fval=0.6057905110013826, rho=-1.1780504359812205, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 32, 37, 38, 39, 41, 42, 43]), old_indices_discarded=array([35]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.83649067, 1.01432978]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 39, 41, 43, 44]), model=ScalarModel(intercept=0.6059146038962105, linear_terms=array([0.00063063, 0.00133693]), square_terms=array([[1.93356047e-05, 1.31514692e-03], + [1.31514692e-03, 1.01583452e-01]]), scale=0.014804534925835772, shift=array([8.83649067, 1.01432978])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=45, candidate_x=array([8.82168485, 1.01432664]), index=45, x=array([8.82168485, 1.01432664]), fval=0.6051564916805549, rho=1.020928649746183, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 39, 41, 43, 44]), old_indices_discarded=array([], dtype=int32), step_length=0.014805816548052337, relative_step_length=1.0000865695695937, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.82168485, 1.01432664]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 31, 37, 39, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.6115555266177718, linear_terms=array([0.00124126, 0.04101205]), square_terms=array([[7.04241015e-05, 3.73186729e-03], + [3.73186729e-03, 2.30210452e-01]]), scale=0.029609069851671544, shift=array([8.82168485, 1.01432664])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=46, candidate_x=array([8.79199479, 1.00954484]), index=45, x=array([8.82168485, 1.01432664]), fval=0.6051564916805549, rho=-1.230884779808124, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 37, 39, 41, 42, 43, 44, 45]), old_indices_discarded=array([32, 35, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.82168485, 1.01432664]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 41, 43, 44, 45, 46]), model=ScalarModel(intercept=0.6052045803357664, linear_terms=array([5.75893275e-04, 2.08251016e-05]), square_terms=array([[2.14888603e-05, 1.39552818e-03], + [1.39552818e-03, 1.02157333e-01]]), scale=0.014804534925835772, shift=array([8.82168485, 1.01432664])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=47, candidate_x=array([8.80688164, 1.01452473]), index=47, x=array([8.80688164, 1.01452473]), fval=0.6045010959198394, rho=1.1411150863578703, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 41, 43, 44, 45, 46]), old_indices_discarded=array([], dtype=int32), step_length=0.014804535502910653, relative_step_length=1.0000000389796022, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.80688164, 1.01452473]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 31, 41, 42, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=0.6105358682194937, linear_terms=array([0.00053509, 0.04163391]), square_terms=array([[1.09771507e-04, 4.72352439e-03], + [4.72352439e-03, 2.30297392e-01]]), scale=0.029609069851671544, shift=array([8.80688164, 1.01452473])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + 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new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 41, 42, 43, 44, 45, 46, 47]), old_indices_discarded=array([32, 37, 39]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.80688164, 1.01452473]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 41, 43, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=0.6045817009488239, linear_terms=array([ 0.00060537, -0.00012916]), square_terms=array([[2.04142540e-05, 1.36233150e-03], + [1.36233150e-03, 1.02744088e-01]]), scale=0.014804534925835772, shift=array([8.80688164, 1.01452473])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + 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44, 45, 46, 47, 48]), old_indices_discarded=array([], dtype=int32), step_length=0.01480454508906136, relative_step_length=1.0000006864940802, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.79207864, 1.01473837]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 41, 43, 44, 45, 46, 47, 48, 49]), model=ScalarModel(intercept=0.6039320017441308, linear_terms=array([1.22941931e-03, 2.77206681e-05]), square_terms=array([[7.95912011e-05, 5.38269834e-03], + [5.38269834e-03, 4.12173721e-01]]), scale=0.029609069851671544, shift=array([8.79207864, 1.01473837])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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old_indices_discarded=array([13, 32, 37, 39, 42]), step_length=0.02960907001757359, relative_step_length=1.000000005603082, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.76247205, 1.01512188]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 42, 43, 44, 45, 46, 47, 49, 50]), model=ScalarModel(intercept=0.6026578745853444, linear_terms=array([0.0024301, 0.0002779]), square_terms=array([[3.16909370e-04, 2.15042159e-02], + [2.15042159e-02, 1.65484964e+00]]), scale=0.05921813970334309, shift=array([8.76247205, 1.01512188])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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13, 29, 30, 32, 34, 35, 36, 37, 38, 39, 40, 41, 48]), step_length=0.05921814073463714, relative_step_length=1.0000000174151713, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.70325877, 1.0158803 ]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 13, 30, 42, 44, 45, 50, 51]), model=ScalarModel(intercept=0.5745601532643059, linear_terms=array([ 0.01933147, -0.20828263]), square_terms=array([[ 3.96552248e-04, -7.70697193e-03], + [-7.70697193e-03, 3.60952666e+00]]), scale=array([0.10496142, 0.09454056]), shift=array([8.70325877, 1.00545944])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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old_indices_discarded=array([ 1, 3, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 46, + 47, 48, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.70325877, 1.0158803 ]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 42, 43, 46, 49, 50, 51]), model=ScalarModel(intercept=0.5848648139367352, linear_terms=array([ 0.01259792, -0.01987223]), square_terms=array([[ 2.31944576e-04, -1.32546665e-02], + [-1.32546665e-02, 2.24249333e+00]]), scale=0.05921813970334309, shift=array([8.70325877, 1.0158803 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + 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old_indices_discarded=array([ 3, 13, 31, 32, 35, 36, 37, 38, 39, 41, 44, 45, 47, 48, 52]), step_length=0.05922045545199665, relative_step_length=1.0000391053934683, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.64403857, 1.01605409]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 42, 43, 50, 51, 52, 53]), model=ScalarModel(intercept=0.6233574060676437, linear_terms=array([ 0.01619734, -0.70631774]), square_terms=array([[2.97413525e-04, 7.75950577e-04], + [7.75950577e-04, 5.79975163e+00]]), scale=array([0.10496142, 0.09445367]), shift=array([8.64403857, 1.00554633])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 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43, 50, 51, 52, 53]), old_indices_discarded=array([ 1, 3, 13, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 44, 45, + 46, 47, 48, 49]), step_length=0.10496625831674383, relative_step_length=0.8862677791178409, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.53907715, 1.01706193]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 30, 42, 50, 51, 52, 53, 54]), model=ScalarModel(intercept=0.7023662250726452, linear_terms=array([ 0.06492856, -1.1147206 ]), square_terms=array([[ 3.47923203e-03, -8.06332216e-02], + [-8.06332216e-02, 3.61802648e+00]]), scale=array([0.20992284, 0.14643046]), shift=array([8.53907715, 0.95356954])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=55, candidate_x=array([8.32915431, 0.99542161]), index=54, x=array([8.53907715, 1.01706193]), fval=0.5930898839617496, rho=-1.7530958161469754, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 30, 42, 50, 51, 52, 53, 54]), old_indices_discarded=array([ 0, 1, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, + 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 44, + 45, 46, 47, 48, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.53907715, 1.01706193]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 42, 51, 52, 53, 54, 55]), model=ScalarModel(intercept=0.615086708073995, linear_terms=array([ 0.00978289, -0.72244786]), square_terms=array([[4.17284358e-04, 3.04748646e-02], + [3.04748646e-02, 5.74255506e+00]]), scale=array([0.10496142, 0.09394975]), shift=array([8.53907715, 1.00605025])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=56, candidate_x=array([8.43411573, 1.01836827]), index=56, x=array([8.43411573, 1.01836827]), fval=0.5885914443329772, rho=0.3283224223143453, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 30, 42, 51, 52, 53, 54, 55]), old_indices_discarded=array([ 1, 3, 13, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 44, + 45, 46, 47, 48, 49, 50]), step_length=0.10496954879325875, relative_step_length=0.8862955617916246, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.43411573, 1.01836827]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 51, 52, 53, 54, 55, 56]), model=ScalarModel(intercept=1.9169446392811247, linear_terms=array([-0.05516787, -6.07713486]), square_terms=array([[2.74937852e-03, 1.66261876e-01], + [1.66261876e-01, 1.36583309e+01]]), scale=array([0.20992284, 0.14577728]), shift=array([8.43411573, 0.95422272])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=57, candidate_x=array([8.22419289, 1.02085937]), index=57, x=array([8.22419289, 1.02085937]), fval=0.5799642053547169, rho=0.46355807804061855, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 30, 51, 52, 53, 54, 55, 56]), old_indices_discarded=array([ 0, 1, 3, 11, 12, 13, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, + 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, + 45, 46, 47, 48, 49, 50]), step_length=0.2099376195491761, relative_step_length=0.8862893219918404, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.22419289, 1.02085937]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 30, 52, 53, 54, 55, 56, 57]), model=ScalarModel(intercept=3.341466430284669, linear_terms=array([-1.05103033, -7.77084409]), square_terms=array([[ 0.18279549, 1.34509322], + [ 1.34509322, 11.10009818]]), scale=array([0.41984568, 0.24949316]), shift=array([8.22419289, 0.85050684])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=58, candidate_x=array([8.64403857, 0.99493627]), index=57, x=array([8.22419289, 1.02085937]), fval=0.5799642053547169, rho=-1.0936320536105784, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 30, 52, 53, 54, 55, 56, 57]), old_indices_discarded=array([ 0, 1, 2, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, + 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, + 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.22419289, 1.02085937]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 52, 53, 54, 55, 56, 57]), model=ScalarModel(intercept=1.9832433701127465, linear_terms=array([-0.11608732, -6.1156747 ]), square_terms=array([[5.73554046e-03, 2.62479555e-01], + [2.62479555e-01, 1.32048414e+01]]), scale=array([0.20992284, 0.14453174]), shift=array([8.22419289, 0.95546826])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=59, candidate_x=array([8.01427005, 1.02527945]), index=59, x=array([8.01427005, 1.02527945]), fval=0.5734330166298551, rho=1.093147952169101, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 30, 52, 53, 54, 55, 56, 57]), old_indices_discarded=array([ 1, 3, 13, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, + 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 58]), step_length=0.20996936856056853, relative_step_length=0.8864233561389423, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.01427005, 1.02527945]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 52, 54, 55, 56, 57, 59]), model=ScalarModel(intercept=9.981376675556422, linear_terms=array([ -0.67948996, -26.92467299]), square_terms=array([[2.66781078e-02, 9.72846991e-01], + [9.72846991e-01, 3.85178573e+01]]), scale=array([0.41984568, 0.24728311]), shift=array([8.01427005, 0.85271689])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=60, candidate_x=array([7.90516021, 1.02719534]), index=60, x=array([7.90516021, 1.02719534]), fval=0.570654333265082, rho=28.312992905781908, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 30, 52, 54, 55, 56, 57, 59]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, + 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, + 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 58]), step_length=0.1091266633945737, relative_step_length=0.23034889296854485, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.90516021, 1.02719534]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([10, 30, 52, 54, 55, 56, 57, 59, 60]), model=ScalarModel(intercept=6.968668322923406, linear_terms=array([ -0.3193095 , -18.62779191]), square_terms=array([[1.10008541e-02, 4.89131866e-01], + [4.89131866e-01, 2.70983093e+01]]), scale=array([0.41984568, 0.24632517]), shift=array([7.90516021, 0.85367483])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=61, candidate_x=array([7.53569564, 1.02680097]), index=61, x=array([7.53569564, 1.02680097]), fval=0.557843378100565, rho=0.7126019978012866, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([10, 30, 52, 54, 55, 56, 57, 59, 60]), old_indices_discarded=array([ 0, 1, 3, 6, 7, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, + 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, + 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 58]), step_length=0.36946477399899075, relative_step_length=0.7798809111740238, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.53569564, 1.02680097]), radius=0.9474902352534894, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([30, 52, 54, 55, 56, 57, 59, 60, 61]), model=ScalarModel(intercept=12.13842084066151, linear_terms=array([ -0.90389113, -30.58695776]), square_terms=array([[ 0.04679947, 1.22766505], + [ 1.22766505, 40.38597232]]), scale=array([0.83969136, 0.3 ]), shift=array([7.53569564, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=62, candidate_x=array([6.69600428, 1.03632926]), index=62, x=array([6.69600428, 1.03632926]), fval=0.5424834330682993, rho=0.7246036090517496, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([30, 52, 54, 55, 56, 57, 59, 60, 61]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, + 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, + 53, 58]), step_length=0.8397454168164292, relative_step_length=0.88628398011064, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69600428, 1.03632926]), radius=1.8949804705069788, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([52, 54, 55, 56, 57, 59, 60, 61, 62]), model=ScalarModel(intercept=13.436478042611053, linear_terms=array([ -1.77594432, -32.96460288]), square_terms=array([[ 0.1709616 , 2.30049165], + [ 2.30049165, 42.12835475]]), scale=array([1.67938272, 0.3 ]), shift=array([6.69600428, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=63, candidate_x=array([6.58843012, 1.03594893]), index=63, x=array([6.58843012, 1.03594893]), fval=0.5414632317465802, rho=0.501065213354614, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([52, 54, 55, 56, 57, 59, 60, 61, 62]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, + 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, + 51, 53, 58]), step_length=0.10757483992757301, relative_step_length=0.05676831059836341, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58843012, 1.03594893]), radius=1.8949804705069788, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([54, 55, 56, 57, 59, 60, 61, 62, 63]), model=ScalarModel(intercept=13.527389310158597, linear_terms=array([ -1.76412438, -33.07272766]), square_terms=array([[ 0.17044821, 2.26794554], + [ 2.26794554, 42.11033571]]), scale=array([1.67938272, 0.3 ]), shift=array([6.58843012, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=64, candidate_x=array([5.99457255, 1.04132824]), index=63, x=array([6.58843012, 1.03594893]), fval=0.5414632317465802, rho=-2.858177349740313, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([54, 55, 56, 57, 59, 60, 61, 62, 63]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, + 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, + 51, 52, 53, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58843012, 1.03594893]), radius=0.9474902352534894, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([55, 56, 57, 59, 60, 61, 62, 63, 64]), model=ScalarModel(intercept=13.033433276409284, linear_terms=array([ -0.7928235 , -31.91157346]), square_terms=array([[3.91876133e-02, 1.01921230e+00], + [1.01921230e+00, 4.07663306e+01]]), scale=array([0.83969136, 0.3 ]), shift=array([6.58843012, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=65, candidate_x=array([6.28160798, 1.03757835]), index=63, x=array([6.58843012, 1.03594893]), fval=0.5414632317465802, rho=-1.670491030486846, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([55, 56, 57, 59, 60, 61, 62, 63, 64]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, + 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, + 51, 52, 53, 54, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58843012, 1.03594893]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([55, 57, 59, 60, 61, 62, 63, 64, 65]), model=ScalarModel(intercept=7.57645714444444, linear_terms=array([ -0.29574748, -19.22814953]), square_terms=array([[9.87830891e-03, 4.07101559e-01], + [4.07101559e-01, 2.62853990e+01]]), scale=array([0.41984568, 0.24194837]), shift=array([6.58843012, 0.85805163])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=66, candidate_x=array([6.34718361, 1.03719355]), index=63, x=array([6.58843012, 1.03594893]), fval=0.5414632317465802, rho=-1.608088619606695, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([55, 57, 59, 60, 61, 62, 63, 64, 65]), old_indices_discarded=array([ 1, 3, 7, 10, 13, 30, 31, 32, 35, 37, 38, 39, 41, 42, 43, 44, 45, + 46, 47, 48, 49, 50, 51, 52, 53, 54, 56, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58843012, 1.03594893]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([61, 62, 63, 64, 65, 66]), model=ScalarModel(intercept=2.51780763816455, linear_terms=array([-0.09621641, -7.35783746]), square_terms=array([[3.42399524e-03, 1.80214137e-01], + [1.80214137e-01, 1.37071817e+01]]), scale=array([0.20992284, 0.13698695]), shift=array([6.58843012, 0.96301305])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=67, candidate_x=array([6.48488598, 1.03743422]), index=63, x=array([6.58843012, 1.03594893]), fval=0.5414632317465802, rho=-1.0724059676808537, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([61, 62, 63, 64, 65, 66]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58843012, 1.03594893]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([62, 63, 65, 66, 67]), model=ScalarModel(intercept=0.7479032481802277, linear_terms=array([-0.01574272, -1.69550858]), square_terms=array([[8.99601089e-04, 6.45218243e-02], + [6.45218243e-02, 6.96729079e+00]]), scale=array([0.10496142, 0.08450624]), shift=array([6.58843012, 1.01549376])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=68, candidate_x=array([6.60273304, 1.03595193]), index=68, x=array([6.60273304, 1.03595193]), fval=0.5414533040238657, rho=1.1451990693564231, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([62, 63, 65, 66, 67]), old_indices_discarded=array([], dtype=int32), step_length=0.014302919721513424, relative_step_length=0.12076468285870495, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.60273304, 1.03595193]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([62, 63, 65, 66, 67, 68]), model=ScalarModel(intercept=0.7438421645946036, linear_terms=array([-0.01554274, -1.67681543]), square_terms=array([[8.98462125e-04, 6.42713904e-02], + [6.42713904e-02, 6.94910166e+00]]), scale=array([0.10496142, 0.08450474]), shift=array([6.60273304, 1.01549526])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=69, candidate_x=array([6.61449989, 1.0357986 ]), index=69, x=array([6.61449989, 1.0357986 ]), fval=0.5414101559834424, rho=10.755716308584471, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([62, 63, 65, 66, 67, 68]), old_indices_discarded=array([], dtype=int32), step_length=0.011767854290167887, relative_step_length=0.09936021588249544, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([62, 63, 65, 66, 67, 68, 69]), model=ScalarModel(intercept=0.7410229267529151, linear_terms=array([-0.01539784, -1.66026198]), square_terms=array([[8.96829948e-04, 6.39617448e-02], + [6.39617448e-02, 6.90724557e+00]]), scale=array([0.10496142, 0.08458141]), shift=array([6.61449989, 1.01541859])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=71, candidate_x=array([6.60701913, 1.03578557]), index=69, x=array([6.61449989, 1.0357986 ]), fval=0.5414101559834424, rho=-16.79541545743258, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([62, 63, 66, 67, 68, 69, 70]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([62, 63, 67, 68, 69, 70, 71]), model=ScalarModel(intercept=0.5414615742402571, linear_terms=array([8.4752230e-05, 4.0438811e-03]), square_terms=array([[6.98826564e-05, 6.29275999e-03], + [6.29275999e-03, 8.41875305e-01]]), scale=0.029609069851671544, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 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candidate_index=72, candidate_x=array([6.58489072, 1.03587769]), index=69, x=array([6.61449989, 1.0357986 ]), fval=0.5414101559834424, rho=-1.588880786010452, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([62, 63, 67, 68, 69, 70, 71]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([63, 68, 69, 70, 71, 72]), model=ScalarModel(intercept=0.5414393947813702, linear_terms=array([-2.11246402e-05, -1.93873062e-03]), square_terms=array([[1.87338726e-05, 1.64576236e-03], + [1.64576236e-03, 2.06670312e-01]]), scale=0.014804534925835772, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 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x=array([6.61449989, 1.0357986 ]), fval=0.5414101559834424, rho=-10.089924952581605, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([63, 68, 69, 70, 71, 72]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.007402267462917886, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([63, 68, 69, 70, 71, 72, 73]), model=ScalarModel(intercept=0.5414619458203902, linear_terms=array([6.9562924e-06, 8.5447900e-04]), square_terms=array([[4.61608585e-06, 4.03102152e-04], + [4.03102152e-04, 5.06614459e-02]]), scale=0.007402267462917886, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, 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69, 70, 71, 73, 74]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.0018505668657294715, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([69, 70, 71, 74, 75]), model=ScalarModel(intercept=0.5414505447289019, linear_terms=array([-8.39195189e-06, -4.82590678e-04]), square_terms=array([[2.93663647e-07, 2.57520778e-05], + [2.57520778e-05, 3.23949356e-03]]), scale=0.0018505668657294715, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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0.6396317123330024, 0.6370780738825651, 0.6434710277309676, 0.6363070785814076, 0.6381871027164296, 0.635593483550837, 0.6382400964218143, 0.6347642471305108, 0.6332046797989737, 0.6303511715372276, 0.6252147257628976, 0.6151402665456016, 0.739995597244996, 0.6238326932454787, 0.6190748428461661, 0.6288150875658687, 0.6138905842835336, 0.6184024304966298, 0.6125286403569352, 0.6167148454543119, 0.6110646963985666, 0.608454913769554, 0.6246685342740161, 0.6057905110013826, 0.6075289839093978, 0.609515213008583, 0.6109902971047441, 0.6051564916805547, 0.610358395328094, 0.6045010959198392, 0.6133471242213953, 0.6038388333603965, 0.6024855719631016, 0.5997907962729061, 0.6050821429018344, 0.5974935659720187, 0.5930898839617496, 0.7118379655434667, 0.5885914443329772, 0.5799642053547169, 0.6905609554101932, 0.5734330166298551, 0.570654333265082, 0.557843378100565, 0.5424834330682992, 0.5414632317465802, 0.5501696210698668, 0.5434588654753454, 0.5427098651478546, 0.5417403799848393, 0.5414533040238656, 0.5414101559834423, 0.5414570248057565, 0.541461501749771, 0.5414940722460778, 0.5415308885058294, 0.5415401970146225, 0.5414670862447538, 0.541630603918333, 0.5417428852802306, 0.5414925969685491, 0.5414213817815339, 0.5415235212662285, 0.5414473204202261, 0.5414098309112865], 'runtime': [0.0, 1.6021721996366978, 1.763861000072211, 1.9364263000898063, 2.105490399990231, 2.2869786000810564, 2.4659180999733508, 2.645659300033003, 2.8395737996324897, 3.035836899653077, 3.223421999718994, 3.405466000083834, 3.596803499851376, 4.85236749984324, 5.994861399754882, 7.14662199979648, 8.282688799779862, 9.429990599863231, 10.606538099702448, 11.902987699955702, 13.051619499921799, 14.198331299703568, 15.403642300050706, 16.55112239997834, 17.69306949991733, 18.856871999800205, 20.002126100007445, 21.29282119963318, 22.501489299815148, 23.68054739991203, 24.829328400082886, 25.971021299716085, 27.116726099979132, 28.259377599693835, 29.404241499956697, 30.751218599732965, 31.8990707998164, 33.04312939988449, 34.19053489994258, 35.35309229977429, 36.50230869976804, 37.67961739981547, 38.97866219980642, 40.138869300018996, 41.282837899867445, 42.43159359972924, 43.61818069964647, 44.7674082997255, 45.915902299806476, 47.063161599915475, 48.3554062996991, 49.515675500035286, 50.720047399867326, 51.87775920005515, 53.02564329979941, 54.18041200004518, 55.32891159970313, 56.47521140007302, 57.76730199996382, 58.93786389986053, 60.09956929972395, 61.246673699934036, 62.39065769966692, 63.537203199695796, 64.6880405000411, 65.84563069976866, 67.12054200004786, 68.2633369998075, 69.40824959985912, 70.57097999984398, 71.71744199981913, 72.86136729968712, 74.00794119993225, 75.30863319989294, 76.46647779969499, 77.64264229964465, 78.78975840006024, 79.93701329967007, 81.08435950009152, 82.23052149964496, 83.39726469991729, 84.69916970003396, 85.85893659992144], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71]}, 'multistart_info': {...}}], 'exploration_sample': array([[ 9.252107, 1. ], + [16.45625 , 0.9125 ], + [14.09375 , 0.9875 ], + [17.046875, 0.63125 ], + [ 7.596875, 0.93125 ], + [18.81875 , 0.5375 ], + [15.275 , 0.65 ], + [11.73125 , 0.7625 ], + [10.55 , 0.8 ], + [ 9.36875 , 0.8375 ], + [ 5.825 , 0.95 ], + [12.9125 , 0.575 ], + [17.6375 , 1.025 ], + [ 8.1875 , 0.725 ], + [12.321875, 1.08125 ], + [ 7.00625 , 0.6125 ], + [ 4.64375 , 0.6875 ], + [ 3.4625 , 0.875 ], + [ 2.871875, 0.78125 ], + [ 2.28125 , 1.0625 ]]), 'exploration_results': array([0.64235819, 0.99955613, 1.1121001 , 1.75705837, 1.77123339, + 1.82132756, 1.94396028, 2.07018913, 2.11467658, 2.15218408, + 2.24402179, 2.54668134, 2.78807842, 2.90740886, 3.01539999, + 3.32546076, 3.5830362 , 4.07905962, 4.08578359, 6.94508729])}}" diff --git a/src/estimark/content/tables/min/Portfolio_estimate_results.csv b/src/estimark/content/tables/min/Portfolio_estimate_results.csv new file mode 100644 index 0000000..934462b --- /dev/null +++ b/src/estimark/content/tables/min/Portfolio_estimate_results.csv @@ -0,0 +1,1084 @@ +CRRA,9.25239894900598 + +time_to_estimate,45.062182664871216 + +params,{'CRRA': 9.25239894900598} + +criterion,0.6423583869781233 + +start_criterion,0.9309824869808929 + +start_params,{'CRRA': 6.614528749695077} + +algorithm,multistart_tranquilo_ls + +direction,minimize + +n_free,1 + +message,Absolute criterion change smaller than tolerance. + +success, + +n_criterion_evaluations, + +n_derivative_evaluations, + +n_iterations, + +history,"{'params': [{'CRRA': 8.1875}, {'CRRA': 7.36875}, {'CRRA': 9.00625}, {'CRRA': 7.36875}, {'CRRA': 7.36875}, {'CRRA': 9.00625}, {'CRRA': 7.36875}, {'CRRA': 7.36875}, {'CRRA': 9.00625}, {'CRRA': 7.36875}, {'CRRA': 9.00625}, {'CRRA': 7.36875}, {'CRRA': 9.00625}, {'CRRA': 9.00625}, {'CRRA': 7.3687499999999995}, {'CRRA': 9.026168169426626}, {'CRRA': 7.388668169426626}, {'CRRA': 9.038959312915338}, {'CRRA': 10.676459312915338}, {'CRRA': 9.290153724085462}, {'CRRA': 7.652653724085462}, {'CRRA': 9.235117007166322}, {'CRRA': 8.416367007166322}, {'CRRA': 9.1520466847697}, {'CRRA': 9.644492007166322}, {'CRRA': 9.248676532750787}, {'CRRA': 9.658051532750788}, {'CRRA': 9.252525969850202}, {'CRRA': 9.661900969850203}, {'CRRA': 9.25239894900598}], 'criterion': [0.6831279025699917, 0.7818234415442017, 0.6443932011688029, 0.7818234415442017, 0.7818234415442017, 0.6443932011688029, 0.7818234415442017, 0.7818234415442017, 0.6443932011688029, 0.7818234415442017, 0.6443932011688029, 0.7818234415442017, 0.6443932011688029, 0.6443932011688029, 0.7818234415442095, 0.6440402472439135, 0.7787614029619838, 0.6439435808488991, 0.7100198965757869, 0.642415138936857, 0.740451029529312, 0.6424025578938513, 0.6665435566959452, 0.6426661753949429, 0.6484718333650715, 0.6423803321514743, 0.6489393340909813, 0.642358861106374, 0.6490529814080408, 0.6423583869781233], 'runtime': [0.0, 3.074203699827194, 3.283573899883777, 3.498749100137502, 3.7044561998918653, 3.926607399713248, 4.110963100101799, 4.243899200111628, 4.541710100136697, 4.7094960999675095, 4.89852079981938, 5.075110800098628, 5.21526509989053, 12.235306499991566, 15.733508999925107, 16.80475839972496, 17.874723799992353, 18.94831439992413, 20.016572599764913, 21.089213099796325, 22.262267199810594, 23.334850599989295, 24.407207199838012, 25.481976099777967, 26.552139800041914, 27.62647379981354, 28.800836499780416, 29.875069699715823, 30.947488199919462, 32.024298400152475], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]}" + +convergence_report, + +multistart_info,"{'start_parameters': [{'CRRA': 8.1875}], 'local_optima': [Minimize with 1 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 7.381e-07* 0.002468 +relative_params_change 1.373e-05 0.02307 +absolute_criterion_change 4.741e-07* 0.001585 +absolute_params_change 0.000127 0.2134 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 8.1875}, {'CRRA': 10.549999999999999}, {'CRRA': 6.614528749695077}, {'CRRA': 12.9125}, {'CRRA': 5.824999999999999}, {'CRRA': 14.093749999999998}, {'CRRA': 15.274999999999999}, {'CRRA': 4.64375}, {'CRRA': 17.6375}, {'CRRA': 3.4625}], 'exploration_results': array([0.6831279 , 0.69939713, 0.93725113, 1.02360977, 1.18227674, + 1.26376333, 1.54278366, 1.78141341, 2.1808234 , 2.89794431])}" + +algorithm_output,"{'states': [State(trustregion=Region(center=array([8.1875]), radius=0.8187500000000001, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=[0], model=ScalarModel(intercept=0.6831279025699917, linear_terms=array([0.]), square_terms=array([[0.]]), scale=0.8187500000000001, shift=array([8.1875])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=0, candidate_x=array([8.1875]), index=0, x=array([8.1875]), fval=0.6831279025699917, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([8.1875]), radius=0.8187500000000001, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=0.6798922337586231, linear_terms=array([-0.06848896]), square_terms=array([[0.06703103]]), scale=0.8187500000000001, shift=array([8.1875])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=13, candidate_x=array([9.00625]), index=13, x=array([9.00625]), fval=0.6443932011688028, rho=1.1075462640286373, accepted=True, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int32), step_length=0.8187499999999996, relative_step_length=0.9999999999999994, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.00625]), radius=1.6375000000000002, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 1, 2, 12, 13, 14]), model=ScalarModel(intercept=0.6451809482519073, linear_terms=array([-0.00325077]), square_terms=array([[0.26725027]]), scale=1.6375000000000002, shift=array([9.00625])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=15, candidate_x=array([9.02616817]), index=15, x=array([9.02616817]), fval=0.6440402472439134, rho=17.852277387983314, accepted=True, new_indices=array([14]), old_indices_used=array([ 0, 1, 2, 12, 13]), old_indices_discarded=array([ 3, 4, 5, 6, 7, 8, 9, 10, 11]), step_length=0.019918169426626164, relative_step_length=0.012163767588779335, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.02616817]), radius=1.6375000000000002, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 2, 12, 13, 15, 16]), model=ScalarModel(intercept=0.6447879200688609, linear_terms=array([-0.00206742]), square_terms=array([[0.26466733]]), scale=1.6375000000000002, shift=array([9.02616817])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=17, candidate_x=array([9.03895931]), index=17, x=array([9.03895931]), fval=0.6439435808488991, rho=11.971513486470478, accepted=True, new_indices=array([16]), old_indices_used=array([ 0, 2, 12, 13, 15]), old_indices_discarded=array([ 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14]), step_length=0.012791143488712464, relative_step_length=0.007811385336618298, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.03895931]), radius=1.6375000000000002, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 2, 13, 15, 17, 18]), model=ScalarModel(intercept=0.640887156447643, linear_terms=array([-0.03350583]), square_terms=array([[0.21841968]]), scale=1.6375000000000002, shift=array([9.03895931])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=19, candidate_x=array([9.29015372]), index=19, x=array([9.29015372]), fval=0.6424151389368569, rho=0.5947436919672805, accepted=True, new_indices=array([18]), old_indices_used=array([ 0, 2, 13, 15, 17]), old_indices_discarded=array([ 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16]), step_length=0.25119441117012364, relative_step_length=0.15340116712679305, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.29015372]), radius=1.6375000000000002, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 2, 15, 18, 19, 20]), model=ScalarModel(intercept=0.6360123591878122, linear_terms=array([0.0077497]), square_terms=array([[0.23057571]]), scale=1.6375000000000002, shift=array([9.29015372])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=21, candidate_x=array([9.23511701]), index=21, x=array([9.23511701]), fval=0.6424025578938514, rho=0.09660301085246886, accepted=True, new_indices=array([20]), old_indices_used=array([ 0, 2, 15, 18, 19]), old_indices_discarded=array([ 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17]), step_length=0.05503671691914036, relative_step_length=0.033610208805581895, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.23511701]), radius=0.8187500000000001, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 2, 15, 17, 19, 21, 22]), model=ScalarModel(intercept=0.6429673760619858, linear_terms=array([0.0061267]), square_terms=array([[0.06038539]]), scale=0.8187500000000001, shift=array([9.23511701])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=23, candidate_x=array([9.15204668]), index=21, x=array([9.23511701]), fval=0.6424025578938514, rho=-0.8481705078488714, accepted=False, new_indices=array([22]), old_indices_used=array([ 2, 15, 17, 19, 21]), old_indices_discarded=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 18, 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.23511701]), radius=0.40937500000000004, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 2, 15, 19, 21, 23, 24]), model=ScalarModel(intercept=0.6421324295697537, linear_terms=array([-0.00046788]), square_terms=array([[0.01412584]]), scale=0.40937500000000004, shift=array([9.23511701])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=25, candidate_x=array([9.24867653]), index=25, x=array([9.24867653]), fval=0.6423803321514744, rho=2.8683063839230534, accepted=True, new_indices=array([24]), old_indices_used=array([ 2, 15, 19, 21, 23]), old_indices_discarded=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, + 20, 22]), step_length=0.013559525584465604, relative_step_length=0.03312250524449613, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.24867653]), radius=0.40937500000000004, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 2, 19, 23, 24, 25, 26]), model=ScalarModel(intercept=0.6421484427735298, linear_terms=array([-0.00013227]), square_terms=array([[0.01406608]]), scale=0.40937500000000004, shift=array([9.24867653])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=27, candidate_x=array([9.25252597]), index=27, x=array([9.25252597]), fval=0.642358861106374, rho=34.526953193374496, accepted=True, new_indices=array([26]), old_indices_used=array([ 2, 19, 23, 24, 25]), old_indices_discarded=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, + 18, 20, 21, 22]), step_length=0.0038494370994150984, relative_step_length=0.009403205128342224, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25252597]), radius=0.40937500000000004, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 2, 19, 23, 26, 27, 28]), model=ScalarModel(intercept=0.64214537534659, linear_terms=array([4.36033832e-06]), square_terms=array([[0.01405292]]), scale=0.40937500000000004, shift=array([9.25252597])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=29, candidate_x=array([9.25239895]), index=29, x=array([9.25239895]), fval=0.6423583869781233, rho=700.8934507833044, accepted=True, new_indices=array([28]), old_indices_used=array([ 2, 19, 23, 26, 27]), old_indices_discarded=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, + 18, 20, 21, 22, 24, 25]), step_length=0.00012702084422144821, relative_step_length=0.0003102799248157513, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 30 entries., 'multistart_info': {'start_parameters': [array([8.1875])], 'local_optima': [{'solution_x': array([9.25239895]), 'solution_criterion': 0.6423583869781233, 'states': [State(trustregion=Region(center=array([8.1875]), radius=0.8187500000000001, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=[0], model=ScalarModel(intercept=0.6831279025699917, linear_terms=array([0.]), square_terms=array([[0.]]), scale=0.8187500000000001, shift=array([8.1875])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=0, candidate_x=array([8.1875]), index=0, x=array([8.1875]), fval=0.6831279025699917, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([8.1875]), radius=0.8187500000000001, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=0.6798922337586231, linear_terms=array([-0.06848896]), square_terms=array([[0.06703103]]), scale=0.8187500000000001, shift=array([8.1875])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=13, candidate_x=array([9.00625]), index=13, x=array([9.00625]), fval=0.6443932011688028, rho=1.1075462640286373, accepted=True, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int32), step_length=0.8187499999999996, relative_step_length=0.9999999999999994, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.00625]), radius=1.6375000000000002, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 1, 2, 12, 13, 14]), model=ScalarModel(intercept=0.6451809482519073, linear_terms=array([-0.00325077]), square_terms=array([[0.26725027]]), scale=1.6375000000000002, shift=array([9.00625])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=15, candidate_x=array([9.02616817]), index=15, x=array([9.02616817]), fval=0.6440402472439134, rho=17.852277387983314, accepted=True, new_indices=array([14]), old_indices_used=array([ 0, 1, 2, 12, 13]), old_indices_discarded=array([ 3, 4, 5, 6, 7, 8, 9, 10, 11]), step_length=0.019918169426626164, relative_step_length=0.012163767588779335, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.02616817]), radius=1.6375000000000002, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 2, 12, 13, 15, 16]), model=ScalarModel(intercept=0.6447879200688609, linear_terms=array([-0.00206742]), square_terms=array([[0.26466733]]), scale=1.6375000000000002, shift=array([9.02616817])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=17, candidate_x=array([9.03895931]), index=17, x=array([9.03895931]), fval=0.6439435808488991, rho=11.971513486470478, accepted=True, new_indices=array([16]), old_indices_used=array([ 0, 2, 12, 13, 15]), old_indices_discarded=array([ 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14]), step_length=0.012791143488712464, relative_step_length=0.007811385336618298, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.03895931]), radius=1.6375000000000002, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 2, 13, 15, 17, 18]), model=ScalarModel(intercept=0.640887156447643, linear_terms=array([-0.03350583]), square_terms=array([[0.21841968]]), scale=1.6375000000000002, shift=array([9.03895931])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=19, candidate_x=array([9.29015372]), index=19, x=array([9.29015372]), fval=0.6424151389368569, rho=0.5947436919672805, accepted=True, new_indices=array([18]), old_indices_used=array([ 0, 2, 13, 15, 17]), old_indices_discarded=array([ 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16]), step_length=0.25119441117012364, relative_step_length=0.15340116712679305, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.29015372]), radius=1.6375000000000002, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 2, 15, 18, 19, 20]), model=ScalarModel(intercept=0.6360123591878122, linear_terms=array([0.0077497]), square_terms=array([[0.23057571]]), scale=1.6375000000000002, shift=array([9.29015372])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=21, candidate_x=array([9.23511701]), index=21, x=array([9.23511701]), fval=0.6424025578938514, rho=0.09660301085246886, accepted=True, new_indices=array([20]), old_indices_used=array([ 0, 2, 15, 18, 19]), old_indices_discarded=array([ 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17]), step_length=0.05503671691914036, relative_step_length=0.033610208805581895, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.23511701]), radius=0.8187500000000001, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 2, 15, 17, 19, 21, 22]), model=ScalarModel(intercept=0.6429673760619858, linear_terms=array([0.0061267]), square_terms=array([[0.06038539]]), scale=0.8187500000000001, shift=array([9.23511701])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=23, candidate_x=array([9.15204668]), index=21, x=array([9.23511701]), fval=0.6424025578938514, rho=-0.8481705078488714, accepted=False, new_indices=array([22]), old_indices_used=array([ 2, 15, 17, 19, 21]), old_indices_discarded=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 18, 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.23511701]), radius=0.40937500000000004, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 2, 15, 19, 21, 23, 24]), model=ScalarModel(intercept=0.6421324295697537, linear_terms=array([-0.00046788]), square_terms=array([[0.01412584]]), scale=0.40937500000000004, shift=array([9.23511701])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=25, candidate_x=array([9.24867653]), index=25, x=array([9.24867653]), fval=0.6423803321514744, rho=2.8683063839230534, accepted=True, new_indices=array([24]), old_indices_used=array([ 2, 15, 19, 21, 23]), old_indices_discarded=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, + 20, 22]), step_length=0.013559525584465604, relative_step_length=0.03312250524449613, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.24867653]), radius=0.40937500000000004, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 2, 19, 23, 24, 25, 26]), model=ScalarModel(intercept=0.6421484427735298, linear_terms=array([-0.00013227]), square_terms=array([[0.01406608]]), scale=0.40937500000000004, shift=array([9.24867653])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=27, candidate_x=array([9.25252597]), index=27, x=array([9.25252597]), fval=0.642358861106374, rho=34.526953193374496, accepted=True, new_indices=array([26]), old_indices_used=array([ 2, 19, 23, 24, 25]), old_indices_discarded=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, + 18, 20, 21, 22]), step_length=0.0038494370994150984, relative_step_length=0.009403205128342224, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25252597]), radius=0.40937500000000004, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 2, 19, 23, 26, 27, 28]), model=ScalarModel(intercept=0.64214537534659, linear_terms=array([4.36033832e-06]), square_terms=array([[0.01405292]]), scale=0.40937500000000004, shift=array([9.25252597])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=29, candidate_x=array([9.25239895]), index=29, x=array([9.25239895]), fval=0.6423583869781233, rho=700.8934507833044, accepted=True, new_indices=array([28]), old_indices_used=array([ 2, 19, 23, 26, 27]), old_indices_discarded=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, + 18, 20, 21, 22, 24, 25]), step_length=0.00012702084422144821, relative_step_length=0.0003102799248157513, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 30 entries., 'history': {'params': [{'CRRA': 8.1875}, {'CRRA': 7.36875}, {'CRRA': 9.00625}, {'CRRA': 7.36875}, {'CRRA': 7.36875}, {'CRRA': 9.00625}, {'CRRA': 7.36875}, {'CRRA': 7.36875}, {'CRRA': 9.00625}, {'CRRA': 7.36875}, {'CRRA': 9.00625}, {'CRRA': 7.36875}, {'CRRA': 9.00625}, {'CRRA': 9.00625}, {'CRRA': 7.3687499999999995}, {'CRRA': 9.026168169426626}, {'CRRA': 7.388668169426626}, {'CRRA': 9.038959312915338}, {'CRRA': 10.676459312915338}, {'CRRA': 9.290153724085462}, {'CRRA': 7.652653724085462}, {'CRRA': 9.235117007166322}, {'CRRA': 8.416367007166322}, {'CRRA': 9.1520466847697}, {'CRRA': 9.644492007166322}, {'CRRA': 9.248676532750787}, {'CRRA': 9.658051532750788}, {'CRRA': 9.252525969850202}, {'CRRA': 9.661900969850203}, {'CRRA': 9.25239894900598}], 'criterion': [0.6831279025699917, 0.7818234415442017, 0.6443932011688029, 0.7818234415442017, 0.7818234415442017, 0.6443932011688029, 0.7818234415442017, 0.7818234415442017, 0.6443932011688029, 0.7818234415442017, 0.6443932011688029, 0.7818234415442017, 0.6443932011688029, 0.6443932011688029, 0.7818234415442095, 0.6440402472439135, 0.7787614029619838, 0.6439435808488991, 0.7100198965757869, 0.642415138936857, 0.740451029529312, 0.6424025578938513, 0.6665435566959452, 0.6426661753949429, 0.6484718333650715, 0.6423803321514743, 0.6489393340909813, 0.642358861106374, 0.6490529814080408, 0.6423583869781233], 'runtime': [0.0, 3.074203699827194, 3.283573899883777, 3.498749100137502, 3.7044561998918653, 3.926607399713248, 4.110963100101799, 4.243899200111628, 4.541710100136697, 4.7094960999675095, 4.89852079981938, 5.075110800098628, 5.21526509989053, 12.235306499991566, 15.733508999925107, 16.80475839972496, 17.874723799992353, 18.94831439992413, 20.016572599764913, 21.089213099796325, 22.262267199810594, 23.334850599989295, 24.407207199838012, 25.481976099777967, 26.552139800041914, 27.62647379981354, 28.800836499780416, 29.875069699715823, 30.947488199919462, 32.024298400152475], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]}, 'multistart_info': {...}}], 'exploration_sample': array([[ 8.1875 ], + [10.55 ], + [ 6.61452875], + [12.9125 ], + [ 5.825 ], + [14.09375 ], + [15.275 ], + [ 4.64375 ], + [17.6375 ], + [ 3.4625 ]]), 'exploration_results': array([0.6831279 , 0.69939713, 0.93725113, 1.02360977, 1.18227674, + 1.26376333, 1.54278366, 1.78141341, 2.1808234 , 2.89794431])}}" diff --git a/src/estimark/content/tables/min/WarmGlowPortfolioBeta_estimate_results.csv b/src/estimark/content/tables/min/WarmGlowPortfolioBeta_estimate_results.csv new file mode 100644 index 0000000..619dfd7 --- /dev/null +++ b/src/estimark/content/tables/min/WarmGlowPortfolioBeta_estimate_results.csv @@ -0,0 +1,13113 @@ +CRRA,4.161270252410767 + +BeqFac,3932.5419104702305 + +DiscFac,0.9235618590930775 + +time_to_estimate,235.31548237800598 + +params,"{'CRRA': 4.161270252410767, 'BeqFac': 3932.5419104702305, 'DiscFac': 0.9235618590930775}" + +criterion,0.07906739186138749 + +start_criterion,0.14974471998399175 + +start_params,"{'CRRA': 4.972165469041286, 'BeqFac': 2235.668701887974, 'DiscFac': 1.0}" + +algorithm,multistart_tranquilo_ls + +direction,minimize + +n_free,3 + +message,Absolute criterion change smaller than tolerance. + +success, + +n_criterion_evaluations, + +n_derivative_evaluations, + +n_iterations, + +history,"{'params': [{'CRRA': 4.059438573758104, 'BeqFac': 3932.2768372119126, 'DiscFac': 0.8720884775777547}, {'CRRA': 1.1865624830963672, 'BeqFac': 3618.0088638043917, 'DiscFac': 1.1}, {'CRRA': 18.864750592221043, 'BeqFac': 3615.3369060843656, 'DiscFac': 1.0141895974609063}, {'CRRA': 20.0, 'BeqFac': 4139.2725657346655, 'DiscFac': 0.967186242030893}, {'CRRA': 11.813333909541619, 'BeqFac': 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69.41409680014476, 70.62386910011992, 71.77614930039272, 72.92142220027745, 74.430033700075, 74.61684990022331, 74.81267850007862, 75.00723180035129, 75.2044039000757, 75.3887424999848, 75.57385200029239, 75.76805710000917, 75.96694570034742, 76.16238610027358, 76.3546518003568, 76.55501760030165, 77.8122900002636], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 10, 11, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 15, 16, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 32, 33, 34, 35, 36, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 38, 39, 40, 41, 42, 43, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 45, 46, 47, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 49]}" + +convergence_report,"{'one_step': {'relative_criterion_change': 0.15773345928132843, 'relative_params_change': 0.4431666467150333, 'absolute_criterion_change': 0.015773345928132843, 'absolute_params_change': 1696.956220326838}, 'five_steps': {'relative_criterion_change': 0.15773345928132843, 'relative_params_change': 0.4431666467150333, 'absolute_criterion_change': 0.015773345928132843, 'absolute_params_change': 1696.956220326838}}" + +multistart_info,"{'start_parameters': [{'CRRA': 4.972165469041287, 'BeqFac': 2235.668701887974, 'DiscFac': 1.0}, {'CRRA': 4.059438573758104, 'BeqFac': 3932.2768372119126, 'DiscFac': 0.8720884775777547}, {'CRRA': 4.466570349505643, 'BeqFac': 4587.182667295986, 'DiscFac': 0.9284133483434123}], 'local_optima': [Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Relative criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 0*** 0.1692 +relative_params_change 0*** 0.05382 +absolute_criterion_change 0*** 0.01692 +absolute_params_change 0*** 0.2525 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 4.245e-07* 5.719e-06* +relative_params_change 4.911e-07* 1.068e-05 +absolute_criterion_change 4.245e-08* 5.719e-07* +absolute_params_change 1.875e-06* 4.663e-05 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 9.277e-06* 0.001793 +relative_params_change 4.611e-05 0.002492 +absolute_criterion_change 9.277e-07* 0.0001793 +absolute_params_change 0.0002187 0.01527 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 4.972165469041286, 'BeqFac': 2235.668701887974, 'DiscFac': 1.0}, {'CRRA': 3.4625, 'BeqFac': 6250.0, 'DiscFac': 0.7250000000000001}, {'CRRA': 5.824999999999999, 'BeqFac': 7500.0, 'DiscFac': 0.9500000000000001}, {'CRRA': 4.053125, 'BeqFac': 1562.5, 'DiscFac': 0.8187500000000001}, {'CRRA': 9.959375, 'BeqFac': 937.5, 'DiscFac': 1.00625}, {'CRRA': 7.596874999999999, 'BeqFac': 7187.5, 'DiscFac': 0.9312500000000001}, {'CRRA': 15.865624999999998, 'BeqFac': 7812.5, 'DiscFac': 0.89375}, {'CRRA': 14.684375, 'BeqFac': 3437.5, 'DiscFac': 0.8562500000000001}, {'CRRA': 5.234375, 'BeqFac': 8437.5, 'DiscFac': 0.55625}, {'CRRA': 19.409375, 'BeqFac': 5937.5, 'DiscFac': 0.70625}, {'CRRA': 8.1875, 'BeqFac': 3750.0, 'DiscFac': 0.875}, {'CRRA': 14.093749999999998, 'BeqFac': 8125.0, 'DiscFac': 0.7625000000000001}, {'CRRA': 12.9125, 'BeqFac': 1250.0, 'DiscFac': 1.0250000000000001}, {'CRRA': 18.81875, 'BeqFac': 625.0, 'DiscFac': 0.9125000000000001}, {'CRRA': 17.046875, 'BeqFac': 2187.5, 'DiscFac': 0.6312500000000001}, {'CRRA': 10.549999999999999, 'BeqFac': 5000.0, 'DiscFac': 0.8}, {'CRRA': 17.6375, 'BeqFac': 8750.0, 'DiscFac': 0.575}, {'CRRA': 15.274999999999999, 'BeqFac': 2500.0, 'DiscFac': 0.65}, {'CRRA': 6.415625, 'BeqFac': 2812.5, 'DiscFac': 0.59375}, {'CRRA': 2.871875, 'BeqFac': 4687.5, 'DiscFac': 0.78125}, {'CRRA': 8.778125, 'BeqFac': 9062.5, 'DiscFac': 0.66875}, {'CRRA': 7.00625, 'BeqFac': 1875.0, 'DiscFac': 0.6875}, {'CRRA': 9.368749999999999, 'BeqFac': 5625.0, 'DiscFac': 0.6125}, {'CRRA': 16.45625, 'BeqFac': 6875.0, 'DiscFac': 0.9875}, {'CRRA': 13.503124999999999, 'BeqFac': 6562.5, 'DiscFac': 0.51875}, {'CRRA': 11.73125, 'BeqFac': 4375.0, 'DiscFac': 0.5375}, {'CRRA': 18.228125, 'BeqFac': 4062.5, 'DiscFac': 0.96875}, {'CRRA': 4.64375, 'BeqFac': 3125.0, 'DiscFac': 1.0625}, {'CRRA': 12.321874999999999, 'BeqFac': 9687.5, 'DiscFac': 1.08125}, {'CRRA': 2.28125, 'BeqFac': 9375.0, 'DiscFac': 0.8375}], 'exploration_results': array([ 0.15183336, 0.16963129, 0.3197984 , 0.64023668, 0.65671129, + 0.81183081, 0.97682658, 1.08889746, 1.16160077, 1.27832959, + 1.4056054 , 1.63187357, 1.68853551, 1.72432777, 1.75966476, + 1.80211806, 1.86015904, 1.93985391, 1.96313642, 2.01014295, + 2.05048939, 2.06445231, 2.29147195, 2.43324345, 2.49173392, + 2.54550964, 3.05245049, 4.00840374, 4.33350039, 58.25645251])}" + +algorithm_output,"{'states': [State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=393.2276837211913, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=[0], model=ScalarModel(intercept=0.12472562593739224, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), scale=393.2276837211913, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 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upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=101.88864067521602, linear_terms=array([-301.48531903, -14.07496772, 134.91730104]), square_terms=array([[ 450.60368506, 21.0269611 , -198.92102364], + [ 21.0269611 , 0.9962047 , -9.22847607], + [-198.92102364, -9.22847607, 89.91310769]]), scale=array([9.45000000e+00, 3.16939931e+02, 3.00000000e-01]), shift=array([1.05500000e+01, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), candidate_index=13, candidate_x=array([1.26959565e+01, 4.24921677e+03, 5.31351377e-01]), index=0, x=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), fval=0.12472562593739225, rho=-0.004991694207075647, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=196.61384186059564, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=102.38671386491994, linear_terms=array([-301.74886293, -7.4096688 , 135.40964517]), square_terms=array([[ 4.49001106e+02, 1.10387141e+01, -1.98898743e+02], + [ 1.10387141e+01, 2.79198932e-01, 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old_indices_used=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_discarded=array([ 2, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=98.30692093029782, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 14]), model=ScalarModel(intercept=116.12151198406633, linear_terms=array([-321.94879911, 8.08795269, 184.9444489 ]), square_terms=array([[ 4.50244477e+02, -1.12411579e+01, -2.55940799e+02], + [-1.12411579e+01, 2.84655841e-01, 6.47245223e+00], + [-2.55940799e+02, 6.47245223e+00, 1.47960373e+02]]), scale=array([ 9.45 , 79.23498278, 0.3 ]), shift=array([1.05500000e+01, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, 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bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 3, 8, 9, 14, 15]), model=ScalarModel(intercept=36.477921419964005, linear_terms=array([-138.85628947, 17.34063992, 102.44589447]), square_terms=array([[ 268.01780029, -33.176478 , -194.8794462 ], + [ -33.176478 , 4.1379421 , 24.44585578], + [-194.8794462 , 24.44585578, 145.53102569]]), scale=array([ 9.45 , 39.61749139, 0.3 ]), shift=array([1.05500000e+01, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], 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shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), candidate_index=16, candidate_x=array([1.21980290e+01, 3.89265935e+03, 7.09268596e-01]), index=0, x=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), fval=0.12472562593739225, rho=-0.0077322181559090096, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 3, 8, 9, 14, 15]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=24.576730232574455, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 8, 9, 15, 16]), model=ScalarModel(intercept=137.95900758299246, linear_terms=array([ 609.72371975, 81.97626858, 1094.06151597]), square_terms=array([[1348.85665695, 181.37053476, 2420.79025551], + [ 181.37053476, 24.39149366, 325.54412825], + [2420.79025551, 325.54412825, 4345.09263337]]), scale=array([ 9.45 , 19.8087457, 0.3 ]), shift=array([1.05500000e+01, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 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old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=12.288365116287228, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 8, 16, 17]), model=ScalarModel(intercept=675.1794019801806, linear_terms=array([ -2064.6019009 , 718.07582713, -10217.06091518]), square_terms=array([[ 3158.11858269, -1098.22603339, 15626.2724666 ], + [-1098.22603339, 381.93267203, -5434.33216723], + [15626.2724666 , -5434.33216723, 77322.68695368]]), scale=array([6.43190571, 9.90437285, 0.3 ]), shift=array([7.53190571e+00, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, 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20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=108.75147073194, linear_terms=array([-309.53365492, -36.53619492, 161.80376403]), square_terms=array([[ 441.33077962, 52.10175602, -230.15109169], + [ 52.10175602, 6.15213254, -27.15013825], + [-230.15109169, -27.15013825, 120.92652934]]), scale=array([3.9558125 , 4.95218642, 0.3 ]), shift=array([5.05581250e+00, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), 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candidate_index=31, candidate_x=array([6.44025805e+00, 3.93722902e+03, 6.65771609e-01]), index=0, x=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), fval=0.12472562593739225, rho=-0.00640748988321916, accepted=False, new_indices=array([19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=3.072091279071807, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31]), model=ScalarModel(intercept=170.59582545012594, linear_terms=array([-277.08221691, 5.5992395 , 233.76105829]), square_terms=array([[ 2.25373873e+02, -4.61116778e+00, -1.89643367e+02], + [-4.61116778e+00, 1.11464760e-01, 3.74415217e+00], + [-1.89643367e+02, 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25, 26, 27, 28, 29, 30, 31]), old_indices_discarded=array([18, 19, 24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=1.5360456395359035, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 20, 21, 22, 23, 26, 27, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=138.3462289851189, linear_terms=array([-147.45463528, -5.65111698, 268.26306503]), square_terms=array([[ 7.86989538e+01, 3.00126776e+00, -1.42903701e+02], + [ 3.00126776e+00, 1.18067220e-01, -5.51395279e+00], + [-1.42903701e+02, -5.51395279e+00, 2.60665493e+02]]), scale=array([1.23804661, 1.23804661, 0.3 ]), shift=array([4.05943857e+00, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , 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bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 23, 27, 32, 33]), model=ScalarModel(intercept=1.5830056559661339, linear_terms=array([ 9.99281175, 14.43614264, -39.57256129]), square_terms=array([[ 33.80983754, 48.83787417, -133.54382225], + [ 48.83787417, 70.60333873, -193.11510202], + [-133.54382225, -193.11510202, 528.45490937]]), scale=array([0.6190233, 0.6190233, 0.3 ]), shift=array([4.05943857e+00, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), candidate_index=34, candidate_x=array([3.77141395e+00, 3.93289586e+03, 8.96820681e-01]), index=0, x=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), fval=0.12472562593739225, rho=-0.0007867459648932072, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 23, 27, 32, 33]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=0.38401140988397586, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=0.5847700831496822, linear_terms=array([-0.41675462, -0.44190085, 2.89851388]), square_terms=array([[ 0.18454675, 0.18631726, -1.20807895], + [ 0.18631726, 0.19477978, 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43, 44, 45, 46]), old_indices_used=array([ 0, 33, 34]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=0.19200570494198793, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 35, 36, 38, 39, 40, 41, 42, 44, 45, 46, 47]), model=ScalarModel(intercept=1.2415835967758433, linear_terms=array([-0.15283733, -0.58871896, 3.13992098]), square_terms=array([[ 0.02040704, 0.03863901, -0.21039381], + [ 0.03863901, 0.14956944, -0.79740606], + [-0.21039381, -0.79740606, 4.2608185 ]]), scale=0.19200570494198793, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, 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upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 35, 36, 39, 40, 41, 42, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=1.0337220474595914, linear_terms=array([-0.20696303, -0.31059194, 1.61730455]), square_terms=array([[ 0.02606724, 0.03394087, -0.17786586], + [ 0.03394087, 0.05073282, -0.26413928], + [-0.17786586, -0.26413928, 1.3775637 ]]), scale=0.09600285247099397, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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8.72088478e-01])), candidate_index=49, candidate_x=array([4.03665655e+00, 3.93225811e+03, 7.80726618e-01]), index=0, x=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), fval=0.12472562593739225, rho=-0.2193495890214448, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 35, 36, 39, 40, 41, 42, 44, 45, 46, 47, 48]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=0.04800142623549698, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=0.11633665599043491, linear_terms=array([ 0.01692106, -0.00599296, -0.07183474]), square_terms=array([[ 0.00430738, -0.00138782, -0.01722234], + [-0.00138782, 0.00047598, 0.00583328], + 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State(trustregion=Region(center=array([4.15738965e+00, 3.93233146e+03, 9.28205841e-01]), radius=0.19200570494198793, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 50, 51, 54, 55, 56, 57, 59, 60, 61, 62, 63]), model=ScalarModel(intercept=0.08002590130028917, linear_terms=array([-0.00526566, 0.000661 , 0.04112777]), square_terms=array([[ 2.05912030e-02, -2.69140553e-03, -1.45369821e-01], + [-2.69140553e-03, 4.27142888e-04, 2.22767331e-02], + [-1.45369821e-01, 2.22767331e-02, 1.17421707e+00]]), scale=array([0.15475583, 0.15475583, 0.15475583]), shift=array([4.15738965e+00, 3.93233146e+03, 9.28205841e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + 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model_indices=array([41, 42, 44, 47, 48, 49, 54, 55, 59, 61, 63, 64]), model=ScalarModel(intercept=0.15326138046317392, linear_terms=array([0.55415723, 0.00612001, 0.65724766]), square_terms=array([[2.16178448e+00, 2.69601220e-02, 2.58028293e+00], + [2.69601220e-02, 3.98421161e-04, 3.15716605e-02], + [2.58028293e+00, 3.15716605e-02, 3.11749315e+00]]), scale=array([0.30951165, 0.30951165, 0.24443416]), shift=array([4.16380226e+00, 3.93248621e+03, 8.55565845e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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3.93227684e+03, 8.72088478e-01])), candidate_index=65, candidate_x=array([4.03801811e+00, 3.93261118e+03, 8.85249640e-01]), index=64, x=array([4.16380226e+00, 3.93248621e+03, 9.20643341e-01]), fval=0.0797111995279562, rho=-0.04919187191422923, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([41, 42, 44, 47, 48, 49, 54, 55, 59, 61, 63, 64]), old_indices_discarded=array([ 0, 33, 34, 35, 36, 37, 38, 39, 40, 43, 45, 46, 50, 51, 52, 53, 56, + 57, 58, 60, 62]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16380226e+00, 3.93248621e+03, 9.20643341e-01]), radius=0.19200570494198793, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 42, 48, 50, 51, 52, 53, 58, 62, 63, 64, 65]), model=ScalarModel(intercept=0.07981427686533094, linear_terms=array([ 8.23222295e-04, -3.01457336e-06, -1.94680885e-04]), square_terms=array([[ 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fval=0.0797111995279562, rho=-0.4172818202820011, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 42, 48, 50, 51, 52, 53, 58, 62, 63, 64, 65]), old_indices_discarded=array([34, 35, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 49, 54, 55, 56, + 57, 59, 60, 61]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16380226e+00, 3.93248621e+03, 9.20643341e-01]), radius=0.09600285247099397, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([42, 48, 50, 51, 52, 53, 58, 62, 63, 64, 65, 66]), model=ScalarModel(intercept=0.0797875761950249, linear_terms=array([ 5.31755989e-04, -2.45518194e-07, -8.66641581e-04]), square_terms=array([[ 1.65529454e-03, -4.13667194e-05, -8.39813544e-03], + [-4.13667194e-05, 8.98752859e-06, 1.51601069e-03], + [-8.39813544e-03, 1.51601069e-03, 2.58158286e-01]]), scale=0.09600285247099397, shift=array([4.16380226e+00, 3.93248621e+03, 9.20643341e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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45, 47, 49, 54, 55, 56, 57, 59, 60, 61]), step_length=0.10215613340917244, relative_step_length=1.0640947719760474, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12942468e+00, 3.93239001e+03, 9.20412203e-01]), radius=0.19200570494198793, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 50, 51, 54, 55, 56, 57, 59, 60, 63, 64, 67]), model=ScalarModel(intercept=0.0794221211152619, linear_terms=array([-8.78763547e-04, -7.32595189e-05, 1.57498998e-03]), square_terms=array([[ 1.88292059e-02, -8.43669110e-04, -1.35637703e-01], + [-8.43669110e-04, 5.18412063e-05, 7.55602994e-03], + [-1.35637703e-01, 7.55602994e-03, 1.13549976e+00]]), scale=array([0.15475583, 0.15475583, 0.15475583]), shift=array([4.12942468e+00, 3.93239001e+03, 9.20412203e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, 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State(trustregion=Region(center=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01]), radius=0.38401140988397586, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([41, 42, 44, 47, 48, 49, 55, 63, 65, 66, 67, 68]), model=ScalarModel(intercept=0.11467755048322094, linear_terms=array([ 0.4301884 , -0.10852269, 0.38403869]), square_terms=array([[ 2.8059872 , -0.69595835, 2.53665519], + [-0.69595835, 0.17347376, -0.6322991 ], + [ 2.53665519, -0.6322991 , 2.32273428]]), scale=array([0.30951165, 0.30951165, 0.24294931]), shift=array([4.16663860e+00, 3.93254477e+03, 8.57050687e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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51, 53, 63, 64, 65, 66, 67, 68]), model=ScalarModel(intercept=0.07972737754656194, linear_terms=array([0.00089106, 0.00026728, 0.00741102]), square_terms=array([[3.83952175e-03, 2.37615327e-04, 1.80862528e-04], + [2.37615327e-04, 1.04844950e-03, 2.29140497e-02], + [1.80862528e-04, 2.29140497e-02, 5.09344565e-01]]), scale=array([0.15475583, 0.15475583, 0.15475583]), shift=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), 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candidate_index=70, candidate_x=array([4.12157965e+00, 3.93269953e+03, 9.14415260e-01]), index=68, x=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01]), fval=0.07908472064494197, rho=-4.697657825504714, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([42, 47, 48, 50, 51, 53, 63, 64, 65, 66, 67, 68]), old_indices_discarded=array([ 0, 34, 35, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 49, 52, 54, 55, + 56, 57, 58, 59, 60, 61, 62, 69]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01]), radius=0.09600285247099397, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([42, 47, 48, 50, 51, 63, 64, 65, 66, 67, 68, 70]), model=ScalarModel(intercept=0.07970568860996624, linear_terms=array([5.17688765e-04, 7.08684557e-05, 2.23636029e-03]), square_terms=array([[1.54434855e-03, 1.10165119e-04, 2.02375366e-03], + [1.10165119e-04, 1.32782811e-04, 4.89488107e-03], + [2.02375366e-03, 4.89488107e-03, 1.84174468e-01]]), scale=0.09600285247099397, shift=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + 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accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([42, 47, 48, 50, 51, 63, 64, 65, 66, 67, 68, 70]), old_indices_discarded=array([ 0, 35, 38, 41, 44, 45, 49, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, + 62, 69]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01]), radius=0.04800142623549698, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([42, 48, 63, 64, 65, 66, 67, 68, 70, 71]), model=ScalarModel(intercept=0.07966045626129445, linear_terms=array([3.20987313e-04, 4.92207090e-05, 2.88283811e-03]), square_terms=array([[ 3.79212673e-04, -2.34327242e-06, -5.41146655e-06], + [-2.34327242e-06, 8.52228643e-06, -6.54738778e-04], + [-5.41146655e-06, -6.54738778e-04, 5.14897659e-02]]), scale=0.04800142623549698, shift=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + 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8.72088478e-01])), candidate_index=89, candidate_x=array([4.15545357e+00, 3.93254035e+03, 9.22788650e-01]), index=87, x=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01]), fval=0.07907999189313741, rho=-0.3798594883018238, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([68, 75, 76, 77, 78, 79, 80, 81, 83, 84, 86, 87]), old_indices_discarded=array([74, 82, 85, 88]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01]), radius=0.0030000891397185614, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([68, 75, 76, 77, 78, 79, 80, 81, 83, 84, 87, 89]), model=ScalarModel(intercept=0.07916583626576876, linear_terms=array([ 3.46835434e-05, 1.37176019e-05, -3.87853430e-06]), square_terms=array([[ 1.16702087e-05, -1.13065279e-06, -1.02787515e-04], + [-1.13065279e-06, 1.19282548e-07, 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x=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), fval=0.07906796375411648, rho=-1.0803986646623365, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 87, 92, 93, 96, 97, 98, 99, 100, 101, 102, 103, 104]), old_indices_discarded=array([68, 77, 83, 89, 90, 91, 94, 95]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), radius=0.0007500222849296404, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 87, 92, 93, 94, 95, 96, 97, 98, 99, 101, 102, 104]), model=ScalarModel(intercept=0.07909160054167635, linear_terms=array([-1.29680002e-05, 2.27817382e-06, 1.03758748e-06]), square_terms=array([[ 9.19566779e-07, -3.49328357e-08, -6.93344117e-06], + [-3.49328357e-08, 6.61278422e-09, 2.68258739e-07], + [-6.93344117e-06, 2.68258739e-07, 5.96608551e-05]]), 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bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 87, 94, 95, 98, 102, 104, 106, 107]), model=ScalarModel(intercept=0.0790879150221595, linear_terms=array([-6.18729811e-06, 2.85069426e-06, 5.24029282e-06]), square_terms=array([[ 6.39029879e-08, -5.52613342e-09, -4.58386220e-07], + [-5.52613342e-09, 1.01866206e-09, 2.93309494e-08], + [-4.58386220e-07, 2.93309494e-08, 3.89516694e-06]]), scale=0.0001875055712324101, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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+ [ 4.53199229e-08, 4.98555993e-08, 7.22290924e-08], + [-4.64131654e-08, 7.22290924e-08, 1.14321313e-06]]), scale=9.375278561620504e-05, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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new_indices=array([], dtype=int32), old_indices_used=array([ 95, 104, 107, 108]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), radius=4.687639280810252e-05, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([104, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, + 120, 121]), model=ScalarModel(intercept=0.07907082869590354, linear_terms=array([ 2.88136945e-06, -3.08394182e-07, -1.07721092e-06]), square_terms=array([[ 4.46329859e-09, -6.05308320e-10, -2.92877945e-08], + [-6.05308320e-10, 1.34067088e-10, 3.43225360e-09], + [-2.92877945e-08, 3.43225360e-09, 2.44027182e-07]]), scale=4.687639280810252e-05, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + 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State(trustregion=Region(center=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), radius=2.343819640405126e-05, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([104, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120]), model=ScalarModel(intercept=0.07907053144557313, linear_terms=array([ 1.94854337e-07, -3.55132795e-07, -5.64262326e-07]), square_terms=array([[ 1.08597546e-09, 1.16175887e-10, -7.41141155e-09], + [ 1.16175887e-10, 3.16258142e-11, -6.25413742e-10], + [-7.41141155e-09, -6.25413742e-10, 5.96099329e-08]]), scale=2.343819640405126e-05, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 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120, 123]), model=ScalarModel(intercept=0.07907067153514401, linear_terms=array([ 2.04407661e-07, -2.51465066e-07, -2.26280892e-07]), square_terms=array([[ 2.06977844e-10, 4.21146920e-11, -1.67593686e-09], + [ 4.21146920e-11, 2.48475075e-11, -3.05589732e-10], + [-1.67593686e-09, -3.05589732e-10, 1.50170769e-08]]), scale=1.171909820202563e-05, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 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candidate_x=array([4.16126418e+00, 3.93254192e+03, 9.23574260e-01]), index=104, x=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), fval=0.07906796375411648, rho=-0.2558930827717976, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([104, 110, 112, 113, 114, 115, 116, 117, 118, 119, 120, 123]), old_indices_discarded=array([111, 121, 122]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), radius=5.859549101012815e-06, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([104, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, + 135, 136]), model=ScalarModel(intercept=0.07906789905966807, linear_terms=array([2.53618684e-09, 2.53785426e-08, 3.95194505e-07]), square_terms=array([[ 5.35172911e-11, -7.04580653e-12, -3.54919326e-10], + [-7.04580653e-12, 3.06788632e-12, 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square_terms=array([[0.29433574, 0.05189857, 0.69211431], + [0.05189857, 0.01569971, 0.200252 ], + [0.69211431, 0.200252 , 2.57727413]]), scale=array([ 7.56714517, 11.26212487, 0.3 ]), shift=array([8.66714517e+00, 2.23566870e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + 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rho=-22860.524324555063, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 9, 15, 16]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), radius=6.986464693399919, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 9, 16, 17]), model=ScalarModel(intercept=467.19314322074354, linear_terms=array([-1065.72378965, 378.28985285, -1698.61903515]), square_terms=array([[1215.92010216, -431.55891012, 1937.88572934], + [-431.55891012, 153.18465907, -687.77512457], + [1937.88572934, -687.77512457, 3088.84974448]]), scale=array([4.75161395, 5.63106244, 0.3 ]), shift=array([5.85161395e+00, 2.23566870e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 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bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 9, 17, 18]), model=ScalarModel(intercept=631.0068374845937, linear_terms=array([ -713.42087297, 227.74637403, -1894.75839998]), square_terms=array([[ 403.38912904, -128.76975477, 1071.25267549], + [-128.76975477, 41.14741941, -341.84595008], + [1071.25267549, -341.84595008, 2845.4276814 ]]), scale=array([2.81553122, 2.81553122, 0.3 ]), shift=array([4.97216547e+00, 2.23566870e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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shift=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00])), candidate_index=19, candidate_x=array([5.22498136e+00, 2.23285317e+03, 9.53585466e-01]), index=0, x=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), fval=0.15183336484019694, rho=-3.163059122264521, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 9, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), radius=1.7466161733499797, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]), model=ScalarModel(intercept=0.14790847307992083, linear_terms=array([-0.33207427, 0.0835986 , 0.80229467]), square_terms=array([[ 1.61949403, -0.29564339, -2.97830876], + [-0.29564339, 0.059162 , 0.58807027], + 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old_indices_used=array([ 0, 18, 19]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), radius=0.8733080866749898, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=0.11589494066709435, linear_terms=array([ 0.16454974, 0.04806004, -0.44293767]), square_terms=array([[ 0.76924547, 0.27501651, -2.87758723], + [ 0.27501651, 0.09970594, -1.04942116], + [-2.87758723, -1.04942116, 11.20509916]]), scale=array([0.7038828, 0.7038828, 0.3 ]), shift=array([4.97216547e+00, 2.23566870e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, 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upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 21, 22, 23, 24, 27, 28, 29, 30, 31, 32, 33]), model=ScalarModel(intercept=0.4031517845766621, linear_terms=array([-0.23678438, -0.13921737, 1.90006104]), square_terms=array([[ 0.14109853, 0.0676179 , -0.91471804], + [ 0.0676179 , 0.03464796, -0.47018588], + [-0.91471804, -0.47018588, 6.40611468]]), scale=array([0.3519414, 0.3519414, 0.2259707]), shift=array([4.97216547e+00, 2.23566870e+03, 8.74029299e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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2.23566870e+03, 1.00000000e+00])), candidate_index=34, candidate_x=array([4.62022407e+00, 2.23602064e+03, 8.03652173e-01]), index=0, x=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), fval=0.15183336484019694, rho=-0.31192568841383445, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 21, 22, 23, 24, 27, 28, 29, 30, 31, 32, 33]), old_indices_discarded=array([20, 25, 26]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), radius=0.21832702166874746, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=0.3311781411225237, linear_terms=array([-0.15829022, -0.12201639, 1.37588212]), square_terms=array([[ 0.07100082, 0.05048831, -0.56524959], + [ 0.05048831, 0.03638561, -0.40733887], + 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old_indices_used=array([ 0, 33, 34]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), radius=0.10916351083437373, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 35, 36, 38, 39, 40, 41, 42, 44, 45, 46, 47]), model=ScalarModel(intercept=0.7819308994641678, linear_terms=array([ 0.0074579 , -0.0259874 , 1.71946661]), square_terms=array([[ 5.56013887e-04, 1.44714906e-04, -1.22278649e-03], + [ 1.44714906e-04, 6.29275388e-04, -3.58652012e-02], + [-1.22278649e-03, -3.58652012e-02, 2.24563302e+00]]), scale=array([0.08798535, 0.08798535, 0.08798535]), shift=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], 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bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 35, 36, 39, 40, 41, 42, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=0.684129096465268, linear_terms=array([-0.0273062 , -0.01343205, 1.05830747]), square_terms=array([[ 1.32604981e-03, 5.31204096e-04, -3.19319385e-02], + [ 5.31204096e-04, 2.27658842e-04, -1.40325629e-02], + [-3.19319385e-02, -1.40325629e-02, 1.00415127e+00]]), scale=0.054581755417186864, shift=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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scale=223.5668701887974, shift=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00])), candidate_index=49, candidate_x=array([4.95637410e+00, 2.23565341e+03, 9.50041339e-01]), index=0, x=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), fval=0.15183336484019694, rho=-0.30404049502893676, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 35, 36, 39, 40, 41, 42, 44, 45, 46, 47, 48]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), radius=0.027290877708593432, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=0.1606884730144318, linear_terms=array([-0.00451786, -0.00405289, 0.12722605]), square_terms=array([[ 0.00114205, 0.00061019, 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radius=0.027290877708593432, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([63, 64, 66, 67]), model=ScalarModel(intercept=0.11957247313888936, linear_terms=array([ 0.00391841, -0.01304193, -0.02667194]), square_terms=array([[ 0.00027908, -0.00230693, -0.00403301], + [-0.00230693, 0.02558587, 0.04322636], + [-0.00403301, 0.04322636, 0.07397422]]), scale=0.027290877708593432, shift=array([4.77904943e+00, 2.23566008e+03, 9.80374550e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 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shift=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00])), candidate_index=68, candidate_x=array([4.75839345e+00, 2.23564895e+03, 9.94942715e-01]), index=64, x=array([4.77904943e+00, 2.23566008e+03, 9.80374550e-01]), fval=0.11957247313888911, rho=-1.8438302886729045, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([63, 64, 66, 67]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.77904943e+00, 2.23566008e+03, 9.80374550e-01]), radius=0.013645438854296716, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([64, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80]), model=ScalarModel(intercept=0.1186316056712768, linear_terms=array([ 0.00347483, 0.00169535, -0.01719098]), square_terms=array([[ 0.00068357, 0.00053277, -0.00534077], + [ 0.00053277, 0.00042502, -0.00424712], + 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67, 68]), old_indices_discarded=array([], dtype=int32), step_length=0.014257763675346667, relative_step_length=1.0448739558755298, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.76525937e+00, 2.23565983e+03, 9.83987893e-01]), radius=0.027290877708593432, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([64, 68, 69, 70, 73, 74, 75, 76, 78, 79, 80, 81]), model=ScalarModel(intercept=0.1138846587026767, linear_terms=array([ 0.00286786, 0.0001056 , -0.00362309]), square_terms=array([[ 7.58823322e-04, 1.78136736e-04, -1.27262497e-02], + [ 1.78136736e-04, 4.56758695e-05, -3.21711156e-03], + [-1.27262497e-02, -3.21711156e-03, 2.34475273e-01]]), scale=0.027290877708593432, shift=array([4.76525937e+00, 2.23565983e+03, 9.83987893e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + 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radius=0.054581755417186864, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([64, 68, 69, 70, 72, 74, 75, 76, 78, 79, 80, 82]), model=ScalarModel(intercept=0.1114368893687521, linear_terms=array([ 0.00547932, 0.00034892, -0.01217237]), square_terms=array([[ 1.63383022e-03, 6.41824887e-04, -3.65795371e-02], + [ 6.41824887e-04, 2.94150536e-04, -1.66544475e-02], + [-3.65795371e-02, -1.66544475e-02, 9.68829317e-01]]), scale=0.054581755417186864, shift=array([4.73797930e+00, 2.23565930e+03, 9.82933296e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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model=ScalarModel(intercept=0.107827872396005, linear_terms=array([0.00271801, 0.00465108, 0.0595416 ]), square_terms=array([[ 0.01913536, -0.00597598, -0.17725347], + [-0.00597598, 0.00225162, 0.06079365], + [-0.17725347, 0.06079365, 1.72780408]]), scale=0.10916351083437373, shift=array([4.68340853e+00, 2.23565787e+03, 9.81541008e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + 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x=array([4.68340853e+00, 2.23565787e+03, 9.81541008e-01]), fval=0.10637576541348984, rho=-0.4435220960006037, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([64, 66, 67, 68, 70, 74, 75, 76, 79, 80, 82, 83]), old_indices_discarded=array([ 0, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 65, 69, 71, + 72, 73, 77, 78, 81]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.68340853e+00, 2.23565787e+03, 9.81541008e-01]), radius=0.054581755417186864, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([64, 66, 67, 68, 71, 72, 75, 77, 80, 81, 82, 83]), model=ScalarModel(intercept=0.10753665968218919, linear_terms=array([0.00205224, 0.00035594, 0.02486512]), square_terms=array([[ 4.13042323e-03, 1.27964751e-03, -4.10755431e-02], + [ 1.27964751e-03, 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model=ScalarModel(intercept=102.38671386491994, linear_terms=array([-301.74886293, -7.4096688 , 135.40964517]), square_terms=array([[ 4.49001106e+02, 1.10387141e+01, -1.98898743e+02], + [ 1.10387141e+01, 2.79198932e-01, -4.83711708e+00], + [-1.98898743e+02, -4.83711708e+00, 9.01425346e+01]]), scale=array([ 9.45 , 158.46996556, 0.3 ]), shift=array([1.05500000e+01, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + 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16]), model=ScalarModel(intercept=137.95900758299246, linear_terms=array([ 609.72371975, 81.97626858, 1094.06151597]), square_terms=array([[1348.85665695, 181.37053476, 2420.79025551], + [ 181.37053476, 24.39149366, 325.54412825], + [2420.79025551, 325.54412825, 4345.09263337]]), scale=array([ 9.45 , 19.8087457, 0.3 ]), shift=array([1.05500000e+01, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + 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3.91246809e+03, 9.14078548e-01]), index=0, x=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), fval=0.12472562593739225, rho=-51.69148171099177, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 8, 9, 15, 16]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=12.288365116287228, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 8, 16, 17]), model=ScalarModel(intercept=675.1794019801806, linear_terms=array([ -2064.6019009 , 718.07582713, -10217.06091518]), square_terms=array([[ 3158.11858269, -1098.22603339, 15626.2724666 ], + [-1098.22603339, 381.93267203, -5434.33216723], + [15626.2724666 , -5434.33216723, 77322.68695368]]), scale=array([6.43190571, 9.90437285, 0.3 ]), shift=array([7.53190571e+00, 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model=ScalarModel(intercept=170.59582545012594, linear_terms=array([-277.08221691, 5.5992395 , 233.76105829]), square_terms=array([[ 2.25373873e+02, -4.61116778e+00, -1.89643367e+02], + [-4.61116778e+00, 1.11464760e-01, 3.74415217e+00], + [-1.89643367e+02, 3.74415217e+00, 1.60694491e+02]]), scale=array([2.47609321, 2.47609321, 0.3 ]), shift=array([4.05943857e+00, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + 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candidate_x=array([5.66682758e+00, 3.92980074e+03, 6.00414976e-01]), index=0, x=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), fval=0.12472562593739225, rho=-0.006388659400412666, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31]), old_indices_discarded=array([18, 19, 24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=1.5360456395359035, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 20, 21, 22, 23, 26, 27, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=138.3462289851189, linear_terms=array([-147.45463528, -5.65111698, 268.26306503]), square_terms=array([[ 7.86989538e+01, 3.00126776e+00, -1.42903701e+02], + [ 3.00126776e+00, 1.18067220e-01, -5.51395279e+00], + [-1.42903701e+02, -5.51395279e+00, 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30, 31, 32]), old_indices_discarded=array([19, 24, 25]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=0.7680228197679517, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 23, 27, 32, 33]), model=ScalarModel(intercept=1.5830056559661339, linear_terms=array([ 9.99281175, 14.43614264, -39.57256129]), square_terms=array([[ 33.80983754, 48.83787417, -133.54382225], + [ 48.83787417, 70.60333873, -193.11510202], + [-133.54382225, -193.11510202, 528.45490937]]), scale=array([0.6190233, 0.6190233, 0.3 ]), shift=array([4.05943857e+00, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, 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34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=0.5847700831496822, linear_terms=array([-0.41675462, -0.44190085, 2.89851388]), square_terms=array([[ 0.18454675, 0.18631726, -1.20807895], + [ 0.18631726, 0.19477978, -1.27421791], + [-1.20807895, -1.27421791, 8.3918659 ]]), scale=array([0.30951165, 0.30951165, 0.26871159]), shift=array([4.05943857e+00, 3.93227684e+03, 8.31288413e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), 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candidate_index=47, candidate_x=array([3.96066338e+00, 3.93258635e+03, 7.66932590e-01]), index=0, x=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), fval=0.12472562593739225, rho=-0.1708886923662552, accepted=False, new_indices=array([35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]), old_indices_used=array([ 0, 33, 34]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=0.19200570494198793, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 35, 36, 38, 39, 40, 41, 42, 44, 45, 46, 47]), model=ScalarModel(intercept=1.2415835967758433, linear_terms=array([-0.15283733, -0.58871896, 3.13992098]), square_terms=array([[ 0.02040704, 0.03863901, -0.21039381], + [ 0.03863901, 0.14956944, -0.79740606], + [-0.21039381, -0.79740606, 4.2608185 ]]), scale=0.19200570494198793, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + 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old_indices_discarded=array([34, 37, 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=0.09600285247099397, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 35, 36, 39, 40, 41, 42, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=1.0337220474595914, linear_terms=array([-0.20696303, -0.31059194, 1.61730455]), square_terms=array([[ 0.02606724, 0.03394087, -0.17786586], + [ 0.03394087, 0.05073282, -0.26413928], + [-0.17786586, -0.26413928, 1.3775637 ]]), scale=0.09600285247099397, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + 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model_indices=array([ 0, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=0.11633665599043491, linear_terms=array([ 0.01692106, -0.00599296, -0.07183474]), square_terms=array([[ 0.00430738, -0.00138782, -0.01722234], + [-0.00138782, 0.00047598, 0.00583328], + [-0.01722234, 0.00583328, 0.07191651]]), scale=0.04800142623549698, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]]), scale=393.2276837211913, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), candidate_index=62, candidate_x=array([4.06698105e+00, 3.93229322e+03, 9.20092104e-01]), index=62, x=array([4.06698105e+00, 3.93229322e+03, 9.20092104e-01]), fval=0.08328203055052266, rho=1.1521105575858042, accepted=True, new_indices=array([50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61]), old_indices_used=array([ 0, 48, 49]), old_indices_discarded=array([], dtype=int32), step_length=0.05128066423434844, relative_step_length=1.0683154284367173, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.06698105e+00, 3.93229322e+03, 9.20092104e-01]), radius=0.09600285247099397, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 50, 51, 54, 55, 56, 57, 58, 59, 60, 61, 62]), model=ScalarModel(intercept=0.08089110047653379, linear_terms=array([-0.00528525, -0.0001229 , 0.02824264]), square_terms=array([[ 1.16009632e-02, 2.30117604e-04, -6.86298394e-02], + [ 2.30117604e-04, 5.27144510e-06, 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50, 51, 54, 55, 56, 57, 58, 59, 60, 61, 62]), old_indices_discarded=array([35, 36, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 52, 53]), step_length=0.09849549625031004, relative_step_length=1.025964267885365, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.15738965e+00, 3.93233146e+03, 9.28205841e-01]), radius=0.19200570494198793, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 50, 51, 54, 55, 56, 57, 59, 60, 61, 62, 63]), model=ScalarModel(intercept=0.08002590130028917, linear_terms=array([-0.00526566, 0.000661 , 0.04112777]), square_terms=array([[ 2.05912030e-02, -2.69140553e-03, -1.45369821e-01], + [-2.69140553e-03, 4.27142888e-04, 2.22767331e-02], + [-1.45369821e-01, 2.22767331e-02, 1.17421707e+00]]), scale=array([0.15475583, 0.15475583, 0.15475583]), shift=array([4.15738965e+00, 3.93233146e+03, 9.28205841e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 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upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 42, 48, 50, 51, 52, 53, 58, 62, 63, 64, 65]), model=ScalarModel(intercept=0.07981427686533094, linear_terms=array([ 8.23222295e-04, -3.01457336e-06, -1.94680885e-04]), square_terms=array([[ 4.38349908e-03, -2.78417852e-04, -2.40946662e-02], + [-2.78417852e-04, 1.45680783e-04, 9.88295037e-03], + [-2.40946662e-02, 9.88295037e-03, 6.79192813e-01]]), scale=array([0.15475583, 0.15475583, 0.15475583]), shift=array([4.16380226e+00, 3.93248621e+03, 9.20643341e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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9.20412203e-01]), fval=0.07939979491586543, rho=3.2737438064381488, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([42, 48, 50, 51, 52, 53, 58, 62, 63, 64, 65, 66]), old_indices_discarded=array([ 0, 35, 38, 41, 44, 45, 47, 49, 54, 55, 56, 57, 59, 60, 61]), step_length=0.10215613340917244, relative_step_length=1.0640947719760474, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12942468e+00, 3.93239001e+03, 9.20412203e-01]), radius=0.19200570494198793, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 50, 51, 54, 55, 56, 57, 59, 60, 63, 64, 67]), model=ScalarModel(intercept=0.0794221211152619, linear_terms=array([-8.78763547e-04, -7.32595189e-05, 1.57498998e-03]), square_terms=array([[ 1.88292059e-02, -8.43669110e-04, -1.35637703e-01], + [-8.43669110e-04, 5.18412063e-05, 7.55602994e-03], + [-1.35637703e-01, 7.55602994e-03, 1.13549976e+00]]), 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67]), old_indices_discarded=array([34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 52, + 53, 58, 61, 62, 65, 66]), step_length=0.1591995195038744, relative_step_length=0.8291395276612978, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01]), radius=0.38401140988397586, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([41, 42, 44, 47, 48, 49, 55, 63, 65, 66, 67, 68]), model=ScalarModel(intercept=0.11467755048322094, linear_terms=array([ 0.4301884 , -0.10852269, 0.38403869]), square_terms=array([[ 2.8059872 , -0.69595835, 2.53665519], + [-0.69595835, 0.17347376, -0.6322991 ], + [ 2.53665519, -0.6322991 , 2.32273428]]), scale=array([0.30951165, 0.30951165, 0.24294931]), shift=array([4.16663860e+00, 3.93254477e+03, 8.57050687e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + 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1.1e+00]))), model_indices=array([42, 47, 48, 50, 51, 63, 64, 65, 66, 67, 68, 70]), model=ScalarModel(intercept=0.07970568860996624, linear_terms=array([5.17688765e-04, 7.08684557e-05, 2.23636029e-03]), square_terms=array([[1.54434855e-03, 1.10165119e-04, 2.02375366e-03], + [1.10165119e-04, 1.32782811e-04, 4.89488107e-03], + [2.02375366e-03, 4.89488107e-03, 1.84174468e-01]]), scale=0.09600285247099397, shift=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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3.93227684e+03, 8.72088478e-01])), candidate_index=71, candidate_x=array([4.13217290e+00, 3.93263940e+03, 9.20311146e-01]), index=68, x=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01]), fval=0.07908472064494197, rho=-2.866324235420139, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([42, 47, 48, 50, 51, 63, 64, 65, 66, 67, 68, 70]), old_indices_discarded=array([ 0, 35, 38, 41, 44, 45, 49, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, + 62, 69]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01]), radius=0.04800142623549698, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([42, 48, 63, 64, 65, 66, 67, 68, 70, 71]), model=ScalarModel(intercept=0.07966045626129445, linear_terms=array([3.20987313e-04, 4.92207090e-05, 2.88283811e-03]), square_terms=array([[ 3.79212673e-04, 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radius=0.012000356558874246, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([64, 68, 72, 73]), model=ScalarModel(intercept=0.07908472064494196, linear_terms=array([-3.38525141e-05, -9.76572645e-05, 8.48540656e-04]), square_terms=array([[ 2.37169869e-04, 1.05566792e-05, -1.97718322e-03], + [ 1.05566792e-05, 7.38132067e-07, -8.17544926e-05], + [-1.97718322e-03, -8.17544926e-05, 1.75933908e-02]]), scale=0.012000356558874246, shift=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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accepted=True, new_indices=array([75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86]), old_indices_used=array([68, 73, 74]), old_indices_discarded=array([], dtype=int32), step_length=0.006052299097263197, relative_step_length=1.0086865448656241, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01]), radius=0.012000356558874246, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([68, 75, 76, 77, 78, 79, 81, 82, 83, 84, 86, 87]), model=ScalarModel(intercept=0.07914371578873403, linear_terms=array([1.82176925e-04, 7.65262019e-05, 2.45374037e-06]), square_terms=array([[ 1.92862702e-04, -9.93628574e-06, -1.67037516e-03], + [-9.93628574e-06, 7.42657125e-07, 7.94573605e-05], + [-1.67037516e-03, 7.94573605e-05, 1.54724452e-02]]), scale=0.012000356558874246, shift=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01])), vector_model=VectorModel(intercepts=array([ 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State(trustregion=Region(center=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01]), radius=0.006000178279437123, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([68, 75, 76, 77, 78, 79, 80, 81, 83, 84, 86, 87]), model=ScalarModel(intercept=0.07914612489984735, linear_terms=array([ 9.04725864e-05, 3.30565774e-05, -2.19601997e-05]), square_terms=array([[ 4.27792898e-05, -4.53845336e-06, -3.94771372e-04], + [-4.53845336e-06, 5.36493868e-07, 4.14551856e-05], + [-3.94771372e-04, 4.14551856e-05, 3.90706076e-03]]), scale=0.006000178279437123, shift=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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model=ScalarModel(intercept=0.07916583626576876, linear_terms=array([ 3.46835434e-05, 1.37176019e-05, -3.87853430e-06]), square_terms=array([[ 1.16702087e-05, -1.13065279e-06, -1.02787515e-04], + [-1.13065279e-06, 1.19282548e-07, 9.91369374e-06], + [-1.02787515e-04, 9.91369374e-06, 9.72885293e-04]]), scale=0.0030000891397185614, shift=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 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3.93254141e+03, 9.23023628e-01]), index=87, x=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01]), fval=0.07907999189313741, rho=-0.5105398504494335, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([68, 75, 76, 77, 78, 79, 80, 81, 83, 84, 87, 89]), old_indices_discarded=array([74, 82, 85, 86, 88]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01]), radius=0.0015000445698592807, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([68, 77, 83, 87, 89, 90]), model=ScalarModel(intercept=0.07909333025755838, linear_terms=array([-1.15137705e-05, -7.91213109e-07, 9.73368802e-05]), square_terms=array([[ 3.75332861e-06, -1.15364186e-07, -2.97559553e-05], + [-1.15364186e-07, 1.95191461e-08, 8.40989334e-07], + [-2.97559553e-05, 8.40989334e-07, 2.56198599e-04]]), scale=0.0015000445698592807, shift=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01]), radius=0.0007500222849296404, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 87, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103]), model=ScalarModel(intercept=0.07910448579668597, linear_terms=array([-9.82149673e-08, 5.86243338e-06, -2.29104355e-05]), square_terms=array([[ 8.28466084e-07, -2.44013709e-08, -6.66789179e-06], + [-2.44013709e-08, 3.18448291e-09, 1.62847966e-07], + [-6.66789179e-06, 1.62847966e-07, 5.95987805e-05]]), scale=0.0007500222849296404, shift=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, 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0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 87, 92, 93, 96, 97, 98, 99, 100, 101, 102, 103, 104]), model=ScalarModel(intercept=0.07909403564536577, linear_terms=array([-1.22102547e-05, 7.60923217e-06, -5.17261450e-06]), square_terms=array([[ 3.36799435e-06, -1.27039621e-07, -2.66224628e-05], + [-1.27039621e-07, 2.41088585e-08, 9.99863513e-07], + [-2.66224628e-05, 9.99863513e-07, 2.37983666e-04]]), scale=0.0015000445698592807, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], 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scale=393.2276837211913, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), candidate_index=105, candidate_x=array([4.16253783e+00, 3.93254113e+03, 9.23735181e-01]), index=104, x=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), fval=0.07906796375411648, rho=-1.0803986646623365, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 87, 92, 93, 96, 97, 98, 99, 100, 101, 102, 103, 104]), old_indices_discarded=array([68, 77, 83, 89, 90, 91, 94, 95]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), radius=0.0007500222849296404, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 87, 92, 93, 94, 95, 96, 97, 98, 99, 101, 102, 104]), model=ScalarModel(intercept=0.07909160054167635, linear_terms=array([-1.29680002e-05, 2.27817382e-06, 1.03758748e-06]), square_terms=array([[ 9.19566779e-07, -3.49328357e-08, -6.93344117e-06], + [-3.49328357e-08, 6.61278422e-09, 2.68258739e-07], + [-6.93344117e-06, 2.68258739e-07, 5.96608551e-05]]), scale=0.0007500222849296404, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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fval=0.07906796375411648, rho=-0.13973162244364834, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 87, 92, 93, 94, 95, 96, 97, 98, 99, 101, 102, 104]), old_indices_discarded=array([ 83, 90, 91, 100, 103, 105]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), radius=0.0003750111424648202, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 87, 92, 94, 95, 96, 97, 98, 99, 101, 102, 104, 106]), model=ScalarModel(intercept=0.07908661788272847, linear_terms=array([-8.35298009e-06, 7.05475745e-06, 1.43187070e-06]), square_terms=array([[ 2.33438460e-07, -1.55439804e-08, -1.73669039e-06], + [-1.55439804e-08, 5.10319166e-09, 8.60392613e-08], + [-1.73669039e-06, 8.60392613e-08, 1.49265352e-05]]), scale=0.0003750111424648202, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), radius=0.0001875055712324101, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 87, 94, 95, 98, 102, 104, 106, 107]), model=ScalarModel(intercept=0.0790879150221595, linear_terms=array([-6.18729811e-06, 2.85069426e-06, 5.24029282e-06]), square_terms=array([[ 6.39029879e-08, -5.52613342e-09, -4.58386220e-07], + [-5.52613342e-09, 1.01866206e-09, 2.93309494e-08], + [-4.58386220e-07, 2.93309494e-08, 3.89516694e-06]]), scale=0.0001875055712324101, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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model=ScalarModel(intercept=0.07906796375411641, linear_terms=array([-2.77746966e-05, -3.22673327e-05, -4.25319102e-05]), square_terms=array([[ 5.39542744e-08, 4.53199229e-08, -4.64131654e-08], + [ 4.53199229e-08, 4.98555993e-08, 7.22290924e-08], + [-4.64131654e-08, 7.22290924e-08, 1.14321313e-06]]), scale=9.375278561620504e-05, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 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3.93254196e+03, 9.23633511e-01]), index=104, x=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), fval=0.07906796375411648, rho=-0.11603470278551233, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 95, 104, 107, 108]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), radius=4.687639280810252e-05, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([104, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, + 120, 121]), model=ScalarModel(intercept=0.07907082869590354, linear_terms=array([ 2.88136945e-06, -3.08394182e-07, -1.07721092e-06]), square_terms=array([[ 4.46329859e-09, -6.05308320e-10, -2.92877945e-08], + [-6.05308320e-10, 1.34067088e-10, 3.43225360e-09], + [-2.92877945e-08, 3.43225360e-09, 2.44027182e-07]]), 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3.93254191e+03, 9.23561859e-01]), fval=0.07906739186138749, rho=1.4495418816296448, accepted=True, new_indices=array([125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136]), old_indices_used=array([104, 123, 124]), old_indices_discarded=array([], dtype=int32), step_length=5.859549088890873e-06, relative_step_length=0.99999999793125, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 138 entries., 'history': {'params': [{'CRRA': 4.059438573758104, 'BeqFac': 3932.2768372119126, 'DiscFac': 0.8720884775777547}, {'CRRA': 1.1865624830963672, 'BeqFac': 3618.0088638043917, 'DiscFac': 1.1}, {'CRRA': 18.864750592221043, 'BeqFac': 3615.3369060843656, 'DiscFac': 1.0141895974609063}, {'CRRA': 20.0, 'BeqFac': 4139.2725657346655, 'DiscFac': 0.967186242030893}, {'CRRA': 11.813333909541619, 'BeqFac': 4191.377523809567, 'DiscFac': 0.5}, {'CRRA': 10.612091741842278, 'BeqFac': 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State(trustregion=Region(center=array([4.46657035e+00, 4.58718267e+03, 9.28413348e-01]), radius=28.669891670599913, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 8, 9, 15, 16]), model=ScalarModel(intercept=4.862823488147589, linear_terms=array([ -73.44883613, -23.31734993, -131.12114654]), square_terms=array([[ 578.17685765, 182.96932654, 1029.31041988], + [ 182.96932654, 57.95652351, 325.84926908], + [1029.31041988, 325.84926908, 1832.92574964]]), scale=array([ 9.45 , 23.10781735, 0.3 ]), shift=array([1.05500000e+01, 4.58718267e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , + 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , + -0.16780024, -0.02088758, -0.04018239, -0.0329881 , -0.04664206, + -0.06960775, -0.0857819 ]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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square_terms=array([[1.95905989e+07, 4.95384419e+06, 4.69506453e+07], + [4.95384419e+06, 1.25267102e+06, 1.18723370e+07], + [4.69506453e+07, 1.18723370e+07, 1.12521478e+08]]), scale=array([ 7.46023951, 11.55390867, 0.3 ]), shift=array([8.56023951e+00, 4.58718267e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , + 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , + -0.16780024, -0.02088758, -0.04018239, -0.0329881 , -0.04664206, + -0.06960775, -0.0857819 ]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], 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fval=0.10144929167716776, rho=-1041.451062119752, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 9, 16, 17]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.46657035e+00, 4.58718267e+03, 9.28413348e-01]), radius=7.167472917649978, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=80.22435907513035, linear_terms=array([-244.91564084, 13.26585911, 83.93950199]), square_terms=array([[ 375.02548269, -20.22669945, -127.79535667], + [ -20.22669945, 1.10349026, 6.99773206], + [-127.79535667, 6.99773206, 44.64312817]]), scale=array([4.57176234, 5.77695434, 0.3 ]), shift=array([5.67176234e+00, 4.58718267e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 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State(trustregion=Region(center=array([4.46657035e+00, 4.58718267e+03, 9.28413348e-01]), radius=3.583736458824989, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 18, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31]), model=ScalarModel(intercept=93.97983279704786, linear_terms=array([-158.16807239, 51.83688273, 84.93922156]), square_terms=array([[133.49622213, -43.70152356, -71.15581564], + [-43.70152356, 14.31811665, 23.35552818], + [-71.15581564, 23.35552818, 39.27068276]]), scale=array([2.88847717, 2.88847717, 0.3 ]), shift=array([4.46657035e+00, 4.58718267e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , + 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , + -0.16780024, -0.02088758, -0.04018239, -0.0329881 , -0.04664206, + -0.06960775, -0.0857819 ]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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4.58718267e+03, 9.28413348e-01]), fval=0.10144929167716776, rho=-0.010827579816131265, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 20, 21, 22, 23, 26, 27, 29, 30, 31, 32]), old_indices_discarded=array([17, 19, 24, 25, 28]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.46657035e+00, 4.58718267e+03, 9.28413348e-01]), radius=0.8959341147062473, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 18, 23, 32, 33]), model=ScalarModel(intercept=5.686343548503185, linear_terms=array([-1.41335436e+01, 6.60802805e-03, -3.48121455e+01]), square_terms=array([[ 1.80359552e+01, 4.54763842e-02, 4.39045113e+01], + [ 4.54763842e-02, 1.82631253e-02, -3.51143342e-02], + [ 4.39045113e+01, -3.51143342e-02, 1.08172460e+02]]), scale=array([0.72211929, 0.72211929, 0.3 ]), shift=array([4.46657035e+00, 4.58718267e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , + 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , + -0.16780024, -0.02088758, -0.04018239, -0.0329881 , -0.04664206, + -0.06960775, -0.0857819 ]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.28097285e+00, 4.58790479e+03, 9.27938755e-01]), radius=0.44796705735312364, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=0.517668701318821, linear_terms=array([-0.17575919, -0.20581096, 2.58725933]), square_terms=array([[ 0.06009513, 0.04506805, -0.56771327], + [ 0.04506805, 0.04943814, -0.6227709 ], + [-0.56771327, -0.6227709 , 7.8595374 ]]), scale=array([0.36105965, 0.36105965, 0.26656045]), shift=array([4.28097285e+00, 4.58790479e+03, 8.33439554e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , + 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , + -0.16780024, -0.02088758, -0.04018239, -0.0329881 , -0.04664206, + -0.06960775, -0.0857819 ]), linear_terms=array([[0., 0., 0.], + 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model=ScalarModel(intercept=2.387795744181831, linear_terms=array([-0.21651854, -1.00620205, 4.39340151]), square_terms=array([[ 0.01868472, 0.0488839 , -0.21713868], + [ 0.0488839 , 0.22001091, -0.96179267], + [-0.21713868, -0.96179267, 4.21118356]]), scale=array([0.18052982, 0.18052982, 0.17629553]), shift=array([4.28097285e+00, 4.58790479e+03, 9.23704466e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , + 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , + -0.16780024, -0.02088758, -0.04018239, -0.0329881 , -0.04664206, + -0.06960775, -0.0857819 ]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + 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8.21257590e-01]), index=34, x=array([4.28097285e+00, 4.58790479e+03, 9.27938755e-01]), fval=0.08450890381661277, rho=-0.05757584370248203, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([34, 35, 36, 37, 39, 40, 41, 42, 44, 45, 46, 47]), old_indices_discarded=array([ 0, 38, 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.28097285e+00, 4.58790479e+03, 9.27938755e-01]), radius=0.11199176433828091, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([34, 35, 36, 39, 40, 41, 42, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=2.3684676382089758, linear_terms=array([-0.10988363, -0.66436773, 2.75173228]), square_terms=array([[ 0.00591366, 0.01676982, -0.07000994], + [ 0.01676982, 0.09679265, -0.40121714], + [-0.07000994, -0.40121714, 1.66585423]]), scale=0.11199176433828091, shift=array([4.28097285e+00, 4.58790479e+03, 9.27938755e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , + 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , + -0.16780024, -0.02088758, -0.04018239, -0.0329881 , -0.04664206, + -0.06960775, -0.0857819 ]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.28097285e+00, 4.58790479e+03, 9.27938755e-01]), radius=0.055995882169140455, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([34, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=0.09028822664173408, linear_terms=array([-0.01061362, 0.00088466, 0.05364829]), square_terms=array([[ 1.15220286e-02, -8.95044594e-04, -5.57609462e-02], + [-8.95044594e-04, 7.85103124e-05, 4.56043843e-03], + [-5.57609462e-02, 4.56043843e-03, 2.76280980e-01]]), scale=0.055995882169140455, shift=array([4.28097285e+00, 4.58790479e+03, 9.27938755e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , + 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , + -0.16780024, -0.02088758, -0.04018239, -0.0329881 , -0.04664206, + -0.06960775, -0.0857819 ]), 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56, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=0.08780533673879214, linear_terms=array([-0.00204222, -0.000514 , 0.02518641]), square_terms=array([[ 9.44732408e-04, 1.61167329e-04, -9.59779358e-03], + [ 1.61167329e-04, 3.11556686e-05, -1.82922922e-03], + [-9.59779358e-03, -1.82922922e-03, 1.08278084e-01]]), scale=0.027997941084570228, shift=array([4.28097285e+00, 4.58790479e+03, 9.27938755e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , + 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , + -0.16780024, -0.02088758, -0.04018239, -0.0329881 , -0.04664206, + -0.06960775, -0.0857819 ]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], 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[1.64562500e+01, 6.87500000e+03, 9.87500000e-01], + [1.35031250e+01, 6.56250000e+03, 5.18750000e-01], + [1.17312500e+01, 4.37500000e+03, 5.37500000e-01], + [1.82281250e+01, 4.06250000e+03, 9.68750000e-01], + [4.64375000e+00, 3.12500000e+03, 1.06250000e+00], + [1.23218750e+01, 9.68750000e+03, 1.08125000e+00], + [2.28125000e+00, 9.37500000e+03, 8.37500000e-01]]), 'exploration_results': array([ 0.15183336, 0.16963129, 0.3197984 , 0.64023668, 0.65671129, + 0.81183081, 0.97682658, 1.08889746, 1.16160077, 1.27832959, + 1.4056054 , 1.63187357, 1.68853551, 1.72432777, 1.75966476, + 1.80211806, 1.86015904, 1.93985391, 1.96313642, 2.01014295, + 2.05048939, 2.06445231, 2.29147195, 2.43324345, 2.49173392, + 2.54550964, 3.05245049, 4.00840374, 4.33350039, 58.25645251])}}" diff --git a/src/estimark/content/tables/min/WarmGlowPortfolioFourParams_estimate_results.csv b/src/estimark/content/tables/min/WarmGlowPortfolioFourParams_estimate_results.csv new file mode 100644 index 0000000..4f04234 --- /dev/null +++ b/src/estimark/content/tables/min/WarmGlowPortfolioFourParams_estimate_results.csv @@ -0,0 +1,41377 @@ +CRRA,4.130262462019113 + +BeqFac,4099.4172974459125 + +BeqShift,1.5605762058493682 + +DiscFac,0.976477264112623 + +time_to_estimate,564.7696936130524 + +params,"{'CRRA': 4.130262462019113, 'BeqFac': 4099.4172974459125, 'BeqShift': 1.5605762058493682, 'DiscFac': 0.976477264112623}" + +criterion,0.03160677727045485 + +start_criterion,0.04761358470737734 + +start_params,"{'CRRA': 4.28809908637635, 'BeqFac': 3985.3577919647823, 'BeqShift': 2.2275788765626143, 'DiscFac': 1.0}" + +algorithm,multistart_tranquilo_ls + +direction,minimize + +n_free,4 + +message,Maximum number of criterion evaluations reached. + +success, + +n_criterion_evaluations, + +n_derivative_evaluations, + +n_iterations, + +history,"{'params': [{'CRRA': 6.3543559970109325, 'BeqFac': 4099.6889222786085, 'BeqShift': 2.382041833192273, 'DiscFac': 0.9802653739222869}, {'CRRA': 1.5997660080756595, 'BeqFac': 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112.69145950023085, 113.88890310004354, 115.12849680008367, 116.31543600000441, 117.6343872002326, 118.81335589988157, 119.98585169995204, 121.1860096999444, 122.37323410017416, 123.57454850012437, 124.76921389997005, 126.4212179002352, 126.60760340001434, 126.79196709999815, 126.97290260018781, 127.15962800011039, 127.34788580005988, 127.53500150004402, 127.73213839996606, 127.93292480008677, 128.12795529980212, 128.32260590000078, 128.51027300022542, 129.75297969998792, 130.94742600014433, 132.1255693999119], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 8, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 12, 13, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 16, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 19, 20, 21, 22, 23, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 25, 26, 27, 28, 29, 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 32, 33, 34, 35, 36, 37, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 87, 88, 89]}" + +convergence_report,"{'one_step': {'relative_criterion_change': 0.001297727019196962, 'relative_params_change': 0.027894310200989765, 'absolute_criterion_change': 0.0001297727019196962, 'absolute_params_change': 113.76595754764212}, 'five_steps': {'relative_criterion_change': 0.001297727019196962, 'relative_params_change': 0.027894310200989765, 'absolute_criterion_change': 0.0001297727019196962, 'absolute_params_change': 113.76595754764212}}" + +multistart_info,"{'start_parameters': [{'CRRA': 4.28809908637635, 'BeqFac': 3985.357791964783, 'BeqShift': 2.2275788765626143, 'DiscFac': 1.0}, {'CRRA': 6.15733678876515, 'BeqFac': 3867.825670007428, 'BeqShift': 22.653265765629147, 'DiscFac': 1.0011343468411}, {'CRRA': 6.3543559970109325, 'BeqFac': 4099.6889222786085, 'BeqShift': 2.382041833192273, 'DiscFac': 0.9802653739222869}, {'CRRA': 6.019017903047592, 'BeqFac': 4094.4713174272265, 'BeqShift': 8.678234369345601, 'DiscFac': 0.9603698649490326}], 'local_optima': [Minimize with 4 free parameters terminated. + +The tranquilo_ls algorithm reported: Maximum number of criterion evaluations reached. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 3.438e-06* 0.001161 +relative_params_change 5.434e-05 0.00258 +absolute_criterion_change 3.438e-07* 0.0001161 +absolute_params_change 0.00019 0.004757 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 4 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 3.703e-08* 1.469e-05 +relative_params_change 3.819e-07* 0.001782 +absolute_criterion_change 2.006e-08* 7.959e-06* +absolute_params_change 3.648e-06* 0.03719 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 4 free parameters terminated. + +The tranquilo_ls algorithm reported: Maximum number of criterion evaluations reached. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 0.0002068 0.02744 +relative_params_change 0.01485 0.1715 +absolute_criterion_change 2.068e-05 0.002744 +absolute_params_change 0.05016 0.2731 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 4 free parameters terminated. + +The tranquilo_ls algorithm reported: Maximum number of criterion evaluations reached. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 0.00129 0.00488 +relative_params_change 0.001706 0.006966 +absolute_criterion_change 0.001137 0.004302 +absolute_params_change 0.02421 0.1485 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 4.28809908637635, 'BeqFac': 3985.3577919647823, 'BeqShift': 2.2275788765626143, 'DiscFac': 1.0}, {'CRRA': 8.1875, 'BeqFac': 3750.0, 'BeqShift': 43.75, 'DiscFac': 1.0250000000000001}, {'CRRA': 11.73125, 'BeqFac': 4375.0, 'BeqShift': 4.375, 'DiscFac': 0.9875}, {'CRRA': 18.228125, 'BeqFac': 4062.5, 'BeqShift': 54.6875, 'DiscFac': 0.8562500000000001}, {'CRRA': 15.865624999999998, 'BeqFac': 7812.5, 'BeqShift': 45.9375, 'DiscFac': 0.9312500000000001}, {'CRRA': 12.9125, 'BeqFac': 1250.0, 'BeqShift': 61.25, 'DiscFac': 0.875}, {'CRRA': 17.6375, 'BeqFac': 8750.0, 'BeqShift': 8.75, 'DiscFac': 0.7250000000000001}, {'CRRA': 9.368749999999999, 'BeqFac': 5625.0, 'BeqShift': 13.125, 'DiscFac': 0.9125000000000001}, {'CRRA': 18.81875, 'BeqFac': 625.0, 'BeqShift': 48.125, 'DiscFac': 0.6125}, {'CRRA': 16.1609375, 'BeqFac': 156.25, 'BeqShift': 66.71875, 'DiscFac': 0.9781250000000001}, {'CRRA': 15.274999999999999, 'BeqFac': 2500.0, 'BeqShift': 17.5, 'DiscFac': 0.65}, {'CRRA': 4.053125, 'BeqFac': 1562.5, 'BeqShift': 37.1875, 'DiscFac': 1.00625}, {'CRRA': 13.503124999999999, 'BeqFac': 6562.5, 'BeqShift': 2.1875, 'DiscFac': 0.70625}, {'CRRA': 11.140624999999998, 'BeqFac': 312.5, 'BeqShift': 28.4375, 'DiscFac': 0.78125}, {'CRRA': 10.549999999999999, 'BeqFac': 5000.0, 'BeqShift': 35.0, 'DiscFac': 0.8}, {'CRRA': 16.45625, 'BeqFac': 6875.0, 'BeqShift': 56.875, 'DiscFac': 0.5375}, {'CRRA': 5.824999999999999, 'BeqFac': 7500.0, 'BeqShift': 52.5, 'DiscFac': 0.9500000000000001}, {'CRRA': 13.798437499999999, 'BeqFac': 3906.25, 'BeqShift': 22.96875, 'DiscFac': 0.603125}, {'CRRA': 14.684375, 'BeqFac': 3437.5, 'BeqShift': 41.5625, 'DiscFac': 0.51875}, {'CRRA': 12.321874999999999, 'BeqFac': 9687.5, 'BeqShift': 67.8125, 'DiscFac': 0.59375}, {'CRRA': 9.073437499999999, 'BeqFac': 1406.25, 'BeqShift': 5.46875, 'DiscFac': 0.753125}, {'CRRA': 7.00625, 'BeqFac': 1875.0, 'BeqShift': 21.875, 'DiscFac': 0.8375}, {'CRRA': 9.959375, 'BeqFac': 937.5, 'BeqShift': 59.0625, 'DiscFac': 0.66875}, {'CRRA': 11.4359375, 'BeqFac': 7656.25, 'BeqShift': 14.21875, 'DiscFac': 0.528125}, {'CRRA': 7.596874999999999, 'BeqFac': 7187.5, 'BeqShift': 50.3125, 'DiscFac': 0.74375}, {'CRRA': 8.778125, 'BeqFac': 9062.5, 'BeqShift': 19.6875, 'DiscFac': 0.55625}, {'CRRA': 6.7109375, 'BeqFac': 5156.25, 'BeqShift': 31.71875, 'DiscFac': 0.6781250000000001}, {'CRRA': 5.234375, 'BeqFac': 8437.5, 'BeqShift': 6.5625, 'DiscFac': 0.8187500000000001}, {'CRRA': 6.415625, 'BeqFac': 2812.5, 'BeqShift': 10.9375, 'DiscFac': 0.6312500000000001}, {'CRRA': 4.64375, 'BeqFac': 3125.0, 'BeqShift': 65.625, 'DiscFac': 0.7625000000000001}, {'CRRA': 4.348437499999999, 'BeqFac': 8906.25, 'BeqShift': 57.96875, 'DiscFac': 0.9031250000000001}, {'CRRA': 3.4625, 'BeqFac': 6250.0, 'BeqShift': 26.25, 'DiscFac': 0.575}, {'CRRA': 19.409375, 'BeqFac': 5937.5, 'BeqShift': 24.0625, 'DiscFac': 0.96875}, {'CRRA': 14.093749999999998, 'BeqFac': 8125.0, 'BeqShift': 30.625, 'DiscFac': 1.0625}, {'CRRA': 2.871875, 'BeqFac': 4687.5, 'BeqShift': 32.8125, 'DiscFac': 0.89375}, {'CRRA': 2.28125, 'BeqFac': 9375.0, 'BeqShift': 39.375, 'DiscFac': 0.6875}, {'CRRA': 1.9859375, 'BeqFac': 2656.25, 'BeqShift': 49.21875, 'DiscFac': 0.828125}, {'CRRA': 17.046875, 'BeqFac': 2187.5, 'BeqShift': 15.3125, 'DiscFac': 1.0437500000000002}, {'CRRA': 18.5234375, 'BeqFac': 6406.25, 'BeqShift': 40.46875, 'DiscFac': 1.053125}, {'CRRA': 1.690625, 'BeqFac': 5312.5, 'BeqShift': 63.4375, 'DiscFac': 1.08125}], 'exploration_results': array([4.85268873e-02, 5.81756522e-01, 7.85434558e-01, 1.08501898e+00, + 1.09147168e+00, 1.15508374e+00, 1.35896335e+00, 1.47857950e+00, + 1.60399255e+00, 1.93344372e+00, 1.94383457e+00, 1.97559443e+00, + 2.00706025e+00, 2.09257083e+00, 2.11464344e+00, 2.13130763e+00, + 2.24401741e+00, 2.32809571e+00, 2.43093035e+00, 2.58864817e+00, + 2.64782870e+00, 2.75968951e+00, 2.76063416e+00, 2.87160809e+00, + 2.96402039e+00, 3.18404442e+00, 3.25640118e+00, 3.28020017e+00, + 3.37801319e+00, 3.50195454e+00, 3.52157483e+00, 3.84631802e+00, + 4.29481111e+00, 4.50920970e+00, 4.92630064e+00, 5.00339546e+00, + 5.43544424e+00, 7.28251427e+00, 1.28207813e+01, 1.15051611e+03])}" + +algorithm_output,"{'states': [State(trustregion=Region(center=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), radius=409.96889222786086, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=[0], model=ScalarModel(intercept=1.0675306002702438, linear_terms=array([0., 0., 0., 0.]), square_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 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candidate_x=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), index=0, x=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), fval=1.0675306002702436, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), radius=409.96889222786086, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=65.05504224478882, linear_terms=array([-178.31283383, 31.69042646, 4.09387681, 166.47545036]), square_terms=array([[ 2.47350263e+02, -4.37095660e+01, -5.86374731e+00, + -2.27026816e+02], + [-4.37095660e+01, 7.78069637e+00, 9.85890588e-01, + 4.03643693e+01], + [-5.86374731e+00, 9.85890588e-01, 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51, 52, 53, 54, 55, 56, 57, 58, 59, 68, 69]), step_length=0.25874156763788225, relative_step_length=1.2925437528754322, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.14283721e+00, 4.09989630e+03, 2.18359957e+00, 1.01755786e+00]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 55, 59, 60, 61, 62, 64, 65, 67, 68, 70, 71, 73, 75, 76]), model=ScalarModel(intercept=0.32097874490276107, linear_terms=array([ 0.05330163, -0.04745503, -0.07450486, 0.04707809]), square_terms=array([[ 0.04450147, -0.0463324 , -0.06634309, -0.27074592], + [-0.0463324 , 0.0485459 , 0.06911336, 0.29211727], + [-0.06634309, 0.06911336, 0.09925331, 0.40416762], + [-0.27074592, 0.29211727, 0.40416762, 2.04202383]]), scale=array([0.07458962, 0.07458962, 0.07458962, 0.07458962]), shift=array([6.14283721e+00, 4.09989630e+03, 2.18359957e+00, 1.01755786e+00])), 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model=ScalarModel(intercept=0.34700850772977443, linear_terms=array([ 0.00130973, -0.00799522, 0.00251598, -0.00868794]), square_terms=array([[ 9.89738156e-04, 1.15998085e-03, 4.51374301e-06, + 4.04254702e-02], + [ 1.15998085e-03, 1.51831925e-03, -1.74229161e-05, + 4.80403131e-02], + [ 4.51374301e-06, -1.74229161e-05, 6.25426571e-05, + -1.14623773e-03], + [ 4.04254702e-02, 4.80403131e-02, -1.14623773e-03, + 1.68845419e+00]]), scale=0.0500450307895338, shift=array([6.13836451e+00, 4.09991995e+03, 2.17603000e+00, 1.02524414e+00])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + 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candidate_x=array([5.92411952e+00, 4.09996773e+03, 1.96127057e+00, 1.02112845e+00]), index=111, x=array([5.92411952e+00, 4.09996773e+03, 1.96127057e+00, 1.02112845e+00]), fval=0.30088852241782776, rho=0.4287596569977097, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 93, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, + 108, 110]), old_indices_discarded=array([ 46, 55, 59, 60, 76, 77, 78, 79, 80, 81, 82, 83, 84, + 85, 86, 87, 88, 89, 90, 91, 92, 94, 107, 109]), step_length=0.12926168890188985, relative_step_length=1.2914537853468868, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.92411952e+00, 4.09996773e+03, 1.96127057e+00, 1.02112845e+00]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 93, 94, 95, 96, 100, 101, 102, 103, 105, 106, 107, 108, 109, + 110, 111]), model=ScalarModel(intercept=0.8362141099479552, 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old_indices_used=array([ 93, 94, 95, 96, 100, 101, 102, 103, 105, 106, 107, 108, 109, + 110, 111]), old_indices_discarded=array([ 0, 46, 47, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, + 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, + 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, + 87, 88, 89, 90, 91, 92, 97, 98, 99, 104]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.92411952e+00, 4.09996773e+03, 1.96127057e+00, 1.02112845e+00]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 93, 94, 95, 96, 100, 101, 102, 103, 105, 106, 107, 108, 109, + 110, 111]), model=ScalarModel(intercept=0.2829838581648968, linear_terms=array([-0.00276684, 0.03094096, 0.0087251 , -0.03929999]), square_terms=array([[ 3.07948101e-03, -8.53430126e-03, -1.46741811e-03, + 1.20443717e-01], + [-8.53430126e-03, 2.48589549e-02, 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89, 90, 91, 92, 97, 98, 99, 104, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.92411952e+00, 4.09996773e+03, 1.96127057e+00, 1.02112845e+00]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 93, 94, 95, 100, 101, 102, 103, 105, 106, 107, 108, 109, 110, + 111, 113]), model=ScalarModel(intercept=0.2841198144599316, linear_terms=array([ 0.01603782, 0.02090471, -0.01285248, -0.03331009]), square_terms=array([[ 0.00299745, 0.00555541, -0.00404166, -0.07264783], + [ 0.00555541, 0.01052299, -0.00768739, -0.14352062], + [-0.00404166, -0.00768739, 0.00567528, 0.10503643], + [-0.07264783, -0.14352062, 0.10503643, 2.14323031]]), scale=0.0500450307895338, shift=array([5.92411952e+00, 4.09996773e+03, 1.96127057e+00, 1.02112845e+00])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 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scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=114, candidate_x=array([5.89553599e+00, 4.09993215e+03, 1.98226579e+00, 1.01756627e+00]), index=111, x=array([5.92411952e+00, 4.09996773e+03, 1.96127057e+00, 1.02112845e+00]), fval=0.30088852241782776, rho=-0.47013410940622297, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 93, 94, 95, 100, 101, 102, 103, 105, 106, 107, 108, 109, 110, + 111, 113]), old_indices_discarded=array([ 59, 96, 97, 98, 99, 104]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.92411952e+00, 4.09996773e+03, 1.96127057e+00, 1.02112845e+00]), radius=0.0250225153947669, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 95, 111, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, + 124, 125, 126]), 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117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_used=array([ 94, 95, 111, 114]), old_indices_discarded=array([], dtype=int32), step_length=0.02511249539583245, relative_step_length=1.0035959614629457, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.92278744e+00, 4.09994717e+03, 1.94692224e+00, 1.02144230e+00]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([111, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, + 126, 127]), model=ScalarModel(intercept=0.2943051049928589, linear_terms=array([ 0.00564431, -0.00188183, 0.00320629, -0.09902661]), square_terms=array([[ 9.27890201e-05, 7.56993238e-06, 6.61699037e-06, + 5.86812035e-03], + [ 7.56993238e-06, 3.99076703e-05, -3.70323399e-05, + 8.89741428e-03], + [ 6.61699037e-06, -3.70323399e-05, 8.59295855e-05, + -8.43245723e-03], + [ 5.86812035e-03, 8.89741428e-03, 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old_indices_discarded=array([ 0, 32, 33, 34, 35, 36, 37, 38, 42, 44, 45, 46, 47, + 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, + 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, + 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, + 87, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, + 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 113, 115, + 116, 117, 118, 119, 120, 121, 122, 123, 124, 126]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.93949008e+00, 4.09922736e+03, 1.26634654e+00, 1.00772852e+00]), radius=0.4003602463162704, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 112, 114, 125, 128, 129, 130, 131, 132, 133, 134, 135, 136, + 137, 138]), model=ScalarModel(intercept=2.3800270993481427, linear_terms=array([ 0.34086296, -0.36587453, 0.39270343, -9.0556698 ]), square_terms=array([[ 0.02975173, 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61, + 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, + 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 113, 115, 116, + 117, 118, 119, 120, 121, 122, 123, 124, 126, 127]), step_length=0.5174774189042238, relative_step_length=1.2925294748056355, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.64113160e+00, 4.09952572e+03, 9.67988057e-01, 9.80718762e-01]), radius=0.8007204926325407, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 114, 125, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, + 138, 139]), model=ScalarModel(intercept=8.622726430625926, linear_terms=array([ 1.2277478 , -1.26958822, 1.35833354, -27.99550346]), square_terms=array([[ 0.11015107, -0.09196504, 0.11241212, -1.95469033], + [-0.09196504, 0.09842539, -0.10250002, 2.07845956], + [ 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n_evals_acceptance=1), State(trustregion=Region(center=array([4.18135530e+00, 4.09927774e+03, 1.04428032e+00, 9.66584319e-01]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([147, 148, 150, 151, 152, 154, 155, 156, 157, 158, 160, 164, 165, + 166, 167]), model=ScalarModel(intercept=0.03573571879718205, linear_terms=array([-0.00103904, -0.00012385, -0.00223924, 0.01668509]), square_terms=array([[ 1.74164536e-02, -2.04572735e-03, 8.03118307e-03, + -2.01535645e-01], + [-2.04572735e-03, 2.71762449e-04, -1.07363247e-03, + 2.63607584e-02], + [ 8.03118307e-03, -1.07363247e-03, 4.49735351e-03, + -1.03958356e-01], + [-2.01535645e-01, 2.63607584e-02, -1.03958356e-01, + 2.58811266e+00]]), scale=0.1000900615790676, shift=array([4.18135530e+00, 4.09927774e+03, 1.04428032e+00, 9.66584319e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 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shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=170, candidate_x=array([4.17459728e+00, 4.09930253e+03, 1.14432864e+00, 9.69177836e-01]), index=170, x=array([4.17459728e+00, 4.09930253e+03, 1.14432864e+00, 9.69177836e-01]), fval=0.034353350340771874, rho=0.9539572104328949, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([147, 148, 150, 151, 152, 154, 155, 156, 157, 158, 160, 164, 165, + 166, 167]), old_indices_discarded=array([138, 141, 144, 145, 146, 149, 153, 159, 161, 162, 163, 168, 169]), step_length=0.10332617043208771, relative_step_length=1.0323319698476128, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.17459728e+00, 4.09930253e+03, 1.14432864e+00, 9.69177836e-01]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([147, 150, 151, 152, 155, 156, 158, 160, 164, 165, 166, 167, 168, + 169, 170]), model=ScalarModel(intercept=0.03898301695071224, linear_terms=array([ 0.00198454, 0.00051353, 0.00218802, -0.12559782]), square_terms=array([[ 4.15194564e-03, 1.58974126e-04, 9.74651446e-04, + -2.78085165e-02], + [ 1.58974126e-04, 9.21555760e-05, 4.16254383e-04, + -1.17750030e-02], + [ 9.74651446e-04, 4.16254383e-04, 2.48459042e-03, + -5.40677130e-02], + [-2.78085165e-02, -1.17750030e-02, -5.40677130e-02, + 1.69234291e+00]]), scale=array([0.14917924, 0.14917924, 0.14917924, 0.1400007 ]), shift=array([4.17459728e+00, 4.09930253e+03, 1.14432864e+00, 9.59999297e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + 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rho=0.0016410550627144686, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([147, 150, 151, 152, 155, 156, 158, 160, 164, 165, 166, 167, 168, + 169, 170]), old_indices_discarded=array([137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 148, 149, 153, + 154, 157, 159, 161, 162, 163]), step_length=0.21107983641988134, relative_step_length=1.0544495282038355, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.17571078e+00, 4.09945171e+03, 1.29350788e+00, 9.75853572e-01]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([143, 145, 147, 150, 151, 152, 155, 158, 165, 166, 167, 168, 169, + 170, 171]), model=ScalarModel(intercept=0.03278405592667447, linear_terms=array([-0.00058539, -0.00037664, -0.0011525 , 0.01693014]), square_terms=array([[ 4.39506574e-03, 1.51749098e-03, 5.21452806e-04, + -4.51551090e-02], + [ 1.51749098e-03, 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relative_step_length=1.002913343419474, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16250866e+00, 4.09943820e+03, 1.39207438e+00, 9.73777078e-01]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([143, 145, 147, 150, 151, 152, 158, 165, 166, 167, 168, 169, 170, + 171, 172]), model=ScalarModel(intercept=0.035448800508248314, linear_terms=array([ 0.01859568, 0.0072831 , -0.00094604, -0.08987951]), square_terms=array([[ 4.78915876e-02, 1.75180826e-02, -6.37182435e-05, + -2.22312021e-01], + [ 1.75180826e-02, 6.76259777e-03, -5.22983618e-05, + -8.70414549e-02], + [-6.37182435e-05, -5.22983618e-05, 7.13103746e-04, + -5.40324974e-04], + [-2.22312021e-01, -8.70414549e-02, -5.40324974e-04, + 1.12940090e+00]]), scale=array([0.14917924, 0.14917924, 0.14917924, 0.13770108]), shift=array([4.16250866e+00, 4.09943820e+03, 1.39207438e+00, 9.62298918e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + 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candidate_x=array([4.15236900e+00, 4.09955913e+03, 1.56526359e+00, 9.74785320e-01]), index=179, x=array([4.13431204e+00, 4.09946163e+03, 1.53745960e+00, 9.76255422e-01]), fval=0.03162745417502315, rho=-3.5033153040189933, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13431204e+00, 4.09946163e+03, 1.53745960e+00, 9.76255422e-01]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([171, 172, 173, 174, 175, 176, 177, 178, 179, 180]), model=ScalarModel(intercept=0.031652111625055504, linear_terms=array([ 4.80604637e-05, 1.39247104e-05, -2.39867534e-05, -1.89127269e-04]), square_terms=array([[ 3.90665224e-03, 1.27857855e-04, 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relative_step_length=1.0023815838258203, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, + 181]), model=ScalarModel(intercept=0.032799850622037344, linear_terms=array([-0.00120015, -0.00149617, 0.00075662, -0.03985981]), square_terms=array([[ 2.96888836e-03, 4.77998749e-04, -2.25595211e-04, + 1.47872526e-02], + [ 4.77998749e-04, 8.86980525e-04, -5.90740802e-04, + 2.76631448e-02], + [-2.25595211e-04, -5.90740802e-04, 6.06547002e-04, + -1.77861139e-02], + [ 1.47872526e-02, 2.76631448e-02, -1.77861139e-02, + 8.69630174e-01]]), scale=0.1000900615790676, shift=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01])), vector_model=VectorModel(intercepts=array([ 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model=ScalarModel(intercept=0.03161885589197164, linear_terms=array([ 1.24235116e-05, 1.65270337e-05, -5.96983212e-07, -3.23007724e-04]), square_terms=array([[ 3.85807274e-03, 1.15384522e-04, 1.32757111e-03, + -5.48814365e-02], + [ 1.15384522e-04, 4.41711422e-06, 4.73125548e-05, + -2.03433014e-03], + [ 1.32757111e-03, 4.73125548e-05, 5.79485173e-04, + -2.24432493e-02], + [-5.48814365e-02, -2.03433014e-03, -2.24432493e-02, + 9.54134964e-01]]), scale=0.0500450307895338, shift=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 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new_indices=array([], dtype=int32), old_indices_used=array([172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01]), radius=0.0250225153947669, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([175, 177, 179, 181, 182, 183]), model=ScalarModel(intercept=0.03159896733455111, linear_terms=array([ 5.72777496e-04, -4.79596153e-06, 1.29876317e-04, -7.46995187e-03]), square_terms=array([[ 1.16620241e-03, 2.24403583e-06, 3.92674408e-04, + -1.60307218e-02], + [ 2.24403583e-06, 7.30065521e-09, 8.86098589e-07, + -3.50743943e-05], + [ 3.92674408e-04, 8.86098589e-07, 1.60868867e-04, + -6.16883815e-03], + [-1.60307218e-02, -3.50743943e-05, -6.16883815e-03, + 2.56901239e-01]]), scale=0.0250225153947669, shift=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + 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fval=0.034972454852913455, rho=0.5924956878197696, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 92, 93, 94, 97, 98, 99, 100, 101, 102, 104, 105, 106, 108, + 109, 110]), old_indices_discarded=array([ 0, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, + 84, 85, 86, 87, 88, 89, 90, 91, 95, 96, 103, 107]), step_length=0.09887174690248846, relative_step_length=1.0161664183052885, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.09950363e+00, 3.98542742e+03, 2.05703239e+00, 9.86875382e-01]), radius=0.19459754843578042, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 60, 69, 73, 90, 92, 94, 95, 98, 103, 104, 106, 108, 109, + 110, 111]), model=ScalarModel(intercept=0.0505341458401392, linear_terms=array([ 0.04469009, -0.16416707, -0.08838394, -0.37577055]), square_terms=array([[ 0.06180679, 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3.98535779e+03, 2.22757888e+00, 1.00000000e+00])), candidate_index=128, candidate_x=array([4.11495548e+00, 3.98542786e+03, 1.99976824e+00, 9.87137937e-01]), index=127, x=array([4.11026593e+00, 3.98544579e+03, 2.04518916e+00, 9.86159995e-01]), fval=0.03465366839257526, rho=-0.12641395049008758, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([111, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, + 126, 127]), old_indices_discarded=array([ 60, 64, 69, 73, 74, 89, 90, 91, 92, 93, 94, 95, 96, + 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, + 110, 112, 113]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.11026593e+00, 3.98544579e+03, 2.04518916e+00, 9.86159995e-01]), radius=0.024324693554472553, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, + 127, 128]), model=ScalarModel(intercept=0.034751392290817265, linear_terms=array([-2.69146791e-04, -5.75666910e-05, 2.52219359e-04, 8.11798361e-03]), square_terms=array([[ 1.69149680e-03, 2.18313716e-04, 2.09746807e-04, + -2.42784879e-02], + [ 2.18313716e-04, 3.24402371e-05, 2.75336798e-05, + -3.60177270e-03], + [ 2.09746807e-04, 2.75336798e-05, 3.53578236e-05, + -3.12695452e-03], + [-2.42784879e-02, -3.60177270e-03, -3.12695452e-03, + 4.03181241e-01]]), scale=0.024324693554472553, shift=array([4.11026593e+00, 3.98544579e+03, 2.04518916e+00, 9.86159995e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, + -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, + 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 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rho=0.8641741939096383, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, + 127, 128]), old_indices_discarded=array([110, 113, 114]), step_length=0.026500868657753678, relative_step_length=1.0894636184586586, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.10105058e+00, 3.98544456e+03, 2.02040400e+00, 9.84912977e-01]), radius=0.048649387108945105, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([111, 115, 116, 117, 118, 119, 120, 122, 123, 124, 125, 126, 127, + 128, 129]), model=ScalarModel(intercept=0.034316551020124, linear_terms=array([ 0.00089676, 0.00027992, 0.00059662, -0.01294432]), square_terms=array([[ 4.88713158e-03, 1.77439451e-03, -5.58524522e-05, + -8.03397617e-02], + [ 1.77439451e-03, 7.75277896e-04, -6.43954059e-05, + -3.52870862e-02], + [-5.58524522e-05, -6.43954059e-05, 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relative_step_length=1.0181716852450686, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.09515246e+00, 3.98544448e+03, 1.97122336e+00, 9.85089031e-01]), radius=0.09729877421789021, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([111, 115, 116, 117, 118, 119, 120, 122, 123, 125, 126, 127, 128, + 129, 130]), model=ScalarModel(intercept=0.03380035774519198, linear_terms=array([-0.00184705, -0.00045507, 0.00099379, 0.03419088]), square_terms=array([[ 2.39152620e-02, 5.11361155e-03, 1.03154248e-03, + -3.61542564e-01], + [ 5.11361155e-03, 1.25349747e-03, 1.65017220e-04, + -8.94376678e-02], + [ 1.03154248e-03, 1.65017220e-04, 2.11101288e-04, + -1.27104666e-02], + [-3.61542564e-01, -8.94376678e-02, -1.27104666e-02, + 6.42934936e+00]]), scale=0.09729877421789021, shift=array([4.09515246e+00, 3.98544448e+03, 1.97122336e+00, 9.85089031e-01])), vector_model=VectorModel(intercepts=array([ 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9.85089031e-01]), radius=0.048649387108945105, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([111, 115, 116, 117, 118, 119, 122, 123, 125, 126, 127, 128, 129, + 130, 131]), model=ScalarModel(intercept=0.03424609268671577, linear_terms=array([-2.25755276e-03, -7.65995429e-04, 6.14041170e-05, 3.76453713e-02]), square_terms=array([[ 6.83395663e-03, 1.94799763e-03, 1.12493586e-03, + -9.72841521e-02], + [ 1.94799763e-03, 6.27388933e-04, 3.36225682e-04, + -3.15954777e-02], + [ 1.12493586e-03, 3.36225682e-04, 2.26528041e-04, + -1.71812036e-02], + [-9.72841521e-02, -3.15954777e-02, -1.71812036e-02, + 1.60139445e+00]]), scale=0.048649387108945105, shift=array([4.09515246e+00, 3.98544448e+03, 1.97122336e+00, 9.85089031e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, + -0.11812791, -0.01940885, 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candidate_x=array([4.09700718e+00, 3.98544671e+03, 1.92248591e+00, 9.83579539e-01]), index=132, x=array([4.09700718e+00, 3.98544671e+03, 1.92248591e+00, 9.83579539e-01]), fval=0.03344278008256837, rho=0.7088480558208017, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([111, 115, 116, 117, 118, 119, 122, 123, 125, 126, 127, 128, 129, + 130, 131]), old_indices_discarded=array([ 60, 69, 109, 110, 113, 114, 120, 121, 124]), step_length=0.04884695631269484, relative_step_length=1.0040610830986894, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.09700718e+00, 3.98544671e+03, 1.92248591e+00, 9.83579539e-01]), radius=0.09729877421789021, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([111, 113, 115, 116, 117, 119, 122, 125, 126, 127, 128, 129, 130, + 131, 132]), model=ScalarModel(intercept=0.03329194108487574, linear_terms=array([ 0.00037861, -0.00120152, 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State(trustregion=Region(center=array([4.10380907e+00, 3.98547361e+03, 1.79311439e+00, 9.81700943e-01]), radius=0.09729877421789021, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, + 141, 142]), model=ScalarModel(intercept=0.0326807222536389, linear_terms=array([-0.00209921, -0.00013072, -0.00011186, 0.03140504]), square_terms=array([[ 2.65673117e-02, 1.24220947e-04, 7.48136948e-03, + -3.80223461e-01], + [ 1.24220947e-04, 1.73529823e-06, 4.33828043e-05, + -2.32018221e-03], + [ 7.48136948e-03, 4.33828043e-05, 2.37936036e-03, + -1.17532688e-01], + [-3.80223461e-01, -2.32018221e-03, -1.17532688e-01, + 6.23372854e+00]]), scale=0.09729877421789021, shift=array([4.10380907e+00, 3.98547361e+03, 1.79311439e+00, 9.81700943e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 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3.98535779e+03, 2.22757888e+00, 1.00000000e+00])), candidate_index=143, candidate_x=array([4.11669376e+00, 3.98550545e+03, 1.69778444e+00, 9.80211212e-01]), index=143, x=array([4.11669376e+00, 3.98550545e+03, 1.69778444e+00, 9.80211212e-01]), fval=0.03220931715356145, rho=1.0262736108824155, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, + 141, 142]), old_indices_discarded=array([ 0, 59, 60, 61, 63, 64, 66, 67, 69, 70, 73, 74, 75, + 78, 79, 82, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, + 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, + 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, + 122, 123, 124, 125, 126, 127]), step_length=0.1013394261738542, relative_step_length=1.0415282925036176, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.11669376e+00, 3.98550545e+03, 1.69778444e+00, 9.80211212e-01]), radius=0.19459754843578042, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([128, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, + 142, 143]), model=ScalarModel(intercept=0.08296945152155982, linear_terms=array([ 7.19376948e-02, -2.23100515e-04, 2.36320257e-02, -1.08022108e+00]), square_terms=array([[ 5.84409748e-02, -8.08424984e-06, 1.70531284e-02, + -7.64859744e-01], + [-8.08424984e-06, 3.61028975e-06, -1.27774505e-05, + 2.03813182e-04], + [ 1.70531284e-02, -1.27774505e-05, 5.62834359e-03, + -2.45854953e-01], + [-7.64859744e-01, 2.03813182e-04, -2.45854953e-01, + 1.14832543e+01]]), scale=array([0.14501897, 0.14501897, 0.14501897, 0.13240388]), shift=array([4.11669376e+00, 3.98550545e+03, 1.69778444e+00, 9.67596123e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, + -0.11812791, -0.01940885, 0.06476132, 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candidate_x=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), index=144, x=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), fval=0.03185233767614412, rho=0.642468592930664, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([128, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, + 142, 143]), old_indices_discarded=array([ 0, 48, 49, 52, 53, 54, 56, 59, 60, 61, 62, 63, 64, + 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, + 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, + 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, + 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, + 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 129]), step_length=0.20552612749559135, relative_step_length=1.0561599010247422, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), radius=0.38919509687156084, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 59, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, + 143, 144]), model=ScalarModel(intercept=0.5158401817968741, linear_terms=array([ 0.48374106, -0.21959826, -0.03034574, -2.45076283]), square_terms=array([[ 2.73512553e-01, -1.10681409e-01, -1.36794804e-02, + -1.22742522e+00], + [-1.10681409e-01, 5.00854227e-02, 7.37119045e-03, + 5.56398366e-01], + [-1.36794804e-02, 7.37119045e-03, 2.82983352e-03, + 7.80570977e-02], + [-1.22742522e+00, 5.56398366e-01, 7.80570977e-02, + 6.20455154e+00]]), scale=array([0.29003793, 0.29003793, 0.29003793, 0.20600944]), shift=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 8.93990557e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, + -0.11812791, -0.01940885, 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candidate_x=array([4.14231731e+00, 3.98555744e+03, 1.48330502e+00, 9.83650146e-01]), index=144, x=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), fval=0.03185233767614412, rho=-19.37116300456786, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 59, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, + 143, 144]), old_indices_discarded=array([ 0, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, + 58, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, + 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, + 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, + 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, + 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, + 124, 125, 126, 127, 128, 129, 130]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), radius=0.19459754843578042, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 59, 131, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, + 144, 145]), model=ScalarModel(intercept=0.034194805708801616, linear_terms=array([ 0.00897016, -0.00531212, 0.00118509, -0.10933048]), square_terms=array([[ 2.60943087e-02, -1.08818961e-02, 2.66055361e-03, + -2.23169348e-01], + [-1.08818961e-02, 6.33814787e-03, -1.14716135e-03, + 1.30275256e-01], + [ 2.66055361e-03, -1.14716135e-03, 6.17083790e-04, + -2.45327892e-02], + [-2.23169348e-01, 1.30275256e-01, -2.45327892e-02, + 2.68630749e+00]]), scale=array([0.14501897, 0.14501897, 0.14501897, 0.13349996]), shift=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.66500041e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, + -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, + 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 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candidate_x=array([4.14173469e+00, 3.98579549e+03, 1.44950026e+00, 9.65493838e-01]), index=144, x=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), fval=0.03185233767614412, rho=-6.7613204113764, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 59, 131, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, + 144, 145]), old_indices_discarded=array([ 0, 48, 52, 53, 54, 56, 60, 61, 62, 63, 64, 65, 66, + 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, + 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, + 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, + 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, + 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), radius=0.09729877421789021, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([131, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, + 145, 146]), model=ScalarModel(intercept=0.03223491974347327, linear_terms=array([-0.00435523, 0.00045162, -0.00113672, 0.06395075]), square_terms=array([[ 2.76484614e-02, -2.97346672e-03, 6.76823088e-03, + -3.49132230e-01], + [-2.97346672e-03, 3.93096161e-04, -8.27242646e-04, + 4.39088173e-02], + [ 6.76823088e-03, -8.27242646e-04, 1.87325427e-03, + -9.27089668e-02], + [-3.49132230e-01, 4.39088173e-02, -9.27089668e-02, + 5.01368528e+00]]), scale=0.09729877421789021, shift=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, + -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, + 0.00122987, -0.01762768]), 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9.75472888e-01]), index=144, x=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), fval=0.03185233767614412, rho=-0.9167851356132374, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([131, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, + 145, 146]), old_indices_discarded=array([ 59, 130, 132]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), radius=0.048649387108945105, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([143, 144, 145, 146, 147]), model=ScalarModel(intercept=0.031852337676144094, linear_terms=array([-0.01334969, -0.000875 , -0.00212241, 0.00556698]), square_terms=array([[ 0.14858061, 0.00959735, 0.02815489, -0.28697043], + [ 0.00959735, 0.00062439, 0.00182044, -0.01825667], + [ 0.02815489, 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bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([144, 145, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, + 158, 159, 160]), model=ScalarModel(intercept=0.032556756784188255, linear_terms=array([-1.38069018e-03, -8.05850091e-05, -2.11446865e-04, 2.23013286e-02]), square_terms=array([[ 1.35249748e-03, 6.51569707e-05, 1.87461961e-04, + -1.84531760e-02], + [ 6.51569707e-05, 3.62304471e-06, 9.61360198e-06, + -1.02222512e-03], + [ 1.87461961e-04, 9.61360198e-06, 3.78176312e-05, + -2.78482419e-03], + [-1.84531760e-02, -1.02222512e-03, -2.78482419e-03, + 2.89607050e-01]]), scale=0.024324693554472553, shift=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, + -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, + 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 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3.98567482e+03, 1.55173969e+00, 9.75875695e-01]), index=144, x=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), fval=0.03185233767614412, rho=-0.3377986236674669, accepted=False, new_indices=array([149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160]), old_indices_used=array([144, 145, 147, 148]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), radius=0.012162346777236276, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([144, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, + 160, 161]), model=ScalarModel(intercept=0.032618873867381576, linear_terms=array([-0.00076276, -0.00023605, -0.00050889, 0.01153604]), square_terms=array([[ 3.73432785e-04, 9.98864964e-05, 2.43531496e-04, + 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), radius=0.006081173388618138, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([144, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, + 161, 162]), model=ScalarModel(intercept=0.03252324635513784, linear_terms=array([-4.14065539e-04, -9.94093190e-05, -9.38607562e-05, 5.40904684e-03]), square_terms=array([[ 1.20853884e-04, 2.50219420e-05, 2.98370200e-05, + -1.40554138e-03], + [ 2.50219420e-05, 5.65327048e-06, 6.58419488e-06, + -3.17355619e-04], + [ 2.98370200e-05, 6.58419488e-06, 8.42877891e-06, + -3.71203286e-04], + [-1.40554138e-03, -3.17355619e-04, -3.71203286e-04, + 1.78905730e-02]]), scale=0.006081173388618138, shift=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, + -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, + 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 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169, 170, 171, 172, 173, + 174, 175]), model=ScalarModel(intercept=0.03190626622045728, linear_terms=array([-5.00916898e-05, -1.60685558e-06, -3.41876508e-05, 8.86221374e-04]), square_terms=array([[ 2.04825629e-05, 2.61345798e-07, 7.17448985e-06, + -2.80536546e-04], + [ 2.61345798e-07, 2.02583758e-08, 6.64912984e-08, + -1.61121192e-06], + [ 7.17448985e-06, 6.64912984e-08, 3.00963453e-06, + -1.09754930e-04], + [-2.80536546e-04, -1.61121192e-06, -1.09754930e-04, + 4.34084080e-03]]), scale=0.003040586694309069, shift=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, + -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, + 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 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rho=0.7825513778471532, accepted=True, new_indices=array([164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175]), old_indices_used=array([144, 162, 163]), old_indices_discarded=array([], dtype=int32), step_length=0.00327686813422291, relative_step_length=1.077709160655106, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12847123e+00, 3.98565079e+03, 1.55561076e+00, 9.77377950e-01]), radius=0.006081173388618138, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([144, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, + 175, 176]), model=ScalarModel(intercept=0.031782193359979234, linear_terms=array([ 1.65934087e-05, -1.10658052e-05, -5.86797451e-05, 1.70650043e-05]), square_terms=array([[ 8.16385660e-05, 4.98651617e-07, 2.63205082e-05, + -1.11853097e-03], + [ 4.98651617e-07, 7.66151913e-08, 9.65330348e-08, + 7.67793318e-07], + [ 2.63205082e-05, 9.65330348e-08, 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State(trustregion=Region(center=array([4.12847123e+00, 3.98565079e+03, 1.55561076e+00, 9.77377950e-01]), radius=0.003040586694309069, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([144, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, + 176, 177]), model=ScalarModel(intercept=0.03179776772837414, linear_terms=array([ 4.32340121e-06, -2.77572135e-06, -1.55094590e-05, 6.03814167e-06]), square_terms=array([[ 2.04941628e-05, 1.12208524e-07, 6.82404742e-06, + -2.79715394e-04], + [ 1.12208524e-07, 1.42062247e-08, 1.38323893e-08, + 1.90493930e-07], + [ 6.82404742e-06, 1.38323893e-08, 2.73772264e-06, + -1.05054792e-04], + [-2.79715394e-04, 1.90493930e-07, -1.05054792e-04, + 4.34256904e-03]]), scale=0.003040586694309069, shift=array([4.12847123e+00, 3.98565079e+03, 1.55561076e+00, 9.77377950e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, + -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, + 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 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accepted=False, new_indices=array([186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197]), old_indices_used=array([179, 182, 184, 185]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12717358e+00, 3.98565134e+03, 1.55653153e+00, 9.77268694e-01]), radius=9.501833419715841e-05, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([182, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, + 197, 198]), model=ScalarModel(intercept=0.03175159379851204, linear_terms=array([ 1.86824538e-06, 1.22995147e-06, -5.54257272e-07, 2.15176219e-06]), square_terms=array([[ 2.36239271e-08, 1.19473473e-09, 8.68439146e-09, + -3.01777243e-07], + [ 1.19473473e-09, 2.91874089e-10, 2.77300727e-10, + -2.04750465e-08], + [ 8.68439146e-09, 2.77300727e-10, 3.78517422e-09, + -1.14699063e-07], + 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candidate_index=14, candidate_x=array([1.27617049e+01, 3.72370564e+03, 7.00000000e+01, 7.10010395e-01]), index=0, x=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00]), fval=1.043279826158364, rho=-0.0030713934660341114, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00]), radius=96.6956417501857, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]), model=ScalarModel(intercept=58.7897463770328, linear_terms=array([-139.45542076, 8.63392131, -28.59473691, 140.29845288]), square_terms=array([[ 168.38132325, -10.3500791 , 33.52617405, 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1.00113435e+00])), candidate_index=16, candidate_x=array([5.23938509e+00, 3.90385568e+03, 5.86832727e+01, 1.10000000e+00]), index=0, x=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00]), fval=1.043279826158364, rho=-3.9804649365152422, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 3, 7, 11, 14, 15]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00]), radius=24.173910437546425, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 7, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=70.61343619628207, linear_terms=array([-149.60881684, -15.20520525, -38.22698962, 69.94687726]), square_terms=array([[161.20374292, 16.2688619 , 40.62015243, 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State(trustregion=Region(center=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00]), radius=12.086955218773213, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 7, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=140.77239003523985, linear_terms=array([-172.90891621, -7.83111137, -2.15766299, 116.60174267]), square_terms=array([[ 1.07105586e+02, 4.80058830e+00, 1.23891759e+00, + -7.05566845e+01], + [ 4.80058830e+00, 2.17979398e-01, 6.07723262e-02, + -3.25273056e+00], + [ 1.23891759e+00, 6.07723262e-02, 2.72332254e-02, + -1.03206277e+00], + [-7.05566845e+01, -3.25273056e+00, -1.03206277e+00, + 5.01595469e+01]]), scale=array([7.03241927, 9.00750174, 9.00750174, 0.3 ]), shift=array([8.13241927e+00, 3.86782567e+03, 2.26532658e+01, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , + 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shift=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00])), candidate_index=30, candidate_x=array([1.51648385e+01, 3.87683317e+03, 1.36457640e+01, 5.78808741e-01]), index=0, x=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00]), fval=1.043279826158364, rho=-0.0038980287602399517, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 7, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]), old_indices_discarded=array([16]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00]), radius=6.043477609386606, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 7, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=187.5605981153043, linear_terms=array([-130.64991462, -7.14678784, 1.4954385 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28, 29, 30]), old_indices_discarded=array([23]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00]), radius=3.021738804693303, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=0.31987187423087415, linear_terms=array([ 0.08669126, -0.1414405 , -0.04511992, -0.93701812]), square_terms=array([[ 9.04354688e-02, -3.61449104e-02, -5.43056331e-04, + -3.12175071e-01], + [-3.61449104e-02, 1.45220794e-01, 6.28594817e-02, + 9.92180671e-01], + [-5.43056331e-04, 6.28594817e-02, 3.01737068e-02, + 4.17797683e-01], + [-3.12175071e-01, 9.92180671e-01, 4.17797683e-01, + 6.88478390e+00]]), scale=array([2.25187544, 2.25187544, 2.25187544, 0.3 ]), shift=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , + 0.06270254, 0.04555533, -0.16182457, -0.31033459, -0.29822499, + -0.63415123, -0.62235824, 0.04878888, 0.06990258, 0.07736138, + 0.06837988, 0.04773419]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + 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model=ScalarModel(intercept=0.36692545777675056, linear_terms=array([ 0.0504758 , -0.0372444 , -0.07437049, -1.26119166]), square_terms=array([[ 2.70404887e-02, -1.63616628e-03, -5.79863045e-03, + -1.65660526e-01], + [-1.63616628e-03, 5.45410262e-03, 1.26900356e-02, + 1.88720243e-01], + [-5.79863045e-03, 1.26900356e-02, 3.03357143e-02, + 4.56342497e-01], + [-1.65660526e-01, 1.88720243e-01, 4.56342497e-01, + 7.14920792e+00]]), scale=array([1.12593772, 1.12593772, 1.12593772, 0.3 ]), shift=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , + 0.06270254, 0.04555533, -0.16182457, -0.31033459, -0.29822499, + -0.63415123, -0.62235824, 0.04878888, 0.06990258, 0.07736138, + 0.06837988, 0.04773419]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 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new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00]), radius=0.7554347011733258, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 32, 33, 35, 37, 39, 40, 41, 44, 45]), model=ScalarModel(intercept=1.1652708843206574, linear_terms=array([-0.07983151, 0.15956484, 0.02243179, -1.84754634]), square_terms=array([[ 9.66381815e-03, -8.77228945e-03, -1.01216987e-03, + 7.11865361e-02], + [-8.77228945e-03, 1.45920997e-02, 1.45705218e-03, + -1.67346793e-01], + [-1.01216987e-03, 1.45705218e-03, 2.60612725e-04, + -1.44287039e-02], + [ 7.11865361e-02, -1.67346793e-01, -1.44287039e-02, + 2.15953524e+00]]), 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2.20813063e+01, 1.03510227e+00]), index=116, x=array([6.69804980e+00, 3.86727830e+03, 2.20808774e+01, 1.03496159e+00]), fval=0.5418064990534898, rho=-0.5974244675011045, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 83, 87, 94, 99, 101, 104, 106, 107, 108, 110, 111, 113, 114, + 115, 116]), old_indices_discarded=array([ 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 102, + 103, 105, 109, 112, 117]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69804980e+00, 3.86727830e+03, 2.20808774e+01, 1.03496159e+00]), radius=0.000368864600182288, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 83, 87, 94, 99, 104, 106, 107, 108, 110, 111, 113, 114, 115, + 116, 118]), model=ScalarModel(intercept=0.5418256537643822, linear_terms=array([ 2.30452043e-06, 8.47089151e-06, -1.51944076e-05, -5.28816905e-05]), 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x=array([6.69790747e+00, 3.86727826e+03, 2.20808983e+01, 1.03484994e+00]), fval=0.5417961421243523, rho=-0.7561149550917772, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([116, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, + 131, 132]), old_indices_discarded=array([ 83, 87, 91, 93, 94, 97, 98, 99, 101, 102, 103, 104, 105, + 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 117, 118]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69790747e+00, 3.86727826e+03, 2.20808983e+01, 1.03484994e+00]), radius=0.000184432300091144, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([116, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, + 132, 133]), model=ScalarModel(intercept=0.5418053134335727, linear_terms=array([ 1.59107911e-06, 1.91604113e-06, -8.39570529e-07, 4.03636914e-06]), 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old_indices_discarded=array([115, 118, 119]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69790747e+00, 3.86727826e+03, 2.20808983e+01, 1.03484994e+00]), radius=9.2216150045572e-05, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([116, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, + 132, 134]), model=ScalarModel(intercept=0.5418036600266991, linear_terms=array([1.90610897e-06, 1.50974387e-06, 9.76797073e-08, 1.95253835e-06]), square_terms=array([[1.20515783e-09, 3.37366272e-11, 4.94787699e-11, 7.52437640e-08], + [3.37366272e-11, 1.02605967e-10, 3.57534723e-11, 1.62531668e-09], + [4.94787699e-11, 3.57534723e-11, 5.57431091e-11, 5.98222773e-09], + [7.52437640e-08, 1.62531668e-09, 5.98222773e-09, 7.49043871e-06]]), scale=9.2216150045572e-05, shift=array([6.69790747e+00, 3.86727826e+03, 2.20808983e+01, 1.03484994e+00])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , + 0.06270254, 0.04555533, -0.16182457, -0.31033459, -0.29822499, + -0.63415123, -0.62235824, 0.04878888, 0.06990258, 0.07736138, + 0.06837988, 0.04773419]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + 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model=ScalarModel(intercept=0.541801225592115, linear_terms=array([-3.85833151e-06, 1.66200121e-06, -7.39584646e-07, 5.43636323e-06]), square_terms=array([[ 5.05560336e-10, 1.28395863e-10, 5.46841377e-11, + 2.23605728e-08], + [ 1.28395863e-10, 9.20178070e-11, -7.88195222e-13, + 2.29617380e-09], + [ 5.46841377e-11, -7.88195222e-13, 3.43892202e-11, + 5.45528958e-09], + [ 2.23605728e-08, 2.29617380e-09, 5.45528958e-09, + 1.89032774e-06]]), scale=4.6108075022786e-05, shift=array([6.69790747e+00, 3.86727826e+03, 2.20808983e+01, 1.03484994e+00])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , + 0.06270254, 0.04555533, -0.16182457, -0.31033459, -0.29822499, + -0.63415123, -0.62235824, 0.04878888, 0.06990258, 0.07736138, + 0.06837988, 0.04773419]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 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rho=-0.7375647236811499, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([151, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 169, + 170, 171]), old_indices_discarded=array([167, 168]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([151, 156, 157, 158, 159, 160, 162, 163, 164, 165, 166, 169, 170, + 171, 172]), model=ScalarModel(intercept=0.5417883710603923, linear_terms=array([ 1.43992102e-07, -3.92495599e-08, -9.39836000e-08, -9.01031376e-08]), square_terms=array([[ 8.53994530e-14, -1.24453204e-14, -2.50307988e-14, + 4.70275038e-12], + [-1.24453204e-14, 1.24566358e-14, 1.43974985e-14, + 6.71297877e-13], + [-2.50307988e-14, 1.43974985e-14, 2.77619876e-14, + 1.47049227e-12], 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1.03486742e+00]), index=151, x=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00]), fval=0.5417880312778971, rho=-0.7821082957639802, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([151, 156, 157, 159, 160, 162, 163, 164, 165, 166, 169, 170, 171, + 172, 173]), old_indices_discarded=array([158, 161, 167, 168]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([151, 156, 157, 159, 160, 162, 163, 164, 165, 169, 170, 171, 172, + 173, 174]), model=ScalarModel(intercept=0.5417883646362189, linear_terms=array([ 1.39054002e-07, -3.67486274e-08, -9.76276358e-08, -9.03367442e-08]), square_terms=array([[ 8.31510649e-14, -1.19535743e-14, -2.39921551e-14, + 4.77544224e-12], + 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.92411952e+00, 4.09996773e+03, 1.96127057e+00, 1.02112845e+00]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 93, 94, 95, 96, 100, 101, 102, 103, 105, 106, 107, 108, 109, + 110, 111]), model=ScalarModel(intercept=0.2829838581648968, linear_terms=array([-0.00276684, 0.03094096, 0.0087251 , -0.03929999]), square_terms=array([[ 3.07948101e-03, -8.53430126e-03, -1.46741811e-03, + 1.20443717e-01], + [-8.53430126e-03, 2.48589549e-02, 4.41427265e-03, + -3.30744391e-01], + [-1.46741811e-03, 4.41427265e-03, 8.88370182e-04, + -5.83631468e-02], + [ 1.20443717e-01, -3.30744391e-01, -5.83631468e-02, + 4.81457600e+00]]), scale=array([0.07458962, 0.07458962, 0.07458962, 0.07458962]), shift=array([5.92411952e+00, 4.09996773e+03, 1.96127057e+00, 1.02112845e+00])), 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rho=-0.47013410940622297, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 93, 94, 95, 100, 101, 102, 103, 105, 106, 107, 108, 109, 110, + 111, 113]), old_indices_discarded=array([ 59, 96, 97, 98, 99, 104]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.92411952e+00, 4.09996773e+03, 1.96127057e+00, 1.02112845e+00]), radius=0.0250225153947669, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 95, 111, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, + 124, 125, 126]), model=ScalarModel(intercept=0.2942454780500071, linear_terms=array([-0.00053836, 0.01012758, 0.00658806, -0.04235502]), square_terms=array([[ 2.85341780e-04, -7.78431906e-04, -4.05296375e-04, + 1.17160568e-02], + [-7.78431906e-04, 2.28701486e-03, 1.21244865e-03, + -3.24691514e-02], + [-4.05296375e-04, 1.21244865e-03, 6.63688875e-04, + 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1.94692224e+00, 1.02144230e+00]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([111, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, + 126, 127]), model=ScalarModel(intercept=0.2943051049928589, linear_terms=array([ 0.00564431, -0.00188183, 0.00320629, -0.09902661]), square_terms=array([[ 9.27890201e-05, 7.56993238e-06, 6.61699037e-06, + 5.86812035e-03], + [ 7.56993238e-06, 3.99076703e-05, -3.70323399e-05, + 8.89741428e-03], + [ 6.61699037e-06, -3.70323399e-05, 8.59295855e-05, + -8.43245723e-03], + [ 5.86812035e-03, 8.89741428e-03, -8.43245723e-03, + 2.25161796e+00]]), scale=0.0500450307895338, shift=array([5.92278744e+00, 4.09994717e+03, 1.94692224e+00, 1.02144230e+00])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 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candidate_x=array([5.87875075e+00, 4.09995833e+03, 1.92582237e+00, 1.02362849e+00]), index=128, x=array([5.87875075e+00, 4.09995833e+03, 1.92582237e+00, 1.02362849e+00]), fval=0.2860214168257209, rho=1.395194798708464, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([111, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, + 126, 127]), old_indices_discarded=array([ 59, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, + 105, 106, 107, 108, 109, 110, 112, 113]), step_length=0.05013889760653118, relative_step_length=1.0018756471025494, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.87875075e+00, 4.09995833e+03, 1.92582237e+00, 1.02362849e+00]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 111, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, + 126, 128]), 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accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 94, 111, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, + 126, 128]), old_indices_discarded=array([ 46, 59, 60, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, + 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, + 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 112, 113, + 125, 127]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.87875075e+00, 4.09995833e+03, 1.92582237e+00, 1.02362849e+00]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([111, 114, 115, 116, 117, 119, 120, 121, 122, 123, 124, 125, 126, + 127, 128]), model=ScalarModel(intercept=0.28601605918758766, linear_terms=array([ 0.00549346, -0.00187638, 0.00228974, -0.00413326]), square_terms=array([[ 9.45819260e-05, 2.05993647e-05, 2.22032641e-06, + 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118, 129]), step_length=0.0500760576419539, relative_step_length=1.0006199786858077, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.83456553e+00, 4.09997325e+03, 1.90758384e+00, 1.02375259e+00]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([111, 114, 115, 116, 117, 119, 122, 123, 124, 125, 126, 127, 128, + 129, 130]), model=ScalarModel(intercept=0.27698506927654987, linear_terms=array([0.00886586, 0.00136488, 0.00678477, 0.02502439]), square_terms=array([[ 1.93358601e-04, 9.16030067e-06, 2.22153096e-05, + 9.68917980e-03], + [ 9.16030067e-06, 2.87649923e-05, 1.19928618e-04, + -1.05626890e-02], + [ 2.22153096e-05, 1.19928618e-04, 5.97417848e-04, + -4.71540074e-02], + [ 9.68917980e-03, -1.05626890e-02, -4.71540074e-02, + 5.10191499e+00]]), scale=array([0.07458962, 0.07458962, 0.07458962, 0.07458962]), shift=array([5.83456553e+00, 4.09997325e+03, 1.90758384e+00, 1.02375259e+00])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 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candidate_x=array([5.46161742e+00, 4.09960030e+03, 1.78847388e+00, 1.01309961e+00]), index=134, x=array([5.46161742e+00, 4.09960030e+03, 1.78847388e+00, 1.01309961e+00]), fval=0.21950785606017936, rho=0.08682007098967585, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 94, 111, 113, 114, 115, 119, 123, 125, 126, 127, 128, 129, 130, + 131, 132]), old_indices_discarded=array([ 46, 49, 54, 55, 59, 60, 61, 62, 65, 70, 75, 76, 77, + 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, + 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, + 105, 106, 107, 108, 109, 110, 112, 116, 117, 118, 120, 121, 122, + 124, 133]), step_length=0.23555149344412082, relative_step_length=1.176697714677913, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.46161742e+00, 4.09960030e+03, 1.78847388e+00, 1.01309961e+00]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 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2.38204183e+00, 9.80265374e-01])), candidate_index=148, candidate_x=array([4.23569943e+00, 4.09922711e+03, 8.81618923e-01, 9.61062476e-01]), index=148, x=array([4.23569943e+00, 4.09922711e+03, 8.81618923e-01, 9.61062476e-01]), fval=0.041423864822835596, rho=0.46328473374724694, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([138, 139, 140, 141, 144, 145, 146, 147]), old_indices_discarded=array([], dtype=int32), step_length=0.10031413807620175, relative_step_length=1.002238748718894, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.23569943e+00, 4.09922711e+03, 8.81618923e-01, 9.61062476e-01]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([137, 138, 139, 140, 141, 143, 144, 145, 146, 147, 148, 149]), model=ScalarModel(intercept=0.03951342557884055, linear_terms=array([ 0.00394695, 0.0005794 , -0.00109741, -0.02210072]), 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160, 164, 165, + 166, 167]), model=ScalarModel(intercept=0.03573571879718205, linear_terms=array([-0.00103904, -0.00012385, -0.00223924, 0.01668509]), square_terms=array([[ 1.74164536e-02, -2.04572735e-03, 8.03118307e-03, + -2.01535645e-01], + [-2.04572735e-03, 2.71762449e-04, -1.07363247e-03, + 2.63607584e-02], + [ 8.03118307e-03, -1.07363247e-03, 4.49735351e-03, + -1.03958356e-01], + [-2.01535645e-01, 2.63607584e-02, -1.03958356e-01, + 2.58811266e+00]]), scale=0.1000900615790676, shift=array([4.18135530e+00, 4.09927774e+03, 1.04428032e+00, 9.66584319e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 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step_length=0.21107983641988134, relative_step_length=1.0544495282038355, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.17571078e+00, 4.09945171e+03, 1.29350788e+00, 9.75853572e-01]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([143, 145, 147, 150, 151, 152, 155, 158, 165, 166, 167, 168, 169, + 170, 171]), model=ScalarModel(intercept=0.03278405592667447, linear_terms=array([-0.00058539, -0.00037664, -0.0011525 , 0.01693014]), square_terms=array([[ 4.39506574e-03, 1.51749098e-03, 5.21452806e-04, + -4.51551090e-02], + [ 1.51749098e-03, 7.34534123e-04, 2.69788430e-04, + -2.37397024e-02], + [ 5.21452806e-04, 2.69788430e-04, 4.58291048e-04, + -9.94393532e-03], + [-4.51551090e-02, -2.37397024e-02, -9.94393532e-03, + 7.84975007e-01]]), scale=0.1000900615790676, shift=array([4.17571078e+00, 4.09945171e+03, 1.29350788e+00, 9.75853572e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + 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scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=175, candidate_x=array([4.14641789e+00, 4.09944333e+03, 1.44169648e+00, 9.75654332e-01]), index=175, x=array([4.14641789e+00, 4.09944333e+03, 1.44169648e+00, 9.75654332e-01]), fval=0.03196020635668493, rho=0.6511332348539848, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([169, 171, 172, 173, 174]), old_indices_discarded=array([], dtype=int32), step_length=0.05245185261086877, relative_step_length=1.0480931230007022, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.14641789e+00, 4.09944333e+03, 1.44169648e+00, 9.75654332e-01]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([150, 167, 168, 169, 170, 171, 172, 173, 174, 175]), model=ScalarModel(intercept=0.03198863275603155, linear_terms=array([ 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scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=177, candidate_x=array([4.13372474e+00, 4.09944339e+03, 1.49057206e+00, 9.75159538e-01]), index=177, x=array([4.13372474e+00, 4.09944339e+03, 1.49057206e+00, 9.75159538e-01]), fval=0.03175931998394472, rho=0.6690416360198942, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([171, 172, 173, 174, 175, 176]), old_indices_discarded=array([], dtype=int32), step_length=0.050499371349656076, relative_step_length=1.0090786348405505, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13372474e+00, 4.09944339e+03, 1.49057206e+00, 9.75159538e-01]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([150, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177]), model=ScalarModel(intercept=0.03248246414257855, 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old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13372474e+00, 4.09944339e+03, 1.49057206e+00, 9.75159538e-01]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([171, 172, 173, 174, 175, 176, 177, 178]), model=ScalarModel(intercept=0.03172573143066533, linear_terms=array([-1.14059094e-04, -1.10807797e-05, -1.03748194e-04, 1.17775825e-03]), square_terms=array([[ 3.64736289e-03, 6.02428702e-05, 1.27706960e-03, + -5.24032340e-02], + [ 6.02428702e-05, 1.22643828e-06, 2.44981157e-05, + -1.05985180e-03], + [ 1.27706960e-03, 2.44981157e-05, 5.72112824e-04, + -2.20276660e-02], + [-5.24032340e-02, -1.05985180e-03, -2.20276660e-02, + 9.34368129e-01]]), scale=0.0500450307895338, shift=array([4.13372474e+00, 4.09944339e+03, 1.49057206e+00, 9.75159538e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + 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177, 178, 179]), model=ScalarModel(intercept=0.032130564354299555, linear_terms=array([-0.00156055, -0.00139965, 0.00036611, -0.025743 ]), square_terms=array([[ 4.05055456e-03, 1.48919579e-03, -6.07642697e-04, + 3.44846053e-02], + [ 1.48919579e-03, 1.66608590e-03, -7.81966055e-04, + 3.87222711e-02], + [-6.07642697e-04, -7.81966055e-04, 5.86968019e-04, + -1.75898376e-02], + [ 3.44846053e-02, 3.87222711e-02, -1.75898376e-02, + 9.06040243e-01]]), scale=0.1000900615790676, shift=array([4.13431204e+00, 4.09946163e+03, 1.53745960e+00, 9.76255422e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 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46, 47, 48, 49]), model=ScalarModel(intercept=0.5792867438043003, linear_terms=array([ 0.0620906 , 0.05580846, -0.01892313, 1.03442734]), square_terms=array([[ 4.84671874e-02, 5.47513283e-02, -1.60884447e-02, + 6.51384141e-01], + [ 5.47513283e-02, 6.23780615e-02, -1.82999844e-02, + 7.33136697e-01], + [-1.60884447e-02, -1.82999844e-02, 5.38147700e-03, + -2.16952904e-01], + [ 6.51384141e-01, 7.33136697e-01, -2.16952904e-01, + 9.00073831e+00]]), scale=array([0.29797877, 0.29797877, 0.29797877, 0.25071503]), shift=array([1.66678434e+01, 4.11488286e+03, 2.87918011e+01, 7.50715032e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 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rho=-0.8879908119721849, accepted=False, new_indices=array([49]), old_indices_used=array([33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 45, 46, 47, 48]), old_indices_discarded=array([29, 36, 40]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.66678434e+01, 4.11488286e+03, 2.87918011e+01, 7.03451296e-01]), radius=0.1999253572962513, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([33, 34, 35, 37, 38, 39, 41, 42, 43, 45, 46, 47, 48, 49, 50]), model=ScalarModel(intercept=0.5573166557730902, linear_terms=array([ 0.00667821, 0.00321645, -0.01330197, -0.63280993]), square_terms=array([[ 1.70163608e-04, -2.35024264e-05, 4.37192061e-05, + 6.94446216e-03], + [-2.35024264e-05, 1.13911009e-04, -3.53635828e-04, + -2.56390159e-02], + [ 4.37192061e-05, -3.53635828e-04, 1.12835620e-03, + 7.88536699e-02], + [ 6.94446216e-03, -2.56390159e-02, 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new_indices=array([], dtype=int32), old_indices_used=array([58, 62, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76]), old_indices_discarded=array([63]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.60937089e+01, 4.11494400e+03, 2.84980015e+01, 8.82845705e-01]), radius=0.012495334831015706, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([64, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77]), model=ScalarModel(intercept=0.9771712815160445, linear_terms=array([ 7.08349946e-04, -1.16870698e-04, -4.18965451e-05, -1.52010472e-04]), square_terms=array([[7.23909237e-06, 9.03950959e-07, 3.43989559e-07, 3.56627210e-04], + [9.03950959e-07, 1.42166792e-07, 5.24006982e-08, 4.77761815e-05], + [3.43989559e-07, 5.24006982e-08, 2.30233807e-08, 1.86942270e-05], + [3.56627210e-04, 4.77761815e-05, 1.86942270e-05, 1.85106502e-02]]), 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0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([62, 64, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78]), model=ScalarModel(intercept=0.9763644405622992, linear_terms=array([ 0.00275007, 0.00163665, -0.00144273, 0.00636749]), square_terms=array([[ 6.35315860e-06, -3.55843436e-06, 3.30181901e-06, + 3.55661488e-04], + [-3.55843436e-06, 2.44703673e-05, -2.19338708e-05, + -1.18618090e-03], + [ 3.30181901e-06, -2.19338708e-05, 1.96996026e-05, + 1.06751709e-03], + [ 3.55661488e-04, -1.18618090e-03, 1.06751709e-03, + 6.44226950e-02]]), scale=0.024990669662031412, shift=array([1.60814026e+01, 4.11494601e+03, 2.84987245e+01, 8.83166952e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 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4.11494601e+03, 2.84987245e+01, 8.83166952e-01]), fval=0.976556545788589, rho=-0.20310450103398583, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([62, 64, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78]), old_indices_discarded=array([58, 63, 65]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.60814026e+01, 4.11494601e+03, 2.84987245e+01, 8.83166952e-01]), radius=0.012495334831015706, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([64, 66, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79]), model=ScalarModel(intercept=0.9765004336646763, linear_terms=array([ 0.00032415, -0.00110989, -0.00031707, -0.00032646]), square_terms=array([[1.13207023e-05, 7.47482876e-06, 2.08750132e-06, 4.54809763e-04], + [7.47482876e-06, 6.16430423e-06, 1.78601165e-06, 3.07296782e-04], + [2.08750132e-06, 1.78601165e-06, 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2.85020219e+01, 8.83259031e-01]), radius=0.024990669662031412, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([64, 66, 67, 68, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80]), model=ScalarModel(intercept=0.9763725317079316, linear_terms=array([ 1.08656622e-03, -5.47582494e-04, 3.41387034e-05, -4.74561289e-04]), square_terms=array([[3.48211055e-05, 6.82186041e-06, 3.65355805e-07, 1.58375732e-03], + [6.82186041e-06, 1.70635189e-06, 7.22717535e-08, 3.29237820e-04], + [3.65355805e-07, 7.22717535e-08, 1.27783897e-08, 1.70473159e-05], + [1.58375732e-03, 3.29237820e-04, 1.70473159e-05, 7.44863963e-02]]), scale=0.024990669662031412, shift=array([1.60779317e+01, 4.11495756e+03, 2.85020219e+01, 8.83259031e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 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candidate_x=array([1.60555751e+01, 4.11496869e+03, 2.85013228e+01, 8.83835082e-01]), index=81, x=array([1.60555751e+01, 4.11496869e+03, 2.85013228e+01, 8.83835082e-01]), fval=0.9753331123125492, rho=0.8596633050863342, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([64, 66, 67, 68, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80]), old_indices_discarded=array([62, 63, 65, 71]), step_length=0.02499235727006561, relative_step_length=1.0000675295242993, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.60555751e+01, 4.11496869e+03, 2.85013228e+01, 8.83835082e-01]), radius=0.049981339324062825, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([64, 65, 66, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81]), model=ScalarModel(intercept=0.9744751065040498, linear_terms=array([ 0.00357018, -0.00154018, -0.00387109, 0.01346815]), square_terms=array([[5.90998341e-05, 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fval=0.9585677871382879, rho=-1.049281869869592, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 57, 89, 90, 93, 96, 99, 100, 101, 102, 103, 104, 105, 106, + 107, 108]), old_indices_discarded=array([ 37, 38, 52, 53, 54, 55, 56, 58, 59, 60, 61, 62, 63, + 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, + 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 91, + 92, 94, 95, 97, 98, 109]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.56825267e+01, 4.11512057e+03, 2.82883168e+01, 8.94314354e-01]), radius=0.09996267864812565, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 57, 89, 90, 93, 96, 99, 101, 102, 103, 104, 105, 106, 107, + 108, 110]), model=ScalarModel(intercept=0.9578113150011967, linear_terms=array([-0.00143314, -0.01180295, 0.00580005, 0.11249413]), square_terms=array([[ 2.42515718e-03, 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77, 78, 79, 80, 81, 82, 83, 84, + 85, 86, 87, 88, 91, 92, 94, 95, 97, 98, 100]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.56825267e+01, 4.11512057e+03, 2.82883168e+01, 8.94314354e-01]), radius=0.049981339324062825, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([107, 108, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, + 121, 122, 123]), model=ScalarModel(intercept=0.9504713730391238, linear_terms=array([ 0.00576359, -0.00071375, -0.00207317, 0.0123183 ]), square_terms=array([[2.92179329e-05, 3.65826669e-06, 1.25596683e-05, 1.93572926e-03], + [3.65826669e-06, 4.33541544e-06, 1.33464902e-05, 1.13225496e-03], + [1.25596683e-05, 1.33464902e-05, 4.21013282e-05, 3.63824896e-03], + [1.93572926e-03, 1.13225496e-03, 3.63824896e-03, 3.48459659e-01]]), scale=0.049981339324062825, shift=array([1.56825267e+01, 4.11512057e+03, 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model_indices=array([108, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, + 123, 124]), model=ScalarModel(intercept=0.9487927595392658, linear_terms=array([-0.00147051, -0.00421324, -0.01362327, -0.01333188]), square_terms=array([[1.63750249e-03, 4.30941647e-04, 1.51329636e-03, 4.72095779e-02], + [4.30941647e-04, 1.22618834e-04, 4.25727547e-04, 1.22384388e-02], + [1.51329636e-03, 4.25727547e-04, 1.48789357e-03, 4.31972207e-02], + [4.72095779e-02, 1.22384388e-02, 4.31972207e-02, 1.38862042e+00]]), scale=0.09996267864812565, shift=array([1.56362376e+01, 4.11512669e+03, 2.83061835e+01, 8.92627861e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 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fval=0.9571523832919526, rho=-0.08704504009952377, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([108, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, + 123, 124]), old_indices_discarded=array([ 37, 38, 55, 57, 60, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105, 106, 107, 110]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.56362376e+01, 4.11512669e+03, 2.83061835e+01, 8.92627861e-01]), radius=0.049981339324062825, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([108, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, + 124, 125]), model=ScalarModel(intercept=0.9509907876084691, linear_terms=array([-0.00126299, -0.00562742, 0.00301372, -0.00840277]), square_terms=array([[ 4.53210625e-04, 3.06801681e-04, -1.65225453e-04, + 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.56362376e+01, 4.11512669e+03, 2.83061835e+01, 8.92627861e-01]), radius=0.024990669662031412, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([108, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, + 124, 126]), model=ScalarModel(intercept=0.9500782107060736, linear_terms=array([-0.00112443, -0.00068154, -0.00348962, -0.00389738]), square_terms=array([[1.43093646e-04, 2.08826756e-05, 1.14016730e-04, 3.46777637e-03], + [2.08826756e-05, 3.26531434e-06, 1.73382031e-05, 4.98398367e-04], + [1.14016730e-04, 1.73382031e-05, 9.50119687e-05, 2.71002409e-03], + [3.46777637e-03, 4.98398367e-04, 2.71002409e-03, 8.62229027e-02]]), scale=0.024990669662031412, shift=array([1.56362376e+01, 4.11512669e+03, 2.83061835e+01, 8.92627861e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, 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model=ScalarModel(intercept=0.9533020080020995, linear_terms=array([-0.00052447, 0.00074427, -0.00086914, -0.00057595]), square_terms=array([[ 3.64492591e-05, -1.07122372e-05, 1.46504039e-05, + 8.39169607e-04], + [-1.07122372e-05, 3.41383803e-06, -4.49722884e-06, + -2.38989063e-04], + [ 1.46504039e-05, -4.49722884e-06, 6.17725791e-06, + 3.31774608e-04], + [ 8.39169607e-04, -2.38989063e-04, 3.31774608e-04, + 1.98382863e-02]]), scale=0.012495334831015706, shift=array([1.56362376e+01, 4.11512669e+03, 2.83061835e+01, 8.92627861e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 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1.1e+00]))), model_indices=array([121, 124, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, + 139, 140, 141]), model=ScalarModel(intercept=0.9564238455642198, linear_terms=array([ 5.84991615e-06, -8.84818253e-05, 1.56587534e-05, -9.32068364e-04]), square_terms=array([[1.18263923e-06, 3.69897073e-07, 6.32096472e-08, 4.10680085e-05], + [3.69897073e-07, 1.49391801e-07, 4.11190267e-08, 1.25676564e-05], + [6.32096472e-08, 4.11190267e-08, 2.30866599e-08, 2.13447938e-06], + [4.10680085e-05, 1.25676564e-05, 2.13447938e-06, 1.46373477e-03]]), scale=0.0031238337077539266, shift=array([1.56306770e+01, 4.11512514e+03, 2.83080151e+01, 8.94188168e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], 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2.83075065e+01, 8.96050335e-01]), fval=0.956359502934222, rho=0.27822067119484756, accepted=True, new_indices=array([130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141]), old_indices_used=array([121, 124, 128, 129]), old_indices_discarded=array([], dtype=int32), step_length=0.0032348789465337065, relative_step_length=1.035547743307893, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.56297684e+01, 4.11512757e+03, 2.83075065e+01, 8.96050335e-01]), radius=0.006247667415507853, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([124, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, + 141, 142]), model=ScalarModel(intercept=0.9563365939003713, linear_terms=array([0.00029643, 0.00017783, 0.00012363, 0.0004537 ]), square_terms=array([[ 2.43210903e-06, 6.37570749e-09, -3.06184031e-07, + 1.14408592e-04], + [ 6.37570749e-09, 5.95696302e-08, 2.69092843e-08, + 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4.11506385e+03, 2.82934868e+01, 8.96967997e-01]), radius=0.049981339324062825, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([129, 130, 131, 132, 135, 136, 138, 139, 140, 141, 142, 143, 144, + 145, 146]), model=ScalarModel(intercept=0.9534135923040981, linear_terms=array([0.00157329, 0.00045408, 0.00051324, 0.00013002]), square_terms=array([[ 1.70921425e-04, 5.11088898e-07, -2.51118969e-05, + 7.66257632e-03], + [ 5.11088898e-07, 1.00617208e-06, 1.91435521e-06, + 1.08035827e-04], + [-2.51118969e-05, 1.91435521e-06, 1.45702183e-05, + -9.15721799e-04], + [ 7.66257632e-03, 1.08035827e-04, -9.15721799e-04, + 3.61387982e-01]]), scale=0.049981339324062825, shift=array([1.55612143e+01, 4.11506385e+03, 2.82934868e+01, 8.96967997e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + 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146, + 147, 148]), model=ScalarModel(intercept=0.9514697127444787, linear_terms=array([ 0.00347102, 0.00456633, -0.00497031, 0.03017323]), square_terms=array([[ 8.67970814e-04, -4.63342592e-04, 5.07629257e-04, + 3.23447862e-02], + [-4.63342592e-04, 2.76014123e-04, -3.02039517e-04, + -1.74676282e-02], + [ 5.07629257e-04, -3.02039517e-04, 3.30858301e-04, + 1.91948157e-02], + [ 3.23447862e-02, -1.74676282e-02, 1.91948157e-02, + 1.23621672e+00]]), scale=0.09996267864812565, shift=array([1.55154556e+01, 4.11505055e+03, 2.82784344e+01, 8.97881759e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + 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142]), step_length=0.049982181792418395, relative_step_length=1.0000168556578708, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.54350420e+01, 4.11500562e+03, 2.83426550e+01, 8.94717087e-01]), radius=0.09996267864812565, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([110, 129, 131, 135, 136, 140, 141, 143, 144, 145, 146, 147, 148, + 149, 150]), model=ScalarModel(intercept=0.9481669388008567, linear_terms=array([ 0.00491681, -0.00191175, 0.00090832, -0.00794232]), square_terms=array([[ 4.53197713e-04, 9.69734758e-05, -4.03223571e-05, + 2.17424813e-02], + [ 9.69734758e-05, 2.70238997e-05, -1.14028932e-05, + 5.09292923e-03], + [-4.03223571e-05, -1.14028932e-05, 4.97292295e-06, + -2.10313696e-03], + [ 2.17424813e-02, 5.09292923e-03, -2.10313696e-03, + 1.09787792e+00]]), scale=0.09996267864812565, shift=array([1.54350420e+01, 4.11500562e+03, 2.83426550e+01, 8.94717087e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], 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0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]]), scale=409.44713174272266, shift=array([6.01901790e+00, 4.09447132e+03, 8.67823437e+00, 9.60369865e-01])), candidate_index=151, candidate_x=array([1.53424000e+01, 4.11503994e+03, 2.83262993e+01, 8.97072705e-01]), index=151, x=array([1.53424000e+01, 4.11503994e+03, 2.83262993e+01, 8.97072705e-01]), fval=0.9449793990676408, rho=0.7464682068241998, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([110, 129, 131, 135, 136, 140, 141, 143, 144, 145, 146, 147, 148, + 149, 150]), old_indices_discarded=array([106, 107, 108, 109, 111, 112, 113, 114, 115, 116, 117, 118, 119, + 120, 121, 122, 123, 124, 125, 126, 127, 128, 130, 132, 133, 134, + 137, 138, 139, 142]), step_length=0.10016713827274805, relative_step_length=1.0020453596020782, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.53424000e+01, 4.11503994e+03, 2.83262993e+01, 8.97072705e-01]), radius=0.1999253572962513, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([110, 130, 131, 135, 136, 141, 143, 144, 145, 146, 147, 148, 149, + 150, 151]), model=ScalarModel(intercept=0.9447208950935511, linear_terms=array([ 0.00747774, -0.00115453, 0.00019389, -0.01790295]), square_terms=array([[ 2.32523138e-03, 1.54081854e-04, -2.97872562e-06, + 1.00174053e-01], + [ 1.54081854e-04, 1.28584982e-05, -4.90411666e-07, + 6.98051990e-03], + [-2.97872562e-06, -4.90411666e-07, 4.85734809e-07, + -6.45942014e-05], + [ 1.00174053e-01, 6.98051990e-03, -6.45942014e-05, + 4.46348961e+00]]), scale=0.1999253572962513, shift=array([1.53424000e+01, 4.11503994e+03, 2.83262993e+01, 8.97072705e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]]), scale=409.44713174272266, shift=array([6.01901790e+00, 4.09447132e+03, 8.67823437e+00, 9.60369865e-01])), candidate_index=152, candidate_x=array([1.51445449e+01, 4.11506845e+03, 2.83213499e+01, 9.02261230e-01]), index=152, x=array([1.51445449e+01, 4.11506845e+03, 2.83213499e+01, 9.02261230e-01]), fval=0.9352639178281498, rho=1.2203446548292984, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([110, 130, 131, 135, 136, 141, 143, 144, 145, 146, 147, 148, 149, + 150, 151]), old_indices_discarded=array([ 37, 38, 55, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, + 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, + 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, + 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, + 106, 107, 108, 109, 111, 112, 113, 114, 115, 116, 117, 118, 119, + 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 132, 133, 134, + 137, 138, 139, 140, 142]), step_length=0.20002826060126488, relative_step_length=1.0005147086212836, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.51445449e+01, 4.11506845e+03, 2.83213499e+01, 9.02261230e-01]), radius=0.3998507145925026, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([109, 110, 135, 136, 141, 143, 144, 145, 146, 147, 148, 149, 150, + 151, 152]), model=ScalarModel(intercept=1.0726433496908907, linear_terms=array([-0.02343661, -0.00889048, -0.00275083, -1.36662877]), square_terms=array([[4.86288425e-03, 6.82588240e-04, 2.46055974e-04, 1.78667944e-01], + [6.82588240e-04, 1.16954213e-04, 3.75054128e-05, 2.65804533e-02], + [2.46055974e-04, 3.75054128e-05, 1.68608179e-05, 9.53055835e-03], + [1.78667944e-01, 2.65804533e-02, 9.53055835e-03, 6.81382039e+00]]), scale=array([0.29797877, 0.29797877, 0.29797877, 0.24785877]), shift=array([1.51445449e+01, 4.11506845e+03, 2.83213499e+01, 8.52141232e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]]), scale=409.44713174272266, shift=array([6.01901790e+00, 4.09447132e+03, 8.67823437e+00, 9.60369865e-01])), candidate_index=153, candidate_x=array([1.48465661e+01, 4.11536643e+03, 2.86193287e+01, 9.07039202e-01]), index=153, x=array([1.48465661e+01, 4.11536643e+03, 2.86193287e+01, 9.07039202e-01]), fval=0.9223250761250302, rho=0.7752349469770148, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([109, 110, 135, 136, 141, 143, 144, 145, 146, 147, 148, 149, 150, + 151, 152]), old_indices_discarded=array([ 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105, 106, 107, 108, 111, 112, 113, 114, 115, 116, + 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, + 130, 131, 132, 133, 134, 137, 138, 139, 140, 142]), step_length=0.5161364791949027, relative_step_length=1.2908229505627111, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.48465661e+01, 4.11536643e+03, 2.86193287e+01, 9.07039202e-01]), radius=0.7997014291850052, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([125, 136, 137, 141, 142, 143, 144, 145, 146, 148, 149, 150, 151, + 152, 153]), model=ScalarModel(intercept=1.8249779819419052, linear_terms=array([-1.76627203e-01, -2.47141400e-03, -2.38775460e-03, -4.85764149e+00]), square_terms=array([[ 2.34532115e-02, 1.92781711e-04, 1.57766385e-04, + 5.44396171e-01], + [ 1.92781711e-04, 6.31379450e-06, -3.02452352e-06, + 5.07453896e-03], + [ 1.57766385e-04, -3.02452352e-06, 9.75935474e-06, + 4.07694325e-03], + [ 5.44396171e-01, 5.07453896e-03, 4.07694325e-03, + 1.30477707e+01]]), scale=array([0.59595753, 0.59595753, 0.59595753, 0.3 ]), shift=array([1.48465661e+01, 4.11536643e+03, 2.86193287e+01, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]]), scale=409.44713174272266, shift=array([6.01901790e+00, 4.09447132e+03, 8.67823437e+00, 9.60369865e-01])), candidate_index=154, candidate_x=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), index=154, x=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), fval=0.8900718166078354, rho=1.1251274278087409, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([125, 136, 137, 141, 142, 143, 144, 145, 146, 148, 149, 150, 151, + 152, 153]), old_indices_discarded=array([ 29, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, + 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, + 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, + 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, + 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, + 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, + 123, 124, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 138, + 139, 140, 147]), step_length=1.0323679882696024, relative_step_length=1.2909417822620541, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), radius=1.5994028583700104, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 59, 125, 127, 136, 143, 144, 145, 146, 148, 149, 150, 151, 152, + 153, 154]), model=ScalarModel(intercept=1.1326265739323547, linear_terms=array([-0.09637569, -0.14379396, 0.09712327, -1.55727297]), square_terms=array([[ 0.04121845, 0.02313126, -0.01642407, 0.42608071], + [ 0.02313126, 0.01788942, -0.01256294, 0.26548283], + [-0.01642407, -0.01256294, 0.00885428, -0.18607463], + [ 0.42608071, 0.26548283, -0.18607463, 4.71648178]]), scale=array([1.19191507, 1.19191507, 1.19191507, 0.3 ]), shift=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], 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120, + 121, 122, 123, 124, 126, 128, 129, 130, 131, 132, 133, 134, 135, + 137, 138, 139, 140, 141, 142, 147]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), radius=0.7997014291850052, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 59, 125, 127, 136, 143, 144, 145, 146, 148, 149, 150, 151, 152, + 153, 154]), model=ScalarModel(intercept=1.1326265739323542, linear_terms=array([-0.04818785, -0.07189698, 0.04856163, -1.55727297]), square_terms=array([[ 1.03046127e-02, 5.78281380e-03, -4.10601701e-03, + 2.13040355e-01], + [ 5.78281380e-03, 4.47235416e-03, -3.14073444e-03, + 1.32741416e-01], + [-4.10601701e-03, -3.14073444e-03, 2.21357021e-03, + -9.30373154e-02], + [ 2.13040355e-01, 1.32741416e-01, -9.30373154e-02, + 4.71648178e+00]]), scale=array([0.59595753, 0.59595753, 0.59595753, 0.3 ]), shift=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + 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103, 104, 105, 106, 107, 108, 109, 110, 111, + 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, + 126, 128, 129, 130, 131, 132, 133, 134, 135, 137, 138, 139, 140, + 141, 142, 147, 155]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), radius=0.3998507145925026, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 59, 125, 127, 136, 143, 145, 146, 148, 149, 150, 151, 152, 153, + 154, 156]), model=ScalarModel(intercept=0.9323612178144098, linear_terms=array([-0.01259834, 0.01279683, -0.02998382, -0.59640575]), square_terms=array([[ 4.02828891e-03, -1.26463784e-03, 2.65461049e-03, + 1.14730233e-01], + [-1.26463784e-03, 4.66105839e-04, -9.83258053e-04, + -3.67550589e-02], + [ 2.65461049e-03, -9.83258053e-04, 2.08826577e-03, + 7.82898537e-02], + [ 1.14730233e-01, -3.67550589e-02, 7.82898537e-02, + 3.40732089e+00]]), scale=array([0.29797877, 0.29797877, 0.29797877, 0.2369916 ]), shift=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 8.63008405e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), radius=0.1999253572962513, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([154, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, + 169]), model=ScalarModel(intercept=0.8621110105805507, linear_terms=array([ 0.05259407, 0.01444784, -0.04650949, 1.3449937 ]), square_terms=array([[ 1.06382832e-02, 1.77761267e-03, -1.34152433e-02, + 2.24240960e-01], + [ 1.77761267e-03, 3.91467956e-04, -2.20266751e-03, + 3.98071647e-02], + [-1.34152433e-02, -2.20266751e-03, 1.72375098e-02, + -2.79030554e-01], + [ 2.24240960e-01, 3.98071647e-02, -2.79030554e-01, + 4.86559567e+00]]), scale=array([0.14898938, 0.14898938, 0.14898938, 0.14898938]), shift=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], 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168, 169, + 170]), model=ScalarModel(intercept=0.8521861809194315, linear_terms=array([ 0.01363795, 0.00599446, -0.00829727, 0.87225482]), square_terms=array([[ 1.25459512e-04, 6.75203715e-05, -2.81566396e-05, + 4.82302182e-03], + [ 6.75203715e-05, 1.45256704e-04, 1.00392769e-04, + -6.60298780e-03], + [-2.81566396e-05, 1.00392769e-04, 3.70387367e-04, + -2.84734924e-02], + [ 4.82302182e-03, -6.60298780e-03, -2.84734924e-02, + 2.26757906e+00]]), scale=0.09996267864812565, shift=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + 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new_indices=array([], dtype=int32), old_indices_used=array([154, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, + 170]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), radius=0.049981339324062825, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([154, 158, 169, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, + 181, 182, 183]), model=ScalarModel(intercept=0.8751612738683985, linear_terms=array([ 0.01069739, -0.0351117 , 0.00524044, 0.03150041]), square_terms=array([[ 2.80038988e-04, -1.92877967e-03, 4.46333870e-04, + -8.04311081e-03], + [-1.92877967e-03, 1.45965092e-02, -3.44462599e-03, + 6.72455897e-02], + [ 4.46333870e-04, -3.44462599e-03, 8.32232273e-04, + -1.63487093e-02], + [-8.04311081e-03, 6.72455897e-02, 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bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([154, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, + 183, 184]), model=ScalarModel(intercept=0.8806140250059065, linear_terms=array([ 0.00348081, 0.00103192, 0.00045499, -0.00120431]), square_terms=array([[ 7.93493802e-06, 1.82672879e-07, 1.06786659e-07, + 2.40577351e-04], + [ 1.82672879e-07, 1.04969220e-05, 4.04967602e-06, + -1.03166451e-03], + [ 1.06786659e-07, 4.04967602e-06, 1.68666246e-06, + -3.96649049e-04], + [ 2.40577351e-04, -1.03166451e-03, -3.96649049e-04, + 1.12239205e-01]]), scale=0.024990669662031412, shift=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], 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2.92122061e+01, 9.24232050e-01]), index=185, x=array([1.42268091e+01, 4.11595541e+03, 2.92122061e+01, 9.24232050e-01]), fval=0.8889267485376284, rho=0.3128499910431618, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([154, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, + 183, 184]), old_indices_discarded=array([], dtype=int32), step_length=0.024992174326975865, relative_step_length=1.0000602090686166, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.42268091e+01, 4.11595541e+03, 2.92122061e+01, 9.24232050e-01]), radius=0.049981339324062825, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([154, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, + 184, 185]), model=ScalarModel(intercept=0.8804191957868968, linear_terms=array([ 0.00262124, -0.00038687, 0.00011566, -0.00743838]), square_terms=array([[ 1.53514229e-04, -1.70092698e-06, 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2.91861680e+01, 9.25856803e-01]), index=188, x=array([1.41081482e+01, 4.11586063e+03, 2.91861680e+01, 9.25856803e-01]), fval=0.8831177501278756, rho=0.6433964616125217, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([154, 171, 174, 175, 177, 178, 179, 180, 181, 182, 183, 184, 185, + 186, 187]), old_indices_discarded=array([163, 168, 172, 173, 176]), step_length=0.04998185651351309, relative_step_length=1.0000103476508886, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.41081482e+01, 4.11586063e+03, 2.91861680e+01, 9.25856803e-01]), radius=0.09996267864812565, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([154, 168, 171, 174, 175, 178, 179, 180, 181, 182, 183, 185, 186, + 187, 188]), model=ScalarModel(intercept=0.8289860145213327, linear_terms=array([7.58494192e-05, 2.23407925e-02, 8.90899480e-02, 3.91983702e-03]), square_terms=array([[ 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2.91363397e+01, 9.25319713e-01]), index=190, x=array([1.41055555e+01, 4.11586221e+03, 2.91362378e+01, 9.26252295e-01]), fval=0.8827327947131193, rho=-0.04838258187938748, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([171, 187, 188, 189, 190]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.41055555e+01, 4.11586221e+03, 2.91362378e+01, 9.26252295e-01]), radius=0.012495334831015706, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, + 201, 202, 203]), model=ScalarModel(intercept=0.8819337429846763, linear_terms=array([ 0.0006271 , 0.00070911, 0.00036049, -0.0046377 ]), square_terms=array([[ 9.38940867e-06, -5.45262110e-06, -2.75044789e-06, + 5.01156031e-04], + [-5.45262110e-06, 4.00284949e-06, 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6.78125000e+01, 5.93750000e-01], + [9.07343750e+00, 1.40625000e+03, 5.46875000e+00, 7.53125000e-01], + [7.00625000e+00, 1.87500000e+03, 2.18750000e+01, 8.37500000e-01], + [9.95937500e+00, 9.37500000e+02, 5.90625000e+01, 6.68750000e-01], + [1.14359375e+01, 7.65625000e+03, 1.42187500e+01, 5.28125000e-01], + [7.59687500e+00, 7.18750000e+03, 5.03125000e+01, 7.43750000e-01], + [8.77812500e+00, 9.06250000e+03, 1.96875000e+01, 5.56250000e-01], + [6.71093750e+00, 5.15625000e+03, 3.17187500e+01, 6.78125000e-01], + [5.23437500e+00, 8.43750000e+03, 6.56250000e+00, 8.18750000e-01], + [6.41562500e+00, 2.81250000e+03, 1.09375000e+01, 6.31250000e-01], + [4.64375000e+00, 3.12500000e+03, 6.56250000e+01, 7.62500000e-01], + [4.34843750e+00, 8.90625000e+03, 5.79687500e+01, 9.03125000e-01], + [3.46250000e+00, 6.25000000e+03, 2.62500000e+01, 5.75000000e-01], + [1.94093750e+01, 5.93750000e+03, 2.40625000e+01, 9.68750000e-01], + [1.40937500e+01, 8.12500000e+03, 3.06250000e+01, 1.06250000e+00], + [2.87187500e+00, 4.68750000e+03, 3.28125000e+01, 8.93750000e-01], + [2.28125000e+00, 9.37500000e+03, 3.93750000e+01, 6.87500000e-01], + [1.98593750e+00, 2.65625000e+03, 4.92187500e+01, 8.28125000e-01], + [1.70468750e+01, 2.18750000e+03, 1.53125000e+01, 1.04375000e+00], + [1.85234375e+01, 6.40625000e+03, 4.04687500e+01, 1.05312500e+00], + [1.69062500e+00, 5.31250000e+03, 6.34375000e+01, 1.08125000e+00]]), 'exploration_results': array([4.85268873e-02, 5.81756522e-01, 7.85434558e-01, 1.08501898e+00, + 1.09147168e+00, 1.15508374e+00, 1.35896335e+00, 1.47857950e+00, + 1.60399255e+00, 1.93344372e+00, 1.94383457e+00, 1.97559443e+00, + 2.00706025e+00, 2.09257083e+00, 2.11464344e+00, 2.13130763e+00, + 2.24401741e+00, 2.32809571e+00, 2.43093035e+00, 2.58864817e+00, + 2.64782870e+00, 2.75968951e+00, 2.76063416e+00, 2.87160809e+00, + 2.96402039e+00, 3.18404442e+00, 3.25640118e+00, 3.28020017e+00, + 3.37801319e+00, 3.50195454e+00, 3.52157483e+00, 3.84631802e+00, + 4.29481111e+00, 4.50920970e+00, 4.92630064e+00, 5.00339546e+00, + 5.43544424e+00, 7.28251427e+00, 1.28207813e+01, 1.15051611e+03])}}" diff --git a/src/estimark/content/tables/min/WarmGlowPortfolioShiftAlt_estimate_results.csv b/src/estimark/content/tables/min/WarmGlowPortfolioShiftAlt_estimate_results.csv new file mode 100644 index 0000000..7220a1d --- /dev/null +++ b/src/estimark/content/tables/min/WarmGlowPortfolioShiftAlt_estimate_results.csv @@ -0,0 +1,13590 @@ +CRRA,4.177600674290874 + +BeqFac,2798.51512022846 + +BeqShift,1.966935700692675 + +time_to_estimate,213.91753792762756 + +params,"{'CRRA': 4.177600674290874, 'BeqFac': 2798.51512022846, 'BeqShift': 1.966935700692675}" + +criterion,0.050872538941762795 + +start_criterion,0.149744421401998 + +start_params,"{'CRRA': 4.972059127341761, 'BeqFac': 2235.66869440018, 'BeqShift': 0.0}" + +algorithm,multistart_tranquilo_ls + +direction,minimize + +n_free,3 + +message,Absolute criterion change smaller than tolerance. + +success, + +n_criterion_evaluations, + +n_derivative_evaluations, + +n_iterations, + +history,"{'params': [{'CRRA': 4.968360850653578, 'BeqFac': 3489.450001032764, 'BeqShift': 5.063898656927828}, {'CRRA': 1.7676219917476326, 'BeqFac': 3208.2017347523847, 'BeqShift': 59.767774879194484}, {'CRRA': 16.954188449436767, 'BeqFac': 3208.2017347523847, 'BeqShift': 59.36329156294464}, {'CRRA': 20.0, 'BeqFac': 3654.6597147490415, 'BeqShift': 68.98140770341323}, {'CRRA': 13.880517002473926, 'BeqFac': 3762.6620464840953, 'BeqShift': 0.0}, {'CRRA': 7.980394574068313, 'BeqFac': 3770.698267313143, 'BeqShift': 68.474214057218}, {'CRRA': 20.0, 'BeqFac': 3766.1176162364645, 'BeqShift': 62.246768898687904}, {'CRRA': 1.1, 'BeqFac': 3768.540325045825, 'BeqShift': 44.85155276807863}, {'CRRA': 1.3101094923283654, 'BeqFac': 3450.623397898459, 'BeqShift': 70.0}, {'CRRA': 20.0, 'BeqFac': 3560.402448203421, 'BeqShift': 2.701409905191556}, {'CRRA': 13.071215358144052, 'BeqFac': 3208.2017347523847, 'BeqShift': 1.1192446483918272}, {'CRRA': 1.1, 'BeqFac': 3756.7786109759786, 'BeqShift': 16.46504634175265}, {'CRRA': 19.298974170870512, 'BeqFac': 3208.2017347523847, 'BeqShift': 69.91718013800757}, {'CRRA': 12.880975106243817, 'BeqFac': 3208.2017347523847, 'BeqShift': 0.0}, {'CRRA': 9.251225991471458, 'BeqFac': 3067.5776016121954, 'BeqShift': 0.0}, {'CRRA': 3.6375521204171073, 'BeqFac': 2788.697983380339, 'BeqShift': 0.0}, {'CRRA': 5.92463589656902, 'BeqFac': 2926.953468472006, 'BeqShift': 0.0}, {'CRRA': 2.8027826445672543, 'BeqFac': 2645.705202191627, 'BeqShift': 0.0}, {'CRRA': 5.2002500994259755, 'BeqFac': 2786.3293353318168, 'BeqShift': 0.0}, {'CRRA': 6.053954286705494, 'BeqFac': 2716.017268761722, 'BeqShift': 0.0}, {'CRRA': 8.187236245412365, 'BeqFac': 2786.5716524216164, 'BeqShift': 18.264936837321702}, {'CRRA': 5.031061131954489, 'BeqFac': 2803.9073519743406, 'BeqShift': 0.0}, {'CRRA': 5.1602411817300915, 'BeqFac': 2790.951969685949, 'BeqShift': 0.0}, {'CRRA': 5.115380638236383, 'BeqFac': 2799.7409780072107, 'BeqShift': 0.0}, {'CRRA': 4.969188948943323, 'BeqFac': 2817.3189946497346, 'BeqShift': 0.0}, {'CRRA': 5.094531333414104, 'BeqFac': 2808.2705494870056, 'BeqShift': 0.0}, {'CRRA': 5.117653729382992, 'BeqFac': 2804.1354821678415, 'BeqShift': 0.0}, {'CRRA': 5.1153639498249674, 'BeqFac': 2799.7464982727965, 'BeqShift': 2.1972486130609576}, {'CRRA': 4.457344957675711, 'BeqFac': 2798.642351967053, 'BeqShift': 1.09862604015773}, {'CRRA': 4.323396337115101, 'BeqFac': 2800.8396040473685, 'BeqShift': 2.1547660163343503}, {'CRRA': 3.9411714212557856, 'BeqFac': 2797.543725926895, 'BeqShift': 2.093983387066586}, {'CRRA': 4.29994611299188, 'BeqFac': 2798.7155134039913, 'BeqShift': 1.7713480594884083}, {'CRRA': 4.056025184478589, 'BeqFac': 2797.346363767581, 'BeqShift': 2.24557407349223}, {'CRRA': 4.138045729552536, 'BeqFac': 2798.031127683044, 'BeqShift': 2.212909490426139}, {'CRRA': 4.059989311567537, 'BeqFac': 2796.6669868735166, 'BeqShift': 2.282453732315161}, {'CRRA': 4.026002824798528, 'BeqFac': 2798.713856857443, 'BeqShift': 2.270987106425888}, {'CRRA': 4.142328704202798, 'BeqFac': 2798.371239838438, 'BeqShift': 2.115320067541048}, {'CRRA': 4.035421021891758, 'BeqFac': 2799.057000705553, 'BeqShift': 2.268072829011086}, {'CRRA': 4.162342849386828, 'BeqFac': 2798.7120382787884, 'BeqShift': 2.0820283126821564}, {'CRRA': 4.189180729803071, 'BeqFac': 2798.4313906514644, 'BeqShift': 1.9567124389800021}, {'CRRA': 4.157129843134547, 'BeqFac': 2798.773188323082, 'BeqShift': 2.0571220165181043}, {'CRRA': 4.197784149507958, 'BeqFac': 2798.2613053833006, 'BeqShift': 1.9049698616184068}, {'CRRA': 4.1882127035206445, 'BeqFac': 2798.516512627961, 'BeqShift': 1.9233372105334974}, {'CRRA': 4.196587606858762, 'BeqFac': 2798.6869627340325, 'BeqShift': 1.9121957926636612}, {'CRRA': 4.187761980105835, 'BeqFac': 2798.6017090857436, 'BeqShift': 1.9286485471909265}, {'CRRA': 4.196242132162183, 'BeqFac': 2798.7722084678076, 'BeqShift': 1.912079557485072}, {'CRRA': 4.190550713034702, 'BeqFac': 2798.5165199690787, 'BeqShift': 1.9263643061813749}, {'CRRA': 4.189871366285182, 'BeqFac': 2798.3461525835946, 'BeqShift': 1.923926585872323}, {'CRRA': 4.187709060456282, 'BeqFac': 2798.601712437453, 'BeqShift': 1.929437283451666}, {'CRRA': 4.177600674290874, 'BeqFac': 2798.51512022846, 'BeqShift': 1.966935700692675}], 'criterion': [1.181105432157004, 144.6645524000311, 3.4037821967481316, 8.319436275338557, 1.4593214606471996, 0.7011180013216807, 8.319436275338557, 1643.2967077523338, 1137.5807967428811, 8.319436275342273, 1.1697753502038721, 1668.6551408328123, 6.776870912396954, 1.1406131109915512, 0.5545985836347646, 2.2278811659484714, 0.232036655988764, 18.119098849415735, 0.16384370556335875, 0.2527959294336996, 0.6819421555415957, 0.1642991669381873, 0.1628347112711602, 0.1625021910643016, 0.16817866367514966, 0.1627923319728763, 0.16259635535169853, 0.42498980410057874, 0.06529563513965843, 0.08399250593750511, 0.10068669909272801, 0.05432022221654956, 0.05415908459581581, 0.05275688891668154, 0.05330674080076367, 0.057419127185494, 0.051318965064022795, 0.05612791206991445, 0.051424151471162374, 0.05091436090216683, 0.051068448210695803, 0.050914579506761565, 0.05088712976930869, 0.050908465316813346, 0.050875284135983245, 0.050910162881492554, 0.0508732681045439, 0.050878919485344336, 0.05087623002457618, 0.050872538941762795], 'runtime': [0.0, 1.504941400140524, 1.6768188998103142, 1.8667818000540137, 2.0520103997550905, 2.234589799772948, 2.4240341000258923, 2.6088847001083195, 2.7994983000680804, 2.995965300127864, 3.1924839997664094, 3.3760963999666274, 3.5580016998574138, 4.759761999826878, 5.9858081000857055, 7.170780099928379, 8.360401600133628, 9.697439599782228, 10.887631500139832, 12.083789399825037, 13.26669319998473, 14.449900799896568, 15.622420799918473, 16.798246500082314, 17.96437279973179, 19.288782899733633, 20.466819599736482, 21.645570000167936, 22.826812400016934, 24.006268199998885, 25.212331799790263, 26.400473299901932, 27.575066099874675, 28.895407499745488, 30.087074699811637, 31.28205809975043, 32.452069099992514, 33.63252570014447, 34.80818759975955, 35.983998800162226, 37.16741700004786, 38.50141609972343, 39.70189919974655, 40.88999589998275, 42.06016460014507, 43.238295500166714, 44.42306770011783, 45.59471370000392, 46.93225569976494, 48.113191300071776], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38]}" + +convergence_report,"{'one_step': {'relative_criterion_change': 0.018714300470562203, 'relative_params_change': 0.22317607955398075, 'absolute_criterion_change': 0.0018714300470562203, 'absolute_params_change': 561.5795603000294}, 'five_steps': {'relative_criterion_change': 0.018714300470562203, 'relative_params_change': 0.22317607955398075, 'absolute_criterion_change': 0.0018714300470562203, 'absolute_params_change': 561.5795603000294}}" + +multistart_info,"{'start_parameters': [{'CRRA': 4.972059127341761, 'BeqFac': 2235.66869440018, 'BeqShift': 0.0}, {'CRRA': 6.333842432535624, 'BeqFac': 3668.9001041779247, 'BeqShift': 6.573413466501883}, {'CRRA': 4.968360850653578, 'BeqFac': 3489.450001032764, 'BeqShift': 5.063898656927828}], 'local_optima': [Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 3.577e-07* 6.797e-06* +relative_params_change 0.0001525 0.0002161 +absolute_criterion_change 3.577e-08* 6.797e-07* +absolute_params_change 0.0007074 0.0007074 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 8.731e-08* 0.000407 +relative_params_change 0.001013 0.02664 +absolute_criterion_change 5.598e-08* 0.000261 +absolute_params_change 0.01402 0.3641 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 7.292e-06* 0.004464 +relative_params_change 0.02086 0.07591 +absolute_criterion_change 7.292e-07* 0.0004464 +absolute_params_change 0.04261 0.2097 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 4.972059127341761, 'BeqFac': 2235.66869440018, 'BeqShift': 0.0}, {'CRRA': 9.368749999999999, 'BeqFac': 5625.0, 'BeqShift': 13.125}, {'CRRA': 8.778125, 'BeqFac': 9062.5, 'BeqShift': 19.6875}, {'CRRA': 9.959375, 'BeqFac': 937.5, 'BeqShift': 59.0625}, {'CRRA': 8.1875, 'BeqFac': 3750.0, 'BeqShift': 43.75}, {'CRRA': 10.549999999999999, 'BeqFac': 5000.0, 'BeqShift': 35.0}, {'CRRA': 7.596874999999999, 'BeqFac': 7187.5, 'BeqShift': 50.3125}, {'CRRA': 7.00625, 'BeqFac': 1875.0, 'BeqShift': 21.875}, {'CRRA': 11.73125, 'BeqFac': 4375.0, 'BeqShift': 4.375}, {'CRRA': 12.321874999999999, 'BeqFac': 9687.5, 'BeqShift': 67.8125}, {'CRRA': 6.415625, 'BeqFac': 2812.5, 'BeqShift': 10.9375}, {'CRRA': 12.9125, 'BeqFac': 1250.0, 'BeqShift': 61.25}, {'CRRA': 5.824999999999999, 'BeqFac': 7500.0, 'BeqShift': 52.5}, {'CRRA': 5.234375, 'BeqFac': 8437.5, 'BeqShift': 6.5625}, {'CRRA': 13.503124999999999, 'BeqFac': 6562.5, 'BeqShift': 2.1875}, {'CRRA': 14.093749999999998, 'BeqFac': 8125.0, 'BeqShift': 30.625}, {'CRRA': 14.684375, 'BeqFac': 3437.5, 'BeqShift': 41.5625}, {'CRRA': 4.64375, 'BeqFac': 3125.0, 'BeqShift': 65.625}, {'CRRA': 15.274999999999999, 'BeqFac': 2500.0, 'BeqShift': 17.5}, {'CRRA': 4.053125, 'BeqFac': 1562.5, 'BeqShift': 37.1875}, {'CRRA': 15.865624999999998, 'BeqFac': 7812.5, 'BeqShift': 45.9375}, {'CRRA': 3.4625, 'BeqFac': 6250.0, 'BeqShift': 26.25}, {'CRRA': 16.45625, 'BeqFac': 6875.0, 'BeqShift': 56.875}, {'CRRA': 2.871875, 'BeqFac': 4687.5, 'BeqShift': 32.8125}, {'CRRA': 17.046875, 'BeqFac': 2187.5, 'BeqShift': 15.3125}, {'CRRA': 17.6375, 'BeqFac': 8750.0, 'BeqShift': 8.75}, {'CRRA': 18.228125, 'BeqFac': 4062.5, 'BeqShift': 54.6875}, {'CRRA': 18.81875, 'BeqFac': 625.0, 'BeqShift': 48.125}, {'CRRA': 19.409375, 'BeqFac': 5937.5, 'BeqShift': 24.0625}, {'CRRA': 2.28125, 'BeqFac': 9375.0, 'BeqShift': 39.375}], 'exploration_results': array([ 0.15183333, 0.64211538, 0.64842412, 0.66164015, 0.68191739, + 0.7044766 , 0.74714867, 0.84822968, 0.86260573, 0.97951322, + 0.98915247, 1.12431839, 1.18220644, 1.20662179, 1.30406801, + 1.52011014, 1.78024917, 1.78135807, 2.0971452 , 2.24437522, + 2.48116605, 2.85787595, 2.94379242, 3.4148927 , 3.4976294 , + 4.16035282, 4.94978648, 5.89491246, 7.00125067, 34.37799292])}" + +algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.96836085, 3489.45000103, 5.06389866]), radius=348.9450001032764, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=[0], model=ScalarModel(intercept=1.181105432157004, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), scale=348.9450001032764, shift=array([ 4.96836085, 3489.45000103, 5.06389866])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, 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old_indices_discarded=array([ 4, 5, 6, 7, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.9246359 , 2926.95346847, 0. ]), radius=174.4725000516382, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([ 0, 1, 2, 8, 9, 10, 12, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=9.571328999106925, linear_terms=array([-45.18448012, 9.36031969, 33.66340283]), square_terms=array([[133.09207534, -26.87423247, -93.65514891], + [-26.87423247, 5.44544862, 19.03151488], + [-93.65514891, 19.03151488, 66.85203657]]), scale=array([ 9.45 , 140.62413314, 35. ]), shift=array([ 10.55 , 2926.95346847, 35. ])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 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5.06389866])), candidate_index=19, candidate_x=array([ 6.05395429, 2716.01726876, 0. ]), index=18, x=array([ 5.2002501 , 2786.32933533, 0. ]), fval=0.1638437055633587, rho=-0.08459209004077528, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 2, 10, 12, 13, 14, 15, 16, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.2002501 , 2786.32933533, 0. ]), radius=43.61812501290955, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 16, 17, 18, 19]), model=ScalarModel(intercept=7.1526635561288225, linear_terms=array([ 54.6285074 , 0.69834955, -14.30532711]), square_terms=array([[ 2.09947491e+02, 2.68478398e+00, -5.46285074e+01], + [ 2.68478398e+00, 3.43920488e-02, -6.98349546e-01], + [-5.46285074e+01, -6.98349546e-01, 1.43053271e+01]]), scale=array([ 9.45 , 35.15603329, 17.57801664]), shift=array([ 10.55 , 2786.32933533, 17.57801664])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 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dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.2002501 , 2786.32933533, 0. ]), radius=21.809062506454776, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 19, 20]), model=ScalarModel(intercept=16.004294311139557, linear_terms=array([70.66939 , 1.13700979, -8.50470948]), square_terms=array([[ 1.58368000e+02, 2.56906467e+00, -1.92507844e+01], + [ 2.56906467e+00, 4.19273077e-02, -3.14468097e-01], + [-1.92507844e+01, -3.14468097e-01, 2.36523947e+00]]), scale=array([ 9.45 , 17.57801664, 8.78900832]), shift=array([ 10.55 , 2786.32933533, 8.78900832])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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model=ScalarModel(intercept=2.9984363121302398, linear_terms=array([20.20379397, 0.14022481, -1.76089925]), square_terms=array([[ 7.28524130e+01, 5.11325102e-01, -6.48101817e+00], + [ 5.11325102e-01, 3.60021596e-03, -4.56833511e-02], + [-6.48101817e+00, -4.56833511e-02, 5.82826690e-01]]), scale=array([6.44462921, 8.78900832, 4.39450416]), shift=array([ 7.54462921, 2786.32933533, 4.39450416])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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5.06389866])), candidate_index=22, candidate_x=array([ 5.16024118, 2790.95196969, 0. ]), index=22, x=array([ 5.16024118, 2790.95196969, 0. ]), fval=0.1628347112711602, rho=1.0555049921680508, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([15, 18, 20, 21]), old_indices_discarded=array([], dtype=int32), step_length=4.622807489556225, relative_step_length=0.4239345444755384, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.16024118, 2790.95196969, 0. ]), radius=10.904531253227388, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 20, 21, 22]), model=ScalarModel(intercept=3.006282375920816, linear_terms=array([20.16458994, 0.14100404, -1.76243727]), square_terms=array([[ 7.23456179e+01, 5.11502709e-01, -6.45362479e+00], + [ 5.11502709e-01, 3.62783851e-03, -4.58243177e-02], + [-6.45362479e+00, -4.58243177e-02, 5.81967789e-01]]), scale=array([6.42462475, 8.78900832, 4.39450416]), shift=array([ 7.52462475, 2790.95196969, 4.39450416])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 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step_length=8.789122808311065, relative_step_length=0.806006476042679, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.11538064, 2799.74097801, 0. ]), radius=21.809062506454776, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=15.956937372018299, linear_terms=array([68.87495584, 1.01962009, -8.11318965]), square_terms=array([[ 1.50828107e+02, 2.25090089e+00, -1.79474940e+01], + [ 2.25090089e+00, 3.37920630e-02, -2.69729608e-01], + [-1.79474940e+01, -2.69729608e-01, 2.15985383e+00]]), scale=array([ 9.45 , 17.57801664, 8.78900832]), shift=array([ 10.55 , 2799.74097801, 8.78900832])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], 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model=ScalarModel(intercept=3.0353080773001073, linear_terms=array([20.20386846, 0.10823129, -1.77493216]), square_terms=array([[ 7.18576918e+01, 3.89097471e-01, -6.44170944e+00], + [ 3.89097471e-01, 2.11348833e-03, -3.50355915e-02], + [-6.44170944e+00, -3.50355915e-02, 5.83744149e-01]]), scale=array([6.40219448, 8.78900832, 4.39450416]), shift=array([ 7.50219448, 2799.74097801, 4.39450416])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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5.06389866])), candidate_index=25, candidate_x=array([ 5.09453133, 2808.27054949, 0. ]), index=23, x=array([ 5.11538064, 2799.74097801, 0. ]), fval=0.1625021910643016, rho=-0.257817250835792, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([15, 18, 20, 21, 22, 23, 24]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.11538064, 2799.74097801, 0. ]), radius=5.452265626613694, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 20, 21, 22, 23, 24, 25]), model=ScalarModel(intercept=0.18941940373737348, linear_terms=array([-0.84702663, -0.00367806, 0.0743278 ]), square_terms=array([[ 3.09008276e+01, 1.15966532e-01, -2.10738689e+00], + [ 1.15966532e-01, 4.36576332e-04, -7.94409134e-03], + [-2.10738689e+00, -7.94409134e-03, 1.45288267e-01]]), scale=array([4.2049424 , 4.39450416, 2.19725208]), shift=array([5.30494240e+00, 2.79974098e+03, 2.19725208e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + 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old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.11538064, 2799.74097801, 0. ]), radius=2.726132813306847, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 21, 22, 23, 25, 26]), model=ScalarModel(intercept=0.040656194310767946, linear_terms=array([-0.04424986, -0.00023592, -0.08131239]), square_terms=array([[8.44848189e+00, 2.55963392e-02, 4.42498564e-02], + [2.55963392e-02, 7.77973037e-05, 2.35916270e-04], + [4.42498564e-02, 2.35916270e-04, 8.13123886e-02]]), scale=array([2.19725208, 2.19725208, 1.09862604]), shift=array([5.11538064e+00, 2.79974098e+03, 1.09862604e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, 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State(trustregion=Region(center=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00]), radius=0.6815332033267117, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([23, 27, 28, 29, 30]), model=ScalarModel(intercept=0.08513789653811096, linear_terms=array([-0.05873789, -0.00640693, -0.05839653]), square_terms=array([[0.29594537, 0.02020028, 0.12960348], + [0.02020028, 0.00146583, 0.00964012], + [0.12960348, 0.00964012, 0.07540158]]), scale=0.6815332033267117, shift=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 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x=array([4.05602518e+00, 2.79734636e+03, 2.24557407e+00]), fval=0.05415908459581581, rho=0.02787984735130772, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([21, 23, 26, 27, 28, 29, 30, 31]), old_indices_discarded=array([], dtype=int32), step_length=1.4693394633669328, relative_step_length=1.0779661623195813, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05602518e+00, 2.79734636e+03, 2.24557407e+00]), radius=0.6815332033267117, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([27, 28, 30, 31, 32]), model=ScalarModel(intercept=0.06019280281239402, linear_terms=array([-0.05311338, -0.00664367, -0.01652794]), square_terms=array([[0.31168089, 0.01963004, 0.09342492], + [0.01963004, 0.00163636, 0.00734946], + [0.09342492, 0.00734946, 0.04083573]]), scale=0.6815332033267117, shift=array([4.05602518e+00, 2.79734636e+03, 2.24557407e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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relative_step_length=1.0130566200764606, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13804573e+00, 2.79803113e+03, 2.21290949e+00]), radius=1.3630664066534235, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([21, 23, 26, 27, 28, 29, 30, 31, 32, 33]), model=ScalarModel(intercept=0.05146650808405816, linear_terms=array([0.05615491, 0.00068073, 0.02123787]), square_terms=array([[1.36450351e+00, 9.46810979e-03, 6.16903067e-01], + [9.46810979e-03, 7.27379915e-05, 3.94676561e-03], + [6.16903067e-01, 3.94676561e-03, 3.53300245e-01]]), scale=1.3630664066534235, shift=array([4.13804573e+00, 2.79803113e+03, 2.21290949e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 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3489.45000103, 5.06389866])), candidate_index=35, candidate_x=array([4.02600282e+00, 2.79871386e+03, 2.27098711e+00]), index=33, x=array([4.13804573e+00, 2.79803113e+03, 2.21290949e+00]), fval=0.05275688891668154, rho=-2.2415296614174687, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([23, 27, 28, 29, 30, 31, 32, 33, 34]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13804573e+00, 2.79803113e+03, 2.21290949e+00]), radius=0.3407666016633559, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([28, 30, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=0.05243167254991798, linear_terms=array([ 2.14835428e-02, -8.53058917e-05, 7.83429373e-03]), square_terms=array([[2.69010071e-01, 2.03422326e-04, 8.75369596e-02], + [2.03422326e-04, 6.23463910e-07, 8.57167056e-05], + [8.75369596e-02, 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radius=0.3407666016633559, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([27, 28, 30, 31, 32, 33, 35, 36, 37]), model=ScalarModel(intercept=0.052532543329590564, linear_terms=array([-0.00426995, -0.00027655, -0.00095162]), square_terms=array([[9.08193291e-02, 1.45774993e-03, 2.59401890e-02], + [1.45774993e-03, 2.50681460e-05, 4.39732923e-04], + [2.59401890e-02, 4.39732923e-04, 1.01466646e-02]]), scale=0.3407666016633559, shift=array([4.14232870e+00, 2.79837124e+03, 2.11532007e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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1.95671244e+00]), fval=0.05091436090216684, rho=1.1516416598700234, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([30, 31, 33, 35, 36, 37, 38]), old_indices_discarded=array([], dtype=int32), step_length=0.1759818526107779, relative_step_length=1.032858570950159, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.18918073e+00, 2.79843139e+03, 1.95671244e+00]), radius=0.3407666016633559, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([27, 28, 30, 31, 32, 33, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=0.05221280899533157, linear_terms=array([-0.00115235, -0.00033548, -0.00127409]), square_terms=array([[8.81488941e-02, 2.02195870e-03, 2.52583882e-02], + [2.02195870e-03, 4.98103348e-05, 6.21228724e-04], + [2.52583882e-02, 6.21228724e-04, 9.98002687e-03]]), scale=0.3407666016633559, shift=array([4.18918073e+00, 2.79843139e+03, 1.95671244e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.18918073e+00, 2.79843139e+03, 1.95671244e+00]), radius=0.17038330083167794, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([31, 33, 35, 36, 37, 38, 39, 40]), model=ScalarModel(intercept=0.05090588732753695, linear_terms=array([ 2.00967052e-03, -4.51302094e-06, 7.70306669e-04]), square_terms=array([[ 6.02617840e-02, -3.14541700e-04, 1.76727179e-02], + [-3.14541700e-04, 1.67055199e-06, -9.60913876e-05], + [ 1.76727179e-02, -9.60913876e-05, 5.78378569e-03]]), scale=0.17038330083167794, shift=array([4.18918073e+00, 2.79843139e+03, 1.95671244e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], 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model_indices=array([31, 36, 38, 39, 40, 41]), model=ScalarModel(intercept=0.05095057563584681, linear_terms=array([ 1.26813383e-03, -6.52109364e-06, 4.29926422e-04]), square_terms=array([[1.02760072e-02, 1.58255954e-05, 2.97902836e-03], + [1.58255954e-05, 2.96488117e-08, 4.90049513e-06], + [2.97902836e-03, 4.90049513e-06, 1.01490054e-03]]), scale=0.08519165041583897, shift=array([4.18918073e+00, 2.79843139e+03, 1.95671244e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 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5.06389866])), candidate_index=46, candidate_x=array([4.19055071e+00, 2.79851652e+03, 1.92636431e+00]), index=46, x=array([4.19055071e+00, 2.79851652e+03, 1.92636431e+00]), fval=0.0508732681045439, rho=0.17615778998900092, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([], dtype=int32), step_length=0.08526535279045368, relative_step_length=1.0008651361284229, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.19055071e+00, 2.79851652e+03, 1.92636431e+00]), radius=0.17038330083167794, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([31, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=0.0508912246008716, linear_terms=array([5.36167036e-05, 8.98970782e-06, 1.86647354e-05]), square_terms=array([[ 5.50035868e-02, -3.87804914e-04, 1.55193422e-02], + [-3.87804914e-04, 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model=ScalarModel(intercept=0.05274738642084201, linear_terms=array([-2.89095722e-03, -1.52762657e-05, -8.14429061e-04]), square_terms=array([[ 4.06012733e-03, -6.72244814e-06, 1.21120648e-03], + [-6.72244814e-06, 2.69342201e-08, -2.08679767e-06], + [ 1.21120648e-03, -2.08679767e-06, 4.31846954e-04]]), scale=0.054581755234379394, shift=array([4.10988644e+00, 2.23693452e+03, 1.78737883e+00])), vector_model=VectorModel(intercepts=array([ 0.06291652, 0.11643581, 0.10823668, 0.12189314, 0.12343863, + 0.12357968, 0.12479589, 0.09434044, 0.01975806, 0.10844278, + -0.14820181, -0.07391777, -0.01698537, -0.01832682, -0.03672083, + -0.06411733, -0.08425918]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 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1.76660455e+00]), index=59, x=array([4.10988644e+00, 2.23693452e+03, 1.78737883e+00]), fval=0.052755606205776504, rho=-1.1250970659194641, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([55, 57, 58, 59, 60]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.10988644e+00, 2.23693452e+03, 1.78737883e+00]), radius=0.027290877617189697, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([55, 58, 59, 60, 61]), model=ScalarModel(intercept=0.05276154511022461, linear_terms=array([-3.86556789e-05, -2.04771871e-06, 6.36275653e-07]), square_terms=array([[ 1.24535705e-03, -1.35375057e-07, 3.72209525e-04], + [-1.35375057e-07, 6.47746067e-10, -2.28047175e-08], + [ 3.72209525e-04, -2.28047175e-08, 1.28885229e-04]]), scale=0.027290877617189697, shift=array([4.10988644e+00, 2.23693452e+03, 1.78737883e+00])), vector_model=VectorModel(intercepts=array([ 0.06291652, 0.11643581, 0.10823668, 0.12189314, 0.12343863, + 0.12357968, 0.12479589, 0.09434044, 0.01975806, 0.10844278, + -0.14820181, -0.07391777, -0.01698537, -0.01832682, -0.03672083, + -0.06411733, -0.08425918]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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State(trustregion=Region(center=array([4.10988644e+00, 2.23693452e+03, 1.78737883e+00]), radius=0.013645438808594848, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([55, 59, 61, 62]), model=ScalarModel(intercept=0.05275560620577649, linear_terms=array([-1.97093175e-05, -5.42896838e-06, -1.19996937e-05]), square_terms=array([[3.10466342e-04, 1.05603109e-07, 9.24001766e-05], + [1.05603109e-07, 3.65759582e-09, 4.18438923e-08], + [9.24001766e-05, 4.18438923e-08, 3.17549202e-05]]), scale=0.013645438808594848, shift=array([4.10988644e+00, 2.23693452e+03, 1.78737883e+00])), vector_model=VectorModel(intercepts=array([ 0.06291652, 0.11643581, 0.10823668, 0.12189314, 0.12343863, + 0.12357968, 0.12479589, 0.09434044, 0.01975806, 0.10844278, + -0.14820181, -0.07391777, -0.01698537, -0.01832682, -0.03672083, + -0.06411733, -0.08425918]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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square_terms=array([[ 8.20272919e-05, -7.03115536e-08, 2.43677150e-05], + [-7.03115536e-08, 1.30003643e-09, -2.13391346e-08], + [ 2.43677150e-05, -2.13391346e-08, 8.30312530e-06]]), scale=0.006822719404297424, shift=array([4.10988644e+00, 2.23693452e+03, 1.78737883e+00])), vector_model=VectorModel(intercepts=array([ 0.06291652, 0.11643581, 0.10823668, 0.12189314, 0.12343863, + 0.12357968, 0.12479589, 0.09434044, 0.01975806, 0.10844278, + -0.14820181, -0.07391777, -0.01698537, -0.01832682, -0.03672083, + -0.06411733, -0.08425918]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], 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rho=0.4983800116405342, accepted=True, new_indices=array([64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75]), old_indices_used=array([59, 62, 63]), old_indices_discarded=array([], dtype=int32), step_length=0.006920117064447186, relative_step_length=1.0142754896366417, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.11051116e+00, 2.23693167e+03, 1.78110356e+00]), radius=0.013645438808594848, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([59, 64, 65, 66, 67, 68, 70, 72, 73, 74, 75, 76]), model=ScalarModel(intercept=0.05274921512205406, linear_terms=array([-2.32187448e-06, -3.03189290e-06, 1.22926474e-05]), square_terms=array([[3.28191192e-04, 2.38863375e-07, 9.75393216e-05], + [2.38863375e-07, 5.14401342e-09, 7.74187507e-08], + [9.75393216e-05, 7.74187507e-08, 3.33310299e-05]]), scale=0.013645438808594848, shift=array([4.11051116e+00, 2.23693167e+03, 1.78110356e+00])), vector_model=VectorModel(intercepts=array([ 0.06291652, 0.11643581, 0.10823668, 0.12189314, 0.12343863, + 0.12357968, 0.12479589, 0.09434044, 0.01975806, 0.10844278, + -0.14820181, -0.07391777, -0.01698537, -0.01832682, -0.03672083, + -0.06411733, -0.08425918]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.3719594 , 3687.3820962 , 10.89861347]), radius=0.716582051597251, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=0.6404926514152791, linear_terms=array([ 0.00985466, -0.00038215, -0.00076534]), square_terms=array([[ 4.62807937e-02, -8.53432130e-05, -1.30425185e-04], + [-8.53432130e-05, 4.67244373e-07, 8.14784874e-07], + [-1.30425185e-04, 8.14784874e-07, 1.55842322e-06]]), scale=0.716582051597251, shift=array([ 9.3719594 , 3687.3820962 , 10.89861347])), vector_model=VectorModel(intercepts=array([ 0.02576631, 0.06184162, 0.05506634, 0.07304972, 0.07539609, + 0.06945224, 0.05266735, -0.15458875, -0.30268525, -0.2894935 , + -0.62376469, -0.61159718, 0.03592713, 0.05727162, 0.065043 , + 0.05645612, 0.0379622 ]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 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candidate_index=86, candidate_x=array([ 9.20663468, 3687.83100372, 11.41243184]), index=86, x=array([ 9.20663468, 3687.83100372, 11.41243184]), fval=0.6411977764871175, rho=0.1114790501708937, accepted=True, new_indices=array([74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85]), old_indices_used=array([69, 72, 73]), old_indices_discarded=array([], dtype=int32), step_length=0.00017494678994204872, relative_step_length=1.0000000000075087, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 87 entries., 'history': {'params': [{'CRRA': 6.333842432535624, 'BeqFac': 3668.9001041779247, 'BeqShift': 6.573413466501883}, {'CRRA': 2.616981898670428, 'BeqFac': 3373.188231776675, 'BeqShift': 62.17867746421218}, {'CRRA': 18.050369850224293, 'BeqFac': 3373.188231776675, 'BeqShift': 61.4640779436021}, {'CRRA': 20.0, 'BeqFac': 3834.1986261245534, 'BeqShift': 65.26986123883711}, {'CRRA': 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step_length=140.67097035304008, relative_step_length=0.806264427410658, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.25122599, 3067.57760161, 0. ]), radius=348.9450001032764, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([ 0, 1, 2, 3, 4, 8, 9, 10, 11, 12, 13, 14]), model=ScalarModel(intercept=5.307226904606869, linear_terms=array([-51.38753027, 26.65823128, 21.44983872]), square_terms=array([[ 305.31648211, -153.36036519, -122.65042123], + [-153.36036519, 77.5107252 , 61.97971585], + [-122.65042123, 61.97971585, 49.61959056]]), scale=array([ 9.45 , 281.24826628, 35. ]), shift=array([ 10.55 , 3067.57760161, 35. ])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 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model=ScalarModel(intercept=3.807942725574409, linear_terms=array([-39.58737235, 12.58807335, 13.34355021]), square_terms=array([[288.79762252, -88.45374867, -92.48750266], + [-88.45374867, 27.21485773, 28.46514941], + [-92.48750266, 28.46514941, 29.83245946]]), scale=array([ 9.45 , 140.62413314, 35. ]), shift=array([ 10.55 , 3067.57760161, 35. ])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]]), scale=348.9450001032764, shift=array([ 4.96836085, 3489.45000103, 5.06389866])), candidate_index=16, candidate_x=array([ 5.9246359 , 2926.95346847, 0. ]), index=16, x=array([ 5.9246359 , 2926.95346847, 0. ]), fval=0.23203665598876397, rho=0.570002726445654, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 2, 3, 8, 9, 10, 11, 12, 13, 14, 15]), old_indices_discarded=array([4, 5, 6, 7]), step_length=140.66347437444162, relative_step_length=0.8062214637424797, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.9246359 , 2926.95346847, 0. ]), radius=348.9450001032764, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([ 0, 1, 2, 3, 8, 9, 10, 12, 13, 14, 15, 16]), model=ScalarModel(intercept=3.9661830657942825, linear_terms=array([-28.12732145, 11.87395531, 18.45752875]), square_terms=array([[150.75705807, -60.44190282, -91.27776749], + [-60.44190282, 24.37668461, 36.9203391 ], + [-91.27776749, 36.9203391 , 56.05439485]]), scale=array([ 9.45 , 281.24826628, 35. ]), shift=array([ 10.55 , 2926.95346847, 35. ])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 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old_indices_discarded=array([ 4, 5, 6, 7, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.9246359 , 2926.95346847, 0. ]), radius=174.4725000516382, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([ 0, 1, 2, 8, 9, 10, 12, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=9.571328999106925, linear_terms=array([-45.18448012, 9.36031969, 33.66340283]), square_terms=array([[133.09207534, -26.87423247, -93.65514891], + [-26.87423247, 5.44544862, 19.03151488], + [-93.65514891, 19.03151488, 66.85203657]]), scale=array([ 9.45 , 140.62413314, 35. ]), shift=array([ 10.55 , 2926.95346847, 35. ])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], 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model_indices=array([ 1, 2, 10, 12, 13, 14, 15, 16, 17, 18]), model=ScalarModel(intercept=2.345400265444784, linear_terms=array([-9.54818498, 0.79259481, 6.47312741]), square_terms=array([[ 71.29706187, -5.72633659, -37.74299647], + [ -5.72633659, 0.46131347, 3.04307064], + [-37.74299647, 3.04307064, 20.58343169]]), scale=array([ 9.45 , 70.31206657, 35. ]), shift=array([ 10.55 , 2786.32933533, 35. ])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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5.06389866])), candidate_index=19, candidate_x=array([ 6.05395429, 2716.01726876, 0. ]), index=18, x=array([ 5.2002501 , 2786.32933533, 0. ]), fval=0.1638437055633587, rho=-0.08459209004077528, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 2, 10, 12, 13, 14, 15, 16, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.2002501 , 2786.32933533, 0. ]), radius=43.61812501290955, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 16, 17, 18, 19]), model=ScalarModel(intercept=7.1526635561288225, linear_terms=array([ 54.6285074 , 0.69834955, -14.30532711]), square_terms=array([[ 2.09947491e+02, 2.68478398e+00, -5.46285074e+01], + [ 2.68478398e+00, 3.43920488e-02, -6.98349546e-01], + [-5.46285074e+01, -6.98349546e-01, 1.43053271e+01]]), scale=array([ 9.45 , 35.15603329, 17.57801664]), shift=array([ 10.55 , 2786.32933533, 17.57801664])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 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dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.2002501 , 2786.32933533, 0. ]), radius=21.809062506454776, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 19, 20]), model=ScalarModel(intercept=16.004294311139557, linear_terms=array([70.66939 , 1.13700979, -8.50470948]), square_terms=array([[ 1.58368000e+02, 2.56906467e+00, -1.92507844e+01], + [ 2.56906467e+00, 4.19273077e-02, -3.14468097e-01], + [-1.92507844e+01, -3.14468097e-01, 2.36523947e+00]]), scale=array([ 9.45 , 17.57801664, 8.78900832]), shift=array([ 10.55 , 2786.32933533, 8.78900832])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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model=ScalarModel(intercept=2.9984363121302398, linear_terms=array([20.20379397, 0.14022481, -1.76089925]), square_terms=array([[ 7.28524130e+01, 5.11325102e-01, -6.48101817e+00], + [ 5.11325102e-01, 3.60021596e-03, -4.56833511e-02], + [-6.48101817e+00, -4.56833511e-02, 5.82826690e-01]]), scale=array([6.44462921, 8.78900832, 4.39450416]), shift=array([ 7.54462921, 2786.32933533, 4.39450416])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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5.06389866])), candidate_index=22, candidate_x=array([ 5.16024118, 2790.95196969, 0. ]), index=22, x=array([ 5.16024118, 2790.95196969, 0. ]), fval=0.1628347112711602, rho=1.0555049921680508, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([15, 18, 20, 21]), old_indices_discarded=array([], dtype=int32), step_length=4.622807489556225, relative_step_length=0.4239345444755384, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.16024118, 2790.95196969, 0. ]), radius=10.904531253227388, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 20, 21, 22]), model=ScalarModel(intercept=3.006282375920816, linear_terms=array([20.16458994, 0.14100404, -1.76243727]), square_terms=array([[ 7.23456179e+01, 5.11502709e-01, -6.45362479e+00], + [ 5.11502709e-01, 3.62783851e-03, -4.58243177e-02], + [-6.45362479e+00, -4.58243177e-02, 5.81967789e-01]]), scale=array([6.42462475, 8.78900832, 4.39450416]), shift=array([ 7.52462475, 2790.95196969, 4.39450416])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 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step_length=8.789122808311065, relative_step_length=0.806006476042679, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.11538064, 2799.74097801, 0. ]), radius=21.809062506454776, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=15.956937372018299, linear_terms=array([68.87495584, 1.01962009, -8.11318965]), square_terms=array([[ 1.50828107e+02, 2.25090089e+00, -1.79474940e+01], + [ 2.25090089e+00, 3.37920630e-02, -2.69729608e-01], + [-1.79474940e+01, -2.69729608e-01, 2.15985383e+00]]), scale=array([ 9.45 , 17.57801664, 8.78900832]), shift=array([ 10.55 , 2799.74097801, 8.78900832])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 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model=ScalarModel(intercept=3.0353080773001073, linear_terms=array([20.20386846, 0.10823129, -1.77493216]), square_terms=array([[ 7.18576918e+01, 3.89097471e-01, -6.44170944e+00], + [ 3.89097471e-01, 2.11348833e-03, -3.50355915e-02], + [-6.44170944e+00, -3.50355915e-02, 5.83744149e-01]]), scale=array([6.40219448, 8.78900832, 4.39450416]), shift=array([ 7.50219448, 2799.74097801, 4.39450416])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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5.06389866])), candidate_index=25, candidate_x=array([ 5.09453133, 2808.27054949, 0. ]), index=23, x=array([ 5.11538064, 2799.74097801, 0. ]), fval=0.1625021910643016, rho=-0.257817250835792, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([15, 18, 20, 21, 22, 23, 24]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.11538064, 2799.74097801, 0. ]), radius=5.452265626613694, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 20, 21, 22, 23, 24, 25]), model=ScalarModel(intercept=0.18941940373737348, linear_terms=array([-0.84702663, -0.00367806, 0.0743278 ]), square_terms=array([[ 3.09008276e+01, 1.15966532e-01, -2.10738689e+00], + [ 1.15966532e-01, 4.36576332e-04, -7.94409134e-03], + [-2.10738689e+00, -7.94409134e-03, 1.45288267e-01]]), scale=array([4.2049424 , 4.39450416, 2.19725208]), shift=array([5.30494240e+00, 2.79974098e+03, 2.19725208e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + 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old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.11538064, 2799.74097801, 0. ]), radius=2.726132813306847, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 21, 22, 23, 25, 26]), model=ScalarModel(intercept=0.040656194310767946, linear_terms=array([-0.04424986, -0.00023592, -0.08131239]), square_terms=array([[8.44848189e+00, 2.55963392e-02, 4.42498564e-02], + [2.55963392e-02, 7.77973037e-05, 2.35916270e-04], + [4.42498564e-02, 2.35916270e-04, 8.13123886e-02]]), scale=array([2.19725208, 2.19725208, 1.09862604]), shift=array([5.11538064e+00, 2.79974098e+03, 1.09862604e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, 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scale=348.9450001032764, shift=array([ 4.96836085, 3489.45000103, 5.06389866])), candidate_index=28, candidate_x=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00]), index=28, x=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00]), fval=0.06529563513965841, rho=1.475519476069878, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([21, 23, 26, 27]), old_indices_discarded=array([], dtype=int32), step_length=1.6872964496840501, relative_step_length=1.2378681195926988, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00]), radius=2.726132813306847, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 21, 22, 23, 25, 26, 27, 28]), model=ScalarModel(intercept=0.07730703358690492, linear_terms=array([-0.21420423, -0.00177457, -0.09323422]), square_terms=array([[7.19945275e+00, 3.82631907e-02, 1.99911955e+00], + [3.82631907e-02, 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rho=-0.24314171126774836, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([15, 18, 21, 22, 23, 25, 26, 27, 28]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00]), radius=1.3630664066534235, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([21, 23, 26, 27, 28, 29]), model=ScalarModel(intercept=0.06904406104722437, linear_terms=array([ 0.00492248, 0.00149362, -0.05074773]), square_terms=array([[ 6.12405684e-01, -2.44364302e-03, 3.09451673e-01], + [-2.44364302e-03, 4.14350874e-05, -2.29602578e-03], + [ 3.09451673e-01, -2.29602578e-03, 2.13954389e-01]]), scale=array([1.09862604, 1.09862604, 1.09862604]), shift=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 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x=array([4.05602518e+00, 2.79734636e+03, 2.24557407e+00]), fval=0.05415908459581581, rho=0.02787984735130772, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([21, 23, 26, 27, 28, 29, 30, 31]), old_indices_discarded=array([], dtype=int32), step_length=1.4693394633669328, relative_step_length=1.0779661623195813, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05602518e+00, 2.79734636e+03, 2.24557407e+00]), radius=0.6815332033267117, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([27, 28, 30, 31, 32]), model=ScalarModel(intercept=0.06019280281239402, linear_terms=array([-0.05311338, -0.00664367, -0.01652794]), square_terms=array([[0.31168089, 0.01963004, 0.09342492], + [0.01963004, 0.00163636, 0.00734946], + [0.09342492, 0.00734946, 0.04083573]]), scale=0.6815332033267117, shift=array([4.05602518e+00, 2.79734636e+03, 2.24557407e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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model_indices=array([23, 27, 28, 29, 30, 31, 32, 33, 34]), model=ScalarModel(intercept=0.05177155574931393, linear_terms=array([ 2.24054327e-02, -6.65931684e-05, 7.36047043e-03]), square_terms=array([[0.29167665, 0.01447643, 0.13152612], + [0.01447643, 0.0008045 , 0.00750585], + [0.13152612, 0.00750585, 0.07813118]]), scale=0.6815332033267117, shift=array([4.13804573e+00, 2.79803113e+03, 2.21290949e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], 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3489.45000103, 5.06389866])), candidate_index=35, candidate_x=array([4.02600282e+00, 2.79871386e+03, 2.27098711e+00]), index=33, x=array([4.13804573e+00, 2.79803113e+03, 2.21290949e+00]), fval=0.05275688891668154, rho=-2.2415296614174687, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([23, 27, 28, 29, 30, 31, 32, 33, 34]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13804573e+00, 2.79803113e+03, 2.21290949e+00]), radius=0.3407666016633559, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([28, 30, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=0.05243167254991798, linear_terms=array([ 2.14835428e-02, -8.53058917e-05, 7.83429373e-03]), square_terms=array([[2.69010071e-01, 2.03422326e-04, 8.75369596e-02], + [2.03422326e-04, 6.23463910e-07, 8.57167056e-05], + [8.75369596e-02, 8.57167056e-05, 3.13897458e-02]]), scale=0.3407666016633559, shift=array([4.13804573e+00, 2.79803113e+03, 2.21290949e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 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old_indices_used=array([28, 30, 31, 32, 33, 34, 35]), old_indices_discarded=array([], dtype=int32), step_length=0.35386200358029707, relative_step_length=1.0384292411668858, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.14232870e+00, 2.79837124e+03, 2.11532007e+00]), radius=0.6815332033267117, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([23, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=0.0515876639767697, linear_terms=array([ 0.00588415, -0.00077363, -0.00241592]), square_terms=array([[0.31118702, 0.01242837, 0.13629499], + [0.01242837, 0.00054706, 0.00615461], + [0.13629499, 0.00615461, 0.07774976]]), scale=0.6815332033267117, shift=array([4.14232870e+00, 2.79837124e+03, 2.11532007e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + 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radius=0.3407666016633559, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([27, 28, 30, 31, 32, 33, 35, 36, 37]), model=ScalarModel(intercept=0.052532543329590564, linear_terms=array([-0.00426995, -0.00027655, -0.00095162]), square_terms=array([[9.08193291e-02, 1.45774993e-03, 2.59401890e-02], + [1.45774993e-03, 2.50681460e-05, 4.39732923e-04], + [2.59401890e-02, 4.39732923e-04, 1.01466646e-02]]), scale=0.3407666016633559, shift=array([4.14232870e+00, 2.79837124e+03, 2.11532007e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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6.84781454e-02, -6.53727045e-05, 2.13071280e-02], + [-6.53727045e-05, 1.19864368e-07, -2.42922328e-05], + [ 2.13071280e-02, -2.42922328e-05, 7.24051619e-03]]), scale=0.17038330083167794, shift=array([4.14232870e+00, 2.79837124e+03, 2.11532007e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + 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1.95671244e+00]), fval=0.05091436090216684, rho=1.1516416598700234, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([30, 31, 33, 35, 36, 37, 38]), old_indices_discarded=array([], dtype=int32), step_length=0.1759818526107779, relative_step_length=1.032858570950159, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.18918073e+00, 2.79843139e+03, 1.95671244e+00]), radius=0.3407666016633559, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([27, 28, 30, 31, 32, 33, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=0.05221280899533157, linear_terms=array([-0.00115235, -0.00033548, -0.00127409]), square_terms=array([[8.81488941e-02, 2.02195870e-03, 2.52583882e-02], + [2.02195870e-03, 4.98103348e-05, 6.21228724e-04], + [2.52583882e-02, 6.21228724e-04, 9.98002687e-03]]), scale=0.3407666016633559, shift=array([4.18918073e+00, 2.79843139e+03, 1.95671244e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.18918073e+00, 2.79843139e+03, 1.95671244e+00]), radius=0.17038330083167794, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([31, 33, 35, 36, 37, 38, 39, 40]), model=ScalarModel(intercept=0.05090588732753695, linear_terms=array([ 2.00967052e-03, -4.51302094e-06, 7.70306669e-04]), square_terms=array([[ 6.02617840e-02, -3.14541700e-04, 1.76727179e-02], + [-3.14541700e-04, 1.67055199e-06, -9.60913876e-05], + [ 1.76727179e-02, -9.60913876e-05, 5.78378569e-03]]), scale=0.17038330083167794, shift=array([4.18918073e+00, 2.79843139e+03, 1.95671244e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 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accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([31, 33, 35, 36, 37, 38, 39, 40, 41, 42]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.18821270e+00, 2.79851651e+03, 1.92333721e+00]), radius=0.08519165041583897, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([31, 36, 38, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=0.0508813659118873, linear_terms=array([-1.44722476e-04, -3.19089966e-06, -5.21530523e-05]), square_terms=array([[1.06023461e-02, 7.49354229e-06, 3.10107179e-03], + [7.49354229e-06, 6.81249566e-09, 2.33378372e-06], + [3.10107179e-03, 2.33378372e-06, 1.05915929e-03]]), scale=0.08519165041583897, shift=array([4.18821270e+00, 2.79851651e+03, 1.92333721e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 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'exploration_results': array([ 0.15183333, 0.64211538, 0.64842412, 0.66164015, 0.68191739, + 0.7044766 , 0.74714867, 0.84822968, 0.86260573, 0.97951322, + 0.98915247, 1.12431839, 1.18220644, 1.20662179, 1.30406801, + 1.52011014, 1.78024917, 1.78135807, 2.0971452 , 2.24437522, + 2.48116605, 2.85787595, 2.94379242, 3.4148927 , 3.4976294 , + 4.16035282, 4.94978648, 5.89491246, 7.00125067, 34.37799292])}}" diff --git a/src/estimark/content/tables/min/WarmGlowPortfolioShift_estimate_results.csv b/src/estimark/content/tables/min/WarmGlowPortfolioShift_estimate_results.csv new file mode 100644 index 0000000..3ff6990 --- /dev/null +++ b/src/estimark/content/tables/min/WarmGlowPortfolioShift_estimate_results.csv @@ -0,0 +1,7596 @@ +CRRA,9.206764951619395 + +BeqFac,51.48903871676966 + +BeqShift,19.177911777729552 + +time_to_estimate,169.86824083328247 + +params,"{'CRRA': 9.206764951619395, 'BeqFac': 51.48903871676966, 'BeqShift': 19.177911777729552}" + +criterion,0.6411981720246629 + 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3.5878502000123262, 4.844878800213337, 6.0633915001526475, 7.2610666998662055, 8.584980400279164, 9.761942900251597, 10.943062500096858, 12.49804690014571, 12.680896100122482, 12.863225900102407, 13.049990000203252, 13.235215300228447, 13.421385299880058, 13.612889300100505, 13.813482000026852, 14.012756899930537, 14.206071800086647, 14.40031210007146, 14.598113900050521, 15.857798700220883, 17.073550200089812, 18.39786330005154, 19.95549810025841, 20.136687899939716, 20.316136700101197, 20.503039999864995, 20.68812690023333, 20.880544799845666, 21.061436000280082, 21.258774400223047, 21.45859370008111, 21.63768619997427, 21.82981210015714, 22.024868600070477, 23.232584199868143, 24.429392700083554, 25.615990200079978, 27.17550570005551, 27.352922200225294, 27.533706800080836, 27.874167799949646, 28.064581600017846, 28.25611580023542, 28.4465796998702, 28.64204559987411, 28.83596420008689, 29.02757350029424, 29.219605300109833, 29.419515199959278, 30.668996000196785, 31.875013300217688, 33.06283670011908, 34.25480200024322, 35.44796109991148, 36.63821780029684], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 10, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 13, 14, 15, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 18, 19, 20, 21, 22]}" + +convergence_report,"{'one_step': {'relative_criterion_change': 2.8119796241753443e-07, 'relative_params_change': 0.3073071893497542, 'absolute_criterion_change': 1.8030361947918294e-07, 'absolute_params_change': 11.624425850056147}, 'five_steps': {'relative_criterion_change': 2.8119796241753443e-07, 'relative_params_change': 0.3073071893497542, 'absolute_criterion_change': 1.8030361947918294e-07, 'absolute_params_change': 11.624425850056147}}" + +multistart_info,"{'start_parameters': [{'CRRA': 9.36875, 'BeqFac': 39.375, 'BeqShift': 13.125}, {'CRRA': 9.02557038439359, 'BeqFac': 50.30567549536045, 'BeqShift': 16.905833666158657}, {'CRRA': 9.344871468725385, 'BeqFac': 43.24486525515954, 'BeqShift': 26.496870084805927}], 'local_optima': [Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 1.358e-07* 2.622e-05 +relative_params_change 6.705e-06* 0.003894 +absolute_criterion_change 8.706e-08* 1.681e-05 +absolute_params_change 0.0001056 0.08828 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 1.904e-07* 0.0007594 +relative_params_change 3.052e-05 0.1282 +absolute_criterion_change 1.221e-07* 0.0004869 +absolute_params_change 0.0006142 2.522 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 2.728e-05 0.000476 +relative_params_change 0.0002827 0.0766 +absolute_criterion_change 1.75e-05 0.0003053 +absolute_params_change 0.006458 2.233 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 9.368749999999999, 'BeqFac': 39.375, 'BeqShift': 13.125}, {'CRRA': 8.778125, 'BeqFac': 63.4375, 'BeqShift': 19.6875}, {'CRRA': 9.959375, 'BeqFac': 6.5625, 'BeqShift': 59.0625}, {'CRRA': 8.1875, 'BeqFac': 26.25, 'BeqShift': 43.75}, {'CRRA': 10.549999999999999, 'BeqFac': 35.0, 'BeqShift': 35.0}, {'CRRA': 7.596874999999999, 'BeqFac': 50.3125, 'BeqShift': 50.3125}, {'CRRA': 7.00625, 'BeqFac': 13.125, 'BeqShift': 21.875}, {'CRRA': 11.73125, 'BeqFac': 30.625, 'BeqShift': 4.375}, {'CRRA': 12.321874999999999, 'BeqFac': 67.8125, 'BeqShift': 67.8125}, {'CRRA': 6.415625, 'BeqFac': 19.6875, 'BeqShift': 10.9375}, {'CRRA': 12.9125, 'BeqFac': 8.75, 'BeqShift': 61.25}, {'CRRA': 5.824999999999999, 'BeqFac': 52.5, 'BeqShift': 52.5}, {'CRRA': 13.503124999999999, 'BeqFac': 45.9375, 'BeqShift': 2.1875}, {'CRRA': 5.234375, 'BeqFac': 59.0625, 'BeqShift': 6.5625}, {'CRRA': 14.093749999999998, 'BeqFac': 56.875, 'BeqShift': 30.625}, {'CRRA': 14.684375, 'BeqFac': 24.0625, 'BeqShift': 41.5625}, {'CRRA': 4.64375, 'BeqFac': 21.875, 'BeqShift': 65.625}, {'CRRA': 15.274999999999999, 'BeqFac': 17.5, 'BeqShift': 17.5}, {'CRRA': 4.053125, 'BeqFac': 10.9375, 'BeqShift': 37.1875}, {'CRRA': 15.865624999999998, 'BeqFac': 54.6875, 'BeqShift': 45.9375}, {'CRRA': 3.4625, 'BeqFac': 43.75, 'BeqShift': 26.25}, {'CRRA': 16.45625, 'BeqFac': 48.125, 'BeqShift': 56.875}, {'CRRA': 17.046875, 'BeqFac': 15.3125, 'BeqShift': 15.3125}, {'CRRA': 2.871875, 'BeqFac': 32.8125, 'BeqShift': 32.8125}, {'CRRA': 2.0, 'BeqFac': 1.0, 'BeqShift': 1.0}, {'CRRA': 2.28125, 'BeqFac': 65.625, 'BeqShift': 39.375}, {'CRRA': 17.6375, 'BeqFac': 61.25, 'BeqShift': 8.75}, {'CRRA': 18.228125, 'BeqFac': 28.4375, 'BeqShift': 54.6875}, {'CRRA': 18.81875, 'BeqFac': 4.375, 'BeqShift': 48.125}, {'CRRA': 19.409375, 'BeqFac': 41.5625, 'BeqShift': 24.0625}], 'exploration_results': array([0.64211564, 0.64842422, 0.66164015, 0.68191739, 0.7044766 , + 0.74714868, 0.84823378, 0.86260589, 0.97951322, 0.98995977, + 1.12431839, 1.18221306, 1.30406647, 1.43719388, 1.52011014, + 1.78024917, 1.78140887, 2.0971452 , 2.2458042 , 2.48116605, + 2.89746811, 2.94379242, 3.4976294 , 3.77142778, 3.92475841, + 4.11872048, 4.16035282, 4.94978648, 5.89491246, 7.00125067])}" + +algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.02557038, 50.3056755 , 16.90583367]), radius=5.030567549536045, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([20., 70., 70.]))), model_indices=[0], model=ScalarModel(intercept=0.642827121621846, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), scale=5.030567549536045, shift=array([ 9.02557038, 50.3056755 , 16.90583367])), vector_model=VectorModel(intercepts=array([ 0.04710459, 0.11986993, 0.14282693, 0.18631824, 0.20833381, + 0.22221482, 0.22216466, 0.05252852, -0.09507962, -0.08224758, + -0.42404204, -0.4316247 , -0.11708532, -0.09095589, -0.08150493, + -0.08549165, -0.09152337]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]]), scale=5.030567549536045, shift=array([ 9.02557038, 50.3056755 , 16.90583367])), candidate_index=0, candidate_x=array([ 9.02557038, 50.3056755 , 16.90583367]), index=0, x=array([ 9.02557038, 50.3056755 , 16.90583367]), fval=0.642827121621846, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([ 9.02557038, 50.3056755 , 16.90583367]), radius=5.030567549536045, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=0.5827725609411891, linear_terms=array([-0.13086618, -0.00121789, -0.0042486 ]), square_terms=array([[ 2.55855317e+00, -1.07192260e-02, -7.64691333e-03], + [-1.07192260e-02, 7.11366225e-05, 1.04068290e-04], + [-7.64691333e-03, 1.04068290e-04, 2.74873956e-04]]), scale=5.030567549536045, shift=array([ 9.02557038, 50.3056755 , 16.90583367])), vector_model=VectorModel(intercepts=array([ 0.04710459, 0.11986993, 0.14282693, 0.18631824, 0.20833381, + 0.22221482, 0.22216466, 0.05252852, -0.09507962, -0.08224758, + -0.42404204, -0.4316247 , -0.11708532, -0.09095589, -0.08150493, + -0.08549165, -0.09152337]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]]), scale=5.030567549536045, shift=array([ 9.02557038, 50.3056755 , 16.90583367])), candidate_index=13, candidate_x=array([ 9.30406119, 52.12275648, 21.61704889]), index=13, x=array([ 9.30406119, 52.12275648, 21.61704889]), fval=0.6416850814352149, rho=0.13938651078239153, accepted=True, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int32), step_length=5.057162180906725, relative_step_length=1.0052866065526012, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.30406119, 52.12275648, 21.61704889]), radius=10.06113509907209, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 1, 2, 3, 4, 6, 7, 8, 10, 11, 12, 13]), model=ScalarModel(intercept=0.5584593849433535, linear_terms=array([-0.06896328, -0.00033275, -0.02272221]), square_terms=array([[ 6.72730407e+00, -2.67483560e-02, -1.16135911e-01], + [-2.67483560e-02, 1.40449052e-04, 8.44834004e-04], + [-1.16135911e-01, 8.44834004e-04, 1.09448589e-02]]), scale=array([8.10923441, 8.10923441, 8.10923441]), shift=array([ 9.30406119, 52.12275648, 21.61704889])), vector_model=VectorModel(intercepts=array([ 0.04710459, 0.11986993, 0.14282693, 0.18631824, 0.20833381, + 0.22221482, 0.22216466, 0.05252852, -0.09507962, -0.08224758, + -0.42404204, -0.4316247 , -0.11708532, -0.09095589, -0.08150493, + -0.08549165, -0.09152337]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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radius=5.030567549536045, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13]), model=ScalarModel(intercept=0.6077375632829374, linear_terms=array([0.06553236, 0.00436375, 0.04509535]), square_terms=array([[2.56639579e+00, 2.98457753e-03, 8.12138719e-02], + [2.98457753e-03, 1.49746064e-04, 1.12713313e-03], + [8.12138719e-02, 1.12713313e-03, 1.00780986e-02]]), scale=5.030567549536045, shift=array([ 9.30406119, 52.12275648, 21.61704889])), vector_model=VectorModel(intercepts=array([ 0.04710459, 0.11986993, 0.14282693, 0.18631824, 0.20833381, + 0.22221482, 0.22216466, 0.05252852, -0.09507962, -0.08224758, + -0.42404204, -0.4316247 , -0.11708532, -0.09095589, -0.08150493, + -0.08549165, -0.09152337]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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index=63, x=array([ 9.20673 , 51.48925053, 19.17733633]), fval=0.6411982941369027, rho=-0.4736163965120041, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([16, 49, 50, 51, 52, 53, 59, 60, 61, 62, 63, 64]), old_indices_discarded=array([54, 55, 56, 57, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20673 , 51.48925053, 19.17733633]), radius=0.0006140829528242242, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([16, 62, 63, 64, 65]), model=ScalarModel(intercept=0.6412018772218984, linear_terms=array([-3.72388782e-06, 2.25369895e-05, -6.12278699e-05]), square_terms=array([[ 3.55640239e-08, -1.41336598e-09, 3.35637644e-09], + [-1.41336598e-09, 7.05971511e-10, -1.79557755e-09], + [ 3.35637644e-09, -1.79557755e-09, 4.68337624e-09]]), scale=0.0006140829528242242, shift=array([ 9.20673 , 51.48925053, 19.17733633])), vector_model=VectorModel(intercepts=array([ 0.04710459, 0.11986993, 0.14282693, 0.18631824, 0.20833381, + 0.22221482, 0.22216466, 0.05252852, -0.09507962, -0.08224758, + -0.42404204, -0.4316247 , -0.11708532, -0.09095589, -0.08150493, + -0.08549165, -0.09152337]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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'tranquilo_history': History for least_squares function with 67 entries., 'multistart_info': {'start_parameters': [array([ 9.36875, 39.375 , 13.125 ]), array([ 9.02557038, 50.3056755 , 16.90583367]), array([ 9.34487147, 43.24486526, 26.49687008])], 'local_optima': [{'solution_x': array([ 9.20671298, 40.69251276, 14.86951258]), 'solution_criterion': 0.6411983523282824, 'states': [State(trustregion=Region(center=array([ 9.36875, 39.375 , 13.125 ]), radius=3.9375, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([20., 70., 70.]))), model_indices=[0], model=ScalarModel(intercept=0.6421156352360291, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), scale=3.9375, shift=array([ 9.36875, 39.375 , 13.125 ])), vector_model=VectorModel(intercepts=array([ 0.04974919, 0.12690831, 0.15323823, 0.19937099, 0.22433814, + 0.23994656, 0.24229912, 0.07800181, -0.06876495, -0.05566806, + -0.39787992, -0.40698777, -0.12931719, -0.10261415, 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1.9999990044023963, 'BeqFac': 1.0000000937305267}, {'CRRA': 1.9999990044023963, 'BeqFac': 1.0000000937305267}], 'criterion': [0.8276471824376574, 101.67310083509217, 101.67310083509217, 101.67310083509217, 101.67310083509217, 101.67310083509217, 101.67310083509217, 101.67310083509217, 101.67310083509217, 101.67310083509217, 101.67310083509217, 101.67310083509217, 101.67310083509217, 101.67310083509217, 101.6631008350922, 101.6581008350922, 101.65560094261426, 101.65435086280652, 101.65372588185996, 101.65341334730257, 101.65325710530595, 101.6531789653836, 101.65313990622937, 101.6531203663422, 101.65311060071718, 101.6531057179047, 101.65310327649843, 101.6531020561841, 101.65310144600937, 101.65310114026798, 101.65310098772137, 101.6531009351404, 101.6531009350922, 101.6531009350922, 101.6531009350922, 101.6531009350922, 101.6531009350922, 101.6531009350922, 101.6531009350922, 101.6531009350922, 101.6531009350922, 101.6531009350922, 101.6531009350922, 101.6531009350922, 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10.825552000198513, 11.05199269996956, 11.422774800099432, 11.64544240012765, 11.868264500051737, 12.090906499885023, 12.31406190013513, 12.537094000261277, 12.759967099875212, 12.982686299830675, 13.205718399956822, 13.428489599842578, 13.651291300076991, 13.87483590003103, 14.097963999956846, 14.32136629987508, 14.54510340001434, 14.768045200034976, 14.991303599905223, 15.215040700044483, 15.438527800142765, 15.661850899923593, 15.885141999926418, 16.108558400068432, 16.332447399850935, 16.556049100123346, 16.779387100134045, 17.002744699828327, 17.226317500229925, 17.44983910024166, 17.673072800040245, 17.89696819987148, 18.12054690020159, 18.343889799900353, 18.567598899826407, 18.935552999842912, 19.158564600162208, 19.382035300135612, 19.605214700102806, 19.82852130010724, 20.05194369982928, 20.275953100062907, 20.49972640024498, 20.72295350022614, 20.946535000111908, 21.169626799877733, 21.394386100117117, 21.617947900202125, 21.842129200231284, 22.066331500187516, 22.290008699987084, 22.513442300260067, 22.73716300027445, 22.96098840003833, 23.18487979983911, 23.409180799964815, 23.633142000064254, 23.857424300163984, 24.081767099909484, 24.305915900040418, 24.530007100198418, 24.754125399980694, 24.97819900000468, 25.203025800175965, 25.42716610012576, 25.651465199887753, 25.875632400158793, 26.0998467002064, 26.45643000025302, 26.680018399842083, 26.903818999882787, 27.127937300130725, 27.352323399856687, 27.57637930009514, 27.80031660012901, 28.02404379984364, 28.248444000259042, 28.4726595999673], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101]}" + +convergence_report, + +multistart_info,"{'start_parameters': [{'CRRA': 2.0, 'BeqFac': 1.0}, {'CRRA': 6.926097823918753, 'BeqFac': 183.76536853959433}], 'local_optima': [Minimize with 2 free parameters terminated., Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 4.069e-09** 7.731e-08* +relative_params_change 4.41e-06* 6.026e-05 +absolute_criterion_change 4.137e-07* 7.859e-06* +absolute_params_change 8.763e-06* 9.915e-05 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 2.0, 'BeqFac': 1.0}, {'CRRA': 18.81875, 'BeqFac': 625.0}, {'CRRA': 12.9125, 'BeqFac': 1250.0}, {'CRRA': 7.00625, 'BeqFac': 1875.0}, {'CRRA': 17.046875, 'BeqFac': 2187.5}, {'CRRA': 15.274999999999999, 'BeqFac': 2500.0}, {'CRRA': 4.64375, 'BeqFac': 3125.0}, {'CRRA': 8.1875, 'BeqFac': 3750.0}, {'CRRA': 11.73125, 'BeqFac': 4375.0}, {'CRRA': 2.871875, 'BeqFac': 4687.5}, {'CRRA': 10.549999999999999, 'BeqFac': 5000.0}, {'CRRA': 9.368749999999999, 'BeqFac': 5625.0}, {'CRRA': 3.4625, 'BeqFac': 6250.0}, {'CRRA': 16.45625, 'BeqFac': 6875.0}, {'CRRA': 7.596874999999999, 'BeqFac': 7187.5}, {'CRRA': 5.824999999999999, 'BeqFac': 7500.0}, {'CRRA': 14.093749999999998, 'BeqFac': 8125.0}, {'CRRA': 17.6375, 'BeqFac': 8750.0}, {'CRRA': 2.28125, 'BeqFac': 9375.0}, {'CRRA': 12.321874999999999, 'BeqFac': 9687.5}], 'exploration_results': array([8.33326931e-01, 1.64075763e+02, 2.26557868e+02, 2.89053770e+02, + 3.20308278e+02, 3.51556627e+02, 4.14053213e+02, 4.76553611e+02, + 5.39054183e+02, 5.70303109e+02, 6.01553832e+02, 6.64053584e+02, + 7.26553118e+02, 7.89054621e+02, 8.20303319e+02, 8.51553198e+02, + 9.14054001e+02, 9.76554498e+02, 1.03905310e+03, 1.07030365e+03])}" + +algorithm_output,"{'states': [State(trustregion=Region(center=array([2., 1.]), radius=0.2, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=[0], model=ScalarModel(intercept=0.8276471824376574, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.2, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=0, candidate_x=array([2., 1.]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([2., 1.]), radius=0.2, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=86.15955603265024, linear_terms=array([ 0.05089577, -0.19533528]), square_terms=array([[ 1.51497851e-05, -5.81440784e-05], + [-5.81440784e-05, 2.23153914e-04]]), scale=0.2, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=13, candidate_x=array([1.94980218, 1.19359798]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-499.88400427494435, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.1, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 4, 7, 8, 9, 10, 11, 13]), model=ScalarModel(intercept=69.9966297435316, linear_terms=array([ 5.52347875, 10.30766762]), square_terms=array([[0.22002817, 0.41060667], + [0.41060667, 0.76625569]]), scale=0.1, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=15, candidate_x=array([1.98543255, 0.95216916]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-11.192688611597553, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 4, 8, 9, 10, 11, 13, 14]), old_indices_discarded=array([ 2, 3, 5, 6, 7, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.025, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 14, 15]), model=ScalarModel(intercept=0.8276471824376403, linear_terms=array([-31.62005899, 14.00478571]), square_terms=array([[ 3117.28536415, -1380.6732394 ], + [-1380.6732394 , 611.51238059]]), scale=0.025, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.0125, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 15, 16]), model=ScalarModel(intercept=0.827647182437657, linear_terms=array([-45.37479674, 16.0066913 ]), square_terms=array([[ 6419.23080123, -2264.48759797], + [-2264.48759797, 798.83466418]]), scale=0.0125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + 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model_indices=array([ 0, 16, 17]), model=ScalarModel(intercept=0.8276471824376554, linear_terms=array([ 22.92925386, -12.7088243 ]), square_terms=array([[1639.19696011, -908.54564757], + [-908.54564757, 503.57291637]]), scale=0.00625, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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scale=0.003125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=19, candidate_x=array([1.9987484 , 0.99713645]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7117679378963, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.0015625, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 18, 19]), model=ScalarModel(intercept=0.8276471824376597, linear_terms=array([-16.19210931, 11.64397652]), square_terms=array([[ 817.44432411, -587.83592419], + [-587.83592419, 422.72123444]]), scale=0.0015625, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=20, candidate_x=array([2.00093264, 1.00125388]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7086956369039, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 19]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.00078125, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 19, 20]), model=ScalarModel(intercept=0.8276471824376584, linear_terms=array([-24.44583172, 12.96813418]), square_terms=array([[1863.20708893, -988.40244714], + [-988.40244714, 524.33216002]]), scale=0.00078125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=21, candidate_x=array([2.00037411, 1.00068591]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7117058020676, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 19, 20]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.000390625, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 20, 21]), model=ScalarModel(intercept=0.8276471824376602, linear_terms=array([ 10.88378502, -10.70264801]), square_terms=array([[ 369.32716463, -363.18052667], + [-363.18052667, 357.13618598]]), scale=0.000390625, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=22, candidate_x=array([1.99972026, 0.99972723]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7109492805971, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 20, 21]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.0001953125, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 21, 22]), model=ScalarModel(intercept=0.8276471824376606, linear_terms=array([ 17.44512723, -11.89823002]), square_terms=array([[ 948.85460568, -647.15437432], + [-647.15437432, 441.38351829]]), scale=0.0001953125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=24, candidate_x=array([2.00008235, 1.00005249]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.71151997705, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 22, 23]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=4.8828125e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 23, 24]), model=ScalarModel(intercept=0.8276471824376598, linear_terms=array([-11.96730467, 10.99081158]), square_terms=array([[ 446.52322965, -410.08838858], + [-410.08838858, 376.62651187]]), scale=4.8828125e-05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=25, candidate_x=array([2.00003373, 1.0000353 ]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.711688055746, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 23, 24]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=2.44140625e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 24, 25]), model=ScalarModel(intercept=0.8276471824376532, linear_terms=array([ 3.08834797, -8.73857385]), square_terms=array([[ 29.73745547, -84.14302869], + [-84.14302869, 238.08524183]]), scale=2.44140625e-05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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1.]), radius=1.220703125e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 25, 26]), model=ScalarModel(intercept=0.8276471824376521, linear_terms=array([ 7.57960308, -10.13596867]), square_terms=array([[ 179.12008348, -239.53174518], + [-239.53174518, 320.31839108]]), scale=1.220703125e-05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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linear_terms=array([-0.15165759, 7.44664151]), square_terms=array([[ 7.17099892e-02, -3.52108049e+00], + [-3.52108049e+00, 1.72890945e+02]]), scale=6.103515625e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=29, candidate_x=array([1.99999724, 0.9999987 ]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7117397453109, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 27, 28]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1.52587890625e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 28, 29]), model=ScalarModel(intercept=0.827647182437653, linear_terms=array([-1.78151146, 13.64486064]), square_terms=array([[ 9.89529152, -75.78950624], + [-75.78950624, 580.48307582]]), scale=1.52587890625e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=30, candidate_x=array([1.99999849, 0.99999977]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7116873932954, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 28, 29]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30]), model=ScalarModel(intercept=0.8276471824376591, linear_terms=array([ 6.78250872, -7.99625076]), square_terms=array([[ 143.4273832 , -169.09396935], + [-169.09396935, 199.3536369 ]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31]), model=ScalarModel(intercept=10.049290130474787, linear_terms=array([-51.9281406 , 88.49493782]), square_terms=array([[ 143.70755383, -244.90364749], + [-244.90364749, 417.3600827 ]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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]), square_terms=array([[ 16.75517786, -19.70434065], + [-19.70434065, 23.17260037]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=34, candidate_x=array([2.00000019, 1.00000098]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-6.003128302627301, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31, 32, 33]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32, 33, 34]), model=ScalarModel(intercept=67.15977327832778, linear_terms=array([-12.95288056, 3.90965354]), square_terms=array([[ 1.26162446, -0.38080445], + [-0.38080445, 0.11494072]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], 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dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=74.40172512316786, linear_terms=array([-4.39913405, -4.74128575]), square_terms=array([[0.13123025, 0.14143696], + [0.14143696, 0.15243751]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=36, candidate_x=array([2.00000054, 1.00000084]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-16.18609474458335, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31, 32, 33, 34, 35]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=78.42574355713958, linear_terms=array([-4.95695165, 1.36114804]), square_terms=array([[ 0.15799804, -0.04338528], + [-0.04338528, 0.01191333]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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linear_terms=array([ 4.95171225, -0.92106365]), square_terms=array([[ 0.15651963, -0.02911408], + [-0.02911408, 0.00541548]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], 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0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=39, candidate_x=array([1.999999 , 0.99999991]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-11.699970979801405, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 31, 32, 33, 34, 35, 36, 37, 38]), old_indices_discarded=array([29, 30]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=79.64023815570656, linear_terms=array([ 0.29251479, -1.21077324]), square_terms=array([[ 0.00054174, -0.00224234], + [-0.00224234, 0.00928148]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=40, candidate_x=array([1.99999977, 1.00000097]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-81.26757484763098, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 36, 37, 38, 39]), old_indices_discarded=array([29, 30, 31]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 36, 38, 39, 40]), model=ScalarModel(intercept=77.91642222342819, linear_terms=array([-7.61877194, 6.60055683]), square_terms=array([[ 0.3757044 , -0.32549317], + [-0.32549317, 0.28199245]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 38, 39, 40, 41]), model=ScalarModel(intercept=79.20315056997187, linear_terms=array([-4.18694583, -2.12920934]), square_terms=array([[0.11160833, 0.05675677], + [0.05675677, 0.02886281]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=44, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=45, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=46, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=47, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=48, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=49, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=50, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=51, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=52, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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52]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=54, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=55, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=59, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=60, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 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36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=63, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=65, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=66, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=67, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=68, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=69, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=70, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=71, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=72, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=73, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=74, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=75, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=77, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=78, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=79, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=80, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=88, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=89, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=90, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=91, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=92, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=93, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=94, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=95, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=96, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94, 95]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=97, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94, 95, 96]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=98, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94, 95, 96, 97]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=99, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=100, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=101, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=102, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=103, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=104, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=105, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=106, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=107, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105, 106]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=108, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105, 106, 107]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=109, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105, 106, 107, 108]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=110, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105, 106, 107, 108, 109]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=111, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105, 106, 107, 108, 109, 110]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=112, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105, 106, 107, 108, 109, 110, 111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 113 entries., 'multistart_info': {'start_parameters': [array([2., 1.]), array([ 6.92609782, 183.76536854])], 'local_optima': [{'solution_x': array([2., 1.]), 'solution_criterion': 0.8276471824376574, 'states': [State(trustregion=Region(center=array([2., 1.]), radius=0.2, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=[0], model=ScalarModel(intercept=0.8276471824376574, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.2, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=13, candidate_x=array([1.94980218, 1.19359798]), index=0, 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6, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 4, 8, 9, 10, 11, 13, 14]), model=ScalarModel(intercept=61.19193548717778, linear_terms=array([3.65115817, 8.68434653]), square_terms=array([[0.11012808, 0.26194167], + [0.26194167, 0.62303309]]), scale=0.05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 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upper=array([ 20., 10000.]))), model_indices=array([ 0, 14, 15]), model=ScalarModel(intercept=0.8276471824376403, linear_terms=array([-31.62005899, 14.00478571]), square_terms=array([[ 3117.28536415, -1380.6732394 ], + [-1380.6732394 , 611.51238059]]), scale=0.025, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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[-2264.48759797, 798.83466418]]), scale=0.0125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=17, candidate_x=array([2.004237 , 1.01176031]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7120258298457, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 15, 16]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.00625, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 16, 17]), model=ScalarModel(intercept=0.8276471824376554, linear_terms=array([ 22.92925386, -12.7088243 ]), square_terms=array([[1639.19696011, -908.54564757], + [-908.54564757, 503.57291637]]), scale=0.00625, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=18, candidate_x=array([1.99690325, 0.99457059]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.6996277758512, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 16, 17]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.003125, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=0.8276471824376497, linear_terms=array([ 33.51528457, -14.29880894]), square_terms=array([[ 3502.17413253, -1494.15175942], + [-1494.15175942, 637.45816047]]), scale=0.003125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.0015625, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 18, 19]), model=ScalarModel(intercept=0.8276471824376597, linear_terms=array([-16.19210931, 11.64397652]), square_terms=array([[ 817.44432411, -587.83592419], + [-587.83592419, 422.72123444]]), scale=0.0015625, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=20, candidate_x=array([2.00093264, 1.00125388]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7086956369039, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 19]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.00078125, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 19, 20]), model=ScalarModel(intercept=0.8276471824376584, linear_terms=array([-24.44583172, 12.96813418]), square_terms=array([[1863.20708893, -988.40244714], + [-988.40244714, 524.33216002]]), scale=0.00078125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=21, candidate_x=array([2.00037411, 1.00068591]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7117058020676, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 19, 20]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.000390625, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 20, 21]), model=ScalarModel(intercept=0.8276471824376602, linear_terms=array([ 10.88378502, -10.70264801]), square_terms=array([[ 369.32716463, -363.18052667], + [-363.18052667, 357.13618598]]), scale=0.000390625, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=22, candidate_x=array([1.99972026, 0.99972723]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7109492805971, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 20, 21]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.0001953125, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 21, 22]), model=ScalarModel(intercept=0.8276471824376606, linear_terms=array([ 17.44512723, -11.89823002]), square_terms=array([[ 948.85460568, -647.15437432], + [-647.15437432, 441.38351829]]), scale=0.0001953125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=23, candidate_x=array([1.99988751, 0.99984033]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.71171318721, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 21, 22]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=9.765625e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 22, 23]), model=ScalarModel(intercept=0.8276471824376566, linear_terms=array([-6.61168567, 9.77707177]), square_terms=array([[ 136.29368845, -201.54515116], + [-201.54515116, 298.03616307]]), scale=9.765625e-05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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State(trustregion=Region(center=array([2., 1.]), radius=4.8828125e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 23, 24]), model=ScalarModel(intercept=0.8276471824376598, linear_terms=array([-11.96730467, 10.99081158]), square_terms=array([[ 446.52322965, -410.08838858], + [-410.08838858, 376.62651187]]), scale=4.8828125e-05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=25, candidate_x=array([2.00003373, 1.0000353 ]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.711688055746, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 23, 24]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=2.44140625e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 24, 25]), model=ScalarModel(intercept=0.8276471824376532, linear_terms=array([ 3.08834797, -8.73857385]), square_terms=array([[ 29.73745547, -84.14302869], + [-84.14302869, 238.08524183]]), scale=2.44140625e-05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=26, candidate_x=array([1.99997671, 0.99999267]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.711721703526, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 24, 25]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1.220703125e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 25, 26]), model=ScalarModel(intercept=0.8276471824376521, linear_terms=array([ 7.57960308, -10.13596867]), square_terms=array([[ 179.12008348, -239.53174518], + [-239.53174518, 320.31839108]]), scale=1.220703125e-05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=27, candidate_x=array([1.99999004, 0.99999294]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7116874138196, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 25, 26]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=6.103515625e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 26, 27]), model=ScalarModel(intercept=0.8276471824376579, linear_terms=array([-0.15165759, 7.44664151]), square_terms=array([[ 7.17099892e-02, -3.52108049e+00], + [-3.52108049e+00, 1.72890945e+02]]), scale=6.103515625e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=28, candidate_x=array([2.00000611, 0.99999986]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.711677104554, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 26, 27]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=3.0517578125e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 27, 28]), model=ScalarModel(intercept=0.8276471824376628, linear_terms=array([-3.97279126, 9.21962533]), square_terms=array([[ 49.20880826, -114.19849305], + [-114.19849305, 265.01954175]]), scale=3.0517578125e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=29, candidate_x=array([1.99999724, 0.9999987 ]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7117397453109, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 27, 28]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1.52587890625e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 28, 29]), model=ScalarModel(intercept=0.827647182437653, linear_terms=array([-1.78151146, 13.64486064]), square_terms=array([[ 9.89529152, -75.78950624], + [-75.78950624, 580.48307582]]), scale=1.52587890625e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=30, candidate_x=array([1.99999849, 0.99999977]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7116873932954, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 28, 29]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30]), model=ScalarModel(intercept=0.8276471824376591, linear_terms=array([ 6.78250872, -7.99625076]), square_terms=array([[ 143.4273832 , -169.09396935], + [-169.09396935, 199.3536369 ]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=31, candidate_x=array([2.00000074, 1.00000067]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7116856529549, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31]), model=ScalarModel(intercept=10.049290130474787, linear_terms=array([-51.9281406 , 88.49493782]), square_terms=array([[ 143.70755383, -244.90364749], + [-244.90364749, 417.3600827 ]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=32, candidate_x=array([1.99999924, 0.99999934]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-10.746700442186407, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32]), model=ScalarModel(intercept=43.05839744142132, linear_terms=array([-37.69007116, 44.3240894 ]), square_terms=array([[ 16.75517786, -19.70434065], + [-19.70434065, 23.17260037]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=33, candidate_x=array([2.00000009, 0.999999 ]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-2.9522733070987055, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31, 32]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32, 33]), model=ScalarModel(intercept=56.93444143408821, linear_terms=array([ -6.96601288, -17.26411453]), square_terms=array([[0.43120475, 1.06866988], + [1.06866988, 2.64852213]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=34, candidate_x=array([2.00000019, 1.00000098]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-6.003128302627301, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31, 32, 33]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32, 33, 34]), model=ScalarModel(intercept=67.15977327832778, linear_terms=array([-12.95288056, 3.90965354]), square_terms=array([[ 1.26162446, -0.38080445], + [-0.38080445, 0.11494072]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=35, candidate_x=array([2.00000098, 0.99999981]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-7.891442250906298, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31, 32, 33, 34]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=74.40172512316786, linear_terms=array([-4.39913405, -4.74128575]), square_terms=array([[0.13123025, 0.14143696], + [0.14143696, 0.15243751]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=36, candidate_x=array([2.00000054, 1.00000084]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-16.18609474458335, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31, 32, 33, 34, 35]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=78.42574355713958, linear_terms=array([-4.95695165, 1.36114804]), square_terms=array([[ 0.15799804, -0.04338528], + [-0.04338528, 0.01191333]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=37, candidate_x=array([2.00000097, 0.99999977]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-19.954818815240166, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31, 32, 33, 34, 35, 36]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 30, 31, 32, 33, 34, 35, 36, 37]), model=ScalarModel(intercept=78.99436811656406, linear_terms=array([ 4.95171225, -0.92106365]), square_terms=array([[ 0.15651963, -0.02911408], + [-0.02911408, 0.00541548]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=38, candidate_x=array([1.99999901, 1.00000016]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-20.352438513412878, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 30, 31, 32, 33, 34, 35, 36, 37]), old_indices_discarded=array([29]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 31, 32, 33, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=77.8470369868507, linear_terms=array([8.80696801, 1.13598751]), square_terms=array([[0.50248075, 0.06481366], + [0.06481366, 0.00836014]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=39, candidate_x=array([1.999999 , 0.99999991]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-11.699970979801405, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 31, 32, 33, 34, 35, 36, 37, 38]), old_indices_discarded=array([29, 30]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=79.64023815570656, linear_terms=array([ 0.29251479, -1.21077324]), square_terms=array([[ 0.00054174, -0.00224234], + [-0.00224234, 0.00928148]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=40, candidate_x=array([1.99999977, 1.00000097]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-81.26757484763098, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 36, 37, 38, 39]), old_indices_discarded=array([29, 30, 31]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 36, 38, 39, 40]), model=ScalarModel(intercept=77.91642222342819, linear_terms=array([-7.61877194, 6.60055683]), square_terms=array([[ 0.3757044 , -0.32549317], + [-0.32549317, 0.28199245]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=41, candidate_x=array([2.00000087, 0.99999951]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-10.540882296216793, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 36, 38, 39, 40]), old_indices_discarded=array([29, 30, 31, 37]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 38, 39, 40, 41]), model=ScalarModel(intercept=79.20315056997187, linear_terms=array([-4.18694583, -2.12920934]), square_terms=array([[0.11160833, 0.05675677], + [0.05675677, 0.02886281]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=42, candidate_x=array([2.00000091, 1.00000041]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-21.818687823671702, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 38, 39, 40, 41]), old_indices_discarded=array([29, 30, 31, 36, 37]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=43, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=44, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=45, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=46, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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[-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=48, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=49, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=50, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=52, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=54, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=55, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=56, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=57, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=58, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=59, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=60, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=61, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=62, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=63, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=64, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=65, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=66, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=67, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=68, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=69, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=70, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=71, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=72, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=73, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=74, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=75, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=76, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=77, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=78, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=79, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=80, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=81, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=82, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=83, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=84, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=85, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=86, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=87, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=88, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=89, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=90, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=91, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=92, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=93, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=94, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=95, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=96, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94, 95]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=97, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94, 95, 96]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=98, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94, 95, 96, 97]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=99, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=100, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=101, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=102, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=103, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=104, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=105, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=106, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=107, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105, 106]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=108, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105, 106, 107]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=109, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105, 106, 107, 108]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=110, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 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-7.65738376], + [-7.65738376, 2.42416309]]), scale=array([ 9.45 , 32.57156351]), shift=array([10.55 , 32.57156351])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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shift=array([10.55 , 16.28578176])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=24, candidate_x=array([1.1, 0. ]), index=18, x=array([1.27677162, 0. ]), fval=101.77651312344997, rho=-0.10692730529367472, accepted=False, new_indices=array([23]), old_indices_used=array([15, 16, 17, 18, 19, 20, 21, 22]), old_indices_discarded=array([ 1, 2, 5, 6, 11, 13, 14]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.27677162, 0. ]), radius=18.376536853959433, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([16, 17, 18, 20, 22, 23, 24, 25]), model=ScalarModel(intercept=117.57203468456304, linear_terms=array([30.73535329, -7.11397569]), square_terms=array([[30.73949783, -7.9121334 ], + [-7.9121334 , 2.03963642]]), scale=array([8.23127669, 8.14289088]), shift=array([9.33127669, 8.14289088])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=26, candidate_x=array([1.1 , 4.95638609]), index=18, x=array([1.27677162, 0. ]), fval=101.77651312344997, rho=-0.5089417781776332, accepted=False, new_indices=array([25]), old_indices_used=array([16, 17, 18, 20, 22, 23, 24]), old_indices_discarded=array([21]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.27677162, 0. ]), radius=9.188268426979716, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([17, 18, 20, 22, 23, 24, 25, 26]), model=ScalarModel(intercept=107.07780108961828, linear_terms=array([13.09888074, 0.39394958]), square_terms=array([[1.63256660e+01, 2.16792836e-02], + [2.16792836e-02, 7.25065498e-04]]), scale=array([4.15983125, 4.07144544]), shift=array([5.25983125, 4.07144544])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=27, candidate_x=array([1.92771902, 0. ]), index=27, x=array([1.92771902, 0. ]), fval=101.75336172178959, rho=0.11582332012959147, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([17, 18, 20, 22, 23, 24, 25, 26]), old_indices_discarded=array([], dtype=int32), step_length=0.650947401024822, relative_step_length=0.07084549240131369, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.92771902, 0. ]), radius=9.188268426979716, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([17, 18, 20, 22, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=104.55965217810557, linear_terms=array([8.8072047 , 0.73944899]), square_terms=array([[14.54343784, 0.6300655 ], + [ 0.6300655 , 0.02792488]]), scale=array([4.48530495, 4.07144544]), shift=array([5.58530495, 4.07144544])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=29, candidate_x=array([3.06341409, 0. ]), index=27, x=array([1.92771902, 0. ]), fval=101.75336172178959, rho=-0.09805438131359735, accepted=False, new_indices=array([28]), old_indices_used=array([17, 18, 20, 22, 24, 25, 26, 27]), old_indices_discarded=array([23]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.92771902, 0. ]), radius=4.594134213489858, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([17, 18, 22, 24, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=101.7963759557694, linear_terms=array([0.88312694, 0.23121981]), square_terms=array([[4.43153858, 0.34430387], + [0.34430387, 0.02688032]]), scale=array([2.44958223, 2.03572272]), shift=array([3.54958223, 2.03572272])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.92771902, 0. ]), radius=2.297067106744929, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([18, 22, 24, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=101.90622993713308, linear_terms=array([0.11164869, 0.06828134]), square_terms=array([[6.11612797e-05, 3.74045927e-05], + [3.74045927e-05, 2.28756423e-05]]), scale=array([1.43172087, 1.01786136]), shift=array([2.53172087, 1.01786136])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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State(trustregion=Region(center=array([1.92771902, 0. ]), radius=1.1485335533724645, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([18, 22, 24, 26, 27, 29, 30, 31]), model=ScalarModel(intercept=101.81701589262005, linear_terms=array([0.00903626, 0.02869081]), square_terms=array([[4.00983753e-07, 1.27315403e-06], + [1.27315403e-06, 4.04236127e-06]]), scale=array([0.92279019, 0.50893068]), shift=array([2.02279019, 0.50893068])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + 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bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([18, 22, 24, 27, 29, 30, 31, 32]), model=ScalarModel(intercept=25.447067538457258, linear_terms=array([ 2.26209655e-03, -5.08941351e+01]), square_terms=array([[ 1.00543625e-07, -2.26209655e-03], + [-2.26209655e-03, 5.08941351e+01]]), scale=array([0.50893068, 0.25446534]), shift=array([1.92771902, 0.25446534])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([18, 22, 24, 27, 29, 30, 31, 32, 33]), model=ScalarModel(intercept=101.70281707849445, linear_terms=array([ 0.00255143, -0.04817497]), square_terms=array([[ 3.20040297e-08, -6.04285701e-07], + [-6.04285701e-07, 1.14098510e-05]]), scale=0.28713338834311614, shift=array([1.92240301, 0.50893056])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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upper=array([ 20., 10000.]))), model_indices=array([18, 22, 24, 27, 29, 31, 32, 33, 34]), model=ScalarModel(intercept=101.6703406833042, linear_terms=array([ 0.00091326, -0.08127158]), square_terms=array([[ 4.10166532e-09, -3.65011616e-07], + [-3.65011616e-07, 3.24827770e-05]]), scale=0.5742667766862323, shift=array([1.9072569, 0.7956642])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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model=ScalarModel(intercept=101.7002045598023, linear_terms=array([-0.01763113, -0.01363475]), square_terms=array([[1.52829874e-06, 1.18188538e-06], + [1.18188538e-06, 9.13992149e-07]]), scale=0.28713338834311614, shift=array([1.9072569, 0.7956642])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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linear_terms=array([-3.30543881e-05, -5.13835828e-02]), square_terms=array([[5.37232193e-12, 8.35136165e-09], + [8.35136165e-09, 1.29823272e-05]]), scale=0.5742667766862323, shift=array([2.13447793, 0.9712096 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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1.46652301e-08, -1.58357648e-07], + [-1.58357648e-07, 1.70997279e-06]]), scale=0.28713338834311614, shift=array([2.13447793, 0.9712096 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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scale=0.14356669417155807, shift=array([2.13447793, 0.9712096 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=39, candidate_x=array([2.26814041, 0.91880982]), index=36, x=array([2.13447793, 0.9712096 ]), fval=101.66685336190018, rho=-2.429463248081529, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([33, 34, 35, 36, 37, 38]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.13447793, 0.9712096 ]), radius=0.07178334708577903, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([34, 36, 38, 39]), model=ScalarModel(intercept=101.6761802726613, linear_terms=array([0.0004533 , 0.00066536]), square_terms=array([[1.01045073e-09, 1.48316938e-09], + [1.48316938e-09, 2.17703977e-09]]), scale=0.07178334708577903, shift=array([2.13447793, 0.9712096 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=40, candidate_x=array([2.09406231, 0.91188484]), index=40, x=array([2.09406231, 0.91188484]), fval=101.66598958952493, rho=1.0728799070913182, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([34, 36, 38, 39]), old_indices_discarded=array([], dtype=int32), step_length=0.07178334708577915, relative_step_length=1.0000000000000016, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.09406231, 0.91188484]), radius=0.14356669417155807, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([33, 34, 35, 36, 37, 38, 39, 40]), model=ScalarModel(intercept=101.68098851485126, linear_terms=array([-0.0050226 , 0.00236335]), square_terms=array([[ 1.24047463e-07, -5.83696319e-08], + [-5.83696319e-08, 2.74654058e-08]]), scale=0.14356669417155807, shift=array([2.09406231, 0.91188484])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=41, candidate_x=array([2.22396921, 0.85076534]), index=40, x=array([2.09406231, 0.91188484]), fval=101.66598958952493, rho=-2.526612521827417, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([33, 34, 35, 36, 37, 38, 39, 40]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.09406231, 0.91188484]), radius=0.07178334708577903, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([34, 36, 38, 39, 40, 41]), model=ScalarModel(intercept=101.67433941881659, linear_terms=array([0.00112428, 0.00054592]), square_terms=array([[6.21590003e-09, 3.01827897e-09], + [3.01827897e-09, 1.46559757e-09]]), scale=0.07178334708577903, shift=array([2.09406231, 0.91188484])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=42, candidate_x=array([2.02948871, 0.88053044]), index=42, x=array([2.02948871, 0.88053044]), fval=101.66540634612655, rho=0.46666748151376614, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([34, 36, 38, 39, 40, 41]), old_indices_discarded=array([], dtype=int32), step_length=0.07178334708577888, relative_step_length=0.9999999999999979, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.02948871, 0.88053044]), radius=0.14356669417155807, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([33, 34, 35, 36, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=101.680416054201, linear_terms=array([-0.00475057, -0.00077785]), square_terms=array([[1.10974697e-07, 1.81707727e-08], + [1.81707727e-08, 2.97524560e-09]]), scale=0.14356669417155807, shift=array([2.02948871, 0.88053044])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=43, candidate_x=array([2.17116917, 0.90372619]), index=42, x=array([2.02948871, 0.88053044]), fval=101.66540634612655, rho=-1.523359554750816, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([33, 34, 35, 36, 38, 39, 40, 41, 42]), old_indices_discarded=array([37]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.02948871, 0.88053044]), radius=0.07178334708577903, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([34, 36, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=101.66905458361765, linear_terms=array([ 0.00365778, -0.00773732]), square_terms=array([[ 6.57986625e-08, -1.39184073e-07], + [-1.39184073e-07, 2.94416414e-07]]), scale=0.07178334708577903, shift=array([2.02948871, 0.88053044])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=44, candidate_x=array([1.99881359, 0.94542948]), index=44, x=array([1.99881359, 0.94542948]), fval=101.658559176471, rho=0.8000735183892913, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([34, 36, 39, 40, 41, 42, 43]), old_indices_discarded=array([], dtype=int32), step_length=0.07178334708577906, relative_step_length=1.0000000000000004, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99881359, 0.94542948]), radius=0.14356669417155807, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([34, 36, 38, 39, 40, 41, 42, 43, 44]), model=ScalarModel(intercept=101.66837110968486, linear_terms=array([0.00492682, 0.00116565]), square_terms=array([[1.19375993e-07, 2.82435251e-08], + [2.82435251e-08, 6.68222052e-09]]), scale=0.14356669417155807, shift=array([1.99881359, 0.94542948])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=45, candidate_x=array([1.85910309, 0.91237844]), index=44, x=array([1.99881359, 0.94542948]), fval=101.658559176471, rho=-2.1991263376100285, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([34, 36, 38, 39, 40, 41, 42, 43, 44]), old_indices_discarded=array([33, 35, 37]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99881359, 0.94542948]), radius=0.07178334708577903, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([34, 36, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=101.66380899320433, linear_terms=array([ 0.00235297, -0.00665188]), square_terms=array([[ 2.72293463e-08, -7.69776254e-08], + [-7.69776254e-08, 2.17616492e-07]]), scale=0.07178334708577903, shift=array([1.99881359, 0.94542948])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=46, candidate_x=array([1.9748781 , 1.01310475]), index=46, x=array([1.9748781 , 1.01310475]), fval=101.6559342855933, rho=0.3720268111515636, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([34, 36, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([38]), step_length=0.07178334708577894, relative_step_length=0.9999999999999987, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9748781 , 1.01310475]), radius=0.14356669417155807, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([34, 36, 38, 40, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=101.66834084528847, linear_terms=array([0.00105253, 0.00192198]), square_terms=array([[5.44817640e-09, 9.94871690e-09], + [9.94871690e-09, 1.81669903e-08]]), scale=0.14356669417155807, shift=array([1.9748781 , 1.01310475])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=47, candidate_x=array([1.90592301, 0.88718179]), index=46, x=array([1.9748781 , 1.01310475]), fval=101.6559342855933, rho=-5.4105642931805455, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([34, 36, 38, 40, 42, 43, 44, 45, 46]), old_indices_discarded=array([33, 35, 37, 39, 41]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9748781 , 1.01310475]), radius=0.07178334708577903, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([34, 36, 40, 42, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=101.65730356105429, linear_terms=array([ 0.00098995, -0.00629255]), square_terms=array([[ 4.82013494e-09, -3.06388241e-08], + [-3.06388241e-08, 1.94753374e-07]]), scale=0.07178334708577903, shift=array([1.9748781 , 1.01310475])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=48, candidate_x=array([1.96372399, 1.0840162 ]), index=46, x=array([1.9748781 , 1.01310475]), fval=101.6559342855933, rho=-0.9918406732109903, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([34, 36, 40, 42, 43, 44, 45, 46, 47]), old_indices_discarded=array([38, 39, 41]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9748781 , 1.01310475]), radius=0.03589167354288952, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([36, 40, 42, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=101.6617873815051, linear_terms=array([-0.00012068, -0.00134631]), square_terms=array([[7.16320381e-11, 7.99106267e-10], + [7.99106267e-10, 8.91459804e-09]]), scale=0.03589167354288952, shift=array([1.9748781 , 1.01310475])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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10000.]))), model_indices=array([44, 46, 48, 49]), model=ScalarModel(intercept=101.65745624776355, linear_terms=array([0.00318564, 0.00116839]), square_terms=array([[4.99142631e-08, 1.83069319e-08], + [1.83069319e-08, 6.71438853e-09]]), scale=0.01794583677144476, shift=array([1.9748781 , 1.01310475])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=50, candidate_x=array([1.9580296 , 1.00692568]), index=46, x=array([1.9748781 , 1.01310475]), fval=101.6559342855933, rho=-0.4185964575065688, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([44, 46, 48, 49]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9748781 , 1.01310475]), radius=0.00897291838572238, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([46, 49, 50]), model=ScalarModel(intercept=101.65593428559325, linear_terms=array([-0.00102209, 0.00072439]), square_terms=array([[ 5.13823243e-09, -3.64162989e-09], + [-3.64162989e-09, 2.58093974e-09]]), scale=0.00897291838572238, shift=array([1.9748781 , 1.01310475])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=51, candidate_x=array([1.98219895, 1.00791645]), index=51, x=array([1.98219895, 1.00791645]), fval=101.6550490329974, rho=0.7066458575122126, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([46, 49, 50]), old_indices_discarded=array([], dtype=int32), step_length=0.008972918385722404, relative_step_length=1.0000000000000027, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.98219895, 1.00791645]), radius=0.01794583677144476, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([44, 46, 48, 49, 50, 51]), model=ScalarModel(intercept=101.65780983947639, linear_terms=array([0.00063274, 0.00063126]), square_terms=array([[1.96914913e-09, 1.96454291e-09], + [1.96454291e-09, 1.95994747e-09]]), scale=0.01794583677144476, shift=array([1.98219895, 1.00791645])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=52, candidate_x=array([1.96947888, 0.99525735]), index=51, x=array([1.98219895, 1.00791645]), fval=101.6550490329974, rho=-1.2760889741412444, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([44, 46, 48, 49, 50, 51]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.98219895, 1.00791645]), radius=0.00897291838572238, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([46, 49, 50, 51, 52]), model=ScalarModel(intercept=101.65521942995059, linear_terms=array([-0.00089145, 0.00057592]), square_terms=array([[ 3.90872913e-09, -2.52522797e-09], + [-2.52522797e-09, 1.63141934e-09]]), scale=0.00897291838572238, shift=array([1.98219895, 1.00791645])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=53, candidate_x=array([1.98973589, 1.00304738]), index=53, x=array([1.98973589, 1.00304738]), fval=101.65417152893576, rho=0.8268176302277769, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([46, 49, 50, 51, 52]), old_indices_discarded=array([], dtype=int32), step_length=0.00897291838572237, relative_step_length=0.999999999999999, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.98973589, 1.00304738]), radius=0.01794583677144476, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([44, 46, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=101.65686345501824, linear_terms=array([-9.30404245e-05, 5.65023341e-04]), square_terms=array([[ 4.25771576e-11, -2.58565972e-10], + [-2.58565972e-10, 1.57024014e-09]]), scale=0.01794583677144476, shift=array([1.98973589, 1.00304738])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=54, candidate_x=array([1.99265166, 0.98534 ]), index=53, x=array([1.98973589, 1.00304738]), fval=101.65417152893576, rho=-0.993946834585867, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([44, 46, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.98973589, 1.00304738]), radius=0.00897291838572238, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([46, 50, 51, 52, 53, 54]), model=ScalarModel(intercept=101.65447794468584, linear_terms=array([-0.00081271, -0.0001358 ]), square_terms=array([[3.24870780e-09, 5.42864165e-10], + [5.42864165e-10, 9.07134529e-11]]), scale=0.00897291838572238, shift=array([1.98973589, 1.00304738])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=55, candidate_x=array([1.9985861 , 1.00452623]), index=55, x=array([1.9985861 , 1.00452623]), fval=101.65357502800954, rho=0.7239326168613196, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([46, 50, 51, 52, 53, 54]), old_indices_discarded=array([], dtype=int32), step_length=0.008972918385722428, relative_step_length=1.0000000000000053, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9985861 , 1.00452623]), radius=0.01794583677144476, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([44, 46, 49, 50, 51, 52, 53, 54, 55]), model=ScalarModel(intercept=101.65497283502123, linear_terms=array([-0.00108512, -0.00030579]), square_terms=array([[5.79156561e-09, 1.63209915e-09], + [1.63209915e-09, 4.59935676e-10]]), scale=0.01794583677144476, shift=array([1.9985861 , 1.00452623])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=56, candidate_x=array([2.0158592 , 1.00939379]), index=55, x=array([1.9985861 , 1.00452623]), fval=101.65357502800954, rho=-1.2143715762219611, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([44, 46, 49, 50, 51, 52, 53, 54, 55]), old_indices_discarded=array([48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9985861 , 1.00452623]), radius=0.00897291838572238, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([46, 50, 51, 52, 53, 54, 55, 56]), model=ScalarModel(intercept=101.65457751207414, linear_terms=array([-0.00046326, 0.00016734]), square_terms=array([[ 1.05559287e-09, -3.81311452e-10], + [-3.81311452e-10, 1.37741006e-10]]), scale=0.00897291838572238, shift=array([1.9985861 , 1.00452623])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=57, candidate_x=array([2.0070253 , 1.00147777]), index=55, x=array([1.9985861 , 1.00452623]), fval=101.65357502800954, rho=-0.49478560723833387, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([46, 50, 51, 52, 53, 54, 55, 56]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9985861 , 1.00452623]), radius=0.00448645919286119, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([51, 53, 54, 55, 56, 57]), model=ScalarModel(intercept=101.65438327884436, linear_terms=array([-3.96566587e-05, 1.41729139e-05]), square_terms=array([[ 7.73528167e-12, -2.76451634e-12], + [-2.76451634e-12, 9.88011935e-13]]), scale=0.00448645919286119, shift=array([1.9985861 , 1.00452623])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=58, candidate_x=array([2.00281085, 1.00301635]), index=58, x=array([2.00281085, 1.00301635]), fval=101.65351313645318, rho=1.4696474008540954, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([51, 53, 54, 55, 56, 57]), old_indices_discarded=array([], dtype=int32), step_length=0.004486459192861143, relative_step_length=0.9999999999999896, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00281085, 1.00301635]), radius=0.00897291838572238, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([46, 51, 52, 53, 54, 55, 56, 57, 58]), model=ScalarModel(intercept=101.65419419312354, linear_terms=array([-0.00041313, 0.00011216]), square_terms=array([[ 8.39487554e-10, -2.27914839e-10], + [-2.27914839e-10, 6.18772410e-11]]), scale=0.00897291838572238, shift=array([2.00281085, 1.00301635])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=59, candidate_x=array([2.01147031, 1.00066538]), index=58, x=array([2.00281085, 1.00301635]), fval=101.65351313645318, rho=-1.7208326131972362, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([46, 51, 52, 53, 54, 55, 56, 57, 58]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00281085, 1.00301635]), radius=0.00448645919286119, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([51, 53, 54, 55, 56, 57, 58, 59]), model=ScalarModel(intercept=101.65422823536524, linear_terms=array([-5.03968696e-05, 6.48567467e-06]), square_terms=array([[ 1.24925668e-11, -1.60769358e-12], + [-1.60769358e-12, 2.06897326e-13]]), scale=0.00448645919286119, shift=array([2.00281085, 1.00301635])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=60, candidate_x=array([2.00726062, 1.0024437 ]), index=58, x=array([2.00281085, 1.00301635]), fval=101.65351313645318, rho=-6.962482671698848, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([51, 53, 54, 55, 56, 57, 58, 59]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00281085, 1.00301635]), radius=0.002243229596430595, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([55, 57, 58, 59, 60]), model=ScalarModel(intercept=101.65364736975285, linear_terms=array([0.00018313, 0.0002147 ]), square_terms=array([[1.64949201e-10, 1.93390034e-10], + [1.93390034e-10, 2.26734687e-10]]), scale=0.002243229596430595, shift=array([2.00281085, 1.00301635])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=61, candidate_x=array([2.00135514, 1.0013096 ]), index=61, x=array([2.00135514, 1.0013096 ]), fval=101.65328928827302, rho=0.793249003571856, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([55, 57, 58, 59, 60]), old_indices_discarded=array([], dtype=int32), step_length=0.0022432295964306204, relative_step_length=1.0000000000000113, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00135514, 1.0013096 ]), radius=0.00448645919286119, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([53, 54, 55, 56, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=101.65397564855002, linear_terms=array([ 0.00011335, -0.00016284]), square_terms=array([[ 6.31929509e-11, -9.07881463e-11], + [-9.07881463e-11, 1.30433654e-10]]), scale=0.00448645919286119, shift=array([2.00135514, 1.0013096 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=62, candidate_x=array([1.99879211, 1.00499188]), index=61, x=array([2.00135514, 1.0013096 ]), fval=101.65328928827302, rho=-1.6387481559100683, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([53, 54, 55, 56, 57, 58, 59, 60, 61]), old_indices_discarded=array([51]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00135514, 1.0013096 ]), radius=0.002243229596430595, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([55, 57, 58, 59, 60, 61, 62]), model=ScalarModel(intercept=101.65328749627801, linear_terms=array([0.00020435, 0.00031288]), square_terms=array([[2.05388937e-10, 3.14474469e-10], + [3.14474469e-10, 4.81497170e-10]]), scale=0.002243229596430595, shift=array([2.00135514, 1.0013096 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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0. ]), upper=array([ 20., 10000.]))), model_indices=array([53, 55, 57, 58, 59, 60, 61, 62, 63]), model=ScalarModel(intercept=101.65353276184649, linear_terms=array([6.33509679e-05, 2.13247340e-04]), square_terms=array([[1.97403131e-11, 6.64483812e-11], + [6.64483812e-11, 2.23673623e-10]]), scale=0.00448645919286119, shift=array([2.0001285 , 0.99943145])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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57, 58, 60, 61, 62, 63, 64]), model=ScalarModel(intercept=101.65357133359919, linear_terms=array([-3.07662222e-05, 5.62449717e-05]), square_terms=array([[ 4.65581493e-12, -8.51148305e-12], + [-8.51148305e-12, 1.55601855e-11]]), scale=0.002243229596430595, shift=array([2.0001285 , 0.99943145])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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model=ScalarModel(intercept=101.6534281159708, linear_terms=array([-3.92952930e-05, 6.94120144e-06]), square_terms=array([[ 7.59502203e-12, -1.34160032e-12], + [-1.34160032e-12, 2.36983043e-13]]), scale=0.0011216147982152974, shift=array([2.0001285 , 0.99943145])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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-1.56138570e-05]), square_terms=array([[ 2.57872636e-11, -5.56080152e-12], + [-5.56080152e-12, 1.19913900e-12]]), scale=0.0005608073991076487, shift=array([2.0001285 , 0.99943145])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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scale=0.00028040369955382435, shift=array([2.0001285 , 0.99943145])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=68, candidate_x=array([2.00007012, 0.9991572 ]), index=63, x=array([2.0001285 , 0.99943145]), fval=101.65315912374548, rho=-0.04231075182430718, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([63, 66, 67]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.0001285 , 0.99943145]), radius=0.00014020184977691218, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([63, 67, 68]), model=ScalarModel(intercept=101.65315912374552, linear_terms=array([-3.56979582e-06, -1.26759543e-05]), square_terms=array([[6.26809943e-14, 2.22573351e-13], + [2.22573351e-13, 7.90333613e-13]]), scale=0.00014020184977691218, shift=array([2.0001285 , 0.99943145])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=69, candidate_x=array([2.00016651, 0.99956641]), index=69, x=array([2.00016651, 0.99956641]), fval=101.65314728156433, rho=0.8992450217010943, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([63, 67, 68]), old_indices_discarded=array([], dtype=int32), step_length=0.0001402018497769173, relative_step_length=1.0000000000000366, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00016651, 0.99956641]), radius=0.00028040369955382435, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([63, 66, 67, 68, 69]), model=ScalarModel(intercept=101.65316287886624, linear_terms=array([ 1.18463065e-05, -2.23664617e-05]), square_terms=array([[ 6.90263703e-13, -1.30325487e-12], + [-1.30325487e-12, 2.46061507e-12]]), scale=0.00028040369955382435, shift=array([2.00016651, 0.99956641])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=70, candidate_x=array([2.00003526, 0.9998142 ]), index=70, x=array([2.00003526, 0.9998142 ]), fval=101.65311974675102, rho=1.087904744350226, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([63, 66, 67, 68, 69]), old_indices_discarded=array([], dtype=int32), step_length=0.0002804036995538049, relative_step_length=0.9999999999999306, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00003526, 0.9998142 ]), radius=0.0005608073991076487, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([61, 63, 65, 66, 67, 68, 69, 70]), model=ScalarModel(intercept=101.6531539010588, linear_terms=array([ 5.98849665e-05, -1.62873618e-05]), square_terms=array([[ 1.76394390e-11, -4.79753001e-12], + [-4.79753001e-12, 1.30482008e-12]]), scale=0.0005608073991076487, shift=array([2.00003526, 0.9998142 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=71, candidate_x=array([1.99949411, 0.99996138]), index=70, x=array([2.00003526, 0.9998142 ]), fval=101.65311974675102, rho=-0.5127930577353325, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([61, 63, 65, 66, 67, 68, 69, 70]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00003526, 0.9998142 ]), radius=0.00028040369955382435, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([63, 66, 67, 68, 69, 70, 71]), model=ScalarModel(intercept=101.65314877678284, linear_terms=array([ 9.71223485e-06, -1.61205500e-05]), square_terms=array([[ 4.63967456e-13, -7.70101908e-13], + [-7.70101908e-13, 1.27822963e-12]]), scale=0.00028040369955382435, shift=array([2.00003526, 0.9998142 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=72, candidate_x=array([1.99989056, 1.00005438]), index=72, x=array([1.99989056, 1.00005438]), fval=101.65311305572654, rho=0.35552372044377767, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([63, 66, 67, 68, 69, 70, 71]), old_indices_discarded=array([], dtype=int32), step_length=0.0002804036995538801, relative_step_length=1.0000000000001987, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99989056, 1.00005438]), radius=0.0005608073991076487, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([61, 63, 66, 67, 68, 69, 70, 71, 72]), model=ScalarModel(intercept=101.65315386439156, linear_terms=array([4.03977606e-05, 5.28856412e-06]), square_terms=array([[8.02719343e-12, 1.05085843e-12], + [1.05085843e-12, 1.37570304e-13]]), scale=0.0005608073991076487, shift=array([1.99989056, 1.00005438])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=73, candidate_x=array([1.9993345 , 0.99998159]), index=72, x=array([1.99989056, 1.00005438]), fval=101.65311305572654, rho=-1.334113586767923, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([61, 63, 66, 67, 68, 69, 70, 71, 72]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99989056, 1.00005438]), radius=0.00028040369955382435, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([63, 67, 68, 69, 70, 71, 72, 73]), model=ScalarModel(intercept=101.65311655752888, linear_terms=array([-1.71237318e-05, -2.37865075e-05]), square_terms=array([[1.44226858e-12, 2.00344952e-12], + [2.00344952e-12, 2.78298373e-12]]), scale=0.00028040369955382435, shift=array([1.99989056, 1.00005438])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=74, candidate_x=array([2.00005439, 1.00028195]), index=72, x=array([1.99989056, 1.00005438]), fval=101.65311305572654, rho=-0.5627670521816485, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([63, 67, 68, 69, 70, 71, 72, 73]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99989056, 1.00005438]), radius=0.00014020184977691218, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([63, 67, 69, 70, 71, 72, 73, 74]), model=ScalarModel(intercept=101.65313160034756, linear_terms=array([-5.77420288e-06, -6.18898976e-06]), square_terms=array([[1.63996024e-13, 1.75776594e-13], + [1.75776594e-13, 1.88403415e-13]]), scale=0.00014020184977691218, shift=array([1.99989056, 1.00005438])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=75, candidate_x=array([1.9999862, 1.0001569]), index=72, x=array([1.99989056, 1.00005438]), fval=101.65311305572654, rho=-0.41697664424331127, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([63, 67, 69, 70, 71, 72, 73, 74]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99989056, 1.00005438]), radius=7.010092488845609e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([70, 72, 74, 75]), model=ScalarModel(intercept=101.65311148563875, linear_terms=array([5.50427867e-06, 9.82268200e-07]), square_terms=array([[1.49021920e-13, 2.65937649e-14], + [2.65937649e-14, 4.74580071e-15]]), scale=7.010092488845609e-05, shift=array([1.99989056, 1.00005438])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=76, candidate_x=array([1.99982155, 1.00004207]), index=72, x=array([1.99989056, 1.00005438]), fval=101.65311305572654, rho=-1.0934080921860894, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([70, 72, 74, 75]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99989056, 1.00005438]), radius=3.5050462444228044e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([72, 75, 76]), model=ScalarModel(intercept=101.65311305572646, linear_terms=array([-3.98365342e-06, 4.92341491e-06]), square_terms=array([[ 7.80571006e-14, -9.64711165e-14], + [-9.64711165e-14, 1.19229080e-13]]), scale=3.5050462444228044e-05, shift=array([1.99989056, 1.00005438])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=77, candidate_x=array([1.99991261, 1.00002713]), index=77, x=array([1.99991261, 1.00002713]), fval=101.65310998586972, rho=0.4847239229932347, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([72, 75, 76]), old_indices_discarded=array([], dtype=int32), step_length=3.5050462444189934e-05, relative_step_length=0.9999999999989128, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99991261, 1.00002713]), radius=7.010092488845609e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([70, 72, 74, 75, 76, 77]), model=ScalarModel(intercept=101.65311599269691, linear_terms=array([2.73513590e-06, 1.10171467e-06]), square_terms=array([[3.67965523e-14, 1.48216772e-14], + [1.48216772e-14, 5.97018206e-15]]), scale=7.010092488845609e-05, shift=array([1.99991261, 1.00002713])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=78, candidate_x=array([1.99984758, 1.00000094]), index=77, x=array([1.99991261, 1.00002713]), fval=101.65310998586972, rho=-2.0657227533912423, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([70, 72, 74, 75, 76, 77]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99991261, 1.00002713]), radius=3.5050462444228044e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([72, 75, 76, 77, 78]), model=ScalarModel(intercept=101.6531107725162, linear_terms=array([-2.89439098e-06, 2.97676454e-06]), square_terms=array([[ 4.12063098e-14, -4.23790299e-14], + [-4.23790299e-14, 4.35851253e-14]]), scale=3.5050462444228044e-05, shift=array([1.99991261, 1.00002713])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + 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upper=array([ 20., 10000.]))), model_indices=array([72, 75, 76, 77, 78, 79, 80]), model=ScalarModel(intercept=101.65310851811418, linear_terms=array([-2.55877294e-06, 2.22965678e-06]), square_terms=array([[ 3.22042240e-14, -2.80620313e-14], + [-2.80620313e-14, 2.44526185e-14]]), scale=3.5050462444228044e-05, shift=array([1.99993704, 1.000002 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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model_indices=array([70, 72, 75, 76, 77, 78, 79, 80, 81]), model=ScalarModel(intercept=101.65311312451377, linear_terms=array([-7.55959616e-07, -6.02717625e-07]), square_terms=array([[2.81090723e-15, 2.24110295e-15], + [2.24110295e-15, 1.78680477e-15]]), scale=7.010092488845609e-05, shift=array([1.99996347, 0.99997898])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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79, 80, 81, 82]), model=ScalarModel(intercept=101.65310380914096, linear_terms=array([-1.03266467e-05, 8.97242802e-06]), square_terms=array([[ 5.24527182e-13, -4.55741590e-13], + [-4.55741590e-13, 3.95976422e-13]]), scale=0.00014020184977691218, shift=array([2.00001828, 1.00002268])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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model=ScalarModel(intercept=101.65311088628962, linear_terms=array([-3.79014379e-07, 1.04483358e-06]), square_terms=array([[ 7.06578965e-16, -1.94783489e-15], + [-1.94783489e-15, 5.36962031e-15]]), scale=7.010092488845609e-05, shift=array([2.00001828, 1.00002268])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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linear_terms=array([1.46452562e-07, 8.40702212e-07]), square_terms=array([[1.05497769e-16, 6.05603664e-16], + [6.05603664e-16, 3.47643178e-15]]), scale=3.5050462444228044e-05, shift=array([2.00001828, 1.00002268])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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21.40902019990608, 21.63227880001068, 21.85612909961492, 22.079848199617118, 22.438155899755657, 22.660994099918753, 22.887149499729276, 23.114062799606472, 23.340816299896687], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78]}}], 'exploration_sample': array([[2.0000000e+00, 1.0000000e+00], + [1.8818750e+01, 6.2500000e+02], + [1.2912500e+01, 1.2500000e+03], + [7.0062500e+00, 1.8750000e+03], + [1.7046875e+01, 2.1875000e+03], + [1.5275000e+01, 2.5000000e+03], + [4.6437500e+00, 3.1250000e+03], + [8.1875000e+00, 3.7500000e+03], + [1.1731250e+01, 4.3750000e+03], + [2.8718750e+00, 4.6875000e+03], + [1.0550000e+01, 5.0000000e+03], + [9.3687500e+00, 5.6250000e+03], + [3.4625000e+00, 6.2500000e+03], + [1.6456250e+01, 6.8750000e+03], + [7.5968750e+00, 7.1875000e+03], + [5.8250000e+00, 7.5000000e+03], + [1.4093750e+01, 8.1250000e+03], + [1.7637500e+01, 8.7500000e+03], + [2.2812500e+00, 9.3750000e+03], + [1.2321875e+01, 9.6875000e+03]]), 'exploration_results': array([8.33326931e-01, 1.64075763e+02, 2.26557868e+02, 2.89053770e+02, + 3.20308278e+02, 3.51556627e+02, 4.14053213e+02, 4.76553611e+02, + 5.39054183e+02, 5.70303109e+02, 6.01553832e+02, 6.64053584e+02, + 7.26553118e+02, 7.89054621e+02, 8.20303319e+02, 8.51553198e+02, + 9.14054001e+02, 9.76554498e+02, 1.03905310e+03, 1.07030365e+03])}}" diff --git a/src/estimark/content/tables/min/WarmGlowPortfolio_estimate_results.csv b/src/estimark/content/tables/min/WarmGlowPortfolio_estimate_results.csv new file mode 100644 index 0000000..3dfdd53 --- /dev/null +++ b/src/estimark/content/tables/min/WarmGlowPortfolio_estimate_results.csv @@ -0,0 +1,6643 @@ +CRRA,4.972059127341761 + +BeqFac,2235.66869440018 + +time_to_estimate,120.60259437561035 + +params,"{'CRRA': 4.972059127341761, 'BeqFac': 2235.66869440018}" + +criterion,0.15183333483370898 + +start_criterion,0.14974471998399175 + +start_params,"{'CRRA': 4.972165469041286, 'BeqFac': 2235.668701887974}" + +algorithm,multistart_tranquilo_ls + +direction,minimize + +n_free,2 + +message,Absolute criterion change smaller than tolerance. + +success, + +n_criterion_evaluations, + +n_derivative_evaluations, + +n_iterations, + +history,"{'params': [{'CRRA': 4.972165469041287, 'BeqFac': 2235.668701887974}, {'CRRA': 1.1, 'BeqFac': 2038.2167591428197}, {'CRRA': 20.0, 'BeqFac': 2200.680349896351}, {'CRRA': 1.1, 'BeqFac': 2429.9412094587697}, {'CRRA': 19.832484492263543, 'BeqFac': 2433.7996818884876}, {'CRRA': 19.93169977776446, 'BeqFac': 2037.53772188746}, {'CRRA': 20.0, 'BeqFac': 2064.4481322185475}, {'CRRA': 1.1, 'BeqFac': 2332.634198828065}, {'CRRA': 20.0, 'BeqFac': 2322.498125139562}, {'CRRA': 18.700329294200046, 'BeqFac': 2433.7996818884876}, {'CRRA': 1.1, 'BeqFac': 2430.302509845014}, {'CRRA': 7.813833384306793, 'BeqFac': 2037.53772188746}, {'CRRA': 8.624550632134087, 'BeqFac': 2433.7996818884876}, {'CRRA': 17.981702297455875, 'BeqFac': 2037.53772188746}, {'CRRA': 18.109298496140873, 'BeqFac': 2136.603211887717}, {'CRRA': 19.930536834777662, 'BeqFac': 2186.135956887845}, {'CRRA': 20.0, 'BeqFac': 2210.9023293879095}, {'CRRA': 4.079012994666991, 'BeqFac': 2248.051888138006}, {'CRRA': 3.121220401269943, 'BeqFac': 2241.86029501299}, {'CRRA': 4.8421373902763785, 'BeqFac': 2239.1595207269393}, {'CRRA': 4.935891618489661, 'BeqFac': 2237.414959439111}, {'CRRA': 4.960910699246413, 'BeqFac': 2234.79536779911}, {'CRRA': 4.975594073396457, 'BeqFac': 2236.1053544720567}, {'CRRA': 5.031762747602235, 'BeqFac': 2235.4503996289804}, {'CRRA': 4.95481938313597, 'BeqFac': 2235.5595356176655}, {'CRRA': 4.962658187223382, 'BeqFac': 2235.723260832982}, {'CRRA': 4.976595860461234, 'BeqFac': 2235.641409064533}, {'CRRA': 4.985643978352027, 'BeqFac': 2235.670829747999}, {'CRRA': 4.965480731774778, 'BeqFac': 2235.6673366817404}, {'CRRA': 4.9715471211216276, 'BeqFac': 2235.672056738335}, {'CRRA': 4.973852096731626, 'BeqFac': 2235.6684476620744}, {'CRRA': 4.9713764830158755, 'BeqFac': 2235.6683781025586}, {'CRRA': 4.972141953092093, 'BeqFac': 2235.669128923647}, {'CRRA': 4.972378447740231, 'BeqFac': 2235.6687118162295}, {'CRRA': 4.972059127341761, 'BeqFac': 2235.66869440018}], 'criterion': [0.15183336484019694, 1599.2256506697936, 8.33792735607635, 1628.996184127626, 7.942067008283367, 8.173798608673742, 8.33777080025052, 1622.3971740217662, 8.338047223703619, 5.70897681494281, 1629.0195228262494, 0.4459772753262474, 0.5044697780491717, 4.621902068591304, 4.800133525579624, 8.171500062212324, 8.337937651588279, 0.5669929910375328, 6.5167800463917995, 0.15533004766705152, 0.1521320528374574, 0.15187972532160543, 0.15186895370600006, 0.15287286421734658, 0.1519085924789972, 0.1518749282368537, 0.15186803665511617, 0.15194576604337082, 0.15186614554536748, 0.15183882851267255, 0.1518437390370875, 0.1518413460232434, 0.15183338142448646, 0.15183419903764392, 0.15183333483370898], 'runtime': [0.0, 3.234110999852419, 3.3567784996703267, 3.607193300034851, 3.782226899638772, 3.974268099758774, 4.16790989972651, 4.338716899976134, 4.589133599773049, 4.924178099725395, 5.076727400068194, 5.272234600037336, 5.437637999653816, 6.631617899984121, 7.839812099933624, 9.029853600077331, 10.224262000061572, 11.426363199949265, 12.594932300038636, 13.766675599850714, 14.943977899849415, 16.25537579972297, 17.437238699756563, 18.621162499766797, 19.853813599795103, 21.03928539995104, 22.241678999736905, 23.42911219969392, 24.757420799694955, 25.978194700088352, 27.192072699777782, 28.37384819984436, 29.543620300013572, 30.743656399659812, 31.96393799968064], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]}" + +convergence_report, + +multistart_info,"{'start_parameters': [{'CRRA': 4.972165469041287, 'BeqFac': 2235.668701887974}, {'CRRA': 5.221879724992187, 'BeqFac': 3777.5556353977354}], 'local_optima': [Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 1.976e-07* 1.976e-07* +relative_params_change 2.139e-05 2.139e-05 +absolute_criterion_change 3.001e-08* 3.001e-08* +absolute_params_change 0.0001066 0.0001066 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 2.639e-07* 0.0006443 +relative_params_change 2.12e-06* 0.002311 +absolute_criterion_change 4.695e-08* 0.0001146 +absolute_params_change 1.126e-05 6.64 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 4.972165469041286, 'BeqFac': 2235.668701887974}, {'CRRA': 5.824999999999999, 'BeqFac': 7500.0}, {'CRRA': 4.64375, 'BeqFac': 3125.0}, {'CRRA': 7.596874999999999, 'BeqFac': 7187.5}, {'CRRA': 7.00625, 'BeqFac': 1875.0}, {'CRRA': 8.1875, 'BeqFac': 3750.0}, {'CRRA': 9.368749999999999, 'BeqFac': 5625.0}, {'CRRA': 10.549999999999999, 'BeqFac': 5000.0}, {'CRRA': 11.73125, 'BeqFac': 4375.0}, {'CRRA': 12.321874999999999, 'BeqFac': 9687.5}, {'CRRA': 12.9125, 'BeqFac': 1250.0}, {'CRRA': 14.093749999999998, 'BeqFac': 8125.0}, {'CRRA': 15.274999999999999, 'BeqFac': 2500.0}, {'CRRA': 16.45625, 'BeqFac': 6875.0}, {'CRRA': 17.046875, 'BeqFac': 2187.5}, {'CRRA': 17.6375, 'BeqFac': 8750.0}, {'CRRA': 18.81875, 'BeqFac': 625.0}, {'CRRA': 3.4625, 'BeqFac': 6250.0}, {'CRRA': 2.871875, 'BeqFac': 4687.5}, {'CRRA': 2.28125, 'BeqFac': 9375.0}], 'exploration_results': array([1.51833365e-01, 2.18154716e-01, 2.63875842e-01, 3.77381203e-01, + 3.83108040e-01, 4.55042643e-01, 5.57986156e-01, 6.96479632e-01, + 8.83084679e-01, 1.01143958e+00, 1.14406241e+00, 1.54612379e+00, + 2.11582828e+00, 2.96340381e+00, 3.51474071e+00, 4.18178301e+00, + 5.91221015e+00, 7.72175220e+00, 2.55327213e+01, 2.20727523e+02])}" + +algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=223.5668701887974, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=[0], model=ScalarModel(intercept=0.15183336484019694, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + 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relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=223.5668701887974, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=196.12406005340955, linear_terms=array([-428.74914009, 49.95239728]), square_terms=array([[475.26532472, -54.98930881], + [-54.98930881, 6.38449896]]), scale=array([ 9.45 , 198.13098]), shift=array([ 10.55 , 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=13, candidate_x=array([ 17.9817023 , 2037.53772189]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.008440716478759814, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=111.7834350943987, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 2, 3, 6, 7, 8, 10, 11]), model=ScalarModel(intercept=242.07061318310053, linear_terms=array([-471.89691667, 101.1889206 ]), square_terms=array([[465.88374262, -99.22450662], + [-99.22450662, 21.20996444]]), scale=array([ 9.45 , 99.06549]), shift=array([ 10.55 , 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=14, candidate_x=array([ 18.1092985 , 2136.60321189]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.007755830036420791, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 2, 3, 6, 7, 8, 10, 11]), old_indices_discarded=array([ 4, 5, 9, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=55.89171754719935, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 2, 3, 6, 7, 8, 10, 14]), model=ScalarModel(intercept=298.87379830095927, linear_terms=array([-576.79607872, 19.05034093]), square_terms=array([[562.44777715, -18.48262992], + [-18.48262992, 0.60886349]]), scale=array([ 9.45 , 49.532745]), shift=array([ 10.55 , 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=15, candidate_x=array([ 19.93053683, 2186.13595689]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.010921826812679862, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 2, 3, 6, 7, 8, 10, 14]), old_indices_discarded=array([ 4, 5, 9, 11, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=27.945858773599674, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 2, 7, 8, 14, 15]), model=ScalarModel(intercept=131.07369976203483, linear_terms=array([-204.19706036, 40.77897837]), square_terms=array([[163.90069793, -32.18422025], + [-32.18422025, 6.38059561]]), scale=array([ 9.45 , 24.7663725]), shift=array([ 10.55 , 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=16, candidate_x=array([ 20. , 2210.90232939]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.029581696796089584, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 2, 7, 8, 14, 15]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=13.972929386799837, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 2, 15, 16]), model=ScalarModel(intercept=0.9873239325348305, linear_terms=array([2.73907339, 0.01391224]), square_terms=array([[4.36743207e+00, 2.75767161e-02], + [2.75767161e-02, 2.41056737e-04]]), scale=array([ 8.12767586, 12.38318625]), shift=array([ 9.22767586, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=17, candidate_x=array([ 4.07901299, 2248.05188814]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-15.504804046764184, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 2, 15, 16]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=6.986464693399919, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 16, 17]), model=ScalarModel(intercept=0.3431675848933713, linear_terms=array([1.33281827, 0.32943084]), square_terms=array([[4.36179571, 1.27691822], + [1.27691822, 0.39380953]]), scale=array([5.0318793 , 6.19159313]), shift=array([ 6.1318793 , 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=18, candidate_x=array([ 3.1212204 , 2241.86029501]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-100.94273410330236, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 16, 17]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=3.4932323466999593, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=0.15183336484019672, linear_terms=array([-0.12431539, -0.0055439 ]), square_terms=array([[59.59210104, 2.34414471], + [ 2.34414471, 0.09228281]]), scale=3.4932323466999593, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=19, candidate_x=array([ 4.84213739, 2239.15952073]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-4.681921220989647, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=1.7466161733499797, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 18, 19]), model=ScalarModel(intercept=0.15183336484019672, linear_terms=array([-0.06103675, -0.00243685]), square_terms=array([[1.34115073e+01, 3.39655978e-01], + [3.39655978e-01, 8.60970305e-03]]), scale=1.7466161733499797, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=20, candidate_x=array([ 4.93589162, 2237.41495944]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.29114147897803394, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 19]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.8733080866749898, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 19, 20]), model=ScalarModel(intercept=0.15183336484019688, linear_terms=array([0.00582774, 0.00013542]), square_terms=array([[3.87857288e-01, 8.27620950e-04], + [8.27620950e-04, 2.06651665e-06]]), scale=0.8733080866749898, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=21, candidate_x=array([ 4.9609107, 2234.7953678]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.27824291525666955, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 19, 20]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.4366540433374949, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 20, 21]), model=ScalarModel(intercept=0.15183336484019702, linear_terms=array([-5.56309005e-04, -4.37454587e-06]), square_terms=array([[ 7.50279995e-02, -3.28466129e-05], + [-3.28466129e-05, 2.15202300e-08]]), scale=0.4366540433374949, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=22, candidate_x=array([ 4.97559407, 2236.10535447]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-5.330119577173148, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 20, 21]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.21832702166874746, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 21, 22]), model=ScalarModel(intercept=0.15183336484019683, linear_terms=array([-4.45403999e-03, 5.16279055e-05]), square_terms=array([[1.62881565e-02, 6.77124384e-06], + [6.77124384e-06, 2.92278248e-08]]), scale=0.21832702166874746, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=23, candidate_x=array([ 5.03176275, 2235.45039963]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-1.5692018274741517, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 21, 22]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.10916351083437373, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 22, 23]), model=ScalarModel(intercept=0.1518333648401971, linear_terms=array([7.05546057e-04, 2.78738861e-06]), square_terms=array([[4.43307710e-03, 7.04906076e-07], + [7.04906076e-07, 5.61544409e-10]]), scale=0.10916351083437373, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=24, candidate_x=array([ 4.95481938, 2235.55953562]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-1.2789338326888193, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 22, 23]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.054581755417186864, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 23, 24]), model=ScalarModel(intercept=0.151833364840197, linear_terms=array([ 2.00591466e-04, -4.01524035e-05]), square_terms=array([[1.12660455e-03, 2.72179550e-06], + [2.72179550e-06, 2.34943617e-08]]), scale=0.054581755417186864, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=25, candidate_x=array([ 4.96265819, 2235.72326083]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.7111299458870474, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 23, 24]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.027290877708593432, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 24, 25]), model=ScalarModel(intercept=0.15183336484019697, linear_terms=array([-4.80890903e-05, 3.50155615e-06]), square_terms=array([[ 2.91958192e-04, -1.28086238e-07], + [-1.28086238e-07, 2.58764850e-09]]), scale=0.027290877708593432, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=26, candidate_x=array([ 4.97659586, 2235.64140906]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-4.660612278998031, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 24, 25]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.013645438854296716, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 25, 26]), model=ScalarModel(intercept=0.15183336484019688, linear_terms=array([-0.0017766 , -0.00030364]), square_terms=array([[4.26905240e-05, 1.06728380e-06], + [1.06728380e-06, 1.30381617e-06]]), scale=0.013645438854296716, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=27, candidate_x=array([ 4.98564398, 2235.67082975]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.06310400218678887, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 25, 26]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.006822719427148358, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 26, 27]), model=ScalarModel(intercept=0.1518333648401969, linear_terms=array([ 3.88679056e-05, -1.31679513e-06]), square_terms=array([[ 1.84940973e-05, -1.00210163e-07], + [-1.00210163e-07, 6.96346430e-10]]), scale=0.006822719427148358, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=28, candidate_x=array([ 4.96548073, 2235.66733668]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-1.131884231761533, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 26, 27]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.003411359713574179, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 27, 28]), model=ScalarModel(intercept=0.151833364840197, linear_terms=array([ 0.00012663, -0.00067966]), square_terms=array([[ 4.68892805e-06, -6.61282339e-07], + [-6.61282339e-07, 5.43609914e-06]]), scale=0.003411359713574179, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=29, candidate_x=array([ 4.97154712, 2235.67205674]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.007935207946049206, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 27, 28]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.0017056798567870895, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 28, 29]), model=ScalarModel(intercept=0.15183336484019697, linear_terms=array([-6.40650622e-06, 1.55695709e-06]), square_terms=array([[ 1.1627052e-06, -3.2284109e-09], + [-3.2284109e-09, 6.7517919e-11]]), scale=0.0017056798567870895, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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model=ScalarModel(intercept=0.151833364840197, linear_terms=array([5.29250449e-06, 2.34437075e-06]), square_terms=array([[3.10374558e-07, 1.18891884e-09], + [1.18891884e-09, 8.34845812e-11]]), scale=0.0008528399283935448, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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[1.44466078e-11, 7.36990793e-13]]), scale=0.0002132099820983862, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=34, candidate_x=array([ 4.97205913, 2235.6686944 ]), index=34, x=array([ 4.97205913, 2235.6686944 ]), fval=0.15183333483370898, rho=0.07308056702929565, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33]), old_indices_discarded=array([], dtype=int32), step_length=0.00010660499104711433, relative_step_length=0.9999999999805003, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 35 entries., 'multistart_info': {'start_parameters': [array([ 4.97216547, 2235.66870189]), array([ 5.22187972, 3777.5556354 ])], 'local_optima': [{'solution_x': array([ 4.97205913, 2235.6686944 ]), 'solution_criterion': 0.15183333483370898, 'states': [State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=223.5668701887974, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), 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scale=array([ 9.45 , 198.13098]), shift=array([ 10.55 , 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=14, candidate_x=array([ 18.1092985 , 2136.60321189]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.007755830036420791, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 2, 3, 6, 7, 8, 10, 11]), old_indices_discarded=array([ 4, 5, 9, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=55.89171754719935, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 2, 3, 6, 7, 8, 10, 14]), model=ScalarModel(intercept=298.87379830095927, linear_terms=array([-576.79607872, 19.05034093]), square_terms=array([[562.44777715, -18.48262992], + [-18.48262992, 0.60886349]]), scale=array([ 9.45 , 49.532745]), shift=array([ 10.55 , 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=15, candidate_x=array([ 19.93053683, 2186.13595689]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.010921826812679862, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 2, 3, 6, 7, 8, 10, 14]), old_indices_discarded=array([ 4, 5, 9, 11, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=27.945858773599674, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 2, 7, 8, 14, 15]), model=ScalarModel(intercept=131.07369976203483, linear_terms=array([-204.19706036, 40.77897837]), square_terms=array([[163.90069793, -32.18422025], + [-32.18422025, 6.38059561]]), scale=array([ 9.45 , 24.7663725]), shift=array([ 10.55 , 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=16, candidate_x=array([ 20. , 2210.90232939]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.029581696796089584, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 2, 7, 8, 14, 15]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=13.972929386799837, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 2, 15, 16]), model=ScalarModel(intercept=0.9873239325348305, linear_terms=array([2.73907339, 0.01391224]), square_terms=array([[4.36743207e+00, 2.75767161e-02], + [2.75767161e-02, 2.41056737e-04]]), scale=array([ 8.12767586, 12.38318625]), shift=array([ 9.22767586, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=17, candidate_x=array([ 4.07901299, 2248.05188814]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-15.504804046764184, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 2, 15, 16]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=6.986464693399919, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 16, 17]), model=ScalarModel(intercept=0.3431675848933713, linear_terms=array([1.33281827, 0.32943084]), square_terms=array([[4.36179571, 1.27691822], + [1.27691822, 0.39380953]]), scale=array([5.0318793 , 6.19159313]), shift=array([ 6.1318793 , 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=18, candidate_x=array([ 3.1212204 , 2241.86029501]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-100.94273410330236, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 16, 17]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=3.4932323466999593, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=0.15183336484019672, linear_terms=array([-0.12431539, -0.0055439 ]), square_terms=array([[59.59210104, 2.34414471], + [ 2.34414471, 0.09228281]]), scale=3.4932323466999593, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=19, candidate_x=array([ 4.84213739, 2239.15952073]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-4.681921220989647, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=1.7466161733499797, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 18, 19]), model=ScalarModel(intercept=0.15183336484019672, linear_terms=array([-0.06103675, -0.00243685]), square_terms=array([[1.34115073e+01, 3.39655978e-01], + [3.39655978e-01, 8.60970305e-03]]), scale=1.7466161733499797, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=20, candidate_x=array([ 4.93589162, 2237.41495944]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.29114147897803394, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 19]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.8733080866749898, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 19, 20]), model=ScalarModel(intercept=0.15183336484019688, linear_terms=array([0.00582774, 0.00013542]), square_terms=array([[3.87857288e-01, 8.27620950e-04], + [8.27620950e-04, 2.06651665e-06]]), scale=0.8733080866749898, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=21, candidate_x=array([ 4.9609107, 2234.7953678]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.27824291525666955, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 19, 20]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.4366540433374949, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 20, 21]), model=ScalarModel(intercept=0.15183336484019702, linear_terms=array([-5.56309005e-04, -4.37454587e-06]), square_terms=array([[ 7.50279995e-02, -3.28466129e-05], + [-3.28466129e-05, 2.15202300e-08]]), scale=0.4366540433374949, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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model_indices=array([ 0, 21, 22]), model=ScalarModel(intercept=0.15183336484019683, linear_terms=array([-4.45403999e-03, 5.16279055e-05]), square_terms=array([[1.62881565e-02, 6.77124384e-06], + [6.77124384e-06, 2.92278248e-08]]), scale=0.21832702166874746, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], 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linear_terms=array([7.05546057e-04, 2.78738861e-06]), square_terms=array([[4.43307710e-03, 7.04906076e-07], + [7.04906076e-07, 5.61544409e-10]]), scale=0.10916351083437373, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], 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2.72179550e-06], + [2.72179550e-06, 2.34943617e-08]]), scale=0.054581755417186864, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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2235.66733668]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-1.131884231761533, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 26, 27]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.003411359713574179, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 27, 28]), model=ScalarModel(intercept=0.151833364840197, linear_terms=array([ 0.00012663, -0.00067966]), square_terms=array([[ 4.68892805e-06, -6.61282339e-07], + [-6.61282339e-07, 5.43609914e-06]]), scale=0.003411359713574179, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=29, candidate_x=array([ 4.97154712, 2235.67205674]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.007935207946049206, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 27, 28]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.0017056798567870895, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 28, 29]), model=ScalarModel(intercept=0.15183336484019697, linear_terms=array([-6.40650622e-06, 1.55695709e-06]), square_terms=array([[ 1.1627052e-06, -3.2284109e-09], + [-3.2284109e-09, 6.7517919e-11]]), scale=0.0017056798567870895, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), 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0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=14, candidate_x=array([ 18.76448582, 3944.94421122]), index=0, x=array([ 5.22187972, 3777.5556354 ]), fval=0.18001363343527702, rho=-0.009561092191515052, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 2, 3, 5, 7, 8, 9, 12]), old_indices_discarded=array([ 4, 6, 10, 11, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.22187972, 3777.5556354 ]), radius=94.43889088494339, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 2, 3, 5, 7, 8, 9, 14]), model=ScalarModel(intercept=298.4276026191575, linear_terms=array([-576.46526059, 6.81414363]), square_terms=array([[ 5.62685577e+02, -6.61818552e+00], + [-6.61818552e+00, 7.80289630e-02]]), scale=array([ 9.45 , 83.69428791]), shift=array([ 10.55 , 3777.5556354])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, 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fval=0.18001363343527702, rho=-0.011495911236178994, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 2, 3, 5, 7, 8, 9, 14]), old_indices_discarded=array([ 4, 6, 10, 11, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.22187972, 3777.5556354 ]), radius=47.219445442471695, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 2, 7, 8, 14, 15]), model=ScalarModel(intercept=125.45685337509687, linear_terms=array([-251.44927845, 30.0900477 ]), square_terms=array([[258.14181154, -30.52536684], + [-30.52536684, 3.63112862]]), scale=array([ 9.45 , 41.84714396]), shift=array([ 10.55 , 3777.5556354])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + 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accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 2, 7, 8, 14, 15]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.22187972, 3777.5556354 ]), radius=23.609722721235848, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 2, 15, 16]), model=ScalarModel(intercept=1.0792632152960797, linear_terms=array([ 2.4820159 , -0.22529719]), square_terms=array([[ 3.14659679, -0.3739566 ], + [-0.3739566 , 0.08599544]]), scale=array([ 9.45 , 20.92357198]), shift=array([ 10.55 , 3777.5556354])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], 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old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.22187972, 3777.5556354 ]), radius=11.804861360617924, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 16, 17]), model=ScalarModel(intercept=25.62010487127418, linear_terms=array([117.78318548, 58.17307866]), square_terms=array([[272.64693372, 135.19717176], + [135.19717176, 67.23744252]]), scale=array([ 7.29183286, 10.46178599]), shift=array([ 8.39183286, 3777.5556354 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.22187972, 3777.5556354 ]), radius=5.902430680308962, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=8.66476096620099, linear_terms=array([145.36114645, -0.79163954]), square_terms=array([[ 1.24486907e+03, -6.74089224e+00], + [-6.74089224e+00, 3.66585907e-02]]), scale=array([4.67638636, 5.23089299]), shift=array([ 5.77638636, 3777.5556354 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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State(trustregion=Region(center=array([ 5.2556554 , 3782.78652839]), radius=11.804861360617924, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 16, 17, 18, 19]), model=ScalarModel(intercept=0.4447192260530918, linear_terms=array([3.28591072, 0.7575156 ]), square_terms=array([[238.5254968 , 72.82153083], + [ 72.82153083, 22.31236539]]), scale=array([ 7.30872069, 10.46178599]), shift=array([ 8.40872069, 3782.78652839])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + 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radius=5.902430680308962, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 17, 18, 19, 20]), model=ScalarModel(intercept=2.0924308511078555, linear_terms=array([64.90826608, -1.00041399]), square_terms=array([[ 1.10459211e+03, -1.69795324e+01], + [-1.69795324e+01, 2.61125536e-01]]), scale=array([4.6932742 , 5.23089299]), shift=array([ 5.7932742 , 3782.78652839])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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upper=array([ 20., 10000.]))), model_indices=array([ 0, 18, 19, 20, 21]), model=ScalarModel(intercept=0.60991129558898, linear_terms=array([-18.6614341 , 0.97529584]), square_terms=array([[405.20779142, -21.11615795], + [-21.11615795, 1.10050394]]), scale=2.951215340154481, shift=array([ 5.2556554 , 3782.78652839])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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model=ScalarModel(intercept=0.41578081033668957, linear_terms=array([-7.17900081, 0.24007703]), square_terms=array([[108.78543013, -3.63193691], + [ -3.63193691, 0.12126679]]), scale=1.4756076700772405, shift=array([ 5.2556554 , 3782.78652839])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], 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linear_terms=array([-5.28954140e-03, 1.23810918e-05]), square_terms=array([[ 1.91842376e-01, -2.99622408e-05], + [-2.99622408e-05, 9.50955359e-09]]), scale=0.7378038350386202, shift=array([ 5.30368819, 3781.308501 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], 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square_terms=array([[ 1.18917531e+02, -1.26160659e+00], + [-1.26160659e+00, 1.33856432e-02]]), scale=1.4756076700772405, shift=array([ 5.32391471, 3780.570694 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=25, candidate_x=array([ 5.2597755 , 3779.09568551]), index=24, x=array([ 5.32391471, 3780.570694 ]), fval=0.17821555165650368, rho=-0.010118979823408489, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 19, 22, 23, 24]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.32391471, 3780.570694 ]), radius=0.7378038350386202, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 19, 22, 23, 24, 25]), model=ScalarModel(intercept=0.17820071640857027, linear_terms=array([-1.27240763e-05, 7.59106514e-06]), square_terms=array([[1.89453695e-01, 2.97857263e-07], + [2.97857263e-07, 1.58135601e-09]]), scale=0.7378038350386202, shift=array([ 5.32391471, 3780.570694 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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scale=1.4756076700772405, shift=array([ 5.32396542, 3779.83289016])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=27, candidate_x=array([ 5.32393177, 3778.35728249]), index=27, x=array([ 5.32393177, 3778.35728249]), fval=0.17818383067605373, rho=1.4687207826788418, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 19, 22, 23, 24, 25, 26]), old_indices_discarded=array([], dtype=int32), step_length=1.475607671493286, relative_step_length=1.0000000009596355, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.32393177, 3778.35728249]), radius=2.951215340154481, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 18, 19, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.23681559100061073, linear_terms=array([ 7.52400864, -0.03697853]), square_terms=array([[ 4.81922118e+02, -2.37216170e+00], + [-2.37216170e+00, 1.16774340e-02]]), scale=2.951215340154481, shift=array([ 5.32393177, 3778.35728249])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=28, candidate_x=array([ 5.26333048, 3775.40633 ]), index=27, x=array([ 5.32393177, 3778.35728249]), fval=0.17818383067605373, rho=-0.008422541073735482, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 19, 22, 23, 24, 25, 26, 27]), old_indices_discarded=array([21]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.32393177, 3778.35728249]), radius=1.4756076700772405, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 19, 22, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=0.17816465874227283, linear_terms=array([1.88966570e-04, 2.76343183e-05]), square_terms=array([[ 7.56470495e-01, -1.03969061e-04], + [-1.03969061e-04, 1.75995384e-08]]), scale=1.4756076700772405, shift=array([ 5.32393177, 3778.35728249])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=29, candidate_x=array([ 5.32336038, 3776.88167488]), index=29, x=array([ 5.32336038, 3776.88167488]), fval=0.17816777800820685, rho=0.5798905880546184, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 19, 22, 23, 24, 25, 26, 27, 28]), old_indices_discarded=array([], dtype=int32), step_length=1.4756077161109993, relative_step_length=1.0000000311964756, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.32336038, 3776.88167488]), radius=2.951215340154481, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 22, 23, 24, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=0.17814522233392394, linear_terms=array([1.70335715e-04, 5.34309034e-05]), square_terms=array([[ 3.02597147e+00, -4.39921704e-04], + [-4.39921704e-04, 7.75356986e-08]]), scale=2.951215340154481, shift=array([ 5.32336038, 3776.88167488])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=30, candidate_x=array([ 5.32276521, 3773.9304596 ]), index=30, x=array([ 5.32276521, 3773.9304596 ]), fval=0.1781338283759018, rho=0.6351225603827869, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 22, 23, 24, 25, 26, 27, 28, 29]), old_indices_discarded=array([18, 19, 21]), step_length=2.9512153448295146, relative_step_length=1.0000000015841046, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.32276521, 3773.9304596 ]), radius=5.902430680308962, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 22, 24, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=0.22227385671774208, linear_terms=array([ 8.27516735e-01, -9.46109851e-05]), square_terms=array([[ 7.75302897e+00, -1.67049083e-03], + [-1.67049083e-03, 3.94188026e-07]]), scale=array([4.7268291 , 5.23089299]), shift=array([ 5.8268291, 3773.9304596])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=31, candidate_x=array([ 5.32129426, 3768.6995666 ]), index=31, x=array([ 5.32129426, 3768.6995666 ]), fval=0.17807394453160602, rho=0.7154013322715937, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 22, 24, 25, 26, 27, 28, 29, 30]), old_indices_discarded=array([17, 18, 19, 20, 21, 23]), step_length=5.230893201328401, relative_step_length=0.8862269604925865, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.32129426, 3768.6995666 ]), radius=11.804861360617924, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 25, 26, 27, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=0.5208780387285807, linear_terms=array([1.12839055, 0.01109011]), square_terms=array([[1.51827200e+00, 2.11161638e-02], + [2.11161638e-02, 4.80103617e-04]]), scale=array([ 7.34154012, 10.46178599]), shift=array([ 8.44154012, 3768.6995666 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=33, candidate_x=array([2.88314895e+00, 3.77916135e+03]), index=31, x=array([ 5.32129426, 3768.6995666 ]), fval=0.17807394453160602, rho=-248.64296126941807, accepted=False, new_indices=array([32]), old_indices_used=array([ 0, 25, 26, 27, 28, 29, 30, 31]), old_indices_discarded=array([16, 17, 18, 19, 20, 21, 22, 23, 24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.32129426, 3768.6995666 ]), radius=5.902430680308962, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 22, 25, 27, 28, 29, 30, 31, 33]), model=ScalarModel(intercept=0.9998902148657269, linear_terms=array([15.98904319, 0.08221525]), square_terms=array([[1.55477573e+02, 8.04599038e-01], + [8.04599038e-01, 4.16601478e-03]]), scale=array([4.72609362, 5.23089299]), shift=array([ 5.82609362, 3768.6995666 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=34, candidate_x=array([ 5.31561276, 3773.9304596 ]), index=31, x=array([ 5.32129426, 3768.6995666 ]), fval=0.17807394453160602, rho=-0.031700914237791734, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 22, 25, 27, 28, 29, 30, 31, 33]), old_indices_discarded=array([17, 18, 19, 20, 21, 23, 24, 26, 32]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.32129426, 3768.6995666 ]), radius=2.951215340154481, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 25, 27, 28, 29, 30, 31, 33, 34]), model=ScalarModel(intercept=0.17875368787838072, linear_terms=array([-0.35344824, -0.00160514]), square_terms=array([[6.02790036e+01, 2.32207919e-01], + [2.32207919e-01, 8.94985816e-04]]), scale=2.951215340154481, shift=array([ 5.32129426, 3768.6995666 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=35, candidate_x=array([ 5.3272299 , 3771.65082671]), index=31, x=array([ 5.32129426, 3768.6995666 ]), fval=0.17807394453160602, rho=-0.056632647014796866, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 25, 27, 28, 29, 30, 31, 33, 34]), old_indices_discarded=array([17, 19, 22, 23, 24, 26]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.32129426, 3768.6995666 ]), radius=1.4756076700772405, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([28, 30, 31, 34, 35]), model=ScalarModel(intercept=0.1780820131294612, linear_terms=array([1.86739648e-03, 1.59075457e-05]), square_terms=array([[ 7.41957927e-01, -1.22029600e-04], + [-1.22029600e-04, 2.23518326e-08]]), scale=1.4756076700772405, shift=array([ 5.32129426, 3768.6995666 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=36, candidate_x=array([ 5.31733777, 3767.22395957]), index=36, x=array([ 5.31733777, 3767.22395957]), fval=0.17803012332934734, rho=2.3606102276820975, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([28, 30, 31, 34, 35]), old_indices_discarded=array([], dtype=int32), step_length=1.4756123434937174, relative_step_length=1.0000031671131642, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.31733777, 3767.22395957]), radius=2.951215340154481, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 17, 28, 29, 30, 31, 34, 35, 36]), model=ScalarModel(intercept=0.18731061364611515, linear_terms=array([-2.78859371, -0.01533278]), square_terms=array([[4.21276635e+02, 2.31394055e+00], + [2.31394055e+00, 1.27109474e-02]]), scale=2.951215340154481, shift=array([ 5.31733777, 3767.22395957])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=37, candidate_x=array([ 5.32066257, 3770.17523664]), index=36, x=array([ 5.31733777, 3767.22395957]), fval=0.17803012332934734, rho=-0.007038929660613454, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 28, 29, 30, 31, 34, 35, 36]), old_indices_discarded=array([22, 23, 24, 25, 26, 27, 33]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.31733777, 3767.22395957]), radius=1.4756076700772405, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([30, 31, 34, 35, 36, 37]), model=ScalarModel(intercept=0.17803733457425416, linear_terms=array([4.67596486e-03, 1.97718973e-05]), square_terms=array([[ 6.86260197e-01, -1.28093076e-04], + [-1.28093076e-04, 2.65774935e-08]]), scale=1.4756076700772405, shift=array([ 5.31733777, 3767.22395957])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=38, candidate_x=array([ 5.30700832, 3765.7483538 ]), index=38, x=array([ 5.30700832, 3765.7483538 ]), fval=0.17798168290601366, rho=1.3244637262751773, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([30, 31, 34, 35, 36, 37]), old_indices_discarded=array([], dtype=int32), step_length=1.4756419211239096, relative_step_length=1.000023211485928, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30700832, 3765.7483538 ]), radius=2.951215340154481, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([17, 28, 30, 31, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=0.1853840653395654, linear_terms=array([-2.53255399, -0.00530967]), square_terms=array([[4.34594855e+02, 9.14605265e-01], + [9.14605265e-01, 1.92494707e-03]]), scale=2.951215340154481, shift=array([ 5.30700832, 3765.7483538 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=39, candidate_x=array([ 5.33041694, 3762.7971812 ]), index=38, x=array([ 5.30700832, 3765.7483538 ]), fval=0.17798168290601366, rho=-0.013094695227833497, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([17, 28, 30, 31, 34, 35, 36, 37, 38]), old_indices_discarded=array([ 0, 22, 25, 26, 27, 29, 33]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30700832, 3765.7483538 ]), radius=1.4756076700772405, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([31, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=0.17798153266437608, linear_terms=array([2.67703532e-03, 1.84532996e-05]), square_terms=array([[ 6.87387208e-01, -1.22416627e-04], + [-1.22416627e-04, 2.49928337e-08]]), scale=1.4756076700772405, shift=array([ 5.30700832, 3765.7483538 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=40, candidate_x=array([ 5.30099892, 3764.27274717]), index=38, x=array([ 5.30700832, 3765.7483538 ]), fval=0.17798168290601366, rho=-1.7785842210147218, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([31, 35, 36, 37, 38, 39]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30700832, 3765.7483538 ]), radius=0.7378038350386202, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([31, 36, 38, 39, 40]), model=ScalarModel(intercept=0.17800937260106672, linear_terms=array([5.95288503e-06, 5.30714257e-06]), square_terms=array([[ 1.72058207e-01, -2.92041969e-05], + [-2.92041969e-05, 7.60304447e-09]]), scale=0.7378038350386202, shift=array([ 5.30700832, 3765.7483538 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=41, candidate_x=array([ 5.30685756, 3765.01054998]), index=41, x=array([ 5.30685756, 3765.01054998]), fval=0.17796989499069643, rho=2.2212294285075673, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 36, 38, 39, 40]), old_indices_discarded=array([], dtype=int32), step_length=0.7378038354799815, relative_step_length=1.0000000005982095, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30685756, 3765.01054998]), radius=1.4756076700772405, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([31, 35, 36, 37, 38, 39, 40, 41]), model=ScalarModel(intercept=0.17799379519456676, linear_terms=array([9.04133766e-04, 1.68120998e-05]), square_terms=array([[ 6.87741442e-01, -1.22064373e-04], + [-1.22064373e-04, 2.53913004e-08]]), scale=1.4756076700772405, shift=array([ 5.30685756, 3765.01054998])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=42, candidate_x=array([ 5.30465582, 3763.53494268]), index=42, x=array([ 5.30465582, 3763.53494268]), fval=0.1779588833874623, rho=0.6269056309320292, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 35, 36, 37, 38, 39, 40, 41]), old_indices_discarded=array([], dtype=int32), step_length=1.4756089451393082, relative_step_length=1.0000008640928708, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30465582, 3763.53494268]), radius=2.951215340154481, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([17, 31, 36, 37, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=0.17947899803072848, linear_terms=array([-1.1742621 , 0.00126434]), square_terms=array([[ 4.44709273e+02, -4.57981892e-01], + [-4.57981892e-01, 4.71712559e-04]]), scale=2.951215340154481, shift=array([ 5.30465582, 3763.53494268])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=43, candidate_x=array([ 5.30940925, 3760.58372087]), index=43, x=array([ 5.30940925, 3760.58372087]), fval=0.17791553246894198, rho=0.027004451558225083, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([17, 31, 36, 37, 38, 39, 40, 41, 42]), old_indices_discarded=array([ 0, 28, 29, 30, 34, 35]), step_length=2.9512256285033613, relative_step_length=1.000003486139673, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=1.4756076700772405, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([17, 36, 38, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=0.1783256875306959, linear_terms=array([-3.07287421e-01, -8.21318424e-05]), square_terms=array([[1.10555074e+02, 3.40646813e-02], + [3.40646813e-02, 1.05016887e-05]]), scale=1.4756076700772405, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=44, candidate_x=array([ 5.31396537, 3759.10811454]), index=43, x=array([ 5.30940925, 3760.58372087]), fval=0.17791553246894198, rho=-0.006804942702733265, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([17, 36, 38, 39, 40, 41, 42, 43]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=0.7378038350386202, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([39, 42, 43, 44]), model=ScalarModel(intercept=0.17792080695215554, linear_terms=array([2.06155976e-03, 1.11830329e-05]), square_terms=array([[ 1.70363006e-01, -3.82501682e-05], + [-3.82501682e-05, 1.24667902e-08]]), scale=0.7378038350386202, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=45, candidate_x=array([ 5.30031607, 3759.84591906]), index=43, x=array([ 5.30940925, 3760.58372087]), fval=0.17791553246894198, rho=-2.0310571357145926, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([39, 42, 43, 44]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=0.3689019175193101, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([43, 44, 45]), model=ScalarModel(intercept=0.17791553246894187, linear_terms=array([-1.17918083e-03, -3.42897503e-06]), square_terms=array([[ 4.43546197e-02, -8.57949761e-06], + [-8.57949761e-06, 4.04624924e-09]]), scale=0.3689019175193101, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=46, candidate_x=array([ 5.31928716, 3760.95262089]), index=43, x=array([ 5.30940925, 3760.58372087]), fval=0.17791553246894198, rho=-2.6149368631946275, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 44, 45]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=0.18445095875965506, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([43, 45, 46]), model=ScalarModel(intercept=0.17791553246894204, linear_terms=array([ 1.84501111e-03, -3.17202089e-05]), square_terms=array([[ 1.06687234e-02, -2.43582255e-06], + [-2.43582255e-06, 7.19700602e-08]]), scale=0.18445095875965506, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=47, candidate_x=array([ 5.27764604, 3760.76817905]), index=43, x=array([ 5.30940925, 3760.58372087]), fval=0.17791553246894198, rho=-1.4433428348566995, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 45, 46]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=0.09222547937982753, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([43, 46, 47]), model=ScalarModel(intercept=0.17791553246894176, linear_terms=array([-2.17795159e-04, 1.46733396e-05]), square_terms=array([[ 2.85029042e-03, -2.85696802e-06], + [-2.85696802e-06, 7.09340428e-09]]), scale=0.09222547937982753, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=48, candidate_x=array([ 5.31632881, 3760.49148841]), index=43, x=array([ 5.30940925, 3760.58372087]), fval=0.17791553246894198, rho=-0.9406271284117825, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 46, 47]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=0.046112739689913765, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([43, 47, 48]), model=ScalarModel(intercept=0.17791553246894187, linear_terms=array([-3.39208594e-04, -3.23222752e-05]), square_terms=array([[7.54306993e-04, 2.98728975e-06], + [2.98728975e-06, 3.90562555e-08]]), scale=0.046112739689913765, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=0.023056369844956882, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([43, 48, 49]), model=ScalarModel(intercept=0.17791553246894187, linear_terms=array([6.09438346e-05, 1.13529285e-06]), square_terms=array([[ 1.66872702e-04, -1.24585747e-07], + [-1.24585747e-07, 1.17301795e-09]]), scale=0.023056369844956882, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=50, candidate_x=array([ 5.30103082, 3760.56067076]), index=43, x=array([ 5.30940925, 3760.58372087]), fval=0.17791553246894198, rho=-3.755110924764953, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 48, 49]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=0.011528184922478441, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([43, 49, 50]), model=ScalarModel(intercept=0.17791553246894215, linear_terms=array([ 0.00048585, -0.00019408]), square_terms=array([[ 5.31813583e-05, -5.75413054e-06], + [-5.75413054e-06, 2.79836435e-06]]), scale=0.011528184922478441, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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10000.]))), model_indices=array([43, 50, 51]), model=ScalarModel(intercept=0.17791553246894196, linear_terms=array([-4.88151251e-05, 9.00611126e-06]), square_terms=array([[ 1.09589251e-05, -5.41537073e-08], + [-5.41537073e-08, 1.87668792e-09]]), scale=0.005764092461239221, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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model=ScalarModel(intercept=0.17791553246894182, linear_terms=array([2.12658543e-05, 1.52919564e-04]), square_terms=array([[ 2.67941071e-06, -5.49830816e-08], + [-5.49830816e-08, 3.68065340e-07]]), scale=0.0028820462306196103, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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5.01858339e-06, -2.73111673e-06]), square_terms=array([[ 6.71301844e-07, -6.77682479e-09], + [-6.77682479e-09, 2.94207799e-10]]), scale=0.0014410231153098052, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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-1.81884533e-09], + [-1.81884533e-09, 3.67816759e-11]]), scale=0.0007205115576549026, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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scale=0.0003602557788274513, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), 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candidate_index=58, candidate_x=array([ 5.30949887, 3760.58372977]), index=43, x=array([ 5.30940925, 3760.58372087]), fval=0.17791553246894198, rho=-7.4227875403825125, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 56, 57]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=4.503197235343141e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([43, 57, 58]), model=ScalarModel(intercept=0.17791553246894215, linear_terms=array([2.49763551e-07, 8.75365248e-07]), square_terms=array([[5.97075122e-10, 9.86414808e-11], + [9.86414808e-11, 3.99870468e-11]]), scale=4.503197235343141e-05, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=59, candidate_x=array([ 5.3093969 , 3760.58367756]), index=43, x=array([ 5.30940925, 3760.58372087]), fval=0.17791553246894198, rho=-0.05588336662878231, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 57, 58]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=2.2515986176715705e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([43, 58, 59]), model=ScalarModel(intercept=0.17791553246894198, linear_terms=array([ 1.75952660e-07, -7.65912652e-08]), square_terms=array([[ 1.54804745e-10, -2.72364838e-12], + [-2.72364838e-12, 1.48003207e-13]]), scale=2.2515986176715705e-05, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=60, candidate_x=array([ 5.3093886 , 3760.58372985]), index=43, x=array([ 5.30940925, 3760.58372087]), fval=0.17791553246894198, rho=-0.4501440029410156, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 58, 59]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=1.1257993088357853e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([43, 59, 60]), model=ScalarModel(intercept=0.17791553246894212, linear_terms=array([-4.69738998e-08, 1.72050290e-10]), square_terms=array([[ 3.44298654e-11, -7.24102661e-15], + [-7.24102661e-15, 1.66529426e-18]]), scale=1.1257993088357853e-05, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=61, candidate_x=array([ 5.30942051, 3760.58372084]), index=61, x=array([ 5.30942051, 3760.58372084]), fval=0.17791548552127476, rho=0.9998013126948817, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([43, 59, 60]), old_indices_discarded=array([], dtype=int32), step_length=1.1257993087530534e-05, relative_step_length=0.9999999999265128, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 62 entries., 'history': {'params': [{'CRRA': 5.221879724992187, 'BeqFac': 3777.5556353977354}, {'CRRA': 1.1, 'BeqFac': 3476.9035708167335}, {'CRRA': 20.0, 'BeqFac': 3688.8944111418195}, {'CRRA': 1.1, 'BeqFac': 4084.9216049500956}, {'CRRA': 19.360304400268685, 'BeqFac': 4112.332787046263}, {'CRRA': 20.0, 'BeqFac': 3463.4160733070307}, {'CRRA': 19.863685373375603, 'BeqFac': 3442.778483749208}, {'CRRA': 1.1, 'BeqFac': 3921.209349974723}, {'CRRA': 20.0, 'BeqFac': 3917.345783490104}, {'CRRA': 20.0, 'BeqFac': 4104.193678537616}, {'CRRA': 1.6231245796393248, 'BeqFac': 4112.332787046263}, {'CRRA': 7.312867713980317, 'BeqFac': 3442.778483749208}, {'CRRA': 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36.06780719989911, 37.38965609995648, 38.56443569995463, 39.75320369983092, 40.928570999763906, 42.1049975999631, 43.29117719968781, 44.52116549992934, 45.709984899964184, 47.021010799799114, 48.199433099944144, 49.37279489962384, 50.552747999783605, 51.74405709980056, 52.95074549969286, 54.13156419992447, 55.446532499976456, 56.624599299859256, 57.809363099746406, 58.98130919970572, 60.15864789998159, 61.33934499975294, 62.50395749974996], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50]}}], 'exploration_sample': array([[4.97216547e+00, 2.23566870e+03], + [5.82500000e+00, 7.50000000e+03], + [4.64375000e+00, 3.12500000e+03], + [7.59687500e+00, 7.18750000e+03], + [7.00625000e+00, 1.87500000e+03], + [8.18750000e+00, 3.75000000e+03], + [9.36875000e+00, 5.62500000e+03], + [1.05500000e+01, 5.00000000e+03], + [1.17312500e+01, 4.37500000e+03], + [1.23218750e+01, 9.68750000e+03], + [1.29125000e+01, 1.25000000e+03], + [1.40937500e+01, 8.12500000e+03], + [1.52750000e+01, 2.50000000e+03], + [1.64562500e+01, 6.87500000e+03], + [1.70468750e+01, 2.18750000e+03], + [1.76375000e+01, 8.75000000e+03], + [1.88187500e+01, 6.25000000e+02], + [3.46250000e+00, 6.25000000e+03], + [2.87187500e+00, 4.68750000e+03], + [2.28125000e+00, 9.37500000e+03]]), 'exploration_results': array([1.51833365e-01, 2.18154716e-01, 2.63875842e-01, 3.77381203e-01, + 3.83108040e-01, 4.55042643e-01, 5.57986156e-01, 6.96479632e-01, + 8.83084679e-01, 1.01143958e+00, 1.14406241e+00, 1.54612379e+00, + 2.11582828e+00, 2.96340381e+00, 3.51474071e+00, 4.18178301e+00, + 5.91221015e+00, 7.72175220e+00, 2.55327213e+01, 2.20727523e+02])}}" diff --git a/src/estimark/content/tables/min/WealthPortfolioBeta_estimate_results.csv b/src/estimark/content/tables/min/WealthPortfolioBeta_estimate_results.csv new file mode 100644 index 0000000..1662749 --- /dev/null +++ b/src/estimark/content/tables/min/WealthPortfolioBeta_estimate_results.csv @@ -0,0 +1,19657 @@ +CRRA,4.844436801414261 + +WealthShare,0.3460128282561451 + +DiscFac,0.9323589063936805 + +time_to_estimate,217.95789170265198 + +params,"{'CRRA': 4.844436801414261, 'WealthShare': 0.3460128282561451, 'DiscFac': 0.9323589063936805}" + +criterion,0.17254703939389457 + +start_criterion,0.23891445185117913 + +start_params,"{'CRRA': 5.338780774481047, 'WealthShare': 0.17065528804872485, 'DiscFac': 1.0}" + +algorithm,multistart_tranquilo_ls + +direction,minimize + +n_free,3 + +message,Absolute criterion change smaller than tolerance. + +success, + +n_criterion_evaluations, + +n_derivative_evaluations, + +n_iterations, + +history,"{'params': [{'CRRA': 5.301397603427972, 'WealthShare': 0.35179042920828707, 'DiscFac': 0.9217000493025207}, {'CRRA': 4.877555010789218, 'WealthShare': 0.01, 'DiscFac': 1.0004206381225442}, {'CRRA': 5.726445053953837, 'WealthShare': 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58.923332899808884, 58.96633999980986, 59.00750799989328, 59.05656659975648, 59.08834679983556, 59.132794899865985, 60.22101899981499, 61.250512999948114, 62.25834989966825, 63.258237699978054, 64.29787159990519, 65.34936949983239, 66.36967970011756, 67.51696019992232, 68.52077089995146, 69.51590960007161, 70.63079519988969, 70.66824259981513, 70.70960419997573, 70.75069979997352, 70.79406809993088, 70.83469379972667, 70.87575369980186, 70.91594929993153, 70.95578130008653, 70.99587279977277, 71.0371133997105, 71.07846489967778, 72.16604539984837], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 51, 52, 53, 54, 55, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 68]}" + +convergence_report,"{'one_step': {'relative_criterion_change': 0.003307242007129943, 'relative_params_change': 0.03956657811621874, 'absolute_criterion_change': 0.0005706548168893932, 'absolute_params_change': 0.19167597945625867}, 'five_steps': {'relative_criterion_change': 0.014922806335674835, 'relative_params_change': 0.0806704377224931, 'absolute_criterion_change': 0.0025748860526691453, 'absolute_params_change': 0.36818918796590866}}" + +multistart_info,"{'start_parameters': [{'CRRA': 5.338780774481047, 'WealthShare': 0.17065528804872487, 'DiscFac': 1.0}, {'CRRA': 4.472854104495062, 'WealthShare': 0.457530286489798, 'DiscFac': 0.8472036933750238}, {'CRRA': 5.301397603427972, 'WealthShare': 0.35179042920828707, 'DiscFac': 0.9217000493025207}], 'local_optima': [Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 6.023e-05 0.0008401 +relative_params_change 0.0007429 0.01168 +absolute_criterion_change 1.055e-05 0.0001471 +absolute_params_change 0.0002607 0.005784 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 4.685e-07* 0.0001119 +relative_params_change 2.549e-06* 0.0008924 +absolute_criterion_change 8.11e-08* 1.938e-05 +absolute_params_change 5.392e-06* 0.0004772 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 1.731e-05 0.0001779 +relative_params_change 8.502e-05 0.003461 +absolute_criterion_change 2.988e-06* 3.07e-05 +absolute_params_change 3.236e-05 0.00824 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 5.338780774481047, 'WealthShare': 0.17065528804872485, 'DiscFac': 1.0}, {'CRRA': 3.4625, 'WealthShare': 0.6225, 'DiscFac': 0.7250000000000001}, {'CRRA': 8.1875, 'WealthShare': 0.3775, 'DiscFac': 0.875}, {'CRRA': 10.549999999999999, 'WealthShare': 0.5, 'DiscFac': 0.8}, {'CRRA': 14.684375, 'WealthShare': 0.346875, 'DiscFac': 0.8562500000000001}, {'CRRA': 9.959375, 'WealthShare': 0.101875, 'DiscFac': 1.00625}, {'CRRA': 19.409375, 'WealthShare': 0.591875, 'DiscFac': 0.70625}, {'CRRA': 13.503124999999999, 'WealthShare': 0.653125, 'DiscFac': 0.51875}, {'CRRA': 2.871875, 'WealthShare': 0.469375, 'DiscFac': 0.78125}, {'CRRA': 9.368749999999999, 'WealthShare': 0.56125, 'DiscFac': 0.6125}, {'CRRA': 18.81875, 'WealthShare': 0.07125, 'DiscFac': 0.9125000000000001}, {'CRRA': 17.046875, 'WealthShare': 0.224375, 'DiscFac': 0.6312500000000001}, {'CRRA': 15.274999999999999, 'WealthShare': 0.255, 'DiscFac': 0.65}, {'CRRA': 12.9125, 'WealthShare': 0.1325, 'DiscFac': 1.0250000000000001}, {'CRRA': 11.73125, 'WealthShare': 0.43875, 'DiscFac': 0.5375}, {'CRRA': 18.228125, 'WealthShare': 0.408125, 'DiscFac': 0.96875}, {'CRRA': 7.00625, 'WealthShare': 0.19375, 'DiscFac': 0.6875}, {'CRRA': 6.415625, 'WealthShare': 0.285625, 'DiscFac': 0.59375}, {'CRRA': 4.053125, 'WealthShare': 0.16312500000000002, 'DiscFac': 0.8187500000000001}, {'CRRA': 5.234375, 'WealthShare': 0.836875, 'DiscFac': 0.55625}, {'CRRA': 16.45625, 'WealthShare': 0.68375, 'DiscFac': 0.9875}, {'CRRA': 14.093749999999998, 'WealthShare': 0.80625, 'DiscFac': 0.7625000000000001}, {'CRRA': 4.64375, 'WealthShare': 0.31625000000000003, 'DiscFac': 1.0625}, {'CRRA': 15.865624999999998, 'WealthShare': 0.775625, 'DiscFac': 0.89375}, {'CRRA': 17.6375, 'WealthShare': 0.8674999999999999, 'DiscFac': 0.575}, {'CRRA': 7.596874999999999, 'WealthShare': 0.714375, 'DiscFac': 0.9312500000000001}, {'CRRA': 5.824999999999999, 'WealthShare': 0.745, 'DiscFac': 0.9500000000000001}, {'CRRA': 8.778125, 'WealthShare': 0.898125, 'DiscFac': 0.66875}, {'CRRA': 2.28125, 'WealthShare': 0.92875, 'DiscFac': 0.8375}, {'CRRA': 12.321874999999999, 'WealthShare': 0.959375, 'DiscFac': 1.08125}], 'exploration_results': array([2.42222292e-01, 3.03201302e-01, 3.75385332e-01, 3.98577685e-01, + 4.64539853e-01, 6.05168236e-01, 6.05724112e-01, 7.88383230e-01, + 8.39981988e-01, 1.04605604e+00, 1.22782446e+00, 1.48069565e+00, + 1.56893759e+00, 1.77192100e+00, 1.95139780e+00, 2.44243013e+00, + 2.75221387e+00, 2.82714102e+00, 3.02109383e+00, 6.88603970e+00, + 8.55954606e+00, 9.85239944e+00, 1.14488917e+01, 1.20953777e+01, + 1.29729655e+01, 1.58640798e+01, 3.65763661e+01, 5.66287991e+01, + 4.39985749e+02, 5.91890647e+02])}" + +algorithm_output,"{'states': [State(trustregion=Region(center=array([5.3013976 , 0.35179043, 0.92170005]), radius=0.5301397603427972, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=[0], 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old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([5.3013976 , 0.35179043, 0.92170005]), radius=0.5301397603427972, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=2.973876171490377, linear_terms=array([ 0.72542682, 14.35685233, 14.83307135]), square_terms=array([[ 0.11413661, 1.93082302, 1.94450878], + [ 1.93082302, 36.39381604, 37.51674902], + [ 1.94450878, 37.51674902, 38.95260216]]), scale=array([0.42729051, 0.38454047, 0.3 ]), shift=array([5.3013976 , 0.39454047, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), 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fval=0.18834408422290103, rho=-0.29383208425185714, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13]), old_indices_discarded=array([ 6, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.3013976 , 0.35179043, 0.92170005]), radius=0.1325349400856993, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 7, 8, 10, 12, 13, 14]), model=ScalarModel(intercept=6.9052436115388875, linear_terms=array([-0.69117403, 7.16500457, 9.13260536]), square_terms=array([[ 0.03812681, -0.36969498, -0.4746088 ], + [-0.36969498, 3.7854496 , 4.79829205], + [-0.4746088 , 4.79829205, 6.13906768]]), scale=0.1325349400856993, shift=array([5.3013976 , 0.35179043, 0.92170005])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 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upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.16542657032258398, linear_terms=array([0.01630779, 0.01475797, 0.02073112]), square_terms=array([[ 0.05951927, -0.12255125, -0.23582986], + [-0.12255125, 0.26987927, 0.50459186], + [-0.23582986, 0.50459186, 0.96563619]]), scale=0.06626747004284965, shift=array([5.3013976 , 0.35179043, 0.92170005])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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candidate_index=28, candidate_x=array([5.23292383, 0.33864579, 0.91062871]), index=0, x=array([5.3013976 , 0.35179043, 0.92170005]), fval=0.18834408422290103, rho=-4.217700932825573, accepted=False, new_indices=array([16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), old_indices_used=array([ 0, 14, 15]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.3013976 , 0.35179043, 0.92170005]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.16782671230459395, linear_terms=array([ 0.0009187 , -0.00784055, -0.02621131]), square_terms=array([[4.57181704e-05, 1.25022351e-03, 2.32119020e-03], + [1.25022351e-03, 9.27555996e-02, 1.94331965e-01], + [2.32119020e-03, 1.94331965e-01, 4.14037852e-01]]), scale=0.033133735021424825, shift=array([5.3013976 , 0.35179043, 0.92170005])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + 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relative_step_length=1.000149803912295, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.29121744, 0.32407875, 0.9367533 ]), radius=0.06626747004284965, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 18, 20, 21, 22, 24, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=0.16770628802381632, linear_terms=array([-0.00491028, -0.01260532, -0.02110318]), square_terms=array([[1.33508452e-03, 2.20746795e-02, 4.37252047e-02], + [2.20746795e-02, 3.94713894e-01, 7.95682111e-01], + [4.37252047e-02, 7.95682111e-01, 1.63156707e+00]]), scale=0.06626747004284965, shift=array([5.29121744, 0.32407875, 0.9367533 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 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linear_terms=array([ 0.00059146, -0.004534 , -0.01522606]), square_terms=array([[3.03577735e-05, 5.05858062e-04, 7.12748102e-04], + [5.05858062e-04, 9.30360296e-02, 1.93666384e-01], + [7.12748102e-04, 1.93666384e-01, 4.10425108e-01]]), scale=0.033133735021424825, shift=array([5.29121744, 0.32407875, 0.9367533 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + 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fval=0.1799906266171576, rho=1.161952729888368, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 20, 21, 24, 25, 26, 27, 28, 29, 30]), old_indices_discarded=array([15, 16, 19, 22, 23]), step_length=0.03324491388144948, relative_step_length=1.0033554581139965, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.27834936, 0.29684139, 0.95081613]), radius=0.06626747004284965, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 18, 20, 24, 25, 26, 27, 28, 29, 30, 31]), model=ScalarModel(intercept=0.17004437609421225, linear_terms=array([-0.00047394, -0.007927 , -0.01239616]), square_terms=array([[3.94062801e-04, 1.07905924e-02, 2.16812211e-02], + [1.07905924e-02, 3.74115243e-01, 7.88602520e-01], + [2.16812211e-02, 7.88602520e-01, 1.69226096e+00]]), scale=0.06626747004284965, shift=array([5.27834936, 0.29684139, 0.95081613])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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State(trustregion=Region(center=array([5.27834936, 0.29684139, 0.95081613]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 18, 20, 24, 25, 26, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=0.1684737651459072, linear_terms=array([0.00169024, 0.00402826, 0.00597484]), square_terms=array([[ 4.51971473e-04, -5.93887978e-03, -1.50514931e-02], + [-5.93887978e-03, 9.28119628e-02, 2.24673825e-01], + [-1.50514931e-02, 2.24673825e-01, 5.53588350e-01]]), scale=0.033133735021424825, shift=array([5.27834936, 0.29684139, 0.95081613])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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old_indices_discarded=array([15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 32, 33, 41]), step_length=0.03319197686050779, relative_step_length=1.001757780674147, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.25969725, 0.34546622, 0.93220909]), radius=0.06626747004284965, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 18, 21, 24, 28, 31, 35, 36, 37, 38, 43]), model=ScalarModel(intercept=0.17516083815911454, linear_terms=array([0.00333895, 0.02345551, 0.04006676]), square_terms=array([[ 9.17825370e-04, -1.49978117e-02, -3.32074698e-02], + [-1.49978117e-02, 3.29043755e-01, 6.85571868e-01], + [-3.32074698e-02, 6.85571868e-01, 1.45766388e+00]]), scale=0.06626747004284965, shift=array([5.25969725, 0.34546622, 0.93220909])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 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bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 18, 28, 29, 34, 36, 37, 39, 40, 42, 43]), model=ScalarModel(intercept=0.17500252339842898, linear_terms=array([0.00143805, 0.00318419, 0.00584766]), square_terms=array([[ 2.32402708e-04, -4.32320857e-03, -9.89988047e-03], + [-4.32320857e-03, 1.00521560e-01, 2.19588637e-01], + [-9.89988047e-03, 2.19588637e-01, 4.87595223e-01]]), scale=0.033133735021424825, shift=array([5.25969725, 0.34546622, 0.93220909])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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scale=0.5301397603427972, shift=array([5.3013976 , 0.35179043, 0.92170005])), candidate_index=45, candidate_x=array([5.22598818, 0.34124333, 0.9330266 ]), index=43, x=array([5.25969725, 0.34546622, 0.93220909]), fval=0.17587143240730305, rho=-0.04773487920840637, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 28, 29, 34, 36, 37, 39, 40, 42, 43]), old_indices_discarded=array([15, 16, 19, 20, 21, 22, 23, 24, 25, 26, 27, 30, 31, 32, 33, 35, 38, + 41, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.25969725, 0.34546622, 0.93220909]), radius=0.016566867510712412, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 18, 28, 29, 34, 36, 37, 39, 42, 43, 45]), model=ScalarModel(intercept=0.1751974222542535, linear_terms=array([0.00059828, 0.00104291, 0.00174938]), square_terms=array([[ 2.32452897e-05, 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old_indices_used=array([ 0, 17, 18, 28, 29, 34, 36, 37, 39, 42, 43, 45]), old_indices_discarded=array([19, 21, 24, 25, 26, 31, 33, 35, 38, 40, 41, 44]), step_length=0.017343162853838502, relative_step_length=1.0468583057493592, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.24271155, 0.34212944, 0.93327545]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([17, 18, 28, 29, 34, 36, 37, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=0.1748745633782247, linear_terms=array([ 0.00075962, -0.00172199, -0.00445794]), square_terms=array([[ 3.54305914e-05, -8.55387782e-04, -2.21218821e-03], + [-8.55387782e-04, 1.02537349e-01, 2.22590300e-01], + [-2.21218821e-03, 2.22590300e-01, 4.91024973e-01]]), scale=0.033133735021424825, shift=array([5.24271155, 0.34212944, 0.93327545])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], 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State(trustregion=Region(center=array([5.20917798, 0.34014316, 0.93432412]), radius=0.06626747004284965, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([17, 18, 21, 28, 33, 36, 37, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=0.17493421876270587, linear_terms=array([-0.00156353, -0.00842537, -0.01190066]), square_terms=array([[0.00281928, 0.03109626, 0.0629877 ], + [0.03109626, 0.34695273, 0.70455773], + [0.0629877 , 0.70455773, 1.45975502]]), scale=0.06626747004284965, shift=array([5.20917798, 0.34014316, 0.93432412])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]]), scale=0.5301397603427972, shift=array([5.3013976 , 0.35179043, 0.92170005])), candidate_index=48, candidate_x=array([5.27749183, 0.35670252, 0.9239295 ]), index=47, x=array([5.20917798, 0.34014316, 0.93432412]), fval=0.1752985258377397, rho=-2.6662319197648348, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([17, 18, 21, 28, 33, 36, 37, 43, 44, 45, 46, 47]), old_indices_discarded=array([ 0, 14, 15, 16, 19, 20, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 34, + 35, 38, 39, 40, 41, 42]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.20917798, 0.34014316, 0.93432412]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([17, 18, 28, 33, 36, 37, 43, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=0.17451083881940163, linear_terms=array([ 0.00045657, 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rho=0.563659202220171, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([17, 18, 28, 33, 36, 37, 43, 44, 45, 46, 47, 48]), old_indices_discarded=array([ 0, 15, 16, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 34, + 35, 38, 39, 40, 41, 42]), step_length=0.033441763641147204, relative_step_length=1.0092965257168625, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.17665032, 0.34743911, 0.93166475]), radius=0.06626747004284965, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([17, 18, 21, 28, 33, 36, 43, 44, 45, 46, 47, 49]), model=ScalarModel(intercept=0.17564926316541823, linear_terms=array([-0.00274284, -0.01384935, -0.02471199]), square_terms=array([[0.0035693 , 0.03481689, 0.07133062], + [0.03481689, 0.34263536, 0.7019492 ], + [0.07133062, 0.7019492 , 1.46757845]]), scale=0.06626747004284965, shift=array([5.17665032, 0.34743911, 0.93166475])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.17665032, 0.34743911, 0.93166475]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([17, 18, 21, 28, 36, 43, 44, 45, 46, 47, 49, 50]), model=ScalarModel(intercept=0.1762502933918212, linear_terms=array([-0.00155691, -0.00561339, -0.01308175]), square_terms=array([[0.00079825, 0.00800458, 0.01674233], + [0.00800458, 0.08167794, 0.17110631], + [0.01674233, 0.17110631, 0.36563479]]), scale=0.033133735021424825, shift=array([5.17665032, 0.34743911, 0.93166475])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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linear_terms=array([0.00019334, 0.00068168, 0.002236 ]), square_terms=array([[6.30086486e-06, 1.31120182e-04, 1.92914840e-04], + [1.31120182e-04, 2.30933740e-02, 4.65644949e-02], + [1.92914840e-04, 4.65644949e-02, 9.56283946e-02]]), scale=0.016566867510712412, shift=array([5.17665032, 0.34743911, 0.93166475])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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fval=0.1749771962623699, rho=-0.48116939358057886, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([18, 28, 36, 44, 45, 46, 47, 49, 50, 51]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.17665032, 0.34743911, 0.93166475]), radius=0.008283433755356206, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([44, 47, 49, 51, 52]), model=ScalarModel(intercept=0.17496972296461294, linear_terms=array([ 6.97863248e-05, -8.21475281e-05, -4.56887150e-04]), square_terms=array([[1.84361126e-06, 2.04013067e-05, 2.19735979e-05], + [2.04013067e-05, 5.26300747e-03, 1.10824804e-02], + [2.19735979e-05, 1.10824804e-02, 2.38107760e-02]]), scale=0.008283433755356206, shift=array([5.17665032, 0.34743911, 0.93166475])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + 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radius=0.016566867510712412, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([18, 28, 36, 44, 45, 46, 47, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.1749826973068216, linear_terms=array([0.0001074 , 0.00038707, 0.00171986]), square_terms=array([[7.58231429e-06, 2.29836559e-04, 3.96776253e-04], + [2.29836559e-04, 2.29465979e-02, 4.65350783e-02], + [3.96776253e-04, 4.65350783e-02, 9.61235255e-02]]), scale=0.016566867510712412, shift=array([5.17030197, 0.34249393, 0.93412279])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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0.]]]), scale=0.5301397603427972, shift=array([5.3013976 , 0.35179043, 0.92170005])), candidate_index=54, candidate_x=array([5.15977125, 0.35427611, 0.92817791]), index=53, x=array([5.17030197, 0.34249393, 0.93412279]), fval=0.17478348430580953, rho=-0.39475885711036196, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([18, 28, 36, 44, 45, 46, 47, 49, 50, 51, 52, 53]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.17030197, 0.34249393, 0.93412279]), radius=0.008283433755356206, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([44, 47, 49, 51, 52, 53, 54]), model=ScalarModel(intercept=0.1747885766485168, linear_terms=array([ 7.81846919e-05, 3.99579562e-05, -9.60174366e-05]), square_terms=array([[1.77118649e-06, 1.19372381e-05, 4.26127615e-06], + [1.19372381e-05, 5.37799886e-03, 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State(trustregion=Region(center=array([5.14420282, 0.34178406, 0.93435027]), radius=0.008283433755356206, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([49, 52, 53, 54, 55, 56, 57, 58]), model=ScalarModel(intercept=0.17448263024954638, linear_terms=array([8.45063966e-05, 2.93674559e-04, 4.11365018e-04]), square_terms=array([[1.72519621e-06, 1.48187759e-05, 1.09062891e-05], + [1.48187759e-05, 6.27266360e-03, 1.32612134e-02], + [1.09062891e-05, 1.32612134e-02, 2.85049653e-02]]), scale=0.008283433755356206, shift=array([5.14420282, 0.34178406, 0.93435027])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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State(trustregion=Region(center=array([5.08741985, 0.34221541, 0.93430985]), radius=0.06626747004284965, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([44, 49, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=0.17399871546423445, linear_terms=array([5.54960169e-04, 2.77020491e-05, 2.46039514e-04]), square_terms=array([[1.12760714e-04, 1.09146716e-03, 9.82283047e-04], + [1.09146716e-03, 3.54762285e-01, 7.53825721e-01], + [9.82283047e-04, 7.53825721e-01, 1.63251500e+00]]), scale=0.06626747004284965, shift=array([5.08741985, 0.34221541, 0.93430985])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 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old_indices_used=array([ 8, 52, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, + 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, + 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, + 53]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.88958301, 0.35997157, 0.92601999]), radius=0.1325349400856993, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 8, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64]), model=ScalarModel(intercept=0.17454954123079744, linear_terms=array([-5.42519807e-05, -1.01429163e-01, -2.30913425e-01]), square_terms=array([[5.22021113e-04, 2.10949241e-02, 3.90868648e-02], + [2.10949241e-02, 8.25980623e+00, 1.71670369e+01], + [3.90868648e-02, 1.71670369e+01, 3.57832291e+01]]), scale=0.1325349400856993, 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27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, + 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.88958301, 0.35997157, 0.92601999]), radius=0.06626747004284965, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 8, 54, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65]), model=ScalarModel(intercept=0.17542324482018204, linear_terms=array([-0.00082497, -0.05406882, -0.10601177]), square_terms=array([[1.43516091e-04, 1.15577625e-03, 8.44733923e-04], + [1.15577625e-03, 1.97661939e+00, 4.19325940e+00], + [8.44733923e-04, 4.19325940e+00, 8.92777781e+00]]), scale=0.06626747004284965, shift=array([4.88958301, 0.35997157, 0.92601999])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + 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77, 78, 79, 80, 81, 82, 83]), model=ScalarModel(intercept=0.17275901380787767, linear_terms=array([ 5.13124779e-05, -7.09231772e-04, -1.50668997e-03]), square_terms=array([[ 5.24206101e-07, 2.61469482e-06, -1.23800127e-07], + [ 2.61469482e-06, 1.62536068e-03, 3.42956073e-03], + [-1.23800127e-07, 3.42956073e-03, 7.35873402e-03]]), scale=0.004141716877678103, shift=array([4.85934517, 0.34730674, 0.93082827])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 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scale=0.008283433755356206, shift=array([4.855221 , 0.34798078, 0.93135837])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + 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shift=array([4.84436454, 0.3458171 , 0.93240807])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.84436454, 0.3458171 , 0.93240807]), radius=0.0020708584388390515, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, + 99, 100]), model=ScalarModel(intercept=0.17258738194234413, linear_terms=array([ 3.33838120e-06, -1.97151771e-08, 5.27903701e-06]), square_terms=array([[ 1.36507244e-07, 3.44142448e-07, -7.87178649e-07], + [ 3.44142448e-07, 4.10506238e-04, 8.69151483e-04], + [-7.87178649e-07, 8.69151483e-04, 1.87129773e-03]]), scale=0.0020708584388390515, shift=array([4.84436454, 0.3458171 , 0.93240807])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], 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linear_terms=array([ 2.41233698e-05, -2.61891003e-06, 5.11296505e-06]), square_terms=array([[ 4.37386430e-08, -5.79574560e-07, -1.57888647e-06], + [-5.79574560e-07, 1.02556710e-04, 2.17206204e-04], + [-1.57888647e-06, 2.17206204e-04, 4.67922792e-04]]), scale=0.0010354292194195258, shift=array([4.84436454, 0.3458171 , 0.93240807])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 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0.93232094]), fval=0.1725670999317009, rho=0.19934711308216232, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([87, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]), old_indices_discarded=array([ 88, 100, 101]), step_length=0.001038810734087629, relative_step_length=1.0032658095837772, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.84334274, 0.34598281, 0.93232094]), radius=0.0020708584388390515, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 87, 90, 92, 93, 94, 95, 97, 98, 99, 100, 101, 102]), model=ScalarModel(intercept=0.17258247058638926, linear_terms=array([-2.68495297e-05, -2.10095588e-06, -2.07300030e-05]), square_terms=array([[1.91163962e-07, 3.32105210e-06, 5.86912190e-06], + [3.32105210e-06, 4.07207519e-04, 8.64705423e-04], + [5.86912190e-06, 8.64705423e-04, 1.86822036e-03]]), scale=0.0020708584388390515, shift=array([4.84334274, 0.34598281, 0.93232094])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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State(trustregion=Region(center=array([4.84334274, 0.34598281, 0.93232094]), radius=0.0010354292194195258, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 87, 92, 93, 94, 95, 97, 98, 99, 100, 101, 102, 103]), model=ScalarModel(intercept=0.17258275627245578, linear_terms=array([-1.33291997e-05, -4.54612468e-06, -9.95213586e-06]), square_terms=array([[4.81840647e-08, 1.07426513e-06, 1.94899377e-06], + [1.07426513e-06, 1.02515718e-04, 2.17008681e-04], + [1.94899377e-06, 2.17008681e-04, 4.67269204e-04]]), scale=0.0010354292194195258, shift=array([4.84334274, 0.34598281, 0.93232094])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]]), scale=0.5301397603427972, shift=array([5.3013976 , 0.35179043, 0.92170005])), candidate_index=104, candidate_x=array([4.84438244, 0.34597441, 0.93234197]), index=104, x=array([4.84438244, 0.34597441, 0.93234197]), fval=0.1725502344785951, rho=1.2557959125922895, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 87, 92, 93, 94, 95, 97, 98, 99, 100, 101, 102, 103]), old_indices_discarded=array([88, 89, 90, 91, 96]), step_length=0.0010399422294573753, relative_step_length=1.0043585886444073, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.84438244, 0.34597441, 0.93234197]), radius=0.0020708584388390515, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 87, 89, 90, 92, 96, 97, 98, 100, 101, 102, 103, 104]), model=ScalarModel(intercept=0.17259245410531784, linear_terms=array([ 2.41019710e-05, -4.48842471e-06, -4.27936350e-05]), square_terms=array([[1.56434406e-07, 3.33318421e-06, 6.03774428e-06], + [3.33318421e-06, 4.15831904e-04, 8.74354178e-04], + [6.03774428e-06, 8.74354178e-04, 1.86706420e-03]]), scale=0.0020708584388390515, shift=array([4.84438244, 0.34597441, 0.93234197])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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0.93234197]), fval=0.1725502344785951, rho=-1.01272054792213, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 87, 89, 90, 92, 96, 97, 98, 100, 101, 102, 103, 104]), old_indices_discarded=array([86, 88, 91, 93, 94, 95, 99]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.84438244, 0.34597441, 0.93234197]), radius=0.0010354292194195258, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 87, 90, 92, 96, 97, 98, 100, 101, 102, 103, 104, 105]), model=ScalarModel(intercept=0.17258372705737116, linear_terms=array([ 4.09478695e-06, -3.55795689e-06, -4.27096698e-05]), square_terms=array([[3.35585618e-08, 2.15061082e-07, 1.37358651e-07], + [2.15061082e-07, 1.03219001e-04, 2.16664028e-04], + [1.37358651e-07, 2.16664028e-04, 4.61793255e-04]]), scale=0.0010354292194195258, shift=array([4.84438244, 0.34597441, 0.93234197])), 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bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 87, 104, 108, 109]), model=ScalarModel(intercept=0.17255023447859502, linear_terms=array([-3.73002754e-05, -6.01751396e-06, -3.13820183e-06]), square_terms=array([[ 3.36899890e-08, -2.15013771e-08, -5.98360204e-08], + [-2.15013771e-08, 4.08581107e-07, 8.66434307e-07], + [-5.98360204e-08, 8.66434307e-07, 1.87671962e-06]]), scale=6.471432621372036e-05, shift=array([4.84438244, 0.34597441, 0.93234197])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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old_indices_used=array([104, 110]), old_indices_discarded=array([], dtype=int32), step_length=3.2357163106865805e-05, relative_step_length=1.0000000000001739, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 124 entries., 'multistart_info': {'start_parameters': [array([5.33878077, 0.17065529, 1. ]), array([4.4728541 , 0.45753029, 0.84720369]), array([5.3013976 , 0.35179043, 0.92170005])], 'local_optima': [{'solution_x': array([5.21248464, 0.33676407, 0.93666301]), 'solution_criterion': 0.17512192544656371, 'states': [State(trustregion=Region(center=array([5.33878077, 0.17065529, 1. ]), radius=0.5338780774481048, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=[0], model=ScalarModel(intercept=0.24222229239256646, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), scale=0.5338780774481048, shift=array([5.33878077, 0.17065529, 1. ])), 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State(trustregion=Region(center=array([5.33878077, 0.17065529, 1. ]), radius=0.1334695193620262, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 8, 10, 12, 13, 14]), model=ScalarModel(intercept=1.8208078161679868, linear_terms=array([-0.19004525, 2.49267386, 3.44430864]), square_terms=array([[ 0.01155517, -0.14231501, -0.19433776], + [-0.14231501, 1.88777533, 2.52140739], + [-0.19433776, 2.52140739, 3.42876704]]), scale=array([0.1075759 , 0.1075759 , 0.10378795]), shift=array([5.33878077, 0.17065529, 0.99621205])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 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x=array([5.30968909, 0.25995847, 0.96850838]), fval=0.18645615650369046, rho=1.932188396931057, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 16, 18, 19, 20, 21, 22, 23, 25, 27, 28, 29]), old_indices_discarded=array([14, 15, 17, 24, 26]), step_length=0.07028379860830133, relative_step_length=1.0531812648198984, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.30968909, 0.25995847, 0.96850838]), radius=0.1334695193620262, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 16, 17, 18, 19, 21, 22, 23, 25, 28, 29, 30]), model=ScalarModel(intercept=0.1877934505138043, linear_terms=array([-0.03082531, -0.06829227, -0.20801109]), square_terms=array([[ 0.02060883, 0.09482843, 0.45039114], + [ 0.09482843, 0.47240082, 2.15885408], + [ 0.45039114, 2.15885408, 10.7559403 ]]), scale=array([0.1075759, 0.1075759, 0.1075759]), shift=array([5.30968909, 0.25995847, 0.96850838])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.30968909, 0.25995847, 0.96850838]), radius=0.0667347596810131, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 16, 17, 18, 19, 21, 22, 23, 28, 29, 30, 31]), model=ScalarModel(intercept=0.18732156451899337, linear_terms=array([-0.00503334, 0.01007561, 0.10159589]), square_terms=array([[0.00372373, 0.03082621, 0.11068236], + [0.03082621, 0.26932161, 0.95090676], + [0.11068236, 0.95090676, 3.51609679]]), scale=0.0667347596810131, shift=array([5.30968909, 0.25995847, 0.96850838])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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0.21124739]), square_terms=array([[ 0.02086028, 0.15586295, 0.51678677], + [ 0.15586295, 1.20259849, 3.90266093], + [ 0.51678677, 3.90266093, 13.13777487]]), scale=0.1334695193620262, shift=array([5.35620118, 0.30725103, 0.95237438])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + 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old_indices_used=array([ 0, 16, 18, 19, 21, 22, 23, 28, 29, 30, 31, 32]), old_indices_discarded=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 20, + 24, 25, 26, 27]), step_length=0.1362670918270145, relative_step_length=1.0209603846508213, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.49059454, 0.32570596, 0.93946857]), radius=0.0667347596810131, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 16, 19, 20, 22, 23, 28, 29, 30, 31, 32, 33]), model=ScalarModel(intercept=0.180667786417314, linear_terms=array([ 0.00742114, -0.04161736, -0.07431271]), square_terms=array([[1.78983921e-03, 2.53213785e-02, 7.69037706e-02], + [2.53213785e-02, 4.62056121e-01, 1.34550576e+00], + [7.69037706e-02, 1.34550576e+00, 4.02143592e+00]]), scale=0.0667347596810131, shift=array([5.49059454, 0.32570596, 0.93946857])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 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square_terms=array([[ 1.50428947e-04, -2.69288377e-03, -7.97567575e-03], + [-2.69288377e-03, 3.75575586e-01, 8.63048473e-01], + [-7.97567575e-03, 8.63048473e-01, 2.02064605e+00]]), scale=0.0667347596810131, shift=array([5.32187633, 0.35441087, 0.92835645])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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new_indices=array([], dtype=int32), old_indices_used=array([30, 31, 32, 34, 36, 37, 38, 39, 40, 41, 42, 43]), old_indices_discarded=array([ 0, 5, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, + 29, 33, 35]), step_length=0.0667568953608683, relative_step_length=1.0003316964047075, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.25577068, 0.36291503, 0.92458747]), radius=0.1334695193620262, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([23, 28, 30, 31, 32, 36, 38, 39, 40, 41, 42, 44]), model=ScalarModel(intercept=0.17756581846293762, linear_terms=array([-0.00064376, 0.02147547, 0.0472283 ]), square_terms=array([[ 6.33696631e-04, -1.27178250e-02, -3.69738106e-02], + [-1.27178250e-02, 1.40423713e+00, 3.24790628e+00], + [-3.69738106e-02, 3.24790628e+00, 7.66915637e+00]]), scale=0.1334695193620262, shift=array([5.25577068, 0.36291503, 0.92458747])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.25577068, 0.36291503, 0.92458747]), radius=0.0667347596810131, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([23, 28, 30, 32, 36, 38, 39, 40, 41, 42, 44, 45]), model=ScalarModel(intercept=0.1766790596574145, linear_terms=array([0.00015191, 0.01298194, 0.03144833]), square_terms=array([[ 1.77245269e-04, -3.57687440e-03, -1.01635519e-02], + [-3.57687440e-03, 3.48864307e-01, 8.05273892e-01], + [-1.01635519e-02, 8.05273892e-01, 1.89795527e+00]]), scale=0.0667347596810131, shift=array([5.25577068, 0.36291503, 0.92458747])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 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45, 46]), model=ScalarModel(intercept=0.17713858067470892, linear_terms=array([-5.23221131e-05, 7.13375398e-03, 1.70648155e-02]), square_terms=array([[ 4.64368650e-05, -9.99022647e-04, -2.78370618e-03], + [-9.99022647e-04, 8.72377819e-02, 2.01029677e-01], + [-2.78370618e-03, 2.01029677e-01, 4.72985220e-01]]), scale=0.03336737984050655, shift=array([5.25577068, 0.36291503, 0.92458747])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + 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index=44, x=array([5.25577068, 0.36291503, 0.92458747]), fval=0.1767393872279913, rho=-1.025962964474662, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([23, 28, 30, 32, 36, 38, 39, 41, 42, 44, 45, 46]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.25577068, 0.36291503, 0.92458747]), radius=0.016683689920253274, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([42, 44, 46, 47]), model=ScalarModel(intercept=0.17673938722799137, linear_terms=array([-0.01008057, -0.11337789, -0.07673132]), square_terms=array([[0.00876524, 0.08404318, 0.03756224], + [0.08404318, 0.81227781, 0.3730522 ], + [0.03756224, 0.3730522 , 0.1876197 ]]), scale=0.016683689920253274, shift=array([5.25577068, 0.36291503, 0.92458747])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 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bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([44, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60]), model=ScalarModel(intercept=0.1767116579411943, linear_terms=array([0.00013823, 0.00116321, 0.0005062 ]), square_terms=array([[ 1.87815586e-06, -2.30392659e-05, -7.02956521e-05], + [-2.30392659e-05, 6.04921935e-03, 1.28857726e-02], + [-7.02956521e-05, 1.28857726e-02, 2.78895960e-02]]), scale=0.008341844960126637, shift=array([5.25577068, 0.36291503, 0.92458747])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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x=array([5.21614051, 0.34083281, 0.93444849]), fval=0.17525877839750098, rho=-0.24091601228165707, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([64, 66, 68, 69]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.21614051, 0.34083281, 0.93444849]), radius=0.0020854612400316593, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([66, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82]), model=ScalarModel(intercept=0.1752716135829646, linear_terms=array([ 2.83868377e-05, -1.19751994e-04, -3.17911081e-04]), square_terms=array([[1.11944911e-07, 1.15337902e-06, 1.26187859e-06], + [1.15337902e-06, 3.96183939e-04, 8.51195967e-04], + [1.26187859e-06, 8.51195967e-04, 1.85994556e-03]]), scale=0.0020854612400316593, shift=array([5.21614051, 0.34083281, 0.93444849])), 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scale=0.44728541044950626, shift=array([4.4728541 , 0.45753029, 0.84720369])), candidate_index=36, candidate_x=array([4.54190424, 0.34416472, 0.93058415]), index=36, x=array([4.54190424, 0.34416472, 0.93058415]), fval=0.17557093407600716, rho=0.20005246667450538, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([20, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]), old_indices_discarded=array([ 0, 15, 16, 17, 18, 19, 21, 22, 23, 24]), step_length=0.0608879546199208, relative_step_length=1.0890219657954956, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.54190424, 0.34416472, 0.93058415]), radius=0.11182135261237656, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([20, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=0.17568176927667278, linear_terms=array([-0.00016853, -0.01916852, -0.03813775]), square_terms=array([[ 5.84778824e-04, -9.07741680e-03, 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fval=0.17319386240144724, rho=-2.368503752157372, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([41, 46, 47, 48, 50, 51, 52, 53, 54, 56, 57, 59]), old_indices_discarded=array([33, 34, 37, 40, 42, 43, 44, 45, 49, 55, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65947059, 0.34493224, 0.93264892]), radius=0.006988834538273535, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([41, 46, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59]), model=ScalarModel(intercept=0.17322335454301357, linear_terms=array([5.84657279e-05, 5.61600646e-05, 1.11287704e-04]), square_terms=array([[ 1.73166366e-06, -5.21647152e-06, -2.70055797e-05], + [-5.21647152e-06, 4.91675196e-03, 1.02038442e-02], + [-2.70055797e-05, 1.02038442e-02, 2.15010081e-02]]), scale=0.006988834538273535, shift=array([4.65947059, 0.34493224, 0.93264892])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, + 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, + -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, + -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65246302, 0.34513124, 0.93250988]), radius=0.01397766907654707, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([41, 45, 47, 48, 50, 51, 53, 54, 56, 57, 59, 61]), model=ScalarModel(intercept=0.17319833291851128, linear_terms=array([-1.51384574e-04, -7.53489850e-06, -1.33953022e-04]), square_terms=array([[7.88278598e-06, 6.56801117e-05, 6.75420704e-05], + [6.56801117e-05, 1.96414914e-02, 4.08747995e-02], + [6.75420704e-05, 4.08747995e-02, 8.63573595e-02]]), scale=0.01397766907654707, shift=array([4.65246302, 0.34513124, 0.93250988])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, + 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, + -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, + -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 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model=ScalarModel(intercept=0.1731417369534366, linear_terms=array([8.53555441e-05, 3.94898724e-05, 4.21614549e-05]), square_terms=array([[ 1.87710384e-06, -1.89887381e-05, -5.59774665e-05], + [-1.89887381e-05, 4.91019732e-03, 1.02046258e-02], + [-5.59774665e-05, 1.02046258e-02, 2.15316921e-02]]), scale=0.006988834538273535, shift=array([4.65246302, 0.34513124, 0.93250988])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, + 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, + -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, + -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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index=61, x=array([4.65246302, 0.34513124, 0.93250988]), fval=0.17314745134607396, rho=-0.7767331150668283, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([41, 46, 48, 49, 50, 51, 54, 55, 56, 58, 59, 61]), old_indices_discarded=array([37, 44, 45, 47, 52, 53, 57, 60, 62]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65246302, 0.34513124, 0.93250988]), radius=0.0034944172691367677, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([41, 46, 48, 49, 50, 51, 54, 56, 58, 59, 61, 63]), model=ScalarModel(intercept=0.17318840104590158, linear_terms=array([8.40920959e-06, 1.60644298e-06, 1.78650732e-05]), square_terms=array([[ 4.56677469e-07, -1.61383200e-06, -7.65761701e-06], + [-1.61383200e-06, 1.22943708e-03, 2.55528223e-03], + [-7.65761701e-06, 2.55528223e-03, 5.39279040e-03]]), scale=0.0034944172691367677, 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65246302, 0.34513124, 0.93250988]), radius=0.0017472086345683838, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([41, 46, 48, 49, 50, 51, 56, 58, 59, 61, 63, 64]), model=ScalarModel(intercept=0.1731833699424632, linear_terms=array([-1.55880845e-06, 2.34892542e-05, 3.50350099e-05]), square_terms=array([[1.14286970e-07, 5.95276640e-07, 1.79303603e-07], + [5.95276640e-07, 3.00448801e-04, 6.27473167e-04], + [1.79303603e-07, 6.27473167e-04, 1.33116839e-03]]), scale=0.0017472086345683838, shift=array([4.65246302, 0.34513124, 0.93250988])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, + 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, + -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, + -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 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model=ScalarModel(intercept=0.17320359100698507, linear_terms=array([-1.04740719e-05, -4.15791794e-05, 1.53390193e-05]), square_terms=array([[ 3.35637081e-08, 1.29446597e-07, -5.58028919e-08], + [ 1.29446597e-07, 7.70487534e-05, 1.63752882e-04], + [-5.58028919e-08, 1.63752882e-04, 3.53849647e-04]]), scale=0.0008736043172841919, shift=array([4.65246302, 0.34513124, 0.93250988])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, + 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, + -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, + -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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index=78, x=array([4.65266665, 0.34590517, 0.93215796]), fval=0.17313846350417914, rho=0.20010394985498053, accepted=True, new_indices=array([66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77]), old_indices_used=array([61, 64, 65]), old_indices_discarded=array([], dtype=int32), step_length=0.0008742235274298533, relative_step_length=1.0007087993195665, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65266665, 0.34590517, 0.93215796]), radius=0.0017472086345683838, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([61, 66, 68, 69, 70, 71, 72, 73, 75, 76, 77, 78]), model=ScalarModel(intercept=0.17316117071960616, linear_terms=array([ 1.17846159e-06, -7.30897908e-05, 3.56834929e-05]), square_terms=array([[ 1.26266358e-07, -2.95552318e-07, -1.85714932e-06], + [-2.95552318e-07, 3.06633677e-04, 6.53705727e-04], + [-1.85714932e-06, 6.53705727e-04, 1.41658448e-03]]), scale=0.0017472086345683838, shift=array([4.65266665, 0.34590517, 0.93215796])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, + 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, + -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, + -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65266665, 0.34590517, 0.93215796]), radius=0.0008736043172841919, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([61, 66, 67, 69, 70, 71, 72, 73, 74, 75, 77, 78]), model=ScalarModel(intercept=0.17315497758754703, linear_terms=array([-1.03031559e-05, -4.11416984e-05, 1.56495230e-05]), square_terms=array([[ 3.02714973e-08, 8.04834807e-08, -1.41448123e-07], + [ 8.04834807e-08, 7.72107726e-05, 1.63938593e-04], + [-1.41448123e-07, 1.63938593e-04, 3.53626006e-04]]), scale=0.0008736043172841919, shift=array([4.65266665, 0.34590517, 0.93215796])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, + 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, + -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, + -0.0551122 , -0.05468477]), 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model=ScalarModel(intercept=0.17317273550338308, linear_terms=array([ 4.91859051e-07, -1.11539853e-05, -3.77793814e-06]), square_terms=array([[ 7.22202027e-09, 2.91274101e-08, -7.91097523e-09], + [ 2.91274101e-08, 1.93478699e-05, 4.10231377e-05], + [-7.91097523e-09, 4.10231377e-05, 8.82835907e-05]]), scale=0.00043680215864209596, shift=array([4.65266665, 0.34590517, 0.93215796])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, + 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, + -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, + -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 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index=78, x=array([4.65266665, 0.34590517, 0.93215796]), fval=0.17313846350417914, rho=-2.0772563083934017, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([61, 66, 67, 69, 71, 72, 73, 74, 75, 77, 78, 80]), old_indices_discarded=array([68, 70, 76, 79]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65266665, 0.34590517, 0.93215796]), radius=0.00021840107932104798, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([61, 66, 67, 69, 74, 75, 78, 80, 81]), model=ScalarModel(intercept=0.1731737579944651, linear_terms=array([-3.79844970e-06, -3.69441907e-06, 9.93700675e-07]), square_terms=array([[2.70592783e-09, 4.30842813e-08, 7.31011322e-08], + [4.30842813e-08, 4.85108303e-06, 1.02966777e-05], + [7.31011322e-08, 1.02966777e-05, 2.21791802e-05]]), scale=0.00021840107932104798, shift=array([4.65266665, 0.34590517, 0.93215796])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, + 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, + -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, + -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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0.12997867, 0.12006044, 0.11918703, 0.09509912, + 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, + -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, + -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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model_indices=array([ 0, 17, 18, 20, 24, 25, 26, 27, 28, 29, 30, 31]), model=ScalarModel(intercept=0.17004437609421225, linear_terms=array([-0.00047394, -0.007927 , -0.01239616]), square_terms=array([[3.94062801e-04, 1.07905924e-02, 2.16812211e-02], + [1.07905924e-02, 3.74115243e-01, 7.88602520e-01], + [2.16812211e-02, 7.88602520e-01, 1.69226096e+00]]), scale=0.06626747004284965, shift=array([5.27834936, 0.29684139, 0.95081613])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), 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candidate_x=array([5.34476989, 0.3113389 , 0.94369486]), index=31, x=array([5.27834936, 0.29684139, 0.95081613]), fval=0.1799906266171576, rho=-2.1259132820697104, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 20, 24, 25, 26, 27, 28, 29, 30, 31]), old_indices_discarded=array([14, 15, 16, 19, 21, 22, 23]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.27834936, 0.29684139, 0.95081613]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 18, 20, 24, 25, 26, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=0.1684737651459072, linear_terms=array([0.00169024, 0.00402826, 0.00597484]), square_terms=array([[ 4.51971473e-04, -5.93887978e-03, -1.50514931e-02], + [-5.93887978e-03, 9.28119628e-02, 2.24673825e-01], + [-1.50514931e-02, 2.24673825e-01, 5.53588350e-01]]), scale=0.033133735021424825, shift=array([5.27834936, 0.29684139, 0.95081613])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + 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fval=0.17755692096608, rho=1.8988432161216091, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 24, 25, 26, 28, 29, 31, 32, 33, 34]), old_indices_discarded=array([15, 16, 19, 20, 21, 22, 23, 27, 30]), step_length=0.033281691430788476, relative_step_length=1.004465431055931, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.30566766, 0.32537105, 0.9393405 ]), radius=0.06626747004284965, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 20, 24, 25, 26, 29, 30, 31, 32, 34, 35]), model=ScalarModel(intercept=0.17237609478517008, linear_terms=array([ 0.00518006, -0.00474084, -0.0125618 ]), square_terms=array([[ 2.10162976e-03, -2.78797705e-02, -6.43090686e-02], + [-2.78797705e-02, 4.31690458e-01, 9.58890944e-01], + [-6.43090686e-02, 9.58890944e-01, 2.16354079e+00]]), scale=0.06626747004284965, shift=array([5.30566766, 0.32537105, 0.9393405 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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n_evals_acceptance=1), State(trustregion=Region(center=array([5.30566766, 0.32537105, 0.9393405 ]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 20, 24, 25, 26, 29, 30, 31, 32, 34, 35]), model=ScalarModel(intercept=0.1723760947851703, linear_terms=array([ 0.00259003, -0.00237042, -0.0062809 ]), square_terms=array([[ 5.25407439e-04, -6.96994262e-03, -1.60772672e-02], + [-6.96994262e-03, 1.07922614e-01, 2.39722736e-01], + [-1.60772672e-02, 2.39722736e-01, 5.40885198e-01]]), scale=0.033133735021424825, shift=array([5.30566766, 0.32537105, 0.9393405 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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linear_terms=array([-0.00039037, -0.0076903 , -0.01984056]), square_terms=array([[1.68083117e-05, 5.99857721e-04, 1.30035997e-03], + [5.99857721e-04, 3.07066045e-02, 7.08873570e-02], + [1.30035997e-03, 7.08873570e-02, 1.65894328e-01]]), scale=0.016566867510712412, shift=array([5.30566766, 0.32537105, 0.9393405 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + 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fval=0.17733908278986127, rho=0.12075915604171515, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 20, 25, 26, 29, 30, 31, 32, 34, 35, 37]), old_indices_discarded=array([22, 24, 27, 28, 33, 36]), step_length=0.01691501274286895, relative_step_length=1.021014547978453, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.31420845, 0.31272412, 0.94663622]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 20, 25, 26, 29, 30, 31, 32, 34, 35, 37, 38]), model=ScalarModel(intercept=0.17451053131095257, linear_terms=array([ 0.00026766, -0.00034627, -0.00190087]), square_terms=array([[ 2.82594589e-05, -3.98670431e-04, -1.30601253e-03], + [-3.98670431e-04, 1.28325976e-01, 2.92377009e-01], + [-1.30601253e-03, 2.92377009e-01, 6.74940821e-01]]), scale=0.033133735021424825, shift=array([5.31420845, 0.31272412, 0.94663622])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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n_evals_acceptance=1), State(trustregion=Region(center=array([5.31420845, 0.31272412, 0.94663622]), radius=0.016566867510712412, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 20, 26, 29, 30, 31, 32, 34, 35, 37, 38, 39]), model=ScalarModel(intercept=0.1771813911491111, linear_terms=array([ 1.44498627e-05, -7.05759415e-04, -8.71906755e-04]), square_terms=array([[6.32366784e-06, 6.97090062e-05, 6.24448079e-05], + [6.97090062e-05, 2.21772805e-02, 4.87945740e-02], + [6.24448079e-05, 4.87945740e-02, 1.09441144e-01]]), scale=0.016566867510712412, shift=array([5.31420845, 0.31272412, 0.94663622])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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linear_terms=array([-8.72839083e-04, 6.46911097e-05, -1.15041490e-03]), square_terms=array([[1.69827888e-04, 4.48946449e-03, 9.85596140e-03], + [4.48946449e-03, 1.31226175e-01, 2.95972451e-01], + [9.85596140e-03, 2.95972451e-01, 6.76192041e-01]]), scale=0.033133735021424825, shift=array([5.30300313, 0.32433431, 0.9416009 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]]), scale=0.5301397603427972, shift=array([5.3013976 , 0.35179043, 0.92170005])), candidate_index=41, candidate_x=array([5.33528377, 0.31537845, 0.9451021 ]), index=40, x=array([5.30300313, 0.32433431, 0.9416009 ]), fval=0.1766329595168218, rho=-0.9795538799146807, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 25, 26, 29, 31, 32, 34, 35, 37, 38, 39, 40]), old_indices_discarded=array([15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 27, 28, 30, 33, 36]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.30300313, 0.32433431, 0.9416009 ]), radius=0.016566867510712412, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 26, 29, 31, 32, 34, 35, 37, 38, 39, 40, 41]), model=ScalarModel(intercept=0.17721715662436963, linear_terms=array([0.00016892, 0.00010182, 0.00044048]), square_terms=array([[6.47390632e-06, 4.70823827e-05, 1.17452629e-05], + [4.70823827e-05, 2.18434219e-02, 4.79874666e-02], + [1.17452629e-05, 4.79874666e-02, 1.07501169e-01]]), scale=0.016566867510712412, shift=array([5.30300313, 0.32433431, 0.9416009 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.28676541, 0.32788588, 0.93995208]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 29, 31, 34, 35, 36, 37, 38, 39, 40, 42]), model=ScalarModel(intercept=0.17584179736570787, linear_terms=array([ 0.00072293, -0.00395619, -0.0056097 ]), square_terms=array([[ 2.77942804e-05, -1.36149379e-04, -6.48042884e-04], + [-1.36149379e-04, 1.20187024e-01, 2.58595324e-01], + [-6.48042884e-04, 2.58595324e-01, 5.64442351e-01]]), scale=0.033133735021424825, shift=array([5.28676541, 0.32788588, 0.93995208])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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x=array([5.25969725, 0.34546622, 0.93220909]), fval=0.17587143240730305, rho=-0.2929960708109051, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 21, 24, 28, 31, 35, 36, 37, 38, 43]), old_indices_discarded=array([14, 15, 16, 19, 20, 22, 23, 25, 26, 27, 29, 30, 32, 33, 34, 39, 40, + 41, 42]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.25969725, 0.34546622, 0.93220909]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 18, 28, 29, 34, 36, 37, 39, 40, 42, 43]), model=ScalarModel(intercept=0.17500252339842898, linear_terms=array([0.00143805, 0.00318419, 0.00584766]), square_terms=array([[ 2.32402708e-04, -4.32320857e-03, -9.89988047e-03], + [-4.32320857e-03, 1.00521560e-01, 2.19588637e-01], + [-9.89988047e-03, 2.19588637e-01, 4.87595223e-01]]), scale=0.033133735021424825, 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27, 29, 30, 31, 32, 34, + 35, 38, 39, 40, 41, 42]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.20917798, 0.34014316, 0.93432412]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([17, 18, 28, 33, 36, 37, 43, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=0.17451083881940163, linear_terms=array([ 0.00045657, -0.00270283, -0.00529828]), square_terms=array([[4.56076655e-05, 1.60381216e-03, 3.26261075e-03], + [1.60381216e-03, 9.80067681e-02, 2.16994269e-01], + [3.26261075e-03, 2.16994269e-01, 4.88889862e-01]]), scale=0.033133735021424825, shift=array([5.20917798, 0.34014316, 0.93432412])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + 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0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([17, 18, 21, 28, 33, 36, 43, 44, 45, 46, 47, 49]), model=ScalarModel(intercept=0.17564926316541823, linear_terms=array([-0.00274284, -0.01384935, -0.02471199]), square_terms=array([[0.0035693 , 0.03481689, 0.07133062], + [0.03481689, 0.34263536, 0.7019492 ], + [0.07133062, 0.7019492 , 1.46757845]]), scale=0.06626747004284965, shift=array([5.17665032, 0.34743911, 0.93166475])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), 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candidate_x=array([5.2442876 , 0.35755943, 0.92465874]), index=49, x=array([5.17665032, 0.34743911, 0.93166475]), fval=0.1749771962623699, rho=-1.2279027031180028, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([17, 18, 21, 28, 33, 36, 43, 44, 45, 46, 47, 49]), old_indices_discarded=array([ 0, 14, 15, 16, 19, 20, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 34, + 35, 37, 38, 39, 40, 41, 42, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.17665032, 0.34743911, 0.93166475]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([17, 18, 21, 28, 36, 43, 44, 45, 46, 47, 49, 50]), model=ScalarModel(intercept=0.1762502933918212, linear_terms=array([-0.00155691, -0.00561339, -0.01308175]), square_terms=array([[0.00079825, 0.00800458, 0.01674233], + [0.00800458, 0.08167794, 0.17110631], + [0.01674233, 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24, 25, 26, 29, 31, 33, 34, 35, 37, 38, 39, 40, 41, + 42, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.17665032, 0.34743911, 0.93166475]), radius=0.016566867510712412, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([18, 28, 36, 44, 45, 46, 47, 49, 50, 51]), model=ScalarModel(intercept=0.17468169315856466, linear_terms=array([0.00019334, 0.00068168, 0.002236 ]), square_terms=array([[6.30086486e-06, 1.31120182e-04, 1.92914840e-04], + [1.31120182e-04, 2.30933740e-02, 4.65644949e-02], + [1.92914840e-04, 4.65644949e-02, 9.56283946e-02]]), scale=0.016566867510712412, shift=array([5.17665032, 0.34743911, 0.93166475])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + 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model=ScalarModel(intercept=0.17496972296461294, linear_terms=array([ 6.97863248e-05, -8.21475281e-05, -4.56887150e-04]), square_terms=array([[1.84361126e-06, 2.04013067e-05, 2.19735979e-05], + [2.04013067e-05, 5.26300747e-03, 1.10824804e-02], + [2.19735979e-05, 1.10824804e-02, 2.38107760e-02]]), scale=0.008283433755356206, shift=array([5.17665032, 0.34743911, 0.93166475])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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index=53, x=array([5.17030197, 0.34249393, 0.93412279]), fval=0.17478348430580953, rho=1.7260767313376242, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([44, 47, 49, 51, 52]), old_indices_discarded=array([], dtype=int32), step_length=0.008414173637941258, relative_step_length=1.0157832954842565, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.17030197, 0.34249393, 0.93412279]), radius=0.016566867510712412, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([18, 28, 36, 44, 45, 46, 47, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.1749826973068216, linear_terms=array([0.0001074 , 0.00038707, 0.00171986]), square_terms=array([[7.58231429e-06, 2.29836559e-04, 3.96776253e-04], + [2.29836559e-04, 2.29465979e-02, 4.65350783e-02], + [3.96776253e-04, 4.65350783e-02, 9.61235255e-02]]), scale=0.016566867510712412, shift=array([5.17030197, 0.34249393, 0.93412279])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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State(trustregion=Region(center=array([5.17030197, 0.34249393, 0.93412279]), radius=0.008283433755356206, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([44, 47, 49, 51, 52, 53, 54]), model=ScalarModel(intercept=0.1747885766485168, linear_terms=array([ 7.81846919e-05, 3.99579562e-05, -9.60174366e-05]), square_terms=array([[1.77118649e-06, 1.19372381e-05, 4.26127615e-06], + [1.19372381e-05, 5.37799886e-03, 1.13872076e-02], + [4.26127615e-06, 1.13872076e-02, 2.45888809e-02]]), scale=0.008283433755356206, shift=array([5.17030197, 0.34249393, 0.93412279])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 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shift=array([5.14420282, 0.34178406, 0.93435027])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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relative_step_length=1.0030673053597174, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.1369295 , 0.33809681, 0.93594391]), radius=0.016566867510712412, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([44, 47, 49, 51, 52, 53, 54, 55, 56, 57, 58, 59]), model=ScalarModel(intercept=0.1744379176106382, linear_terms=array([ 0.00014316, -0.0007016 , -0.00153491]), square_terms=array([[6.93968654e-06, 4.93334629e-05, 2.09902799e-05], + [4.93334629e-05, 2.19685008e-02, 4.66549901e-02], + [2.09902799e-05, 4.66549901e-02, 1.00998973e-01]]), scale=0.016566867510712412, shift=array([5.1369295 , 0.33809681, 0.93594391])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 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model=ScalarModel(intercept=0.17429973881169664, linear_terms=array([ 2.70298754e-04, -3.09106006e-05, 1.40978604e-06]), square_terms=array([[2.77478710e-05, 2.26332451e-04, 1.44291033e-04], + [2.26332451e-04, 8.82227523e-02, 1.87432209e-01], + [1.44291033e-04, 1.87432209e-01, 4.05886882e-01]]), scale=0.033133735021424825, shift=array([5.12036855, 0.33903597, 0.93576554])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], 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x=array([5.08741985, 0.34221541, 0.93430985]), fval=0.17398999224518474, rho=1.2760574583541893, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([44, 49, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60]), old_indices_discarded=array([17, 18, 21, 24, 28, 31, 33, 34, 36, 37, 39, 43, 45, 46, 47, 48, 50]), step_length=0.03313373505039257, relative_step_length=1.0000000008742673, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.08741985, 0.34221541, 0.93430985]), radius=0.06626747004284965, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([44, 49, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=0.17399871546423445, linear_terms=array([5.54960169e-04, 2.77020491e-05, 2.46039514e-04]), square_terms=array([[1.12760714e-04, 1.09146716e-03, 9.82283047e-04], + [1.09146716e-03, 3.54762285e-01, 7.53825721e-01], + [9.82283047e-04, 7.53825721e-01, 1.63251500e+00]]), scale=0.06626747004284965, shift=array([5.08741985, 0.34221541, 0.93430985])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + 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25, 26, 27, 28, + 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 45, 46, + 47, 48, 50, 51]), step_length=0.0662677999938465, relative_step_length=1.000004979079428, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.02154349, 0.34875659, 0.93132 ]), radius=0.1325349400856993, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([49, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62]), model=ScalarModel(intercept=0.17340700850452878, linear_terms=array([0.00109183, 0.00472696, 0.00984298]), square_terms=array([[4.56127079e-04, 4.44510297e-03, 4.16551232e-03], + [4.44510297e-03, 1.53012536e+00, 3.24711918e+00], + [4.16551232e-03, 3.24711918e+00, 7.01257022e+00]]), scale=0.1325349400856993, shift=array([5.02154349, 0.34875659, 0.93132 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 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n_evals_acceptance=1), State(trustregion=Region(center=array([4.88958301, 0.35997157, 0.92601999]), radius=0.2650698801713986, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 8, 52, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]), model=ScalarModel(intercept=0.6100711093200124, linear_terms=array([-0.01326482, -4.36274037, -8.18834305]), square_terms=array([[1.44837362e-03, 7.48035154e-02, 1.27938079e-01], + [7.48035154e-02, 2.17667442e+01, 4.07631921e+01], + [1.27938079e-01, 4.07631921e+01, 7.65829433e+01]]), scale=array([0.21364526, 0.21364526, 0.19381264]), shift=array([4.88958301, 0.35997157, 0.90618736])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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model_indices=array([67, 72, 73, 75, 76, 77, 78, 80, 81, 84, 86, 87]), model=ScalarModel(intercept=0.1725764987304485, linear_terms=array([7.05977087e-06, 2.29884404e-05, 1.21152777e-05]), square_terms=array([[5.86956811e-07, 1.16384872e-05, 1.94432612e-05], + [1.16384872e-05, 1.63380924e-03, 3.44140991e-03], + [1.94432612e-05, 3.44140991e-03, 7.36782486e-03]]), scale=0.004141716877678103, shift=array([4.84436454, 0.3458171 , 0.93240807])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), 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candidate_x=array([4.84072397, 0.34397348, 0.93327128]), index=87, x=array([4.84436454, 0.3458171 , 0.93240807]), fval=0.1725720095711579, rho=-1.0641290402854422, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([67, 72, 73, 75, 76, 77, 78, 80, 81, 84, 86, 87]), old_indices_discarded=array([74, 79, 82, 83, 85]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.84436454, 0.3458171 , 0.93240807]), radius=0.0020708584388390515, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, + 99, 100]), model=ScalarModel(intercept=0.17258738194234413, linear_terms=array([ 3.33838120e-06, -1.97151771e-08, 5.27903701e-06]), square_terms=array([[ 1.36507244e-07, 3.44142448e-07, -7.87178649e-07], + [ 3.44142448e-07, 4.10506238e-04, 8.69151483e-04], + [-7.87178649e-07, 8.69151483e-04, 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.84436454, 0.3458171 , 0.93240807]), radius=0.0010354292194195258, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([87, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]), model=ScalarModel(intercept=0.1725830437240501, linear_terms=array([ 2.41233698e-05, -2.61891003e-06, 5.11296505e-06]), square_terms=array([[ 4.37386430e-08, -5.79574560e-07, -1.57888647e-06], + [-5.79574560e-07, 1.02556710e-04, 2.17206204e-04], + [-1.57888647e-06, 2.17206204e-04, 4.67922792e-04]]), scale=0.0010354292194195258, shift=array([4.84436454, 0.3458171 , 0.93240807])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), 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100, 101, 102]), model=ScalarModel(intercept=0.17258247058638926, linear_terms=array([-2.68495297e-05, -2.10095588e-06, -2.07300030e-05]), square_terms=array([[1.91163962e-07, 3.32105210e-06, 5.86912190e-06], + [3.32105210e-06, 4.07207519e-04, 8.64705423e-04], + [5.86912190e-06, 8.64705423e-04, 1.86822036e-03]]), scale=0.0020708584388390515, shift=array([4.84334274, 0.34598281, 0.93232094])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 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relative_step_length=1.0043585886444073, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.84438244, 0.34597441, 0.93234197]), radius=0.0020708584388390515, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 87, 89, 90, 92, 96, 97, 98, 100, 101, 102, 103, 104]), model=ScalarModel(intercept=0.17259245410531784, linear_terms=array([ 2.41019710e-05, -4.48842471e-06, -4.27936350e-05]), square_terms=array([[1.56434406e-07, 3.33318421e-06, 6.03774428e-06], + [3.33318421e-06, 4.15831904e-04, 8.74354178e-04], + [6.03774428e-06, 8.74354178e-04, 1.86706420e-03]]), scale=0.0020708584388390515, shift=array([4.84438244, 0.34597441, 0.93234197])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), 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105]), model=ScalarModel(intercept=0.17258372705737116, linear_terms=array([ 4.09478695e-06, -3.55795689e-06, -4.27096698e-05]), square_terms=array([[3.35585618e-08, 2.15061082e-07, 1.37358651e-07], + [2.15061082e-07, 1.03219001e-04, 2.16664028e-04], + [1.37358651e-07, 2.16664028e-04, 4.61793255e-04]]), scale=0.0010354292194195258, shift=array([4.84438244, 0.34597441, 0.93234197])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 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index=104, x=array([4.84438244, 0.34597441, 0.93234197]), fval=0.1725502344785951, rho=-2.013845980078064, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 87, 90, 92, 96, 97, 98, 100, 101, 102, 103, 104, 105]), old_indices_discarded=array([88, 89, 91, 93, 94, 95, 99]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.84438244, 0.34597441, 0.93234197]), radius=0.0005177146097097629, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 87, 90, 92, 96, 97, 98, 100, 102, 103, 104, 105, 106]), model=ScalarModel(intercept=0.1725840937366451, linear_terms=array([ 3.96813424e-06, -3.12252958e-06, -1.95411424e-05]), square_terms=array([[ 8.35946962e-09, 7.31232339e-09, -6.36196010e-08], + [ 7.31232339e-09, 2.56886777e-05, 5.40554793e-05], + [-6.36196010e-08, 5.40554793e-05, 1.15524825e-04]]), scale=0.0005177146097097629, shift=array([4.84438244, 0.34597441, 0.93234197])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.84438244, 0.34597441, 0.93234197]), radius=0.00025885730485488144, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 87, 102, 103, 104, 106, 107]), model=ScalarModel(intercept=0.17255976175891496, linear_terms=array([-1.36437344e-07, -4.51532084e-05, -6.73471361e-05]), square_terms=array([[2.54545043e-09, 5.61432011e-08, 9.86416386e-08], + [5.61432011e-08, 7.19930049e-06, 1.53720182e-05], + [9.86416386e-08, 1.53720182e-05, 3.34119316e-05]]), scale=0.00025885730485488144, shift=array([4.84438244, 0.34597441, 0.93234197])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 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5.57565527e-05]), square_terms=array([[ 2.22263556e-08, -7.54974445e-08, -1.28829298e-07], + [-7.54974445e-08, 1.63871850e-06, 3.49355539e-06], + [-1.28829298e-07, 3.49355539e-06, 7.66893361e-06]]), scale=0.00012942865242744072, shift=array([4.84438244, 0.34597441, 0.93234197])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 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scale=0.5301397603427972, shift=array([5.3013976 , 0.35179043, 0.92170005])), candidate_index=123, candidate_x=array([4.8444368 , 0.34601283, 0.93235891]), index=123, x=array([4.8444368 , 0.34601283, 0.93235891]), fval=0.17254703939389457, rho=0.23884848773610878, accepted=True, new_indices=array([111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_used=array([104, 110]), old_indices_discarded=array([], dtype=int32), step_length=3.2357163106865805e-05, relative_step_length=1.0000000000001739, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 124 entries., 'history': {'params': [{'CRRA': 5.301397603427972, 'WealthShare': 0.35179042920828707, 'DiscFac': 0.9217000493025207}, {'CRRA': 4.877555010789218, 'WealthShare': 0.01, 'DiscFac': 1.0004206381225442}, {'CRRA': 5.726445053953837, 'WealthShare': 0.038959478718889294, 'DiscFac': 1.1}, {'CRRA': 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+WealthShare,0.1943943314688513 + +WealthShift,0.679443956662166 + +time_to_estimate,224.97277808189392 + +params,"{'CRRA': 5.371266391432918, 'WealthShare': 0.1943943314688513, 'WealthShift': 0.679443956662166}" + +criterion,0.2295381734816966 + +start_criterion,0.23891445185117913 + +start_params,"{'CRRA': 5.338780774481047, 'WealthShare': 0.17065528804872485, 'WealthShift': 0.0}" + +algorithm,multistart_tranquilo_ls + +direction,minimize + +n_free,3 + +message,Absolute criterion change smaller than tolerance. + +success, + +n_criterion_evaluations, + +n_derivative_evaluations, + +n_iterations, + +history,"{'params': [{'CRRA': 5.338780774481047, 'WealthShare': 0.17065528804872487, 'WealthShift': 0.0}, {'CRRA': 4.9314527326473225, 'WealthShare': 0.01, 'WealthShift': 0.35816358982006313}, {'CRRA': 5.608800970109241, 'WealthShare': 0.01, 'WealthShift': 0.41982482970398044}, {'CRRA': 5.76908435711711, 'WealthShare': 0.5158471609068197, 'WealthShift': 0.3981698683104749}, {'CRRA': 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'WealthShift': 0.6946412707076063}, {'CRRA': 5.362530858243278, 'WealthShare': 0.19371235845028822, 'WealthShift': 0.67917279093371}, {'CRRA': 5.371252044414068, 'WealthShare': 0.19515406179895806, 'WealthShift': 0.6748494653651586}, {'CRRA': 5.371040357003462, 'WealthShare': 0.19512570949497798, 'WealthShift': 0.6804574712649102}, {'CRRA': 5.370362032234467, 'WealthShare': 0.19301496853268316, 'WealthShift': 0.6791361950445876}, {'CRRA': 5.370417235247351, 'WealthShare': 0.1946836539449308, 'WealthShift': 0.6789290949944624}, {'CRRA': 5.371700118481283, 'WealthShare': 0.19407592989865619, 'WealthShift': 0.679466131617694}, {'CRRA': 5.370996809733228, 'WealthShare': 0.1932546349189789, 'WealthShift': 0.6779038972651502}, {'CRRA': 5.371282416198062, 'WealthShare': 0.1928112110018877, 'WealthShift': 0.6787645124805319}, {'CRRA': 5.3710903886459365, 'WealthShare': 0.1934205187933144, 'WealthShift': 0.6797526277529055}, {'CRRA': 5.3704769790314755, 'WealthShare': 0.19429995636141595, 'WealthShift': 0.6780101915875183}, {'CRRA': 5.371545682473069, 'WealthShare': 0.19411655863874636, 'WealthShift': 0.6779925063945972}, {'CRRA': 5.370195456559467, 'WealthShare': 0.19397745856737997, 'WealthShift': 0.6794840078126839}, {'CRRA': 5.370050397316498, 'WealthShare': 0.19350353913218477, 'WealthShift': 0.6783649794283761}, {'CRRA': 5.371962513757795, 'WealthShare': 0.19354151125781988, 'WealthShift': 0.6787284059664366}, {'CRRA': 5.37138823145291, 'WealthShare': 0.19474863411072363, 'WealthShift': 0.6787693935361694}, {'CRRA': 5.3710792065342154, 'WealthShare': 0.19443913068868013, 'WealthShift': 0.679621235613119}, {'CRRA': 5.3704034370938185, 'WealthShare': 0.19458142919799615, 'WealthShift': 0.6776431911313305}, {'CRRA': 5.371320550989001, 'WealthShare': 0.19446077911052448, 'WealthShift': 0.678607008167103}, {'CRRA': 5.3713519822030475, 'WealthShare': 0.1944618633719279, 'WealthShift': 0.6791772691529462}, {'CRRA': 5.371266391432918, 'WealthShare': 0.1943943314688513, 'WealthShift': 0.679443956662166}], 'criterion': [0.24222229239256646, 1.478112872055069, 1.1738993056575204, 7.66561489336726, 18.738700547401976, 19.22108795656615, 15.506705787934324, 12.31515961934518, 0.23576937639679463, 9.707223917852335, 1.2482439718131173, 20.90388989527211, 0.7443512934453255, 0.24479444524730848, 0.2614718904910609, 0.270639050852218, 0.24095668076641172, 0.2399553333009884, 0.2377361837426363, 0.2568729292948785, 0.23880933776883706, 0.2357308733203971, 0.23672808947597423, 0.23462018780083854, 0.23526585311770365, 0.23386060852077306, 0.23281729277673047, 0.23109088667954464, 0.23231088686731985, 0.22984874056451765, 0.2321432347041099, 0.2311825152488482, 0.2298724029405645, 0.23262807844020333, 0.23060109795757228, 0.2333646350605909, 0.22998277512898735, 0.23771152142310734, 0.23273062652367485, 0.23024047517833396, 0.23539451131722727, 0.23520968963219913, 0.23708932322781573, 0.23014224071617864, 0.24191165473844944, 0.23307446207864127, 0.22981068942013977, 0.22970924468774562, 0.22961013657146467, 0.22981789832329552, 0.22973138822808242, 0.22962474605065475, 0.22963112451216366, 0.22958113184400364, 0.22959160086907093, 0.22960873266061366, 0.22963318479728462, 0.22961335501541685, 0.22966650476648243, 0.22957347491137076, 0.22954657256751673, 0.2296255367268235, 0.2297114465837029, 0.22962539796630416, 0.2295392655379997, 0.2295439897979808, 0.22956976961891207, 0.22960795862685532, 0.22960143762896604, 0.2295819805696318, 0.22953900680686223, 0.22956535348444274, 0.2295484781109926, 0.2295457973718279, 0.22953817348169658], 'runtime': [0.0, 1.078445800114423, 1.1149003999307752, 1.1535559999756515, 1.1922693997621536, 1.2300137002021074, 1.2728542000986636, 1.3101439997553825, 1.3493002001196146, 1.3861357998102903, 1.4237115997821093, 1.4617324001155794, 1.5002863998524845, 2.6419434999115765, 3.6615598001517355, 4.674209500197321, 5.7083767000585794, 6.739310999866575, 7.906286799814552, 8.925660899840295, 9.94611269980669, 10.963380999863148, 11.982361000031233, 13.00331459986046, 14.01534829987213, 15.032428000122309, 16.052119500003755, 17.067473100032657, 18.08538859989494, 19.095149700064212, 20.1070157000795, 21.11918029980734, 22.27491270005703, 23.287277800031006, 24.42046120017767, 24.446721899788827, 24.48540070001036, 24.524533899966627, 24.56690760003403, 24.604305800050497, 24.64112879987806, 24.678917500190437, 24.717683599796146, 24.75592500017956, 24.79437270015478, 24.83348789997399, 25.926029299851507, 26.96925489977002, 28.003000599797815, 29.01732419990003, 30.056552299764007, 31.073117999825627, 32.104939199984074, 33.1367243998684, 34.14407660020515, 35.153611599933356, 36.16626589978114, 37.30677030002698, 38.441172500140965, 38.47760089999065, 38.51622289977968, 38.55528979981318, 38.59801559988409, 38.63672780012712, 38.67566310008988, 38.71459549991414, 38.75300749996677, 38.79171779984608, 38.83782930020243, 38.870700700208545, 39.96831429982558, 40.98857139982283, 42.03050069976598, 43.07035129982978, 44.10055600013584], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 37, 38, 39, 40, 41]}" + +convergence_report, + +multistart_info,"{'start_parameters': [{'CRRA': 5.338780774481047, 'WealthShare': 0.17065528804872487, 'WealthShift': 0.0}, {'CRRA': 5.812664276213293, 'WealthShare': 0.23295294896519178, 'WealthShift': 6.996179195190264}, {'CRRA': 5.6712914737321025, 'WealthShare': 0.19427609444130123, 'WealthShift': 6.289245513555226}], 'local_optima': [Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 3.63e-06* 0.0001872 +relative_params_change 0.0003499 0.003202 +absolute_criterion_change 8.333e-07* 4.296e-05 +absolute_params_change 0.0002617 0.001639 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 1.133e-05 7.917e-05 +relative_params_change 0.0002288 0.001211 +absolute_criterion_change 2.602e-06* 1.818e-05 +absolute_params_change 8.54e-05 0.001737 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 2.534e-06* 0.001255 +relative_params_change 5.552e-05 0.0427 +absolute_criterion_change 5.817e-07* 0.000288 +absolute_params_change 8.297e-05 0.08045 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 5.338780774481047, 'WealthShare': 0.17065528804872485, 'WealthShift': 0.0}, {'CRRA': 6.415625, 'WealthShare': 0.285625, 'WealthShift': 15.625}, {'CRRA': 7.00625, 'WealthShare': 0.19375, 'WealthShift': 31.25}, {'CRRA': 9.959375, 'WealthShare': 0.101875, 'WealthShift': 84.375}, {'CRRA': 8.1875, 'WealthShare': 0.3775, 'WealthShift': 62.5}, {'CRRA': 4.64375, 'WealthShare': 0.31625000000000003, 'WealthShift': 93.75}, {'CRRA': 12.9125, 'WealthShare': 0.1325, 'WealthShift': 87.5}, {'CRRA': 4.053125, 'WealthShare': 0.16312500000000002, 'WealthShift': 53.125}, {'CRRA': 2.871875, 'WealthShare': 0.469375, 'WealthShift': 46.875}, {'CRRA': 18.81875, 'WealthShare': 0.07125, 'WealthShift': 68.75}, {'CRRA': 15.274999999999999, 'WealthShare': 0.255, 'WealthShift': 25.0}, {'CRRA': 11.73125, 'WealthShare': 0.43875, 'WealthShift': 6.25}, {'CRRA': 17.046875, 'WealthShare': 0.224375, 'WealthShift': 21.875}, {'CRRA': 3.4625, 'WealthShare': 0.6225, 'WealthShift': 37.5}, {'CRRA': 14.684375, 'WealthShare': 0.346875, 'WealthShift': 59.375}, {'CRRA': 10.549999999999999, 'WealthShare': 0.5, 'WealthShift': 50.0}, {'CRRA': 9.368749999999999, 'WealthShare': 0.56125, 'WealthShift': 18.75}, {'CRRA': 13.503124999999999, 'WealthShare': 0.653125, 'WealthShift': 3.125}, {'CRRA': 18.228125, 'WealthShare': 0.408125, 'WealthShift': 78.125}, {'CRRA': 5.824999999999999, 'WealthShare': 0.745, 'WealthShift': 75.0}, {'CRRA': 7.596874999999999, 'WealthShare': 0.714375, 'WealthShift': 71.875}, {'CRRA': 19.409375, 'WealthShare': 0.591875, 'WealthShift': 34.375}, {'CRRA': 16.45625, 'WealthShare': 0.68375, 'WealthShift': 81.25}, {'CRRA': 17.6375, 'WealthShare': 0.8674999999999999, 'WealthShift': 12.5}, {'CRRA': 15.865624999999998, 'WealthShare': 0.775625, 'WealthShift': 65.625}, {'CRRA': 14.093749999999998, 'WealthShare': 0.80625, 'WealthShift': 43.75}, {'CRRA': 5.234375, 'WealthShare': 0.836875, 'WealthShift': 9.375}, {'CRRA': 8.778125, 'WealthShare': 0.898125, 'WealthShift': 28.125}, {'CRRA': 2.28125, 'WealthShare': 0.92875, 'WealthShift': 56.25}, {'CRRA': 12.321874999999999, 'WealthShare': 0.959375, 'WealthShift': 96.875}], 'exploration_results': array([2.42222292e-01, 4.68695243e-01, 6.55725054e-01, 7.71100519e-01, + 1.19425789e+00, 1.58158215e+00, 1.58170972e+00, 2.10988168e+00, + 2.53770191e+00, 3.19288105e+00, 3.47314230e+00, 3.48315006e+00, + 3.92293459e+00, 4.56511870e+00, 5.33390087e+00, 5.34621006e+00, + 6.48994604e+00, 9.32970623e+00, 1.17999718e+01, 1.57570577e+01, + 1.66647063e+01, 2.65180559e+01, 4.77377487e+01, 8.29741042e+01, + 8.62618718e+01, 8.81150175e+01, 1.31509063e+02, 1.97370096e+02, + 6.42774449e+02, 7.33084130e+02])}" + +algorithm_output,"{'states': [State(trustregion=Region(center=array([5.33878077, 0.17065529, 0. ]), radius=0.5338780774481048, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=[0], model=ScalarModel(intercept=0.24222229239256646, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), scale=0.5338780774481048, shift=array([5.33878077, 0.17065529, 0. ])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]]), scale=0.5338780774481048, shift=array([5.33878077, 0.17065529, 0. ])), candidate_index=0, candidate_x=array([5.33878077, 0.17065529, 0. ]), index=0, x=array([5.33878077, 0.17065529, 0. ]), fval=0.24222229239256646, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([5.33878077, 0.17065529, 0. ]), radius=0.5338780774481048, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=2.225040705891025, linear_terms=array([-0.41697775, 7.48428914, -0.17091496]), square_terms=array([[ 4.61705353e-02, -7.15556597e-01, 1.51914511e-02], + [-7.15556597e-01, 1.37527722e+01, -2.97488982e-01], + [ 1.51914511e-02, -2.97488982e-01, 6.91651527e-03]]), scale=array([0.43030358, 0.29547944, 0.21515179]), shift=array([5.33878077, 0.30547944, 0.21515179])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), 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vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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State(trustregion=Region(center=array([5.33002354, 0.17880899, 0.16290546]), radius=0.03336737984050655, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 0, 16, 17, 18, 19, 20, 21, 22]), model=ScalarModel(intercept=0.2353621409779833, linear_terms=array([-8.14409317e-06, 3.93183005e-03, -1.42315911e-03]), square_terms=array([[ 3.70635713e-05, 8.65491261e-04, -3.88139805e-05], + [ 8.65491261e-04, 9.48655967e-02, -3.68813140e-03], + [-3.88139805e-05, -3.68813140e-03, 1.59862148e-04]]), scale=0.03336737984050655, shift=array([5.33002354, 0.17880899, 0.16290546])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], 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0.18962821, 0.56908272]), radius=0.016683689920253274, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([29, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.22990493283945845, linear_terms=array([-1.03738771e-04, -1.68226271e-03, -6.74238130e-05]), square_terms=array([[ 9.10673381e-06, 2.28039022e-04, -8.01399215e-06], + [ 2.28039022e-04, 2.35834730e-02, -6.68846684e-04], + [-8.01399215e-06, -6.68846684e-04, 1.97600773e-05]]), scale=0.016683689920253274, shift=array([5.33291758, 0.18962821, 0.56908272])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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State(trustregion=Region(center=array([5.37107921, 0.19443913, 0.67962124]), radius=0.0002606826550039574, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([53, 57, 59, 60, 63, 66, 69, 70, 72, 73]), model=ScalarModel(intercept=0.22956635142348697, linear_terms=array([-2.97036256e-06, 1.56065050e-06, 2.79682150e-06]), square_terms=array([[ 2.37222388e-09, 3.94839739e-08, -1.54347078e-09], + [ 3.94839739e-08, 5.65438269e-06, -1.39090087e-07], + [-1.54347078e-09, -1.39090087e-07, 3.86479519e-09]]), scale=0.0002606826550039574, shift=array([5.37107921, 0.19443913, 0.67962124])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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'states': [State(trustregion=Region(center=array([5.33878077, 0.17065529, 0. ]), radius=0.5338780774481048, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=[0], model=ScalarModel(intercept=0.24222229239256646, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), scale=0.5338780774481048, shift=array([5.33878077, 0.17065529, 0. ])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), 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candidate_x=array([5.33878077, 0.17065529, 0. ]), index=0, x=array([5.33878077, 0.17065529, 0. ]), fval=0.24222229239256646, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([5.33878077, 0.17065529, 0. ]), radius=0.5338780774481048, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=2.225040705891025, linear_terms=array([-0.41697775, 7.48428914, -0.17091496]), square_terms=array([[ 4.61705353e-02, -7.15556597e-01, 1.51914511e-02], + [-7.15556597e-01, 1.37527722e+01, -2.97488982e-01], + [ 1.51914511e-02, -2.97488982e-01, 6.91651527e-03]]), scale=array([0.43030358, 0.29547944, 0.21515179]), shift=array([5.33878077, 0.30547944, 0.21515179])), vector_model=VectorModel(intercepts=array([ 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bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 0, 1, 2, 4, 5, 7, 8, 9, 10, 11, 12, 13]), model=ScalarModel(intercept=0.425381512628019, linear_terms=array([-0.05492339, 1.6515207 , -0.01207703]), square_terms=array([[ 8.17527296e-03, -1.66195924e-01, 1.66474843e-03], + [-1.66195924e-01, 5.78105326e+00, -4.38301566e-02], + [ 1.66474843e-03, -4.38301566e-02, 4.71218119e-04]]), scale=array([0.21515179, 0.18790354, 0.1075759 ]), shift=array([5.33878077, 0.19790354, 0.1075759 ])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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scale=0.5338780774481048, shift=array([5.33878077, 0.17065529, 0. ])), candidate_index=14, candidate_x=array([5.55393257, 0.1482009 , 0. ]), index=0, x=array([5.33878077, 0.17065529, 0. ]), fval=0.24222229239256646, rho=-0.2762680450576785, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 2, 4, 5, 7, 8, 9, 10, 11, 12, 13]), old_indices_discarded=array([3, 6]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33878077, 0.17065529, 0. ]), radius=0.1334695193620262, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 0, 1, 2, 4, 7, 8, 9, 10, 11, 12, 13, 14]), model=ScalarModel(intercept=0.25159110698009957, linear_terms=array([-0.01524465, 0.47423814, -0.00627023]), square_terms=array([[ 2.17792939e-03, -4.77186682e-02, 7.80957785e-04], + [-4.77186682e-02, 1.83079477e+00, -2.76050226e-02], + [ 7.80957785e-04, -2.76050226e-02, 4.40535462e-04]]), scale=array([0.1075759 , 0.1075759 , 0.05378795]), shift=array([5.33878077, 0.17065529, 0.05378795])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33878077, 0.17065529, 0. ]), radius=0.0667347596810131, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 0, 10, 14, 15]), model=ScalarModel(intercept=0.1884967041414315, linear_terms=array([-0.00049831, 0.00705515, -0.03692845]), square_terms=array([[ 1.08791636e-04, 3.07187991e-03, -1.03846231e-03], + [ 3.07187991e-03, 2.45954542e-01, 6.85505606e-03], + [-1.03846231e-03, 6.85505606e-03, 3.35942789e-02]]), scale=array([0.05378795, 0.05378795, 0.02689397]), shift=array([5.33878077, 0.17065529, 0.02689397])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 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State(trustregion=Region(center=array([5.32798039, 0.17024985, 0.12990877]), radius=0.03336737984050655, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 0, 16, 17, 18, 19, 20]), model=ScalarModel(intercept=0.23750709163817948, linear_terms=array([-0.00028041, -0.02014843, -0.00082547]), square_terms=array([[ 3.87878112e-05, 8.30222831e-04, -3.48271727e-05], + [ 8.30222831e-04, 9.10354447e-02, -3.71220454e-03], + [-3.48271727e-05, -3.71220454e-03, 1.74706201e-04]]), scale=0.03336737984050655, shift=array([5.32798039, 0.17024985, 0.12990877])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]), old_indices_discarded=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]), step_length=0.13980703182363227, relative_step_length=1.047482844711653, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.31458516, 0.18690146, 0.43567585]), radius=0.2669390387240524, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 2, 8, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.24889256895749373, linear_terms=array([ 0.00121002, 0.32434574, -0.015755 ]), square_terms=array([[ 1.70973791e-03, 4.47870922e-02, -1.80519470e-03], + [ 4.47870922e-02, 2.63148581e+00, -8.32303744e-02], + [-1.80519470e-03, -8.32303744e-02, 2.99733583e-03]]), scale=array([0.21515179, 0.19602663, 0.21515179]), shift=array([5.31458516, 0.20602663, 0.43567585])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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n_evals_acceptance=1), State(trustregion=Region(center=array([5.31458516, 0.18690146, 0.43567585]), radius=0.1334695193620262, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 8, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=0.23229852733260054, linear_terms=array([ 2.12256197e-05, 1.30675034e-02, -1.76767877e-03]), square_terms=array([[ 6.85846279e-04, 1.62602126e-02, -6.27129917e-04], + [ 1.62602126e-02, 1.52275966e+00, -4.64589216e-02], + [-6.27129917e-04, -4.64589216e-02, 1.49949621e-03]]), scale=0.1334695193620262, shift=array([5.31458516, 0.18690146, 0.43567585])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 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model=ScalarModel(intercept=0.2472192748047931, linear_terms=array([ 0.00288894, 0.29428743, -0.01173496]), square_terms=array([[ 1.76702756e-03, 4.10334146e-02, -1.66808852e-03], + [ 4.10334146e-02, 2.64561735e+00, -8.66366920e-02], + [-1.66808852e-03, -8.66366920e-02, 3.03240965e-03]]), scale=array([0.21515179, 0.19739 , 0.21515179]), shift=array([5.33291758, 0.20739 , 0.56908272])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + 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index=29, x=array([5.33291758, 0.18962821, 0.56908272]), fval=0.22984874056451765, rho=-0.5690444447139047, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 2, 8, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29]), old_indices_discarded=array([ 0, 1, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, + 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33291758, 0.18962821, 0.56908272]), radius=0.1334695193620262, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 2, 8, 19, 21, 23, 24, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=0.23259268781303988, linear_terms=array([-0.00018402, 0.03913342, -0.00193276]), square_terms=array([[ 6.90417570e-04, 1.72993592e-02, -6.33886092e-04], + [ 1.72993592e-02, 1.20998840e+00, -3.61884661e-02], + [-6.33886092e-04, -3.61884661e-02, 1.14819645e-03]]), scale=0.1334695193620262, shift=array([5.33291758, 0.18962821, 0.56908272])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([29, 31, 32, 33, 35, 38, 40, 43, 44, 46, 47, 48]), model=ScalarModel(intercept=0.22974098770427356, linear_terms=array([-1.09021468e-05, 4.08548402e-04, 5.20757068e-06]), square_terms=array([[ 1.53966444e-04, 3.96473554e-03, -1.32542901e-04], + [ 3.96473554e-03, 3.75251867e-01, -1.03635926e-02], + [-1.32542901e-04, -1.03635926e-02, 2.96619546e-04]]), scale=0.0667347596810131, shift=array([5.37932077, 0.19348564, 0.67857404])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 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x=array([5.67791134, 0.28170285, 6.82432251]), fval=0.31575165439857333, rho=0.914691189691689, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 16, 17, 18, 19, 20, 22, 24, 25, 26, 27, 28]), old_indices_discarded=array([ 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 21, 23]), step_length=0.1764090125632903, relative_step_length=1.0086020248570422, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.67791134, 0.28170285, 6.82432251]), radius=0.3498089597595132, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 0, 15, 16, 18, 19, 20, 21, 22, 24, 25, 27, 29]), model=ScalarModel(intercept=0.31717082762321236, linear_terms=array([0.00096363, 0.06934767, 0.00608098]), square_terms=array([[ 1.42032894e-02, 1.94451298e-01, -6.13273089e-03], + [ 1.94451298e-01, 3.46553930e+00, -9.97951198e-02], + [-6.13273089e-03, -9.97951198e-02, 3.08594422e-03]]), scale=array([0.28194461, 0.27682373, 0.28194461]), shift=array([5.67791134, 0.28682373, 6.82432251])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, + 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 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17, 23, 26, + 28]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.67791134, 0.28170285, 6.82432251]), radius=0.1749044798797566, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 0, 16, 17, 18, 19, 21, 23, 24, 26, 27, 28, 29]), model=ScalarModel(intercept=0.318487878323986, linear_terms=array([ 0.00061757, -0.00732461, -0.0023864 ]), square_terms=array([[ 3.07250367e-03, 5.17438327e-02, -1.42203067e-05], + [ 5.17438327e-02, 1.41079054e+00, 5.03649803e-03], + [-1.42203067e-05, 5.03649803e-03, 9.93209172e-05]]), scale=0.1749044798797566, shift=array([5.67791134, 0.28170285, 6.82432251])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, + 0.04033998, 0.06941292]), 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31]), model=ScalarModel(intercept=0.3170759930491773, linear_terms=array([ 0.00040458, 0.00060017, -0.00011948]), square_terms=array([[ 6.33756213e-04, 1.16412855e-02, -7.85554926e-05], + [ 1.16412855e-02, 3.68078048e-01, -1.19418009e-03], + [-7.85554926e-05, -1.19418009e-03, 1.56957188e-05]]), scale=0.0874522399398783, shift=array([5.67791134, 0.28170285, 6.82432251])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, + 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + 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x=array([5.67791134, 0.28170285, 6.82432251]), fval=0.31575165439857333, rho=-2.5002315308908742, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 16, 17, 18, 19, 21, 23, 24, 27, 28, 29, 31]), old_indices_discarded=array([15, 20, 22, 25, 26, 30]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.67791134, 0.28170285, 6.82432251]), radius=0.04372611996993915, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([18, 21, 23, 24, 27, 28, 29, 31, 32]), model=ScalarModel(intercept=0.3165892646918602, linear_terms=array([-5.70867066e-05, 1.95773124e-03, 3.81449161e-04]), square_terms=array([[ 1.65202139e-04, 3.01148727e-03, -3.85488572e-05], + [ 3.01148727e-03, 8.92181217e-02, -8.68640185e-04], + [-3.85488572e-05, -8.68640185e-04, 1.07665106e-05]]), scale=0.04372611996993915, shift=array([5.67791134, 0.28170285, 6.82432251])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, + 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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State(trustregion=Region(center=array([5.68911594, 0.27992637, 6.77848331]), radius=0.0874522399398783, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 0, 16, 18, 21, 23, 24, 27, 28, 29, 31, 32, 33]), model=ScalarModel(intercept=0.31490091077750826, linear_terms=array([0.00056326, 0.00239357, 0.00169179]), square_terms=array([[ 5.75896146e-04, 1.04942751e-02, -1.87864830e-04], + [ 1.04942751e-02, 3.55429643e-01, -5.22363671e-03], + [-1.87864830e-04, -5.22363671e-03, 8.85281587e-05]]), scale=0.0874522399398783, shift=array([5.68911594, 0.27992637, 6.77848331])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, + 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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x=array([5.67220534, 0.2794848 , 6.39080358]), fval=0.3078435872229235, rho=1.1650054937337413, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([35, 36, 37, 38, 39, 40, 41, 42]), old_indices_discarded=array([], dtype=int32), step_length=0.04628805319266375, relative_step_length=1.0585904540463658, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.67220534, 0.2794848 , 6.39080358]), radius=0.0874522399398783, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([10, 30, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=0.312469012000744, linear_terms=array([0.0006424 , 0.02844622, 0.0008161 ]), square_terms=array([[ 5.62722956e-04, 7.63491905e-03, -8.19951703e-05], + [ 7.63491905e-03, 2.06477523e-01, -1.47782624e-03], + [-8.19951703e-05, -1.47782624e-03, 1.63030415e-05]]), scale=0.0874522399398783, shift=array([5.67220534, 0.2794848 , 6.39080358])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, + 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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State(trustregion=Region(center=array([5.67220534, 0.2794848 , 6.39080358]), radius=0.04372611996993915, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([35, 36, 37, 38, 39, 40, 41, 42, 43, 44]), model=ScalarModel(intercept=0.30810133617053853, linear_terms=array([-5.37601995e-05, -7.00924716e-04, 6.13829298e-04]), square_terms=array([[ 1.69906512e-04, 3.48813268e-03, -4.47825909e-05], + [ 3.48813268e-03, 1.22865562e-01, -1.30475046e-03], + [-4.47825909e-05, -1.30475046e-03, 1.56904743e-05]]), scale=0.04372611996993915, shift=array([5.67220534, 0.2794848 , 6.39080358])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, + 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 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41, 42, 43, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=1.0215763649772467, linear_terms=array([ 0.14375631, 4.04635095, -0.05668364]), square_terms=array([[ 2.61048432e-02, 4.02728139e-01, -7.94395225e-03], + [ 4.02728139e-01, 1.13099345e+01, -1.85248296e-01], + [-7.94395225e-03, -1.85248296e-01, 3.39233697e-03]]), scale=array([0.56388923, 0.41557528, 0.56388923]), shift=array([5.52684234, 0.42557528, 5.80609831])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, + 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), 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candidate_x=array([5.47795249, 0.27137126, 5.24220908]), index=49, x=array([5.47795249, 0.27137126, 5.24220908]), fval=0.2889275687162242, rho=0.9414379695286359, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([36, 37, 38, 39, 41, 42, 43, 44, 45, 46, 47, 48]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, + 34, 35, 40]), step_length=0.5660353098175912, relative_step_length=0.8090634816883041, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.47795249, 0.27137126, 5.24220908]), radius=1.3992358390380528, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 4, 10, 11, 30, 35, 37, 41, 44, 46, 47, 48, 49]), model=ScalarModel(intercept=8.401379808041456, linear_terms=array([20.48337965, 36.87386893, -0.97232092]), square_terms=array([[ 2.73718279e+01, 4.74699169e+01, 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old_indices_used=array([ 4, 10, 11, 30, 35, 37, 41, 44, 46, 47, 48, 49]), old_indices_discarded=array([ 0, 1, 2, 3, 5, 6, 7, 8, 9, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 36, 38, 39, + 40, 42, 43, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.47795249, 0.27137126, 5.24220908]), radius=0.6996179195190264, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([36, 37, 38, 41, 42, 43, 44, 45, 46, 47, 48, 49]), model=ScalarModel(intercept=1.0328048943255177, linear_terms=array([ 0.14706337, 4.05118743, -0.06028908]), square_terms=array([[ 2.63006632e-02, 3.99779202e-01, -8.22079292e-03], + [ 3.99779202e-01, 1.10294613e+01, -1.88747720e-01], + [-8.22079292e-03, -1.88747720e-01, 3.58770611e-03]]), scale=array([0.56388923, 0.41263024, 0.56388923]), shift=array([5.47795249, 0.42263024, 5.24220908])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, + 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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+ 34, 35, 39, 40, 50]), step_length=0.569074661100749, relative_step_length=0.8134077833397645, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.4014905 , 0.26603536, 4.67831985]), radius=1.3992358390380528, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([37, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51]), model=ScalarModel(intercept=1.629273584089034, linear_terms=array([ 0.61497725, 5.59543793, -0.20069807]), square_terms=array([[ 0.18910149, 1.29985268, -0.05669145], + [ 1.29985268, 11.59567242, -0.45769195], + [-0.05669145, -0.45769195, 0.01956068]]), scale=array([1.12777846, 0.49 , 1.12777846]), shift=array([5.4014905 , 0.5 , 4.67831985])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, + 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + 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radius=2.7984716780761056, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([37, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52]), model=ScalarModel(intercept=1.488299231526368, linear_terms=array([ 0.68708864, 4.68077783, -0.25965598]), square_terms=array([[ 0.36780394, 1.27824646, -0.10258544], + [ 1.27824646, 8.94190812, -0.55744366], + [-0.10258544, -0.55744366, 0.04079265]]), scale=array([2.25555691, 0.49 , 2.25555691]), shift=array([5.58019624, 0.5 , 3.55054139])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, + 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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scale=0.6996179195190264, shift=array([5.81266428, 0.23295295, 6.9961792 ])), candidate_index=53, candidate_x=array([5.08211349, 0.22842295, 1.29498448]), index=53, x=array([5.08211349, 0.22842295, 1.29498448]), fval=0.24778697688048176, rho=0.5404450920215796, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([37, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, + 34, 35, 36, 38, 39, 40, 41]), step_length=2.309907709026206, relative_step_length=0.8254175760014203, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.08211349, 0.22842295, 1.29498448]), radius=5.596943356152211, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 4, 9, 11, 14, 29, 30, 47, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=11.714007670319425, linear_terms=array([24.50039079, 41.60470357, -4.28313711]), square_terms=array([[27.62848504, 45.16147148, -4.78880059], + [45.16147148, 75.49315113, -7.88257189], + [-4.78880059, -7.88257189, 0.83480515]]), scale=array([4.24661366, 0.49 , 2.90304915]), shift=array([5.34661366, 0.5 , 2.90304915])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, + 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 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rho=-2.6229548840900434, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 4, 9, 11, 14, 29, 30, 47, 49, 50, 51, 52, 53]), old_indices_discarded=array([ 0, 1, 2, 3, 5, 6, 7, 8, 10, 12, 13, 15, 16, 17, 18, 19, 20, + 21, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, + 40, 41, 42, 43, 44, 45, 46, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.08211349, 0.22842295, 1.29498448]), radius=2.7984716780761056, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([37, 42, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54]), model=ScalarModel(intercept=1.7564665441870613, linear_terms=array([ 0.48912219, 5.07900078, -0.29740787]), square_terms=array([[ 0.1529226 , 0.60855853, -0.04226286], + [ 0.60855853, 8.41899891, -0.52187489], + [-0.04226286, -0.52187489, 0.03505869]]), scale=array([2.25555691, 0.49 , 1.7752707 ]), 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24, 25, 26, 27, 28, 29, 30, 31, 32, 33, + 34, 35, 36, 38, 39, 40, 41, 43, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.08211349, 0.22842295, 1.29498448]), radius=1.3992358390380528, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([37, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55]), model=ScalarModel(intercept=1.413470861366983, linear_terms=array([-0.20579534, 3.45109869, -0.17983883]), square_terms=array([[ 0.08818994, -0.14324532, 0.0077864 ], + [-0.14324532, 5.59046525, -0.27774519], + [ 0.0077864 , -0.27774519, 0.01448073]]), scale=array([1.12777846, 0.49 , 1.12777846]), shift=array([5.08211349, 0.5 , 1.29498448])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 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radius=0.6996179195190264, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([50, 51, 52, 53, 54, 55, 56]), model=ScalarModel(intercept=0.8726934072149458, linear_terms=array([-0.0783212 , 2.00091325, -0.09456309]), square_terms=array([[ 0.0203444 , -0.03857019, 0.00397376], + [-0.03857019, 3.83246439, -0.14592643], + [ 0.00397376, -0.14592643, 0.00645507]]), scale=array([0.56388923, 0.39115609, 0.56388923]), shift=array([5.08211349, 0.40115609, 1.29498448])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, + 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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shift=array([5.81266428, 0.23295295, 6.9961792 ])), candidate_index=57, candidate_x=array([5.64600272, 0.21576561, 1.85887371]), index=57, x=array([5.64600272, 0.21576561, 1.85887371]), fval=0.23908436329164598, rho=0.11444149218119434, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([50, 51, 52, 53, 54, 55, 56]), old_indices_discarded=array([], dtype=int32), step_length=0.7975602376166286, relative_step_length=1.1399940101090258, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.64600272, 0.21576561, 1.85887371]), radius=1.3992358390380528, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57]), model=ScalarModel(intercept=1.2708776238087864, linear_terms=array([-0.1402638 , 3.42033159, -0.18005848]), square_terms=array([[ 0.08812702, -0.11093614, 0.00637266], + [-0.11093614, 5.92951868, -0.29885612], + [ 0.00637266, 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0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, + 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + 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bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([52, 53, 56, 57, 58, 59]), model=ScalarModel(intercept=0.3260410148049849, linear_terms=array([ 0.01437464, 1.07237161, -0.01626796]), square_terms=array([[ 2.71464654e-03, 7.59150546e-02, -1.84772164e-03], + [ 7.59150546e-02, 6.63954598e+00, -1.23092279e-01], + [-1.84772164e-03, -1.23092279e-01, 2.43780682e-03]]), scale=array([0.28194461, 0.24385511, 0.28194461]), shift=array([5.64600272, 0.25385511, 1.85887371])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, + 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 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relative_step_length=1.0600922623051872, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33869833, 0.20073019, 0.94626119]), radius=0.3498089597595132, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([53, 57, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69]), model=ScalarModel(intercept=0.34007729667017145, linear_terms=array([ 0.02037288, 1.09201706, -0.02759842]), square_terms=array([[ 3.67439486e-03, 9.39073880e-02, -3.03941614e-03], + [ 9.39073880e-02, 5.48630084e+00, -1.48255420e-01], + [-3.03941614e-03, -1.48255420e-01, 4.16295796e-03]]), scale=array([0.28194461, 0.2363374 , 0.28194461]), shift=array([5.33869833, 0.2463374 , 0.94626119])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, + 0.04033998, 0.06941292]), 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model=ScalarModel(intercept=0.23105408610332923, linear_terms=array([-0.0006138 , -0.00195249, 0.00093336]), square_terms=array([[ 1.24533163e-03, 2.89626025e-02, -9.10786552e-04], + [ 2.89626025e-02, 2.67981548e+00, -6.39153209e-02], + [-9.10786552e-04, -6.39153209e-02, 1.59136186e-03]]), scale=0.1749044798797566, shift=array([5.33869833, 0.20073019, 0.94626119])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, + 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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x=array([5.37701979, 0.19637249, 0.77552158]), fval=0.22975800522408243, rho=0.8228733999861161, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([53, 60, 62, 64, 65, 66, 67, 68, 69, 70]), old_indices_discarded=array([], dtype=int32), step_length=0.17504153263478317, relative_step_length=1.0007835863044834, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.37701979, 0.19637249, 0.77552158]), radius=0.3498089597595132, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([53, 57, 60, 62, 64, 65, 66, 67, 68, 69, 70, 71]), model=ScalarModel(intercept=0.32928826166731434, linear_terms=array([ 0.01408983, 0.97330408, -0.02555177]), square_terms=array([[ 3.33165512e-03, 6.78396125e-02, -2.45923055e-03], + [ 6.78396125e-02, 4.77687728e+00, -1.33384029e-01], + [-2.45923055e-03, -1.33384029e-01, 3.89493474e-03]]), scale=array([0.28194461, 0.23415855, 0.28194461]), shift=array([5.37701979, 0.24415855, 0.77552158])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, + 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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relative_step_length=0.07369657850734845, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.37273054, 0.19594589, 0.75010477]), radius=0.3498089597595132, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([53, 60, 62, 64, 65, 66, 67, 68, 69, 70, 71, 72]), model=ScalarModel(intercept=0.33015631732120326, linear_terms=array([ 0.01200808, 0.97883005, -0.02664989]), square_terms=array([[ 3.15748987e-03, 6.07783540e-02, -2.29315594e-03], + [ 6.07783540e-02, 4.77347700e+00, -1.36482392e-01], + [-2.29315594e-03, -1.36482392e-01, 4.07018190e-03]]), scale=array([0.28194461, 0.23394525, 0.28194461]), shift=array([5.37273054, 0.24394525, 0.75010477])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, + 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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model=ScalarModel(intercept=0.23011185928296796, linear_terms=array([-0.00054442, 0.0073945 , 0.00026198]), square_terms=array([[ 1.16376582e-03, 2.55336284e-02, -8.48105173e-04], + [ 2.55336284e-02, 2.77348932e+00, -6.94401574e-02], + [-8.48105173e-04, -6.94401574e-02, 1.80580668e-03]]), scale=0.1749044798797566, shift=array([5.37273054, 0.19594589, 0.75010477])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, + 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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x=array([5.37273054, 0.19594589, 0.75010477]), fval=0.2296397835138107, rho=-0.38591559280998283, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([53, 62, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.37273054, 0.19594589, 0.75010477]), radius=0.0874522399398783, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([67, 68, 69, 70, 71, 72, 73, 74]), model=ScalarModel(intercept=0.22990165696401943, linear_terms=array([-0.00100953, -0.06567983, 0.00157013]), square_terms=array([[ 2.70902173e-04, 3.76414885e-03, -1.47804139e-04], + [ 3.76414885e-03, 2.99310015e-01, -8.25967854e-03], + [-1.47804139e-04, -8.25967854e-03, 2.46108361e-04]]), scale=0.0874522399398783, shift=array([5.37273054, 0.19594589, 0.75010477])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, + 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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State(trustregion=Region(center=array([5.37273054, 0.19594589, 0.75010477]), radius=0.04372611996993915, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([69, 71, 72, 74, 75]), model=ScalarModel(intercept=0.22959788201018697, linear_terms=array([ 0.00085113, -0.00459338, 0.00042293]), square_terms=array([[ 9.41236784e-05, 2.54061871e-03, -7.55487293e-05], + [ 2.54061871e-03, 1.55880986e-01, -3.87667223e-03], + [-7.55487293e-05, -3.87667223e-03, 1.00624396e-04]]), scale=0.04372611996993915, shift=array([5.37273054, 0.19594589, 0.75010477])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, + 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 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x=array([5.81071335, 0.1944286 , 5.9101968 ]), fval=0.4418926380517428, rho=0.06149806074678534, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 4, 9, 10, 14]), old_indices_discarded=array([], dtype=int32), step_length=0.1701936646856471, relative_step_length=1.0824424921484033, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.81071335, 0.1944286 , 5.9101968 ]), radius=0.07861556891944033, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([10, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.42076836552635977, linear_terms=array([ 0.0093902 , -0.20121153, 0.01715171]), square_terms=array([[ 3.61193666e-04, -1.49324928e-03, 1.63080186e-04], + [-1.49324928e-03, 2.01402114e-01, -1.10062539e-02], + [ 1.63080186e-04, -1.10062539e-02, 6.88503559e-04]]), scale=0.07861556891944033, shift=array([5.81071335, 0.1944286 , 5.9101968 ])), 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linear_terms=array([0.00063314, 0.00259505, 0.00077565]), square_terms=array([[ 4.13948227e-04, 8.30470975e-03, -6.37384456e-05], + [ 8.30470975e-03, 3.46745608e-01, -1.95444378e-03], + [-6.37384456e-05, -1.95444378e-03, 1.51370328e-05]]), scale=0.07861556891944033, shift=array([5.59967088, 0.27409353, 5.71352946])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, + 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, + -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , + 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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fval=0.29502502944321474, rho=1.0288115202626666, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([18, 21, 23, 28, 29, 31, 32, 33, 34, 35, 36, 37]), old_indices_discarded=array([10, 15, 16, 17, 19, 20, 22, 24, 25, 26, 27, 30]), step_length=0.0786238847613481, relative_step_length=1.0001057785629752, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.55934649, 0.27409052, 5.64603383]), radius=0.15723113783888065, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([10, 21, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=0.3048638716861844, linear_terms=array([ 0.00790086, 0.02682295, -0.0070564 ]), square_terms=array([[ 1.11247314e-03, -4.07052752e-03, -3.87937482e-04], + [-4.07052752e-03, 7.45474880e-01, 2.67385638e-02], + [-3.87937482e-04, 2.67385638e-02, 1.19103427e-03]]), scale=0.15723113783888065, shift=array([5.55934649, 0.27409052, 5.64603383])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, + 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, + -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , + 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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model=ScalarModel(intercept=0.29355339341176606, linear_terms=array([-0.00019797, -0.00071454, 0.00299404]), square_terms=array([[ 2.02380756e-03, 4.26701579e-02, -6.18734942e-04], + [ 4.26701579e-02, 1.62234158e+00, -2.01145634e-02], + [-6.18734942e-04, -2.01145634e-02, 2.76548771e-04]]), scale=0.15723113783888065, shift=array([5.59413123, 0.27222346, 5.57554581])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, + 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, + -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , + 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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59]), model=ScalarModel(intercept=0.28737808987475366, linear_terms=array([ 0.01118616, 0.68903588, -0.02221227]), square_terms=array([[ 2.12646123e-03, 5.77559465e-02, -2.19605509e-03], + [ 5.77559465e-02, 4.22779772e+00, -1.32213361e-01], + [-2.19605509e-03, -1.32213361e-01, 4.33400566e-03]]), scale=array([0.25345533, 0.21784008, 0.25345533]), shift=array([5.42539954, 0.22784008, 0.72197945])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, + 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, + -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , + 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 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vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, + 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, + -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , + 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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n_evals_acceptance=1), State(trustregion=Region(center=array([5.25872555, 0.19502188, 0.69471579]), radius=0.3144622756777613, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([47, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=0.2843406451397853, linear_terms=array([ 0.00864362, 0.66880793, -0.02153665]), square_terms=array([[ 2.02864798e-03, 5.20711154e-02, -2.00548145e-03], + [ 5.20711154e-02, 4.18844306e+00, -1.31694121e-01], + [-2.00548145e-03, -1.31694121e-01, 4.32121474e-03]]), scale=array([0.25345533, 0.2192386 , 0.25345533]), shift=array([5.25872555, 0.2292386 , 0.69471579])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, + 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, + -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , + 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], 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bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([61, 64, 66, 67, 68, 69]), model=ScalarModel(intercept=0.22976603634266932, linear_terms=array([-3.93622165e-05, 1.50604279e-04, 5.41389841e-06]), square_terms=array([[ 1.35190060e-05, 3.13851058e-04, -1.12419690e-05], + [ 3.13851058e-04, 3.28274831e-02, -9.01455848e-04], + [-1.12419690e-05, -9.01455848e-04, 2.59810151e-05]]), scale=0.01965389222986008, shift=array([5.32802432, 0.19433656, 0.66406887])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, + 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, + -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , + 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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71, 72]), model=ScalarModel(intercept=0.22972063519629007, linear_terms=array([-1.88791572e-05, -8.79738089e-05, 2.06131045e-06]), square_terms=array([[ 5.35764225e-05, 1.28688003e-03, -4.41054673e-05], + [ 1.28688003e-03, 1.29771660e-01, -3.44584171e-03], + [-4.41054673e-05, -3.44584171e-03, 9.54802399e-05]]), scale=0.03930778445972016, shift=array([5.37502587, 0.19456137, 0.68757298])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, + 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, + -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , + 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + 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index=71, x=array([5.37502587, 0.19456137, 0.68757298]), fval=0.22954649382876313, rho=-2.030261445123912, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 71, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 104]), old_indices_discarded=array([ 91, 92, 103]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.37502587, 0.19456137, 0.68757298]), radius=7.677301652289094e-05, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 71, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, + 116, 117]), model=ScalarModel(intercept=0.22954896050293003, linear_terms=array([ 3.28680984e-06, -4.77347360e-07, 1.37109067e-07]), square_terms=array([[ 2.55761258e-10, 4.01597052e-09, -1.56505450e-10], + [ 4.01597052e-09, 5.03977931e-07, -1.41568103e-08], + [-1.56505450e-10, -1.41568103e-08, 4.17172147e-10]]), scale=7.677301652289094e-05, shift=array([5.37502587, 0.19456137, 0.68757298])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, + 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, + -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , + 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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6.55725054e-01, 7.71100519e-01, + 1.19425789e+00, 1.58158215e+00, 1.58170972e+00, 2.10988168e+00, + 2.53770191e+00, 3.19288105e+00, 3.47314230e+00, 3.48315006e+00, + 3.92293459e+00, 4.56511870e+00, 5.33390087e+00, 5.34621006e+00, + 6.48994604e+00, 9.32970623e+00, 1.17999718e+01, 1.57570577e+01, + 1.66647063e+01, 2.65180559e+01, 4.77377487e+01, 8.29741042e+01, + 8.62618718e+01, 8.81150175e+01, 1.31509063e+02, 1.97370096e+02, + 6.42774449e+02, 7.33084130e+02])}}" diff --git a/src/estimark/content/tables/min/WealthPortfolio_estimate_results.csv b/src/estimark/content/tables/min/WealthPortfolio_estimate_results.csv new file mode 100644 index 0000000..d8b4f14 --- /dev/null +++ b/src/estimark/content/tables/min/WealthPortfolio_estimate_results.csv @@ -0,0 +1,7059 @@ +CRRA,5.338780774481047 + +WealthShare,0.17065528804872485 + +time_to_estimate,87.05302119255066 + +params,"{'CRRA': 5.338780774481047, 'WealthShare': 0.17065528804872485}" + +criterion,0.24222229239256732 + +start_criterion,2.0450859311174514 + +start_params,"{'CRRA': 4.844436801414261, 'WealthShare': 0.3460128282561451}" + +algorithm,multistart_tranquilo_ls + +direction,minimize + +n_free,2 + +message,Absolute criterion change smaller than tolerance. + +success, + +n_criterion_evaluations, + +n_derivative_evaluations, + +n_iterations, + +history,"{'params': [{'CRRA': 7.00625, 'WealthShare': 0.19375}, {'CRRA': 6.385337260354661, 'WealthShare': 0.0589657827107465}, {'CRRA': 7.627162739645338, 'WealthShare': 0.2909337034881926}, {'CRRA': 6.385337260354661, 'WealthShare': 0.4422314188083436}, {'CRRA': 7.627162739645338, 'WealthShare': 0.6717516331999257}, {'CRRA': 7.627162739645338, 'WealthShare': 0.010000476301985028}, {'CRRA': 7.556316959078161, 'WealthShare': 0.01}, {'CRRA': 6.385337260354661, 'WealthShare': 0.4716198511344087}, {'CRRA': 7.627162739645338, 'WealthShare': 0.751444172509113}, {'CRRA': 7.435546062237209, 'WealthShare': 0.8146627396453386}, {'CRRA': 6.407783701100443, 'WealthShare': 0.8146627396453386}, {'CRRA': 6.6651666175265545, 'WealthShare': 0.01}, {'CRRA': 6.48493214278919, 'WealthShare': 0.8146627396453386}, {'CRRA': 6.385337260354661, 'WealthShare': 0.1237617248187634}, {'CRRA': 6.0748808905319915, 'WealthShare': 0.20052322401594325}, {'CRRA': 6.2532427503011325, 'WealthShare': 0.19438789449563343}, {'CRRA': 6.3382227136719385, 'WealthShare': 0.1578819528196836}, {'CRRA': 6.493450898583273, 'WealthShare': 0.13740842870128234}, {'CRRA': 6.4256753849363895, 'WealthShare': 0.15294566470705245}, {'CRRA': 6.294459741840304, 'WealthShare': 0.16000090800258274}, {'CRRA': 6.382143835462875, 'WealthShare': 0.16046020191691993}, {'CRRA': 6.250679533322458, 'WealthShare': 0.16091446504625906}, {'CRRA': 6.163102752485386, 'WealthShare': 0.16148208803528766}, {'CRRA': 6.007874567574052, 'WealthShare': 0.16384849824047343}, {'CRRA': 5.697418197751382, 'WealthShare': 0.17249887954427012}, {'CRRA': 5.236146802438669, 'WealthShare': 0.17615356090029882}, {'CRRA': 5.425169945432182, 'WealthShare': 0.18194646890715233}, {'CRRA': 4.9779501150979595, 'WealthShare': 0.1792004931237239}, {'CRRA': 5.546603172261339, 'WealthShare': 0.48660993072296815}, {'CRRA': 5.215889834469423, 'WealthShare': 0.1769811233053123}, {'CRRA': 5.274171299031127, 'WealthShare': 0.17638843122996792}, {'CRRA': 5.259275228360069, 'WealthShare': 0.1764508677785662}, {'CRRA': 5.361747939266923, 'WealthShare': 0.17587789924445918}, {'CRRA': 5.500670574988, 'WealthShare': 0.1750124128151594}, {'CRRA': 5.361212623821769, 'WealthShare': 0.08830141029970982}, {'CRRA': 5.448965805526602, 'WealthShare': 0.16786346171002578}, {'CRRA': 5.521017950385663, 'WealthShare': 0.16734145638561265}, {'CRRA': 5.361392263936586, 'WealthShare': 0.1687594482598058}, {'CRRA': 5.408532775180341, 'WealthShare': 0.16829680110424536}, {'CRRA': 5.4146764839922925, 'WealthShare': 0.16830439279425335}, {'CRRA': 5.317605072265502, 'WealthShare': 0.16916422003343717}, {'CRRA': 5.230029569186239, 'WealthShare': 0.16984189173088587}, {'CRRA': 5.361391583162623, 'WealthShare': 0.16869150649947118}, {'CRRA': 5.339508482161973, 'WealthShare': 0.17080481516331386}, {'CRRA': 5.368957473427081, 'WealthShare': 0.1704982684436756}, {'CRRA': 5.3283746442860265, 'WealthShare': 0.170903000375076}, {'CRRA': 5.328563448185613, 'WealthShare': 0.17111248155527758}, {'CRRA': 5.334036845737762, 'WealthShare': 0.17107114051345143}, {'CRRA': 5.342235745297582, 'WealthShare': 0.1697818067119096}, {'CRRA': 5.3408803356615, 'WealthShare': 0.17109939320809675}, {'CRRA': 5.338823528524706, 'WealthShare': 0.17065422827949905}, {'CRRA': 5.340193277829363, 'WealthShare': 0.17078798862632172}, {'CRRA': 5.338140970895526, 'WealthShare': 0.17083226059338175}, {'CRRA': 5.338518273170905, 'WealthShare': 0.17049811328240777}, {'CRRA': 5.338781916239757, 'WealthShare': 0.1708201405184341}, {'CRRA': 5.338915212045181, 'WealthShare': 0.17067448290561968}, {'CRRA': 5.338780774481047, 'WealthShare': 0.17065528804872485}], 'criterion': [0.3273843758368489, 0.5699740200328935, 0.842575503738206, 3.810658336120194, 20.623483705368287, 0.6910521757749679, 0.6995225156927494, 4.9395441503578565, 42.99134020288109, 86.6456059148058, 105.27402392078089, 0.8474994237274145, 103.66743022564344, 0.30690596890028216, 0.29210255832128873, 0.28576369985789285, 0.26145704727673624, 0.28242718110045806, 0.26615570282764395, 0.2594527871161228, 0.26221365404602937, 0.2577870645585467, 0.25523533119834385, 0.25064066883423725, 0.24497093468209197, 0.24406000199271466, 0.24743835677691237, 0.24950377050231112, 6.8114635382632684, 0.244474337584774, 0.24364090572406066, 0.24383728429031426, 0.2434506325404963, 0.24360441582812653, 0.5131462702699118, 0.24274413357026287, 0.24315059044862256, 0.2425520875633037, 0.24267761171614233, 0.24262874555716651, 0.24241985946629965, 0.24303623087126985, 0.24256813594664275, 0.24222716621340298, 0.24232942620846887, 0.24226459021544208, 0.24228552258028713, 0.2422627008915168, 0.24227972390907074, 0.24224781333659, 0.24222315394360727, 0.2422366702780749, 0.24222456968889083, 0.2422369639832645, 0.24222420706924647, 0.2422233576238524, 0.2422222923925673], 'runtime': [0.0, 3.0856837001629174, 3.092791900038719, 3.145945300348103, 3.198699700180441, 3.1877071000635624, 3.257912500295788, 3.279559100046754, 3.320520000066608, 3.3423851002007723, 3.399885100312531, 3.4487606999464333, 3.469572300091386, 4.5710685001686215, 5.594150300137699, 6.609584300313145, 7.625805400311947, 8.643067300319672, 9.653973900247365, 10.668333900161088, 11.684152700006962, 12.696534700226039, 13.830191600136459, 14.843372600153089, 15.85343430005014, 16.867383300326765, 17.886160700116307, 18.908616400323808, 19.927985700313002, 20.95058420021087, 21.96936420025304, 22.98745770007372, 24.00510139996186, 25.026439000386745, 26.052888700272888, 27.192030200269073, 28.2193304002285, 29.282562700100243, 30.321563200093806, 31.38996300008148, 32.42062650015578, 33.43918160023168, 34.45591269992292, 35.487439500167966, 36.49907220015302, 37.50863410020247, 38.52129440009594, 39.53660099999979, 40.67266120016575, 41.69906500028446, 42.72594740008935, 43.75388570036739, 44.77801220025867, 45.799775400198996, 46.81848719995469, 47.845374200027436, 48.87321070022881], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45]}" + +convergence_report, + +multistart_info,"{'start_parameters': [{'CRRA': 7.00625, 'WealthShare': 0.19375}, {'CRRA': 7.557071776832622, 'WealthShare': 0.15947986291737937}], 'local_optima': [Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 3.557e-06* 0.002154 +relative_params_change 1.013e-05 0.02634 +absolute_criterion_change 8.616e-07* 0.0005218 +absolute_params_change 4.277e-05 0.1102 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 1.266e-06* 0.2375 +relative_params_change 0.006515 0.369 +absolute_criterion_change 3.068e-07* 0.05754 +absolute_params_change 0.02362 1.523 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 7.00625, 'WealthShare': 0.19375}, {'CRRA': 12.9125, 'WealthShare': 0.1325}, {'CRRA': 4.64375, 'WealthShare': 0.31625000000000003}, {'CRRA': 8.1875, 'WealthShare': 0.3775}, {'CRRA': 4.844436801414261, 'WealthShare': 0.3460128282561451}, {'CRRA': 15.274999999999999, 'WealthShare': 0.255}, {'CRRA': 17.046875, 'WealthShare': 0.224375}, {'CRRA': 11.73125, 'WealthShare': 0.43875}, {'CRRA': 18.81875, 'WealthShare': 0.07125}, {'CRRA': 10.549999999999999, 'WealthShare': 0.5}, {'CRRA': 9.368749999999999, 'WealthShare': 0.56125}, {'CRRA': 16.45625, 'WealthShare': 0.68375}, {'CRRA': 2.871875, 'WealthShare': 0.469375}, {'CRRA': 7.596874999999999, 'WealthShare': 0.714375}, {'CRRA': 14.093749999999998, 'WealthShare': 0.80625}, {'CRRA': 3.4625, 'WealthShare': 0.6225}, {'CRRA': 17.6375, 'WealthShare': 0.8674999999999999}, {'CRRA': 5.824999999999999, 'WealthShare': 0.745}, {'CRRA': 12.321874999999999, 'WealthShare': 0.959375}, {'CRRA': 2.28125, 'WealthShare': 0.92875}], 'exploration_results': array([3.27384376e-01, 1.14034055e+00, 1.50312179e+00, 1.79325436e+00, + 2.03621762e+00, 2.08879824e+00, 2.54067782e+00, 2.64114350e+00, + 2.73814230e+00, 3.83228381e+00, 6.46248546e+00, 9.30118040e+00, + 1.59966394e+01, 3.03715662e+01, 3.38771067e+01, 4.26926083e+01, + 5.84656546e+01, 5.90505617e+01, 5.22500282e+02, 9.01924413e+02])}" + +algorithm_output,"{'states': [State(trustregion=Region(center=array([7.00625, 0.19375]), radius=0.700625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=[0], model=ScalarModel(intercept=0.3273843758368489, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.700625, shift=array([7.00625, 0.19375])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=0, candidate_x=array([7.00625, 0.19375]), index=0, x=array([7.00625, 0.19375]), fval=0.32738437583684893, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([7.00625, 0.19375]), radius=0.700625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=11.224951550903995, linear_terms=array([-1.37625354, 32.5154691 ]), square_terms=array([[ 0.09351784, -2.07104056], + [-2.07104056, 48.22141916]]), scale=array([0.62091274, 0.40233137]), shift=array([7.00625 , 0.41233137])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=13, candidate_x=array([6.38533726, 0.12376172]), index=13, x=array([6.38533726, 0.12376172]), fval=0.30690596890028216, rho=0.04742895556261184, accepted=True, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int32), step_length=0.6248447718567584, relative_step_length=0.8918391034530002, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.38533726, 0.12376172]), radius=0.3503125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 3, 6, 7, 10, 11, 12, 13]), model=ScalarModel(intercept=0.7313551856043823, linear_terms=array([0.5632349 , 4.14900217]), square_terms=array([[ 0.31889798, 2.36106382], + [ 2.36106382, 17.56884109]]), scale=array([0.31045637, 0.21210905]), shift=array([6.38533726, 0.22210905])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=14, candidate_x=array([6.07488089, 0.20052322]), index=14, x=array([6.07488089, 0.20052322]), fval=0.2921025583212886, rho=0.03221406908218355, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 3, 6, 7, 10, 11, 12, 13]), old_indices_discarded=array([2, 4, 5, 8, 9]), step_length=0.3198053866376689, relative_step_length=0.9129145738095811, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.07488089, 0.20052322]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 7, 10, 11, 12, 13, 14]), model=ScalarModel(intercept=0.5857130929401587, linear_terms=array([-0.66733221, -2.49758335]), square_terms=array([[ 0.71346148, 2.85346922], + [ 2.85346922, 11.61097172]]), scale=0.17515625, shift=array([6.07488089, 0.20052322])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=15, candidate_x=array([6.25324275, 0.19438789]), index=15, x=array([6.25324275, 0.19438789]), fval=0.28576369985789285, rho=0.020008399324012414, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 3, 7, 10, 11, 12, 13, 14]), old_indices_discarded=array([], dtype=int32), step_length=0.17846735076374473, relative_step_length=1.0189036974914953, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.25324275, 0.19438789]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 7, 13, 14, 15]), model=ScalarModel(intercept=0.2909351370686484, linear_terms=array([0.01016955, 0.239779 ]), square_terms=array([[0.00441972, 0.04993243], + [0.04993243, 0.684917 ]]), scale=0.087578125, shift=array([6.25324275, 0.19438789])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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10, 11, 13, 14, 15, 16]), model=ScalarModel(intercept=0.2600470730558603, linear_terms=array([-0.10978373, -0.38806688]), square_terms=array([[0.34292001, 1.58579951], + [1.58579951, 7.51750345]]), scale=array([0.15522818, 0.15155507]), shift=array([6.33822271, 0.16155507])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=17, candidate_x=array([6.4934509 , 0.13740843]), index=16, x=array([6.33822271, 0.15788195]), fval=0.2614570472767362, rho=-0.4623888253617076, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 3, 7, 10, 11, 13, 14, 15, 16]), old_indices_discarded=array([ 0, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.33822271, 0.15788195]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 7, 11, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=0.24686579583420382, linear_terms=array([-0.00725813, -0.0120927 ]), square_terms=array([[0.00459974, 0.05116398], + [0.05116398, 0.68633371]]), scale=0.087578125, shift=array([6.33822271, 0.15788195])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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1.19696569e-01]]), scale=0.0437890625, shift=array([6.33822271, 0.15788195])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=19, candidate_x=array([6.29445974, 0.16000091]), index=19, x=array([6.29445974, 0.16000091]), fval=0.2594527871161228, rho=0.5388880366161921, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 13, 15, 16, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.04381424054578142, relative_step_length=1.0005749848099947, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29445974, 0.16000091]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 13, 14, 15, 16, 17, 18, 19]), model=ScalarModel(intercept=0.2541677101008941, linear_terms=array([-0.00368238, -0.03323817]), square_terms=array([[0.00145555, 0.02969193], + [0.02969193, 0.6672799 ]]), scale=0.087578125, shift=array([6.29445974, 0.16000091])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=20, candidate_x=array([6.38214384, 0.1604602 ]), index=19, x=array([6.29445974, 0.16000091]), fval=0.2594527871161228, rho=-0.930670096284453, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 3, 13, 14, 15, 16, 17, 18, 19]), old_indices_discarded=array([ 7, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29445974, 0.16000091]), radius=0.0437890625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 13, 15, 16, 17, 18, 19, 20]), model=ScalarModel(intercept=0.25622931078220434, linear_terms=array([ 0.0032415 , -0.00073259]), square_terms=array([[1.03290011e-04, 1.82810922e-03], + [1.82810922e-03, 1.19546137e-01]]), scale=0.0437890625, shift=array([6.29445974, 0.16000091])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=21, candidate_x=array([6.25067953, 0.16091447]), index=21, x=array([6.25067953, 0.16091447]), fval=0.2577870645585467, rho=0.517848980651204, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 13, 15, 16, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int32), step_length=0.043789739030257874, relative_step_length=1.0000154497543279, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.25067953, 0.16091447]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 13, 14, 15, 16, 18, 19, 20, 21]), model=ScalarModel(intercept=0.25500561017058476, linear_terms=array([0.00514188, 0.00157206]), square_terms=array([[3.29759763e-04, 4.68476327e-03], + [4.68476327e-03, 4.75402508e-01]]), scale=0.087578125, shift=array([6.25067953, 0.16091447])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=22, candidate_x=array([6.16310275, 0.16148209]), index=22, x=array([6.16310275, 0.16148209]), fval=0.2552353311983439, rho=0.5116649557870464, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 13, 14, 15, 16, 18, 19, 20, 21]), old_indices_discarded=array([ 3, 7, 17]), step_length=0.08757862032278296, relative_step_length=1.0000056557819998, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.16310275, 0.16148209]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 13, 14, 15, 16, 19, 20, 21, 22]), model=ScalarModel(intercept=0.2515880836028085, linear_terms=array([0.00754998, 0.01225116]), square_terms=array([[8.67053730e-04, 1.69602107e-02], + [1.69602107e-02, 1.46374627e+00]]), scale=array([0.15522818, 0.15335514]), shift=array([6.16310275, 0.16335514])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=23, candidate_x=array([6.00787457, 0.1638485 ]), index=23, x=array([6.00787457, 0.1638485 ]), fval=0.25064066883423725, rho=0.6486363276282296, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 13, 14, 15, 16, 19, 20, 21, 22]), old_indices_discarded=array([ 0, 3, 7, 10, 11, 12, 17, 18]), step_length=0.1552462214938793, relative_step_length=0.8863298996974375, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.00787457, 0.1638485 ]), radius=0.3503125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([13, 14, 15, 16, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=0.43332719746500753, linear_terms=array([0.03146291, 1.14425379]), square_terms=array([[2.68439891e-03, 7.19387302e-02], + [7.19387302e-02, 3.57398202e+00]]), scale=array([0.31045637, 0.23215243]), shift=array([6.00787457, 0.24215243])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=24, candidate_x=array([5.6974182 , 0.17249888]), index=24, x=array([5.6974182 , 0.17249888]), fval=0.244970934682092, rho=0.6800504779079048, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([13, 14, 15, 16, 19, 20, 21, 22, 23]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 17, 18]), step_length=0.3105768611152018, relative_step_length=0.8865708791870166, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.6974182 , 0.17249888]), radius=0.700625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 13, 14, 15, 16, 19, 22, 23, 24]), model=ScalarModel(intercept=1.9359689768705584, linear_terms=array([0.14431675, 5.7192291 ]), square_terms=array([[0.01061289, 0.22387827], + [0.22387827, 9.66222181]]), scale=array([0.62091274, 0.39170581]), shift=array([5.6974182 , 0.40170581])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=25, candidate_x=array([5.2361468 , 0.17615356]), index=25, x=array([5.2361468 , 0.17615356]), fval=0.2440600019927147, rho=0.12172165982234218, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 13, 14, 15, 16, 19, 22, 23, 24]), old_indices_discarded=array([ 0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 17, 18, 20, 21]), step_length=0.46128587321697967, relative_step_length=0.6583919689091592, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2361468 , 0.17615356]), radius=1.40125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 7, 10, 14, 16, 17, 22, 24, 25]), model=ScalarModel(intercept=19.556928495798605, linear_terms=array([ 2.66108959, 60.1251518 ]), square_terms=array([[ 0.19711181, 4.14595444], + [ 4.14595444, 93.60232406]]), scale=array([1.24182548, 0.49 ]), shift=array([5.2361468, 0.5 ])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=26, candidate_x=array([5.42516995, 0.18194647]), index=25, x=array([5.2361468 , 0.17615356]), fval=0.2440600019927147, rho=-0.2074474380333275, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 7, 10, 14, 16, 17, 22, 24, 25]), old_indices_discarded=array([ 0, 2, 3, 4, 5, 6, 8, 9, 11, 12, 13, 15, 18, 19, 20, 21, 23]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2361468 , 0.17615356]), radius=0.700625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 15, 19, 21, 22, 23, 24, 25, 26]), model=ScalarModel(intercept=2.023039226033913, linear_terms=array([0.11112348, 6.16604764]), square_terms=array([[9.99812684e-03, 1.87643817e-01], + [1.87643817e-01, 1.06798417e+01]]), scale=array([0.62091274, 0.39353315]), shift=array([5.2361468 , 0.40353315])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=27, candidate_x=array([4.97795012, 0.17920049]), index=25, x=array([5.2361468 , 0.17615356]), fval=0.2440600019927147, rho=-9.379349103371986, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([14, 15, 19, 21, 22, 23, 24, 25, 26]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17, 18, + 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2361468 , 0.17615356]), radius=0.3503125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 21, 22, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=0.5871067495424316, linear_terms=array([0.02508987, 2.27718757]), square_terms=array([[2.83476082e-03, 8.32114910e-02], + [8.32114910e-02, 7.59032634e+00]]), scale=array([0.31045637, 0.23830497]), shift=array([5.2361468 , 0.24830497])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=29, candidate_x=array([5.21588983, 0.17698112]), index=25, x=array([5.2361468 , 0.17615356]), fval=0.2440600019927147, rho=-12.575485760554423, accepted=False, new_indices=array([28]), old_indices_used=array([14, 21, 22, 23, 24, 25, 26, 27]), old_indices_discarded=array([ 1, 3, 7, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2361468 , 0.17615356]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 23, 24, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=0.24537978825844767, linear_terms=array([-0.00017886, -0.00847909]), square_terms=array([[6.40294020e-04, 1.45475512e-02], + [1.45475512e-02, 3.24873183e+00]]), scale=array([0.15522818, 0.15522818]), shift=array([5.2361468 , 0.17615356])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=30, candidate_x=array([5.2741713 , 0.17638843]), index=30, x=array([5.2741713 , 0.17638843]), fval=0.24364090572406066, rho=14.798075799096576, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([14, 23, 24, 25, 26, 27, 28, 29]), old_indices_discarded=array([], dtype=int32), step_length=0.03802522196097743, relative_step_length=0.21709314946499153, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2741713 , 0.17638843]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 23, 24, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=0.24506470035985778, linear_terms=array([5.54445372e-05, 8.93470786e-05]), square_terms=array([[6.38751471e-04, 1.45480845e-02], + [1.45480845e-02, 3.24873994e+00]]), scale=array([0.15522818, 0.15522818]), shift=array([5.2741713 , 0.17638843])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2741713 , 0.17638843]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([24, 25, 26, 27, 28, 29, 30, 31]), model=ScalarModel(intercept=0.24474192813793477, linear_terms=array([-0.00048722, -0.00034972]), square_terms=array([[2.32194110e-04, 6.38817516e-03], + [6.38817516e-03, 1.03554328e+00]]), scale=0.087578125, shift=array([5.2741713 , 0.17638843])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=33, candidate_x=array([5.50067057, 0.17501241]), index=32, x=array([5.36174794, 0.1758779 ]), fval=0.24345063254049631, rho=-0.6330830612424933, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([24, 25, 26, 27, 28, 29, 30, 31, 32]), old_indices_discarded=array([14, 22, 23]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.36174794, 0.1758779 ]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([24, 25, 26, 29, 30, 31, 32, 33, 34]), model=ScalarModel(intercept=0.2435599842214549, linear_terms=array([0.00032528, 0.05273933]), square_terms=array([[2.32924566e-04, 6.28242993e-03], + [6.28242993e-03, 6.44662821e-01]]), scale=0.087578125, shift=array([5.36174794, 0.1758779 ])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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0.99]))), model_indices=array([25, 26, 29, 30, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=0.2411702136529787, linear_terms=array([-0.00029148, -0.00161231]), square_terms=array([[7.59338494e-04, 1.81333427e-02], + [1.81333427e-02, 2.02348592e+00]]), scale=array([0.15522818, 0.15522818]), shift=array([5.44896581, 0.16786346])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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35, 36]), model=ScalarModel(intercept=0.24163478548943507, linear_terms=array([ 0.00016458, -0.00055621]), square_terms=array([[2.57908296e-04, 6.00198503e-03], + [6.00198503e-03, 6.44118485e-01]]), scale=0.087578125, shift=array([5.44896581, 0.16786346])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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linear_terms=array([-0.00018881, 0.00035398]), square_terms=array([[8.05230298e-04, 1.86970634e-02], + [1.86970634e-02, 2.02386702e+00]]), scale=array([0.15522818, 0.15522818]), shift=array([5.36139226, 0.16875945])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], 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square_terms=array([[2.60518703e-04, 5.70133267e-03], + [5.70133267e-03, 6.43763482e-01]]), scale=0.087578125, shift=array([5.36139226, 0.16875945])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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1.60944325e-01]]), scale=0.0437890625, shift=array([5.36139226, 0.16875945])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=40, candidate_x=array([5.31760507, 0.16916422]), index=40, x=array([5.31760507, 0.16916422]), fval=0.24241985946629963, rho=7.194700985609317, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([26, 30, 31, 32, 34, 35, 37, 38, 39]), old_indices_discarded=array([25, 29, 33, 36]), step_length=0.043789062500000274, relative_step_length=1.0000000000000062, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.31760507, 0.16916422]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([25, 30, 31, 32, 34, 37, 38, 39, 40]), model=ScalarModel(intercept=0.24205486457802278, linear_terms=array([0.00023275, 0.000779 ]), square_terms=array([[3.03100394e-04, 5.73493197e-03], + [5.73493197e-03, 6.43806788e-01]]), scale=0.087578125, shift=array([5.31760507, 0.16916422])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=41, candidate_x=array([5.23002957, 0.16984189]), index=40, x=array([5.31760507, 0.16916422]), fval=0.24241985946629963, rho=-6.146531737571127, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([25, 30, 31, 32, 34, 37, 38, 39, 40]), old_indices_discarded=array([24, 26, 27, 28, 29, 33, 35, 36]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.31760507, 0.16916422]), radius=0.0437890625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([25, 30, 31, 32, 34, 37, 38, 40, 41]), model=ScalarModel(intercept=0.24213302949529256, linear_terms=array([-3.78622622e-05, 2.41519221e-04]), square_terms=array([[6.90966248e-05, 1.48098964e-03], + [1.48098964e-03, 1.60965844e-01]]), scale=0.0437890625, shift=array([5.31760507, 0.16916422])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=42, candidate_x=array([5.36139158, 0.16869151]), index=40, x=array([5.31760507, 0.16916422]), fval=0.24241985946629963, rho=-11.833264403894546, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([25, 30, 31, 32, 34, 37, 38, 40, 41]), old_indices_discarded=array([26, 29, 33, 35, 36, 39]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.31760507, 0.16916422]), radius=0.02189453125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([25, 30, 31, 32, 37, 38, 40, 41, 42]), model=ScalarModel(intercept=0.24265765523257948, linear_terms=array([-0.00010296, -0.00348716]), square_terms=array([[1.52341867e-05, 3.50476207e-04], + [3.50476207e-04, 4.17971723e-02]]), scale=0.02189453125, shift=array([5.31760507, 0.16916422])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=43, candidate_x=array([5.33950848, 0.17080482]), index=43, x=array([5.33950848, 0.17080482]), fval=0.24222716621340298, rho=0.9043831615651715, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([25, 30, 31, 32, 37, 38, 40, 41, 42]), old_indices_discarded=array([26, 29, 34, 39]), step_length=0.021964765363485655, relative_step_length=1.0032078381895322, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.0437890625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([30, 31, 32, 37, 38, 39, 40, 42, 43]), model=ScalarModel(intercept=0.2423976703093635, linear_terms=array([-3.01430010e-05, 2.34992933e-04]), square_terms=array([[5.91513482e-05, 1.37667035e-03], + [1.37667035e-03, 1.65820314e-01]]), scale=0.0437890625, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=44, candidate_x=array([5.36895747, 0.17049827]), index=43, x=array([5.33950848, 0.17080482]), fval=0.24222716621340298, rho=-9.331642519757773, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([30, 31, 32, 37, 38, 39, 40, 42, 43]), old_indices_discarded=array([25, 26, 29, 33, 34, 35, 36, 41]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.02189453125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([30, 32, 37, 38, 39, 40, 42, 43, 44]), model=ScalarModel(intercept=0.24235348524537714, linear_terms=array([ 5.95198861e-06, -8.48814584e-06]), square_terms=array([[1.47776329e-05, 3.48481891e-04], + [3.48481891e-04, 4.14093403e-02]]), scale=0.02189453125, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=45, candidate_x=array([5.32837464, 0.170903 ]), index=43, x=array([5.33950848, 0.17080482]), fval=0.24222716621340298, rho=-24.42199537270614, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([30, 32, 37, 38, 39, 40, 42, 43, 44]), old_indices_discarded=array([25, 26, 31, 34]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.010947265625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([32, 37, 40, 42, 43, 44, 45]), model=ScalarModel(intercept=0.2422806237585476, linear_terms=array([ 2.67171372e-05, -2.05431480e-04]), square_terms=array([[3.68279483e-06, 8.49740419e-05], + [8.49740419e-05, 1.03070513e-02]]), scale=0.010947265625, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=46, candidate_x=array([5.32856345, 0.17111248]), index=43, x=array([5.33950848, 0.17080482]), fval=0.24222716621340298, rho=-2.0149510031231594, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([32, 37, 40, 42, 43, 44, 45]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.0054736328125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([32, 37, 40, 42, 43, 45, 46]), model=ScalarModel(intercept=0.24229025426011608, linear_terms=array([ 1.48827190e-05, -1.05452116e-04]), square_terms=array([[9.09604685e-07, 2.07560472e-05], + [2.07560472e-05, 2.57873989e-03]]), scale=0.0054736328125, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.00273681640625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([43, 45, 46, 47]), model=ScalarModel(intercept=0.24222624543591792, linear_terms=array([-7.33525171e-06, 2.20337847e-04]), square_terms=array([[2.41748426e-07, 5.48721051e-06], + [5.48721051e-06, 5.94912648e-04]]), scale=0.00273681640625, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=48, candidate_x=array([5.34223575, 0.16978181]), index=43, x=array([5.33950848, 0.17080482]), fval=0.24222716621340298, rho=-1.050453647514022, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 45, 46, 47]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.001368408203125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([43, 47, 48]), model=ScalarModel(intercept=0.24222716621340284, linear_terms=array([-1.02551120e-05, -3.95148592e-05]), square_terms=array([[6.98218857e-08, 1.88148453e-06], + [1.88148453e-06, 1.65041401e-04]]), scale=0.001368408203125, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=49, candidate_x=array([5.34088034, 0.17109939]), index=43, x=array([5.33950848, 0.17080482]), fval=0.24222716621340298, rho=-1.4217767819138583, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 47, 48]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.0006842041015625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([43, 48, 49]), model=ScalarModel(intercept=0.24222716621340307, linear_terms=array([6.14670909e-06, 1.02989082e-05]), square_terms=array([[1.43372945e-08, 1.96028127e-07], + [1.96028127e-07, 3.98197561e-05]]), scale=0.0006842041015625, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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linear_terms=array([-3.89600052e-06, -1.76546459e-05]), square_terms=array([[6.25029901e-08, 1.54407834e-06], + [1.54407834e-06, 1.61121526e-04]]), scale=0.001368408203125, shift=array([5.33882353, 0.17065423])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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[3.63400305e-07, 3.86016620e-05]]), scale=0.0006842041015625, shift=array([5.33882353, 0.17065423])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=52, candidate_x=array([5.33814097, 0.17083226]), index=50, x=array([5.33882353, 0.17065423]), fval=0.24222315394360727, rho=-0.14512140450342684, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 49, 50, 51]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33882353, 0.17065423]), radius=0.00034210205078125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([43, 50, 51, 52]), model=ScalarModel(intercept=0.24222319841032375, linear_terms=array([1.99763146e-06, 5.57738554e-06]), square_terms=array([[3.72373755e-09, 9.11821416e-08], + [9.11821416e-08, 9.85528901e-06]]), scale=0.00034210205078125, shift=array([5.33882353, 0.17065423])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=53, candidate_x=array([5.33851827, 0.17049811]), index=50, x=array([5.33882353, 0.17065423]), fval=0.24222315394360727, rho=-4.232464647796792, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 50, 51, 52]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33882353, 0.17065423]), radius=0.000171051025390625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([43, 50, 52, 53]), model=ScalarModel(intercept=0.24222851970904374, linear_terms=array([ 9.20804052e-08, -5.32837905e-06]), square_terms=array([[9.45290458e-10, 2.30234760e-08], + [2.30234760e-08, 2.43134636e-06]]), scale=0.000171051025390625, shift=array([5.33882353, 0.17065423])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=54, candidate_x=array([5.33878192, 0.17082014]), index=50, x=array([5.33882353, 0.17065423]), fval=0.24222315394360727, rho=-0.2598782806832573, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 50, 52, 53]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33882353, 0.17065423]), radius=8.55255126953125e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([50, 53, 54]), model=ScalarModel(intercept=0.2422231539436072, linear_terms=array([-3.14907959e-06, -8.42234292e-07]), square_terms=array([[3.63526659e-10, 1.13163660e-09], + [1.13163660e-09, 6.14271368e-07]]), scale=8.55255126953125e-05, shift=array([5.33882353, 0.17065423])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=55, candidate_x=array([5.33891521, 0.17067448]), index=50, x=array([5.33882353, 0.17065423]), fval=0.24222315394360727, rho=-0.05725279410002452, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([50, 53, 54]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33882353, 0.17065423]), radius=4.276275634765625e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([50, 54, 55]), model=ScalarModel(intercept=0.24222315394360722, linear_terms=array([ 8.70513659e-08, -4.37510681e-09]), square_terms=array([[8.02169837e-11, 1.60425228e-09], + [1.60425228e-09, 1.54231322e-07]]), scale=4.276275634765625e-05, shift=array([5.33882353, 0.17065423])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 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step_length=4.276717619883001e-05, relative_step_length=1.000103357490285, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 57 entries., 'multistart_info': {'start_parameters': [array([7.00625, 0.19375]), array([7.55707178, 0.15947986])], 'local_optima': [{'solution_x': array([5.33878077, 0.17065529]), 'solution_criterion': 0.24222229239256732, 'states': [State(trustregion=Region(center=array([7.00625, 0.19375]), radius=0.700625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=[0], model=ScalarModel(intercept=0.3273843758368489, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.700625, shift=array([7.00625, 0.19375])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, 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step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([7.00625, 0.19375]), radius=0.700625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=11.224951550903995, linear_terms=array([-1.37625354, 32.5154691 ]), square_terms=array([[ 0.09351784, -2.07104056], + [-2.07104056, 48.22141916]]), scale=array([0.62091274, 0.40233137]), shift=array([7.00625 , 0.41233137])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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n_evals_acceptance=1), State(trustregion=Region(center=array([6.38533726, 0.12376172]), radius=0.3503125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 3, 6, 7, 10, 11, 12, 13]), model=ScalarModel(intercept=0.7313551856043823, linear_terms=array([0.5632349 , 4.14900217]), square_terms=array([[ 0.31889798, 2.36106382], + [ 2.36106382, 17.56884109]]), scale=array([0.31045637, 0.21210905]), shift=array([6.38533726, 0.22210905])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], 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0.20052322]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 7, 10, 11, 12, 13, 14]), model=ScalarModel(intercept=0.5857130929401587, linear_terms=array([-0.66733221, -2.49758335]), square_terms=array([[ 0.71346148, 2.85346922], + [ 2.85346922, 11.61097172]]), scale=0.17515625, shift=array([6.07488089, 0.20052322])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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0.99]))), model_indices=array([ 1, 3, 7, 13, 14, 15]), model=ScalarModel(intercept=0.2909351370686484, linear_terms=array([0.01016955, 0.239779 ]), square_terms=array([[0.00441972, 0.04993243], + [0.04993243, 0.684917 ]]), scale=0.087578125, shift=array([6.25324275, 0.19438789])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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linear_terms=array([-0.10978373, -0.38806688]), square_terms=array([[0.34292001, 1.58579951], + [1.58579951, 7.51750345]]), scale=array([0.15522818, 0.15155507]), shift=array([6.33822271, 0.16155507])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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0.68633371]]), scale=0.087578125, shift=array([6.33822271, 0.15788195])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=18, candidate_x=array([6.42567538, 0.15294566]), index=16, x=array([6.33822271, 0.15788195]), fval=0.2614570472767362, rho=-0.7750579998984433, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 3, 7, 11, 13, 14, 15, 16, 17]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.33822271, 0.15788195]), radius=0.0437890625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 13, 15, 16, 17, 18]), model=ScalarModel(intercept=0.2588317231036329, linear_terms=array([ 0.00363168, -0.00423961]), square_terms=array([[1.17813939e-04, 1.72772939e-03], + [1.72772939e-03, 1.19696569e-01]]), scale=0.0437890625, shift=array([6.33822271, 0.15788195])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=19, candidate_x=array([6.29445974, 0.16000091]), index=19, x=array([6.29445974, 0.16000091]), fval=0.2594527871161228, rho=0.5388880366161921, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 13, 15, 16, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.04381424054578142, relative_step_length=1.0005749848099947, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29445974, 0.16000091]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 13, 14, 15, 16, 17, 18, 19]), model=ScalarModel(intercept=0.2541677101008941, linear_terms=array([-0.00368238, -0.03323817]), square_terms=array([[0.00145555, 0.02969193], + [0.02969193, 0.6672799 ]]), scale=0.087578125, shift=array([6.29445974, 0.16000091])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=20, candidate_x=array([6.38214384, 0.1604602 ]), index=19, x=array([6.29445974, 0.16000091]), fval=0.2594527871161228, rho=-0.930670096284453, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 3, 13, 14, 15, 16, 17, 18, 19]), old_indices_discarded=array([ 7, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29445974, 0.16000091]), radius=0.0437890625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 13, 15, 16, 17, 18, 19, 20]), model=ScalarModel(intercept=0.25622931078220434, linear_terms=array([ 0.0032415 , -0.00073259]), square_terms=array([[1.03290011e-04, 1.82810922e-03], + [1.82810922e-03, 1.19546137e-01]]), scale=0.0437890625, shift=array([6.29445974, 0.16000091])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=21, candidate_x=array([6.25067953, 0.16091447]), index=21, x=array([6.25067953, 0.16091447]), fval=0.2577870645585467, rho=0.517848980651204, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 13, 15, 16, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int32), step_length=0.043789739030257874, relative_step_length=1.0000154497543279, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.25067953, 0.16091447]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 13, 14, 15, 16, 18, 19, 20, 21]), model=ScalarModel(intercept=0.25500561017058476, linear_terms=array([0.00514188, 0.00157206]), square_terms=array([[3.29759763e-04, 4.68476327e-03], + [4.68476327e-03, 4.75402508e-01]]), scale=0.087578125, shift=array([6.25067953, 0.16091447])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=22, candidate_x=array([6.16310275, 0.16148209]), index=22, x=array([6.16310275, 0.16148209]), fval=0.2552353311983439, rho=0.5116649557870464, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 13, 14, 15, 16, 18, 19, 20, 21]), old_indices_discarded=array([ 3, 7, 17]), step_length=0.08757862032278296, relative_step_length=1.0000056557819998, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.16310275, 0.16148209]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 13, 14, 15, 16, 19, 20, 21, 22]), model=ScalarModel(intercept=0.2515880836028085, linear_terms=array([0.00754998, 0.01225116]), square_terms=array([[8.67053730e-04, 1.69602107e-02], + [1.69602107e-02, 1.46374627e+00]]), scale=array([0.15522818, 0.15335514]), shift=array([6.16310275, 0.16335514])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=23, candidate_x=array([6.00787457, 0.1638485 ]), index=23, x=array([6.00787457, 0.1638485 ]), fval=0.25064066883423725, rho=0.6486363276282296, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 13, 14, 15, 16, 19, 20, 21, 22]), old_indices_discarded=array([ 0, 3, 7, 10, 11, 12, 17, 18]), step_length=0.1552462214938793, relative_step_length=0.8863298996974375, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.00787457, 0.1638485 ]), radius=0.3503125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([13, 14, 15, 16, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=0.43332719746500753, linear_terms=array([0.03146291, 1.14425379]), square_terms=array([[2.68439891e-03, 7.19387302e-02], + [7.19387302e-02, 3.57398202e+00]]), scale=array([0.31045637, 0.23215243]), shift=array([6.00787457, 0.24215243])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=24, candidate_x=array([5.6974182 , 0.17249888]), index=24, x=array([5.6974182 , 0.17249888]), fval=0.244970934682092, rho=0.6800504779079048, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([13, 14, 15, 16, 19, 20, 21, 22, 23]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 17, 18]), step_length=0.3105768611152018, relative_step_length=0.8865708791870166, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.6974182 , 0.17249888]), radius=0.700625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 13, 14, 15, 16, 19, 22, 23, 24]), model=ScalarModel(intercept=1.9359689768705584, linear_terms=array([0.14431675, 5.7192291 ]), square_terms=array([[0.01061289, 0.22387827], + [0.22387827, 9.66222181]]), scale=array([0.62091274, 0.39170581]), shift=array([5.6974182 , 0.40170581])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=25, candidate_x=array([5.2361468 , 0.17615356]), index=25, x=array([5.2361468 , 0.17615356]), fval=0.2440600019927147, rho=0.12172165982234218, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 13, 14, 15, 16, 19, 22, 23, 24]), old_indices_discarded=array([ 0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 17, 18, 20, 21]), step_length=0.46128587321697967, relative_step_length=0.6583919689091592, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2361468 , 0.17615356]), radius=1.40125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 7, 10, 14, 16, 17, 22, 24, 25]), model=ScalarModel(intercept=19.556928495798605, linear_terms=array([ 2.66108959, 60.1251518 ]), square_terms=array([[ 0.19711181, 4.14595444], + [ 4.14595444, 93.60232406]]), scale=array([1.24182548, 0.49 ]), shift=array([5.2361468, 0.5 ])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2361468 , 0.17615356]), radius=0.700625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 15, 19, 21, 22, 23, 24, 25, 26]), model=ScalarModel(intercept=2.023039226033913, linear_terms=array([0.11112348, 6.16604764]), square_terms=array([[9.99812684e-03, 1.87643817e-01], + [1.87643817e-01, 1.06798417e+01]]), scale=array([0.62091274, 0.39353315]), shift=array([5.2361468 , 0.40353315])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2361468 , 0.17615356]), radius=0.3503125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 21, 22, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=0.5871067495424316, linear_terms=array([0.02508987, 2.27718757]), square_terms=array([[2.83476082e-03, 8.32114910e-02], + [8.32114910e-02, 7.59032634e+00]]), scale=array([0.31045637, 0.23830497]), shift=array([5.2361468 , 0.24830497])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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State(trustregion=Region(center=array([5.2361468 , 0.17615356]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 23, 24, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=0.24537978825844767, linear_terms=array([-0.00017886, -0.00847909]), square_terms=array([[6.40294020e-04, 1.45475512e-02], + [1.45475512e-02, 3.24873183e+00]]), scale=array([0.15522818, 0.15522818]), shift=array([5.2361468 , 0.17615356])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + 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0.17638843]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 23, 24, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=0.24506470035985778, linear_terms=array([5.54445372e-05, 8.93470786e-05]), square_terms=array([[6.38751471e-04, 1.45480845e-02], + [1.45480845e-02, 3.24873994e+00]]), scale=array([0.15522818, 0.15522818]), shift=array([5.2741713 , 0.17638843])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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upper=array([20. , 0.99]))), model_indices=array([24, 25, 26, 27, 28, 29, 30, 31]), model=ScalarModel(intercept=0.24474192813793477, linear_terms=array([-0.00048722, -0.00034972]), square_terms=array([[2.32194110e-04, 6.38817516e-03], + [6.38817516e-03, 1.03554328e+00]]), scale=0.087578125, shift=array([5.2741713 , 0.17638843])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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31, 32]), model=ScalarModel(intercept=0.2442299698961134, linear_terms=array([-0.00054183, 0.00016305]), square_terms=array([[7.30553468e-04, 2.00849094e-02], + [2.00849094e-02, 3.25315131e+00]]), scale=array([0.15522818, 0.15522818]), shift=array([5.36174794, 0.1758779 ])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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linear_terms=array([0.00032528, 0.05273933]), square_terms=array([[2.32924566e-04, 6.28242993e-03], + [6.28242993e-03, 6.44662821e-01]]), scale=0.087578125, shift=array([5.36174794, 0.1758779 ])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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1.81333427e-02], + [1.81333427e-02, 2.02348592e+00]]), scale=array([0.15522818, 0.15522818]), shift=array([5.44896581, 0.16786346])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=36, candidate_x=array([5.52101795, 0.16734146]), index=35, x=array([5.44896581, 0.16786346]), fval=0.24274413357026287, rho=-6.259192132860256, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([25, 26, 29, 30, 31, 32, 33, 34, 35]), old_indices_discarded=array([14, 15, 19, 21, 22, 23, 24, 27, 28]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.44896581, 0.16786346]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([25, 26, 30, 31, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=0.24163478548943507, linear_terms=array([ 0.00016458, -0.00055621]), square_terms=array([[2.57908296e-04, 6.00198503e-03], + [6.00198503e-03, 6.44118485e-01]]), scale=0.087578125, shift=array([5.44896581, 0.16786346])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=37, candidate_x=array([5.36139226, 0.16875945]), index=37, x=array([5.36139226, 0.16875945]), fval=0.24255208756330368, rho=2.7825937304103108, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([25, 26, 30, 31, 32, 33, 34, 35, 36]), old_indices_discarded=array([24, 28, 29]), step_length=0.08757812500000042, relative_step_length=1.0000000000000047, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.36139226, 0.16875945]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([25, 26, 29, 31, 33, 34, 35, 36, 37]), model=ScalarModel(intercept=0.2417800168998051, linear_terms=array([-0.00018881, 0.00035398]), square_terms=array([[8.05230298e-04, 1.86970634e-02], + [1.86970634e-02, 2.02386702e+00]]), scale=array([0.15522818, 0.15522818]), shift=array([5.36139226, 0.16875945])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=38, candidate_x=array([5.40853278, 0.1682968 ]), index=37, x=array([5.36139226, 0.16875945]), fval=0.24255208756330368, rho=-4.2991999497290685, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([25, 26, 29, 31, 33, 34, 35, 36, 37]), old_indices_discarded=array([14, 22, 23, 24, 27, 28, 30, 32]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.36139226, 0.16875945]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([25, 26, 30, 31, 32, 34, 35, 37, 38]), model=ScalarModel(intercept=0.24170158357350824, linear_terms=array([-0.00012888, -0.00012381]), square_terms=array([[2.60518703e-04, 5.70133267e-03], + [5.70133267e-03, 6.43763482e-01]]), scale=0.087578125, shift=array([5.36139226, 0.16875945])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=39, candidate_x=array([5.41467648, 0.16830439]), index=37, x=array([5.36139226, 0.16875945]), fval=0.24255208756330368, rho=-1.9714007957034316, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([25, 26, 30, 31, 32, 34, 35, 37, 38]), old_indices_discarded=array([24, 27, 28, 29, 33, 36]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.36139226, 0.16875945]), radius=0.0437890625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([26, 30, 31, 32, 34, 35, 37, 38, 39]), model=ScalarModel(intercept=0.2417553910728286, linear_terms=array([ 4.54049262e-05, -4.56249946e-05]), square_terms=array([[6.76405313e-05, 1.43316796e-03], + [1.43316796e-03, 1.60944325e-01]]), scale=0.0437890625, shift=array([5.36139226, 0.16875945])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=40, candidate_x=array([5.31760507, 0.16916422]), index=40, x=array([5.31760507, 0.16916422]), fval=0.24241985946629963, rho=7.194700985609317, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([26, 30, 31, 32, 34, 35, 37, 38, 39]), old_indices_discarded=array([25, 29, 33, 36]), step_length=0.043789062500000274, relative_step_length=1.0000000000000062, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.31760507, 0.16916422]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([25, 30, 31, 32, 34, 37, 38, 39, 40]), model=ScalarModel(intercept=0.24205486457802278, linear_terms=array([0.00023275, 0.000779 ]), square_terms=array([[3.03100394e-04, 5.73493197e-03], + [5.73493197e-03, 6.43806788e-01]]), scale=0.087578125, shift=array([5.31760507, 0.16916422])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=41, candidate_x=array([5.23002957, 0.16984189]), index=40, x=array([5.31760507, 0.16916422]), fval=0.24241985946629963, rho=-6.146531737571127, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([25, 30, 31, 32, 34, 37, 38, 39, 40]), old_indices_discarded=array([24, 26, 27, 28, 29, 33, 35, 36]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.31760507, 0.16916422]), radius=0.0437890625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([25, 30, 31, 32, 34, 37, 38, 40, 41]), model=ScalarModel(intercept=0.24213302949529256, linear_terms=array([-3.78622622e-05, 2.41519221e-04]), square_terms=array([[6.90966248e-05, 1.48098964e-03], + [1.48098964e-03, 1.60965844e-01]]), scale=0.0437890625, shift=array([5.31760507, 0.16916422])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=42, candidate_x=array([5.36139158, 0.16869151]), index=40, x=array([5.31760507, 0.16916422]), fval=0.24241985946629963, rho=-11.833264403894546, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([25, 30, 31, 32, 34, 37, 38, 40, 41]), old_indices_discarded=array([26, 29, 33, 35, 36, 39]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.31760507, 0.16916422]), radius=0.02189453125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([25, 30, 31, 32, 37, 38, 40, 41, 42]), model=ScalarModel(intercept=0.24265765523257948, linear_terms=array([-0.00010296, -0.00348716]), square_terms=array([[1.52341867e-05, 3.50476207e-04], + [3.50476207e-04, 4.17971723e-02]]), scale=0.02189453125, shift=array([5.31760507, 0.16916422])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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old_indices_discarded=array([26, 29, 34, 39]), step_length=0.021964765363485655, relative_step_length=1.0032078381895322, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.0437890625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([30, 31, 32, 37, 38, 39, 40, 42, 43]), model=ScalarModel(intercept=0.2423976703093635, linear_terms=array([-3.01430010e-05, 2.34992933e-04]), square_terms=array([[5.91513482e-05, 1.37667035e-03], + [1.37667035e-03, 1.65820314e-01]]), scale=0.0437890625, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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41]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.02189453125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([30, 32, 37, 38, 39, 40, 42, 43, 44]), model=ScalarModel(intercept=0.24235348524537714, linear_terms=array([ 5.95198861e-06, -8.48814584e-06]), square_terms=array([[1.47776329e-05, 3.48481891e-04], + [3.48481891e-04, 4.14093403e-02]]), scale=0.02189453125, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.010947265625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([32, 37, 40, 42, 43, 44, 45]), model=ScalarModel(intercept=0.2422806237585476, linear_terms=array([ 2.67171372e-05, -2.05431480e-04]), square_terms=array([[3.68279483e-06, 8.49740419e-05], + [8.49740419e-05, 1.03070513e-02]]), scale=0.010947265625, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=46, candidate_x=array([5.32856345, 0.17111248]), index=43, x=array([5.33950848, 0.17080482]), fval=0.24222716621340298, rho=-2.0149510031231594, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([32, 37, 40, 42, 43, 44, 45]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.0054736328125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([32, 37, 40, 42, 43, 45, 46]), model=ScalarModel(intercept=0.24229025426011608, linear_terms=array([ 1.48827190e-05, -1.05452116e-04]), square_terms=array([[9.09604685e-07, 2.07560472e-05], + [2.07560472e-05, 2.57873989e-03]]), scale=0.0054736328125, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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0.99]))), model_indices=array([43, 45, 46, 47]), model=ScalarModel(intercept=0.24222624543591792, linear_terms=array([-7.33525171e-06, 2.20337847e-04]), square_terms=array([[2.41748426e-07, 5.48721051e-06], + [5.48721051e-06, 5.94912648e-04]]), scale=0.00273681640625, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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linear_terms=array([-1.02551120e-05, -3.95148592e-05]), square_terms=array([[6.98218857e-08, 1.88148453e-06], + [1.88148453e-06, 1.65041401e-04]]), scale=0.001368408203125, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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3.98197561e-05]]), scale=0.0006842041015625, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=50, candidate_x=array([5.33882353, 0.17065423]), index=50, x=array([5.33882353, 0.17065423]), fval=0.24222315394360727, rho=0.5418085141054687, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([43, 48, 49]), old_indices_discarded=array([], dtype=int32), step_length=0.0007013115532927016, relative_step_length=1.0250034334654436, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33882353, 0.17065423]), radius=0.001368408203125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([43, 47, 48, 49, 50]), model=ScalarModel(intercept=0.24223982659455429, linear_terms=array([-3.89600052e-06, -1.76546459e-05]), square_terms=array([[6.25029901e-08, 1.54407834e-06], + [1.54407834e-06, 1.61121526e-04]]), scale=0.001368408203125, shift=array([5.33882353, 0.17065423])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, 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linear_terms=array([0.00244388, 0.06648202]), square_terms=array([[1.19336081e-03, 3.24541529e-02], + [3.24541529e-02, 2.37275707e+00]]), scale=array([0.16743201, 0.16451471]), shift=array([5.34091551, 0.17451471])), vector_model=VectorModel(intercepts=array([ 0.04372593, 0.09723622, 0.10188573, 0.13262325, 0.15154936, + 0.16923117, 0.18861608, 0.18401939, 0.09682264, 0.1552954 , + -0.16489717, -0.1798028 , -0.14271083, -0.11811719, -0.10957416, + -0.11108474, -0.1079118 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], 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0.17159741])), vector_model=VectorModel(intercepts=array([ 0.04372593, 0.09723622, 0.10188573, 0.13262325, 0.15154936, + 0.16923117, 0.18861608, 0.18401939, 0.09682264, 0.1552954 , + -0.16489717, -0.1798028 , -0.14271083, -0.11811719, -0.10957416, + -0.11108474, -0.1079118 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7557071776832622, shift=array([7.55707178, 0.15947986])), candidate_index=26, candidate_x=array([5.36452368, 0.17077702]), index=26, x=array([5.36452368, 0.17077702]), fval=0.24232590102301266, rho=0.004636863515290328, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([17, 18, 21, 22, 24, 25]), old_indices_discarded=array([], dtype=int32), step_length=0.023622419090119764, relative_step_length=1.0002781939973293, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 27 entries., 'history': {'params': [{'CRRA': 7.557071776832622, 'WealthShare': 0.15947986291737937}, {'CRRA': 6.887343728211803, 'WealthShare': 0.013137857270913678}, {'CRRA': 8.22679982545344, 'WealthShare': 0.33278788031954126}, {'CRRA': 6.887343728211803, 'WealthShare': 0.4927195085070417}, {'CRRA': 8.22679982545344, 'WealthShare': 0.8233666446704474}, {'CRRA': 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3.4625 , 0.6225 ], + [17.6375 , 0.8675 ], + [ 5.825 , 0.745 ], + [12.321875 , 0.959375 ], + [ 2.28125 , 0.92875 ]]), 'exploration_results': array([3.27384376e-01, 1.14034055e+00, 1.50312179e+00, 1.79325436e+00, + 2.03621762e+00, 2.08879824e+00, 2.54067782e+00, 2.64114350e+00, + 2.73814230e+00, 3.83228381e+00, 6.46248546e+00, 9.30118040e+00, + 1.59966394e+01, 3.03715662e+01, 3.38771067e+01, 4.26926083e+01, + 5.84656546e+01, 5.90505617e+01, 5.22500282e+02, 9.01924413e+02])}}" diff --git a/src/estimark/estimation.py b/src/estimark/estimation.py new file mode 100644 index 0000000..ad8bef3 --- /dev/null +++ b/src/estimark/estimation.py @@ -0,0 +1,903 @@ +"""Demonstrates an example estimation of microeconomic dynamic stochastic optimization +problem, as described in Section 9 of Chris Carroll's EstimatingMicroDSOPs.pdf notes. +The estimation attempts to match the age-conditional wealth profile of simulated +consumers to the median wealth holdings of seven age groups in the 2004 SCF by +varying only two parameters: the coefficient of relative risk aversion and a scaling +factor for an age-varying sequence of discount factors. The estimation uses a +consumption-saving model with idiosyncratic shocks to permanent and transitory +income as defined in ConsIndShockModel. +""" + +from __future__ import annotations + +import csv +from pathlib import Path +from time import time + +import estimagic as em +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +# Estimation methods +from estimagic.inference import get_bootstrap_samples +from scipy.optimize import approx_fprime +from statsmodels.stats.weightstats import DescrStatsW + +# Import modules from core HARK libraries: +# The consumption-saving micro model +from estimark.agents import ( + BequestWarmGlowLifeCycleConsumerType, + BequestWarmGlowLifeCyclePortfolioType, + IndShkLifeCycleConsumerType, + PortfolioLifeCycleConsumerType, + WealthPortfolioLifeCycleConsumerType, +) + +# Parameters for the consumer type and the estimation +from estimark.parameters import ( + age_mapping, + bootstrap_options, + init_calibration, + init_params_options, + init_subjective_labor, + init_subjective_stock, + minimize_options, + sim_mapping, +) + +# SCF 2004 data on household wealth +from estimark.scf import scf_data +from estimark.snp import snp_data + +# ===================================================== +# Define objects and functions used for the estimation +# ===================================================== + +agent_types = { + "IndShock": IndShkLifeCycleConsumerType, + "Portfolio": PortfolioLifeCycleConsumerType, + "WarmGlow": BequestWarmGlowLifeCycleConsumerType, + "WarmGlowPortfolio": BequestWarmGlowLifeCyclePortfolioType, + "WealthPortfolio": WealthPortfolioLifeCycleConsumerType, +} + + +def make_agent(agent_name): + for key, value in agent_types.items(): + if key in agent_name: + agent_type = value + + calibration = init_calibration.copy() + + if "Sub" in agent_name: + if "(Stock)" in agent_name: + calibration.update(init_subjective_stock) + if "(Labor)" in agent_name: + calibration.update(init_subjective_labor) + + # Make a lifecycle consumer to be used for estimation, including simulated + # shocks (plus an initial distribution of wealth) + # Make a TempConsumerType for estimation + agent = agent_type(**calibration) + # Set the number of periods to simulate + agent.T_sim = agent.T_cycle + 1 + # Choose to track bank balances as wealth + track_vars = ["bNrm"] + if "Portfolio" in agent_name: + track_vars += ["Share"] + agent.track_vars = track_vars + + agent.name = agent_name + + return agent + + +def weighted_median(values, weights): + stats = DescrStatsW(values, weights=weights) + return stats.quantile(0.5, return_pandas=False) + + +def winsored_mean(values, weights, limits): + stats = DescrStatsW(values, weights=weights) + qs = stats.quantile(limits, return_pandas=False) + + # discard values outside qs + mask = (values >= qs[0]) & (values <= qs[1]) + return np.average(values[mask], weights=weights[mask]) + + +def get_weighted_moments( + data, + variable=None, + weights=None, + groups=None, + mapping=None, +): + # Common variables that don't depend on whether weights are None or not + data_variable = data[variable] + data_groups = data[groups] + data_weights = data[weights] if weights else None + + emp_moments = {} + for key in mapping: + group_data = data_variable[data_groups == key] + group_weights = data_weights[data_groups == key] if weights else None + + # Check if the group has any data + if not group_data.empty: + if weights is None: + emp_moments[key] = group_data.median() + else: + emp_moments[key] = weighted_median( + group_data.to_numpy(), + group_weights.to_numpy(), + ) + # else: + # print(f"Warning: Group {key} does not have any data.") + + return emp_moments + + +def get_moments_cov(agent_name, emp_moments): + moments_cov = em.get_moments_cov( + scf_data, + get_weighted_moments, + moment_kwargs={ + "variable": "wealth_income_ratio", + "weights": "weight", + "groups": "age_group", + "mapping": age_mapping, + }, + ) + + if "Port" in agent_name: + for key1 in emp_moments: + # Check if key1 exists in moments_cov dictionary + if key1 not in moments_cov: + # If it doesn't exist, create a new dictionary for this key + moments_cov[key1] = {} + + for key2 in emp_moments: + # Check if key2 exists in the nested dictionary under key1 + if key2 not in moments_cov[key1]: + # If it doesn't exist, we need to add it + if key1 == key2: + # If key1 is equal to key2, set the value to 1.0 + moments_cov[key1][key2] = 1.0 + else: + # Otherwise, set the value to 0.0 + moments_cov[key1][key2] = 0.0 + + return moments_cov + + +def get_empirical_moments(agent_name): + emp_moments = get_weighted_moments( + data=scf_data, + variable="wealth_income_ratio", + weights="weight", + groups="age_group", + mapping=age_mapping, + ) + + # Add share moments if agent is a portfolio type + + if "Portfolio" in agent_name: + share_moments = get_weighted_moments( + data=snp_data, + variable="share", + groups="age_group", + mapping=age_mapping, + ) + + suffix = "_port" + for key, value in share_moments.items(): + emp_moments[key + suffix] = value + + return emp_moments + + +def get_initial_guess(agent, params_to_estimate, save_dir): + agent_name = agent.name + + agent_params = [] + for key in params_to_estimate: + if hasattr(agent, key): + agent_params.append(key) + else: + print(f"Agent {agent_name} does not have parameter: {key}") + + # start from previous estimation results if available + csv_file_path = save_dir / (agent_name + "_estimate_results.csv") + + try: + res = pd.read_csv(csv_file_path, header=None) + temp_dict = res.set_index(res.columns[0])[res.columns[1]].to_dict() + except (FileNotFoundError, IndexError): + temp_dict = init_params_options.get("init_guess", {}) + + initial_guess = { + key: float(temp_dict.get(key, init_params_options["init_guess"][key])) + for key in agent_params + } + + return initial_guess + + +# Define the objective function for the simulated method of moments estimation +def simulate_moments(params, agent=None, emp_moments=None): + """A quick check to make sure that the parameter values are within bounds. + Far flung falues of DiscFac or CRRA might cause an error during solution or + simulation, so the objective function doesn't even bother with them. + """ + # Update the agent with a new path of DiscFac based on this DiscFac (and a new CRRA) + + agent.assign_parameters(**params) + + if hasattr(agent, "BeqCRRA"): + agent.BeqCRRA = agent.CRRA + + # ensure subjective beliefs are used for solution + if "(Stock)" in agent.name and "Portfolio" in agent.name: + agent.RiskyAvg = init_subjective_stock["RiskyAvg"] + agent.RiskyStd = init_subjective_stock["RiskyStd"] + agent.Rfree = init_subjective_stock["Rfree"] + agent.update_RiskyDstn() + if "(Labor)" in agent.name: + agent.TranShkStd = init_subjective_labor["TranShkStd"] + agent.PermShkStd = init_subjective_labor["PermShkStd"] + agent.update_income_process() + + agent.update() + + # Solve the model for these parameters, then simulate wealth data + agent.solve() # Solve the microeconomic model + + # simulate with true parameters (override subjective beliefs) + if "(Stock)" in agent.name and "Portfolio" in agent.name: + agent.RiskyAvg = init_subjective_stock["RiskyAvgTrue"] + agent.RiskyStd = init_subjective_stock["RiskyStdTrue"] + agent.Rfree = init_subjective_stock["Rfree"] + agent.update_RiskyDstn() + # for labor keep same process as subjective beliefs + if "(Labor)" in agent.name: + agent.TranShkStd = init_subjective_labor["TranShkStd"] + agent.PermShkStd = init_subjective_labor["PermShkStd"] + agent.update_income_process() + + agent.update() + + max_sim_age = agent.T_cycle + 1 + # Initialize the simulation by clearing histories, resetting initial values + agent.initialize_sim() + # agent.make_shock_history() + agent.simulate(max_sim_age) # Simulate histories of consumption and wealth + # Take "wealth" to mean bank balances before receiving labor income + sim_w_history = agent.history["bNrm"] + + # Find the distance between empirical data and simulated medians for each age group + + sim_moments = { + key: np.median(sim_w_history[cohort_idx]) + for key, cohort_idx in sim_mapping.items() + if key in emp_moments + } + + if "Portfolio" in agent.name: + sim_share_history = agent.history["Share"] + share_moments = {} + for key, cohort_idx in sim_mapping.items(): + key_port = key + "_port" + if key_port in emp_moments: + share_moments[key_port] = np.median(sim_share_history[cohort_idx]) + sim_moments.update(share_moments) + + return sim_moments + + +def calculate_weights(emp_moments): + n_port_stats = sum(1 for k in emp_moments if "_port" in k) + max_w_stat = max(emp_moments.values()) + + port_fac = ( + (len(emp_moments) - n_port_stats) / n_port_stats if n_port_stats != 0 else 1.0 + ) + + port_fac = 1.0 + + # Using dictionary comprehension to create weights + weights = { + k: (1 / max_w_stat if "_port" not in k else port_fac) + for k, v in emp_moments.items() + } + + return weights + + +def msm_criterion(params, agent=None, emp_moments=None, weights=None): + """The objective function for the SMM estimation. Given values of discount factor + adjuster DiscFac, coeffecient of relative risk aversion CRRA, a base consumer + agent type, empirical data, and calibrated parameters, this function calculates + the weighted distance between data and the simulated wealth-to-permanent + income ratio. + + Steps: + a) solve for consumption functions for (DiscFac, CRRA) + b) simulate wealth holdings for many consumers over time + c) sum distances between empirical data and simulated medians within + seven age groupings + + Parameters + ---------- + DiscFac : float + An adjustment factor to a given age-varying sequence of discount factors. + I.e. DiscFac[t] = DiscFac*timevary_DiscFac[t]. + CRRA : float + Coefficient of relative risk aversion. + agent : ConsumerType + The consumer type to be used in the estimation, with all necessary para- + meters defined except the discount factor and CRRA. + bounds_DiscFac : (float,float) + Lower and upper bounds on DiscFac; if outside these bounds, the function + simply returns a "penalty value". + bounds_DiscFac : (float,float) + Lower and upper bounds on CRRA; if outside these bounds, the function + simply returns a "penalty value". + empirical_data : np.array + Array of wealth-to-permanent-income ratios in the data. + empirical_weights : np.array + Weights for each observation in empirical_data. + empirical_groups : np.array + Array of integers listing the age group for each observation in empirical_data. + mapping : [np.array] + List of arrays of "simulation ages" for each age grouping. E.g. if the + 0th element is [1,2,3,4,5], then these time indices from the simulation + correspond to the 0th empirical age group. + + Returns + ------- + distance_sum : float + Sum of distances between empirical data observations and the corresponding + median wealth-to-permanent-income ratio in the simulation. + + """ + emp_moments = emp_moments.copy() + sim_moments = simulate_moments(params, agent, emp_moments) + + # TODO: make sure all keys in moments have a corresponding + # key in sim_moments, raise an error if not + errors = np.array( + [ + float(weights[key] * (sim_moments[key] - emp_moments[key])) + for key in emp_moments + ], + ) + + squared_errors = np.square(errors) + loss = np.sum(squared_errors) + + return { + "value": loss, + "contributions": squared_errors, + "root_contributions": errors, + } + + +# Define the bootstrap procedure +def calculate_se_bootstrap( + agent, + initial_estimate, + n_draws=50, + seed=0, + verbose=False, +): + """Calculates standard errors by repeatedly re-estimating the model with datasets + resampled from the actual data. + + Parameters + ---------- + initial_estimate : [float,float] + The estimated [DiscFac,CRRA], for use as an initial guess for each + re-estimation in the bootstrap procedure. + N : int + Number of times to resample data and re-estimate the model. + seed : int + Seed for the random number generator. + verbose : boolean + Indicator for whether extra output should be printed for the user. + + Returns + ------- + standard_errors : [float,float] + Standard errors calculated by bootstrap: [DiscFac_std_error, CRRA_std_error]. + + """ + t_0 = time() + + # Generate a list of seeds for generating bootstrap samples + RNG = np.random.default_rng(seed) + seed_list = RNG.integers(2**31 - 1, size=n_draws) + + # Estimate the model N times, recording each set of estimated parameters + estimate_list = [] + for n in range(n_draws): + t_start = time() + + # Bootstrap a new dataset by resampling from the original data + bootstrap_data = get_bootstrap_samples(data=scf_data, rng=RNG) + + # Find moments with bootstrapped sample + bootstrap_moments = get_weighted_moments( + data=bootstrap_data, + variable="wealth_income_ratio", + weights="weight", + groups="age_group", + mapping=age_mapping, + ) + + # Estimate the model with the bootstrap data and add to list of estimates + this_estimate = em.minimize( + msm_criterion, + initial_estimate, + criterion_kwargs={"agent": agent, "emp_moments": bootstrap_moments}, + **minimize_options, + ).params + estimate_list.append(this_estimate) + t_now = time() + + # Report progress of the bootstrap + if verbose: + print( + f"Finished bootstrap estimation #{n + 1} of {n_draws} in {t_now - t_start} seconds ({t_now - t_0} cumulative)", + ) + + # Calculate the standard errors for each parameter + estimate_array = (np.array(estimate_list)).T + DiscFac_std_error = np.std(estimate_array[0]) + CRRA_std_error = np.std(estimate_array[1]) + + return [DiscFac_std_error, CRRA_std_error] + + +# ================================================================= +# Done defining objects and functions. Now run them (if desired). +# ================================================================= + + +def do_estimate_model( + agent, + initial_guess, + estimate_method="min", + emp_moments=None, + moments_cov=None, + minimize_options=None, + criterion_kwargs=None, + save_dir=None, +): + fmt_init_guess = [f"{key} = {value:.3f}" for key, value in initial_guess.items()] + multistart_text = " with multistart" if minimize_options.get("multistart") else "" + statement1 = f"Estimating model using {minimize_options['algorithm']}{multistart_text} from an initial guess of" + statement2 = ", ".join(fmt_init_guess) + max_len = max(len(statement1), len(statement2)) + dash_line = "-" * max_len + + # Use f-string for padding + statement1 = f"{statement1:^{max_len}}" + statement2 = f"{statement2:^{max_len}}" + + print(dash_line) + print(statement1) + print(statement2) + print(dash_line) + + upper_bounds = { + key: value + for key, value in init_params_options["upper_bounds"].items() + if key in initial_guess + } + + lower_bounds = { + key: value + for key, value in init_params_options["lower_bounds"].items() + if key in initial_guess + } + + estimagic_options = {"upper_bounds": upper_bounds, "lower_bounds": lower_bounds} + + if estimate_method == "min": + res, time_to_estimate = estimate_min( + agent, + msm_criterion, + initial_guess, + emp_moments, + minimize_options, + criterion_kwargs=criterion_kwargs, + estimagic_options=estimagic_options, + ) + + model_estimate = res.params + + elif estimate_method == "msm": + res, time_to_estimate = estimate_msm( + agent, + simulate_moments, + emp_moments, + moments_cov, + initial_guess, + minimize_options, + estimagic_options=estimagic_options, + ) + + model_estimate = res._params + + else: + raise ValueError(f"Invalid estimate_method: {estimate_method}") + + # Calculate minutes and remaining seconds + minutes, seconds = divmod(time_to_estimate, 60) + statement1 = f"Estimated model: {agent.name}" + statement2 = f"Time to estimate: {int(minutes)} min, {int(seconds)} sec." + estimates = [f"{key} = {value:.3f}" for key, value in model_estimate.items()] + statement3 = "Estimated values: " + ", ".join(estimates) + dash_len = max(len(statement1), len(statement2), len(statement3)) + print(statement1) + print(statement2) + print(statement3) + print("-" * dash_len) + + # Create the simple estimate table + estimate_results_file = save_dir / (agent.name + "_estimate_results.csv") + + keys_to_save = vars(res) + + with open(estimate_results_file, "w") as f: + writer = csv.writer(f) + + for key in model_estimate: + writer.writerow([key, model_estimate[key]]) + + writer.writerow(["time_to_estimate", time_to_estimate]) + + if keys_to_save is not None: + for key in keys_to_save: + writer.writerow([key, getattr(res, key)]) + + return model_estimate, res, time_to_estimate + + +def do_compute_se_boostrap( + agent, + model_estimate, + time_to_estimate, + bootstrap_size=50, + seed=0, + save_dir=None, +): + # Estimate the model: + print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~") + print( + f"Computing standard errors using {bootstrap_size} bootstrap replications.", + ) + print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~") + + t_bootstrap_guess = time_to_estimate * bootstrap_size + minutes, seconds = divmod(t_bootstrap_guess, 60) + print(f"This will take approximately {int(minutes)} min, {int(seconds)} sec.") + + t_start_bootstrap = time() + std_errors = calculate_se_bootstrap( + agent, + model_estimate, + n_draws=bootstrap_size, + seed=seed, + verbose=True, + ) + t_end_bootstrap = time() + time_to_bootstrap = t_end_bootstrap - t_start_bootstrap + + # Calculate minutes and remaining seconds + minutes, seconds = divmod(time_to_bootstrap, 60) + print(f"Time to bootstrap: {int(minutes)} min, {int(seconds)} sec.") + + print(f"Standard errors: DiscFac--> {std_errors[0]}, CRRA--> {std_errors[1]}") + + # Create the simple bootstrap table + bootstrap_results_file = save_dir + agent.name + "_bootstrap_results.csv" + + with open(bootstrap_results_file, "w") as f: + writer = csv.writer(f) + writer.writerow( + [ + "DiscFac", + "DiscFac_standard_error", + "CRRA", + "CRRA_standard_error", + ], + ) + writer.writerow( + [model_estimate[0], std_errors[0], model_estimate[1], std_errors[1]], + ) + + +def do_compute_sensitivity(agent, model_estimate, emp_moments, save_dir=None): + print("``````````````````````````````````````````````````````````````````````") + print("Computing sensitivity measure.") + print("``````````````````````````````````````````````````````````````````````") + + # Find the Jacobian of the function that simulates moments + + n_moments = len(emp_moments) + jac = np.array( + [ + approx_fprime( + model_estimate, + lambda params: simulate_moments(params, agent=agent)[j], + epsilon=0.01, + ) + for j in range(n_moments) + ], + ) + + # Compute sensitivity measure. (all moments weighted equally) + sensitivity = np.dot(np.linalg.inv(np.dot(jac.T, jac)), jac.T) + + # Create lables for moments in the plots + moment_labels = emp_moments.keys() + + # Plot + fig, axs = plt.subplots(len(model_estimate)) + fig.set_tight_layout(True) + + axs[0].bar(range(n_moments), sensitivity[0, :], tick_label=moment_labels) + axs[0].set_title("DiscFac") + axs[0].set_ylabel("Sensitivity") + axs[0].set_xlabel("Median W/Y Ratio") + + axs[1].bar(range(n_moments), sensitivity[1, :], tick_label=moment_labels) + axs[1].set_title("CRRA") + axs[1].set_ylabel("Sensitivity") + axs[1].set_xlabel("Median W/Y Ratio") + + plt.savefig(save_dir + agent.name + "Sensitivity.pdf") + plt.savefig(save_dir + agent.name + "Sensitivity.png") + plt.savefig(save_dir + agent.name + "Sensitivity.svg") + + plt.show() + + +def do_make_contour_plot(agent, model_estimate, emp_moments, save_dir=None): + print("``````````````````````````````````````````````````````````````````````") + print("Creating the contour plot.") + print("``````````````````````````````````````````````````````````````````````") + t_start_contour = time() + DiscFac_star, CRRA_star = model_estimate + grid_density = 20 # Number of parameter values in each dimension + level_count = 100 # Number of contour levels to plot + DiscFac_list = np.linspace( + max(DiscFac_star - 0.25, 0.5), + min(DiscFac_star + 0.25, 1.05), + grid_density, + ) + CRRA_list = np.linspace(max(CRRA_star - 5, 2), min(CRRA_star + 5, 8), grid_density) + CRRA_mesh, DiscFac_mesh = np.meshgrid(CRRA_list, DiscFac_list) + smm_obj_levels = np.empty([grid_density, grid_density]) + for j in range(grid_density): + DiscFac = DiscFac_list[j] + for k in range(grid_density): + CRRA = CRRA_list[k] + smm_obj_levels[j, k] = msm_criterion( + np.array([DiscFac, CRRA]), + agent=agent, + emp_moments=emp_moments, + ) + smm_contour = plt.contourf(CRRA_mesh, DiscFac_mesh, smm_obj_levels, level_count) + t_end_contour = time() + time_to_contour = t_end_contour - t_start_contour + + # Calculate minutes and remaining seconds + minutes, seconds = divmod(time_to_contour, 60) + print(f"Time to contour: {int(minutes)} min, {int(seconds)} sec.") + + plt.colorbar(smm_contour) + plt.plot(model_estimate[1], model_estimate[0], "*r", ms=15) + plt.xlabel(r"coefficient of relative risk aversion $\rho$", fontsize=14) + plt.ylabel(r"discount factor adjustment $\beth$", fontsize=14) + plt.savefig(save_dir + agent.name + "SMMcontour.pdf") + plt.savefig(save_dir + agent.name + "SMMcontour.png") + plt.savefig(save_dir + agent.name + "SMMcontour.svg") + plt.show() + + +def estimate_msm( + agent, + simulate_moments=None, + emp_moments=None, + moments_cov=None, + initial_params=None, + minimize_options=None, + simulate_moments_kwargs=None, + estimagic_options=None, +): + t0 = time() + + simulate_moments_kwargs = simulate_moments_kwargs or {} + simulate_moments_kwargs.setdefault("agent", agent) + simulate_moments_kwargs.setdefault("emp_moments", emp_moments) + + res = em.estimate_msm( + simulate_moments, + emp_moments, + moments_cov, + initial_params, + optimize_options=minimize_options, + simulate_moments_kwargs=simulate_moments_kwargs, + **estimagic_options, + ) + + run_time = time() - t0 + + return res, run_time + + +def estimate_min( + agent, + criterion=None, + initial_params=None, + emp_moments=None, + minimize_options={}, + criterion_kwargs=None, + estimagic_options=None, +): + t0 = time() + + criterion_kwargs = criterion_kwargs or {} + criterion_kwargs.setdefault("agent", agent) + criterion_kwargs.setdefault("emp_moments", emp_moments) + + res = em.minimize( + criterion, + initial_params, + criterion_kwargs=criterion_kwargs, + **minimize_options, + **estimagic_options, + ) + + run_time = time() - t0 + + return res, run_time + + +def estimate( + agent_name, + params_to_estimate, + estimate_model=True, + estimate_method="min", + compute_se_bootstrap=False, + compute_sensitivity=False, + make_contour_plot=False, + save_dir=None, + emp_moments=None, + moments_cov=None, +): + """Run the main estimation procedure for EstimatingMicroDSOPs. + + Parameters + ---------- + estimate_model : bool + Whether to estimate the model using Nelder-Mead. When True, this is a low-time, low-memory operation. + + compute_standard_errors : bool + Whether to compute standard errors on the estiamtion of the model. + + make_contour_plot : bool + Whether to make the contour plot associate with the estiamte. + + Returns + ------- + None + + """ + save_dir = Path(save_dir).resolve() if save_dir is not None else Path.cwd() + save_dir.mkdir(parents=True, exist_ok=True) + + ############################################################ + # Make agent + ############################################################ + + agent = make_agent(agent_name) + + ############################################################ + # Get initial guess + ############################################################ + + initial_guess = get_initial_guess(agent, params_to_estimate, save_dir) + + ############################################################ + # Get empirical moments + ############################################################ + + if emp_moments is None: + emp_moments = get_empirical_moments(agent_name) + + print("Calculated empirical moments.") + + weights = calculate_weights(emp_moments) + + ############################################################ + # Get moments covariance matrix + ############################################################ + + if moments_cov is None and estimate_method == "msm": + moments_cov = get_moments_cov(agent_name, emp_moments) + + print("Calculated moments covariance matrix.") + + ############################################################ + # Estimate model + ############################################################ + + if estimate_model: + model_estimate, res, time_to_estimate = do_estimate_model( + agent, + initial_guess, + estimate_method=estimate_method, + emp_moments=emp_moments, + moments_cov=moments_cov, + minimize_options=minimize_options, + criterion_kwargs={"weights": weights}, + save_dir=save_dir, + ) + + # Compute standard errors by bootstrap + if compute_se_bootstrap: + do_compute_se_boostrap( + agent, + model_estimate, + time_to_estimate, + save_dir=save_dir, + **bootstrap_options, + ) + + # Compute sensitivity measure + if compute_sensitivity: + do_compute_sensitivity( + agent, + model_estimate, + initial_guess, + save_dir=save_dir, + ) + + # Make a contour plot of the objective function + if make_contour_plot: + do_make_contour_plot( + agent, + model_estimate, + emp_moments, + save_dir=save_dir, + ) + + +if __name__ == "__main__": + # Set booleans to determine which tasks should be done + # Which agent type to estimate ("IndShock" or "Portfolio") + local_agent_name = "WealthPortfolio" + local_params_to_estimate = ["CRRA", "WealthShare", "WealthShift"] + local_estimate_model = True # Whether to estimate the model + # Whether to get standard errors via bootstrap + local_compute_se_bootstrap = False + # Whether to compute a measure of estimates' sensitivity to moments + local_compute_sensitivity = False + # Whether to make a contour map of the objective function + local_make_contour_plot = False + local_save_dir = "content/tables/min" + + estimate( + agent_name=local_agent_name, + params_to_estimate=local_params_to_estimate, + estimate_model=local_estimate_model, + compute_se_bootstrap=local_compute_se_bootstrap, + compute_sensitivity=local_compute_sensitivity, + make_contour_plot=local_make_contour_plot, + save_dir=local_save_dir, + ) diff --git a/src/estimark/options.py b/src/estimark/options.py new file mode 100644 index 0000000..5c6f690 --- /dev/null +++ b/src/estimark/options.py @@ -0,0 +1,49 @@ +# Define settings for "main()" function in StructuralEstiamtion.py based on +# resource requirements: +from __future__ import annotations + +params_to_estimate = ["CRRA", "BeqShift", "BeqFac", "WealthShare"] + +low_resource = { + "estimate_model": True, + "params_to_estimate": params_to_estimate, + "make_contour_plot": False, + "compute_se_bootstrap": False, + "compute_sensitivity": False, +} +# Author note: +# This takes approximately 90 seconds on a laptop with the following specs: +# Linux, Ubuntu 14.04.1 LTS, 8G of RAM, Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz + +medium_resource = { + "estimate_model": True, + "params_to_estimate": params_to_estimate, + "make_contour_plot": True, + "compute_se_bootstrap": False, + "compute_sensitivity": True, +} +# Author note: +# This takes approximately 7 minutes on a laptop with the following specs: +# Linux, Ubuntu 14.04.1 LTS, 8G of RAM, Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz + +high_resource = { + "estimate_model": True, + "params_to_estimate": params_to_estimate, + "make_contour_plot": False, + "compute_se_bootstrap": True, + "compute_sensitivity": True, +} +# Author note: +# This takes approximately 30 minutes on a laptop with the following specs: +# Linux, Ubuntu 14.04.1 LTS, 8G of RAM, Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz + +all_replications = { + "estimate_model": True, + "params_to_estimate": params_to_estimate, + "make_contour_plot": True, + "compute_se_bootstrap": True, + "compute_sensitivity": True, +} +# Author note: +# This takes approximately 40 minutes on a laptop with the following specs: +# Linux, Ubuntu 14.04.1 LTS, 8G of RAM, Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz diff --git a/src/estimark/parameters.py b/src/estimark/parameters.py new file mode 100644 index 0000000..c5c4b88 --- /dev/null +++ b/src/estimark/parameters.py @@ -0,0 +1,278 @@ +"""Specifies the full set of calibrated values required to estimate the EstimatingMicroDSOPs +model. The empirical data is stored in a separate csv file and is loaded in setup_scf_data. +""" + +# Discount Factor of 1.0 always +# income uncertainty doubles at retirement +# only estimate CRRA, Bequest params +from __future__ import annotations + +import warnings + +warnings.simplefilter(action="ignore", category=FutureWarning) + +import numpy as np +from HARK.Calibration.Income.IncomeTools import Cagetti_income, parse_income_spec +from HARK.Calibration.life_tables.us_ssa.SSATools import parse_ssa_life_table +from HARK.distribution import DiscreteDistribution + +# --------------------------------------------------------------------------------- +# - Define all of the model parameters for EstimatingMicroDSOPs and ConsumerExamples - +# --------------------------------------------------------------------------------- + +# Assets grid +exp_nest = 1 # Number of times to "exponentially nest" when constructing a_grid +aXtraMin = 0.001 # Minimum end-of-period "assets above minimum" value +aXtraMax = 100 # Maximum end-of-period "assets above minimum" value +aXtraCount = 20 # Number of points in the grid of "assets above minimum" + +# Artificial borrowing constraint +BoroCnstArt = 0.0 # imposed minimum level of end-of period assets +Rfree = 1.03 # Interest factor on assets + +# Use cubic spline interpolation when True, linear interpolation when False +CubicBool = False +vFuncBool = False # Whether to calculate the value function during solution + +# Income process parameters +# Number of points in discrete approximation to permanent income shocks +PermShkCount = 7 +# Number of points in discrete approximation to transitory income shocks +TranShkCount = 7 +UnempPrb = 0.05 # Probability of unemployment while working +UnempPrbRet = 0.005 # Probability of "unemployment" while retired +IncUnemp = 0.3 # Unemployment benefits replacement rate +IncUnempRet = 0.0 # "Unemployment" benefits when retired +ss_variances = False # Use the Sabelhaus-Song variance profiles +education = "College" # Education level for income process + +# Population age parameters +final_age = 120 # Age at which the problem ends (die with certainty) +retirement_age = 65 # Age at which the consumer retires +initial_age = 25 # Age at which the consumer enters the model +final_age_data = 95 # Age at which the data ends +age_interval = 5 # Interval between age groups + +# Three point discrete distribution of initial w +init_w_to_y = np.array([0.17, 0.5, 0.83]) +# Equiprobable discrete distribution of initial w +prob_w_to_y = np.array([0.33333, 0.33333, 0.33334]) +num_agents = 10000 # Number of agents to simulate + +# Bootstrap options +bootstrap_size = 50 # Number of re-estimations to do during bootstrap +seed = 1132023 # Just an integer to seed the estimation + +params_to_estimate = ["CRRA"] + +# Initial guess of the coefficient of relative risk aversion during estimation (rho) +init_CRRA = 2.0 +# Bounds for rho; if violated, objective function returns "penalty value" +bounds_CRRA = [1.1, 20.0] + +# Initial guess of the adjustment to the discount factor during estimation (beth) +init_DiscFac = 1.0 +# Bounds for beth; if violated, objective function returns "penalty value" +bounds_DiscFac = [0.5, 1.1] + +init_WealthShare = 0.05 # Initial guess of the wealth share parameter +bounds_WealthShare = [0.01, 0.99] # Bounds for the wealth share parameter + +init_WealthShift = 0.0 # Initial guess of the wealth shift parameter +bounds_WealthShift = [0.0, 100.0] # Bounds for the wealth shift parameter + +init_BeqFac = 1.0 # Initial guess of the bequest factor +bounds_BeqFac = [0.0, 100.0] # Bounds for the bequest factor + +init_BeqShift = 0.0 # Initial guess of the bequest shift parameter +bounds_BeqShift = [0.0, 70.0] # Bounds for the bequest shift parameter + +###################################################################### +# Constructed parameters +###################################################################### + +# Total number of periods in the model +terminal_t = final_age - initial_age +retirement_t = retirement_age - initial_age - 1 + +# Income +income_spec = Cagetti_income[education] +# Replace retirement age +income_spec["age_ret"] = retirement_age +inc_calib = parse_income_spec( + age_min=initial_age, + age_max=final_age, + **income_spec, + SabelhausSong=ss_variances, +) + +inc_calib["PermGroFac"][retirement_age - initial_age] = 0.9389 + +# use permgrofac = 0.9389 at retirement + +# Age groups for the estimation: calculate average wealth-to-permanent income ratio +# for consumers within each of these age groups, compare actual to simulated data + +age_groups = [ + list(range(start, start + age_interval)) + for start in range(initial_age + 1, final_age_data + 1, age_interval) +] + +# generate labels as (25,30], (30,35], ... +age_labels = [f"({group[0]-1},{group[-1]}]" for group in age_groups] + +# Generate mappings between the real ages in the groups and the indices of simulated data +age_mapping = dict(zip(age_labels, map(np.array, age_groups))) +sim_mapping = { + label: np.array(group) - initial_age for label, group in zip(age_labels, age_groups) +} + +remove_ages_from_scf = np.arange( + retirement_age - age_interval + 1, + retirement_age + age_interval + 1, +) # remove retirement ages 61-70 +remove_ages_from_snp = np.arange( + retirement_age + age_interval + 1, +) # only match ages 71 and older + +init_params_options = { + "init_guess": { + "CRRA": init_CRRA, + "DiscFac": init_DiscFac, + "WealthShare": init_WealthShare, + "WealthShift": init_WealthShift, + "BeqFac": init_BeqFac, + "BeqShift": init_BeqShift, + }, + "upper_bounds": { + "CRRA": bounds_CRRA[1], + "DiscFac": bounds_DiscFac[1], + "WealthShare": bounds_WealthShare[1], + "WealthShift": bounds_WealthShift[1], + "BeqFac": bounds_BeqFac[1], + "BeqShift": bounds_BeqShift[1], + }, + "lower_bounds": { + "CRRA": bounds_CRRA[0], + "DiscFac": bounds_DiscFac[0], + "WealthShare": bounds_WealthShare[0], + "WealthShift": bounds_WealthShift[0], + "BeqFac": bounds_BeqFac[0], + "BeqShift": bounds_BeqShift[0], + }, +} + +# Survival probabilities over the lifecycle +liv_prb = parse_ssa_life_table( + female=False, + min_age=initial_age, + max_age=final_age - 1, + cohort=1960, +) + +aNrmInit = DiscreteDistribution( + prob_w_to_y, + init_w_to_y, + seed=seed, +).draw(N=num_agents) + +bootstrap_options = { + "bootstrap_size": bootstrap_size, + "seed": seed, +} + +minimize_options = { + "algorithm": "tranquilo_ls", + "multistart": True, + "error_handling": "continue", + "algo_options": { + "convergence.absolute_params_tolerance": 1e-6, + "convergence.absolute_criterion_tolerance": 1e-6, + "stopping.max_iterations": 100, + "stopping.max_criterion_evaluations": 200, + "n_cores": 12, + }, + "numdiff_options": {"n_cores": 12}, +} + +# ----------------------------------------------------------------------------- +# -- Set up the dictionary "container" for making a basic lifecycle type ------ +# ----------------------------------------------------------------------------- + +# Dictionary that can be passed to ConsumerType to instantiate +init_calibration = { + "CRRA": init_CRRA, + "DiscFac": init_DiscFac, + "Rfree": Rfree, + "PermGroFac": inc_calib["PermGroFac"], + "PermGroFacAgg": 1.0, + "BoroCnstArt": BoroCnstArt, + "PermShkStd": inc_calib["PermShkStd"][: retirement_t + 1] + + [inc_calib["PermShkStd"][retirement_t]] * (terminal_t - retirement_t - 1), + "PermShkCount": PermShkCount, + "TranShkStd": inc_calib["TranShkStd"][: retirement_t + 1] + + [inc_calib["TranShkStd"][retirement_t]] * (terminal_t - retirement_t - 1), + "TranShkCount": TranShkCount, + "T_cycle": terminal_t, + "UnempPrb": UnempPrb, + "UnempPrbRet": UnempPrbRet, + "T_retire": retirement_t, + "T_age": terminal_t, + "IncUnemp": IncUnemp, + "IncUnempRet": IncUnempRet, + "aXtraMin": aXtraMin, + "aXtraMax": aXtraMax, + "aXtraCount": aXtraCount, + "aXtraNestFac": exp_nest, + "LivPrb": liv_prb, + "AgentCount": num_agents, + "seed": seed, + "tax_rate": 0.0, + "vFuncBool": vFuncBool, + "CubicBool": CubicBool, + "aNrmInit": aNrmInit, + "neutral_measure": True, # Harmemberg + "sim_common_Rrisky": False, # idiosyncratic risky return + "WealthShift": init_WealthShift, + "ChiFromOmega_N": 501, # Number of gridpoints in chi-from-omega function + "ChiFromOmega_bound": 15, # Highest gridpoint to use for it +} + +Eq_prem = 0.03 +RiskyStd = 0.20 + +init_calibration["RiskyAvg"] = Rfree + Eq_prem +init_calibration["RiskyStd"] = RiskyStd + +# from Mateo's JMP for College Educated +ElnR_nom = 0.020 +VlnR = 0.424**2 + +TrueElnR_nom = 0.085 +TrueVlnR = 0.170**2 + +logInflation = 0.024 +logRfree_nom = 0.043 +Rfree_real = np.exp(logRfree_nom - logInflation) # 1.019 + +ElnR_real = ElnR_nom - logInflation +TrueElnR_real = TrueElnR_nom - logInflation + + +init_subjective_stock = { + "Rfree": Rfree_real, # from Mateo's JMP + "RiskyAvg": np.exp(ElnR_real + 0.5 * VlnR), + "RiskyStd": np.sqrt(np.exp(2 * ElnR_real + VlnR) * (np.exp(VlnR) - 1)), + "RiskyAvgTrue": np.exp(TrueElnR_real + 0.5 * TrueVlnR), + "RiskyStdTrue": np.sqrt( + np.exp(2 * TrueElnR_real + TrueVlnR) * (np.exp(TrueVlnR) - 1), + ), +} + +# from Tao's JMP +init_subjective_labor = { + "TranShkStd": [0.03] * (retirement_t + 1) + + [0.03 * np.sqrt(2)] * (terminal_t - retirement_t - 1), + "PermShkStd": [0.03] * (retirement_t + 1) + + [0.03 * np.sqrt(2)] * (terminal_t - retirement_t - 1), +} diff --git a/src/estimark/py.typed b/src/estimark/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/src/estimark/scf.py b/src/estimark/scf.py new file mode 100644 index 0000000..08ca84d --- /dev/null +++ b/src/estimark/scf.py @@ -0,0 +1,32 @@ +"""Sets up the SCF data for use in the EstimatingMicroDSOPs estimation.""" + +from __future__ import annotations + +from pathlib import Path + +import pandas as pd + +from estimark.parameters import ( + education, + final_age_data, + initial_age, + remove_ages_from_scf, +) + +# Get the directory containing the current file and construct the full path to the CSV file +csv_file_path = Path(__file__).resolve().parent / ".." / "data" / "SCFdata.csv" + +# Define the variables to keep +keep_vars = ["age", "age_group", "wealth_income_ratio", "weight", "wave"] + +# Read the CSV file and filter data in one step +scf_data = pd.read_csv(csv_file_path) +scf_data_full = scf_data.loc[ + (scf_data.norminc > 0.0) + & (scf_data.education == education) + & (scf_data.age > initial_age) + & (scf_data.age <= final_age_data), + keep_vars, +] + +scf_data = scf_data_full.loc[~scf_data.age.isin(remove_ages_from_scf)] diff --git a/src/estimark/snp.py b/src/estimark/snp.py new file mode 100644 index 0000000..798f818 --- /dev/null +++ b/src/estimark/snp.py @@ -0,0 +1,43 @@ +"""Sets up the S&P data for use in the EstimatingMicroDSOPs estimation.""" + +from __future__ import annotations + +from pathlib import Path + +import pandas as pd + +from estimark.parameters import ( + age_mapping, + final_age_data, + initial_age, + remove_ages_from_snp, +) + +file_path = ( + Path(__file__).resolve().parent / ".." / "data" / "S&P Target Date glidepath.xlsx" +) + +# Define column mapping and columns to keep +column_mapping = {"Current Age": "age", "S&P Target Date Equity allocation": "share"} + +# Load data, rename columns, filter data +snp_data = ( + pd.read_excel(file_path, usecols=column_mapping.keys()) + .rename(columns=column_mapping) + .query(f"{initial_age} < age <= {final_age_data}") +) + +# Assign age groups +bins = [initial_age + 1] + [group[-1] + 1 for group in age_mapping.values()] +labels = list(age_mapping.keys()) + +snp_data = snp_data.assign( + age_group=pd.cut(snp_data["age"], bins=bins, labels=labels, right=False), +) + +snp_data_full = snp_data.copy() +# Remove ages +snp_data = snp_data.loc[ + ~snp_data.age.isin(remove_ages_from_snp), + ["age", "share", "age_group"], +] diff --git a/src/msm_notebooks/FinAssets_Cov.ipynb b/src/msm_notebooks/FinAssets_Cov.ipynb new file mode 100644 index 0000000..5a6cf68 --- /dev/null +++ b/src/msm_notebooks/FinAssets_Cov.ipynb @@ -0,0 +1,676 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Method of Simulated Moments (MSM) for Structural Estimation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Steps of MSM estimation\n", + "1. Load empirical data\n", + "2. Define a function to calculate empirical moments from the data\n", + "3. Calculate the covariance matrix of the empirical moments (for the weighting matrix)\n", + "4. Define a `HARK` agent type with the model parameters to be estimated\n", + "5. Define a function to simulate the model and calculate the simulated moments\n", + "6. Estimate the model parameters by minimizing the distance between the empirical and simulated moments" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import annotations\n", + "\n", + "import warnings\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "from statsmodels.stats.weightstats import DescrStatsW\n", + "\n", + "warnings.simplefilter(action=\"ignore\", category=FutureWarning)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Load empirical data" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "scf_data = pd.read_stata(\"../data/scf_processed.dta\")\n", + "\n", + "scf_data = scf_data.replace([np.inf, -np.inf], np.nan)\n", + "scf_data = scf_data.dropna()\n", + "\n", + "scf_data[\"networththou\"] = scf_data[\"networth\"] / 1000\n", + "scf_data[\"finthou\"] = scf_data[\"fin\"] / 1000" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Calculate Moments" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['(21-25]', '(26-30]', '(31-35]', '(36-40]', '(41-45]', ..., '(71-75]', '(76-80]', '(81-85]', '(86-90]', '(91-95]']\n", + "Length: 15\n", + "Categories (15, object): ['(21-25]' < '(26-30]' < '(31-35]' < '(36-40]' ... '(76-80]' < '(81-85]' < '(86-90]' < '(91-95]']" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "indices = scf_data[\"age_lbl\"].unique().sort_values()\n", + "indices" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def calculate_weighted_median(data):\n", + " stats = DescrStatsW(data[\"finthou\"], weights=data[\"wgt\"])\n", + " return stats.quantile(0.5, return_pandas=False)[0]\n", + "\n", + "\n", + "def calculate_moments(data):\n", + " medians = data.groupby([\"age_lbl\"]).apply(\n", + " calculate_weighted_median,\n", + " include_groups=False,\n", + " )\n", + " return medians.reindex(indices, fill_value=0.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "age_lbl\n", + "(21-25] 3.920167\n", + "(26-30] 10.731806\n", + "(31-35] 19.474649\n", + "(36-40] 26.711670\n", + "(41-45] 38.846864\n", + "(46-50] 58.384827\n", + "(51-55] 66.187000\n", + "(56-60] 82.265840\n", + "(61-65] 76.000000\n", + "(66-70] 84.800000\n", + "(71-75] 77.500000\n", + "(76-80] 54.837487\n", + "(81-85] 68.688031\n", + "(86-90] 62.365245\n", + "(91-95] 60.545638\n", + "dtype: float64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "empirical_moments = calculate_moments(scf_data)\n", + "empirical_moments" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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(91-95]-0.025521-0.0110970.1065350.080925-0.0085360.675545-0.1041340.4576101.271103-0.9714610.5906050.265623-1.006280-0.28252239.800669
\n", + "
" + ], + "text/plain": [ + "age_lbl (21-25] (26-30] (31-35] (36-40] (41-45] (46-50] (51-55] \\\n", + "age_lbl \n", + "(21-25] 0.016801 -0.000749 -0.002685 0.002035 0.004864 0.003656 0.000328 \n", + "(26-30] -0.000749 0.039618 -0.002933 0.009414 0.003510 0.002786 -0.008376 \n", + "(31-35] -0.002685 -0.002933 0.163474 0.010342 -0.001325 0.027629 0.015236 \n", + "(36-40] 0.002035 0.009414 0.010342 0.456068 0.033588 0.067388 0.012491 \n", + "(41-45] 0.004864 0.003510 -0.001325 0.033588 1.034562 -0.090549 0.170603 \n", + "(46-50] 0.003656 0.002786 0.027629 0.067388 -0.090549 3.038064 -0.052855 \n", + "(51-55] 0.000328 -0.008376 0.015236 0.012491 0.170603 -0.052855 5.028821 \n", + "(56-60] 0.014750 0.004830 0.014758 0.010992 -0.015446 0.310041 0.282000 \n", + "(61-65] -0.002181 0.007423 0.043892 0.035021 -0.045916 -0.003971 0.367655 \n", + "(66-70] -0.007886 0.012024 0.035808 -0.094145 0.011664 0.088649 -0.379126 \n", + "(71-75] 0.008624 0.031356 -0.019665 0.030651 0.170984 -0.124644 -0.045354 \n", + "(76-80] 0.003455 0.006701 0.015921 0.011621 -0.039976 0.129644 -0.216652 \n", + "(81-85] -0.003829 0.015580 -0.024180 -0.051711 -0.157313 -0.110991 0.033490 \n", + "(86-90] 0.013656 0.021508 0.017549 0.010470 0.009196 -0.129766 0.805542 \n", + "(91-95] -0.025521 -0.011097 0.106535 0.080925 -0.008536 0.675545 -0.104134 \n", + "\n", + "age_lbl (56-60] (61-65] (66-70] (71-75] (76-80] (81-85] \\\n", + "age_lbl \n", + "(21-25] 0.014750 -0.002181 -0.007886 0.008624 0.003455 -0.003829 \n", + "(26-30] 0.004830 0.007423 0.012024 0.031356 0.006701 0.015580 \n", + "(31-35] 0.014758 0.043892 0.035808 -0.019665 0.015921 -0.024180 \n", + "(36-40] 0.010992 0.035021 -0.094145 0.030651 0.011621 -0.051711 \n", + "(41-45] -0.015446 -0.045916 0.011664 0.170984 -0.039976 -0.157313 \n", + "(46-50] 0.310041 -0.003971 0.088649 -0.124644 0.129644 -0.110991 \n", + "(51-55] 0.282000 0.367655 -0.379126 -0.045354 -0.216652 0.033490 \n", + "(56-60] 6.389591 -0.060930 0.353854 -0.243218 0.053496 -0.107208 \n", + "(61-65] -0.060930 7.981681 -0.130882 -0.410915 -0.106258 -0.196294 \n", + "(66-70] 0.353854 -0.130882 8.679183 -0.033774 0.014155 0.245623 \n", + "(71-75] -0.243218 -0.410915 -0.033774 10.775534 -0.266371 0.182973 \n", + "(76-80] 0.053496 -0.106258 0.014155 -0.266371 4.181164 -0.287834 \n", + "(81-85] -0.107208 -0.196294 0.245623 0.182973 -0.287834 10.765353 \n", + "(86-90] -0.010087 0.000222 0.119023 -0.194918 -0.085448 0.475150 \n", + "(91-95] 0.457610 1.271103 -0.971461 0.590605 0.265623 -1.006280 \n", + "\n", + "age_lbl (86-90] (91-95] \n", + "age_lbl \n", + "(21-25] 0.013656 -0.025521 \n", + "(26-30] 0.021508 -0.011097 \n", + "(31-35] 0.017549 0.106535 \n", + "(36-40] 0.010470 0.080925 \n", + "(41-45] 0.009196 -0.008536 \n", + "(46-50] -0.129766 0.675545 \n", + "(51-55] 0.805542 -0.104134 \n", + "(56-60] -0.010087 0.457610 \n", + "(61-65] 0.000222 1.271103 \n", + "(66-70] 0.119023 -0.971461 \n", + "(71-75] -0.194918 0.590605 \n", + "(76-80] -0.085448 0.265623 \n", + "(81-85] 0.475150 -1.006280 \n", + "(86-90] 17.571577 -0.282522 \n", + "(91-95] -0.282522 39.800669 " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# moments_cov = em.get_moments_cov(\n", + "# scf_data,\n", + "# calculate_moments,\n", + "# bootstrap_kwargs={\n", + "# \"seed\": 11323,\n", + "# \"n_cores\": 12,\n", + "# \"error_handling\": \"continue\",\n", + "# },\n", + "# )\n", + "\n", + "# moments_cov.to_pickle(\"finassets_cov.pkl\")\n", + "\n", + "moments_cov = pd.read_pickle(\"finassets_cov.pkl\")\n", + "\n", + "moments_cov" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(21-25] 0.129620\n", + "(26-30] 0.199043\n", + "(31-35] 0.404319\n", + "(36-40] 0.675328\n", + "(41-45] 1.017134\n", + "(46-50] 1.743004\n", + "(51-55] 2.242503\n", + "(56-60] 2.527764\n", + "(61-65] 2.825187\n", + "(66-70] 2.946045\n", + "(71-75] 3.282611\n", + "(76-80] 2.044789\n", + "(81-85] 3.281060\n", + "(86-90] 4.191847\n", + "(91-95] 6.308777\n", + "dtype: float64" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series(np.sqrt(np.diag(moments_cov)), index=indices)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "hark-dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/msm_notebooks/MSM Full Bequest model.ipynb b/src/msm_notebooks/MSM Full Bequest model.ipynb new file mode 100644 index 0000000..5fa0764 --- /dev/null +++ b/src/msm_notebooks/MSM Full Bequest model.ipynb @@ -0,0 +1,750 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Method of Simulated Moments (MSM) for Structural Estimation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Steps of MSM estimation\n", + "1. Load empirical data\n", + "2. Define a function to calculate empirical moments from the data\n", + "3. Calculate the covariance matrix of the empirical moments (for the weighting matrix)\n", + "4. Define a `HARK` agent type with the model parameters to be estimated\n", + "5. Define a function to simulate the model and calculate the simulated moments\n", + "6. Estimate the model parameters by minimizing the distance between the empirical and simulated moments" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import annotations\n", + "\n", + "from copy import copy\n", + "from pathlib import Path\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from estimagic.utilities import read_pickle\n", + "from HARK.Calibration.Income.IncomeTools import (\n", + " Cagetti_income,\n", + " parse_income_spec,\n", + " parse_time_params,\n", + ")\n", + "from HARK.Calibration.life_tables.us_ssa.SSATools import parse_ssa_life_table\n", + "from HARK.Calibration.SCF.WealthIncomeDist.SCFDistTools import (\n", + " income_wealth_dists_from_scf,\n", + ")\n", + "from HARK.ConsumptionSaving.ConsBequestModel import BequestWarmGlowConsumerType\n", + "from HARK.ConsumptionSaving.ConsIndShockModel import init_lifecycle\n", + "from HARK.utilities import plot_funcs\n", + "\n", + "figs_dir = Path(\"../../content/slides/figures/\")\n", + "figs_dir.mkdir(parents=True, exist_ok=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Load empirical data\n", + "\n", + "To avoid the expensive calculation and recalculation of the empirical moments and the covariance matrix, we calculate these in a separate notebook and save them to be loaded here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Calculate Moments\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "empirical_moments = pd.read_pickle(\"networth_mom.pkl\")\n", + "empirical_moments.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Calculate the covariance matrix of empirical moments" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "moments_cov = pd.read_pickle(\"networth_cov.pkl\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Define an agent type to simulate data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "birth_age = 25\n", + "death_age = 100\n", + "adjust_infl_to = 1992\n", + "income_calib = Cagetti_income\n", + "education = \"HS\"\n", + "\n", + "# Income specification\n", + "income_params = parse_income_spec(\n", + " age_min=birth_age,\n", + " age_max=death_age,\n", + " adjust_infl_to=adjust_infl_to,\n", + " **income_calib[education],\n", + " SabelhausSong=True,\n", + ")\n", + "\n", + "# Initial distribution of wealth and permanent income\n", + "dist_params = income_wealth_dists_from_scf(\n", + " base_year=adjust_infl_to,\n", + " age=birth_age,\n", + " education=education,\n", + " wave=1995,\n", + ")\n", + "\n", + "# We need survival probabilities only up to death_age-1, because survival\n", + "# probability at death_age is 0.\n", + "liv_prb = parse_ssa_life_table(\n", + " female=True,\n", + " cross_sec=True,\n", + " year=2004,\n", + " min_age=birth_age,\n", + " max_age=death_age - 1,\n", + ")\n", + "\n", + "# Parameters related to the number of periods implied by the calibration\n", + "time_params = parse_time_params(age_birth=birth_age, age_death=death_age)\n", + "\n", + "# Update all the new parameters\n", + "params = copy(init_lifecycle)\n", + "params.update(time_params)\n", + "params.update(dist_params)\n", + "params.update(income_params)\n", + "params[\"LivPrb\"] = liv_prb\n", + "params[\"AgentCount\"] = 1_000\n", + "params[\"T_sim\"] = 75\n", + "params[\"track_vars\"] = [\"aNrm\", \"bNrm\", \"cNrm\", \"pLvl\", \"t_age\", \"mNrm\"]\n", + "params[\"PermGroFacAgg\"] = 1.0\n", + "\n", + "\n", + "### Define some initial constraints\n", + "params[\"BeqCRRA\"] = 0.0\n", + "params[\"BeqCRRATerm\"] = 0.0\n", + "params[\"BeqFac\"] = 0.0\n", + "params[\"BeqFacTerm\"] = 0.0\n", + "params[\"BeqShift\"] = 0.0\n", + "params[\"BeqShiftTerm\"] = 0.0" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "LifeCycleAgent = BequestWarmGlowConsumerType(**params)\n", + "LifeCycleAgent.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Consumption functions\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "LifeCycleAgent.unpack(\"cFunc\")\n", + "# Plot the consumption functions\n", + "print(\"Consumption functions\")\n", + "plot_funcs(LifeCycleAgent.cFunc, 0, 5)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Turn off death for simulation\n", + "LifeCycleAgent.LivPrb = [1.0] * LifeCycleAgent.T_cycle\n", + "\n", + "# Run the simulations\n", + "LifeCycleAgent.initialize_sim()\n", + "history = LifeCycleAgent.simulate()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "raw_data = {\n", + " \"Age\": history[\"t_age\"].flatten() + birth_age,\n", + " \"pIncome\": history[\"pLvl\"].flatten(),\n", + " \"nrmM\": history[\"mNrm\"].flatten(),\n", + " \"nrmC\": history[\"cNrm\"].flatten(),\n", + "}\n", + "\n", + "sim_data = pd.DataFrame(raw_data)\n", + "sim_data[\"Cons\"] = sim_data.nrmC * sim_data.pIncome\n", + "sim_data[\"M\"] = sim_data.nrmM * sim_data.pIncome\n", + "\n", + "# Find the mean of each variable at every age\n", + "AgeMeans = sim_data.groupby([\"Age\"]).median().reset_index()\n", + "\n", + "plt.figure()\n", + "plt.plot(AgeMeans.Age, AgeMeans.pIncome, label=\"Permanent Income\")\n", + "plt.plot(AgeMeans.Age, AgeMeans.M, label=\"Market resources\")\n", + "plt.plot(AgeMeans.Age, AgeMeans.Cons, label=\"Consumption\")\n", + "plt.legend()\n", + "plt.xlabel(\"Age\")\n", + "plt.ylabel(f\"Thousands of {adjust_infl_to} USD\")\n", + "plt.title(\"Variable Medians Conditional on Survival\")\n", + "plt.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "age_groups = [\n", + " list(range(start, start + 5)) for start in range(birth_age + 1, 95 + 1, 5)\n", + "]\n", + "\n", + "# generate labels as (25,30], (30,35], ...\n", + "age_labels = [f\"({group[0]-1},{group[-1]}]\" for group in age_groups]\n", + "\n", + "# Generate mappings between the real ages in the groups and the indices of simulated data\n", + "age_mapping = dict(zip(age_labels, map(np.array, age_groups)))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'(25,30]': array([26, 27, 28, 29, 30]),\n", + " '(30,35]': array([31, 32, 33, 34, 35]),\n", + " '(35,40]': array([36, 37, 38, 39, 40]),\n", + " '(40,45]': array([41, 42, 43, 44, 45]),\n", + " '(45,50]': array([46, 47, 48, 49, 50]),\n", + " '(50,55]': array([51, 52, 53, 54, 55]),\n", + " '(55,60]': array([56, 57, 58, 59, 60]),\n", + " '(60,65]': array([61, 62, 63, 64, 65]),\n", + " '(65,70]': array([66, 67, 68, 69, 70]),\n", + " '(70,75]': array([71, 72, 73, 74, 75]),\n", + " '(75,80]': array([76, 77, 78, 79, 80]),\n", + " '(80,85]': array([81, 82, 83, 84, 85]),\n", + " '(85,90]': array([86, 87, 88, 89, 90]),\n", + " '(90,95]': array([91, 92, 93, 94, 95])}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "age_mapping" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Define a function to calculate simulated moments" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def simulate_moments(params, agent=None):\n", + " agent.assign_parameters(**params) # new guess\n", + " agent.LivPrb = liv_prb # perceived mortality\n", + " agent.BeqCRRA = agent.CRRA\n", + "\n", + " agent.update()\n", + " agent.solve()\n", + "\n", + " agent.LivPrb = [1.0] * agent.T_cycle # ignore mortality\n", + " agent.initialize_sim()\n", + " history = agent.simulate()\n", + "\n", + " raw_data = {\n", + " \"age\": history[\"t_age\"].flatten() + birth_age,\n", + " \"b_nrm\": history[\"bNrm\"].flatten(),\n", + " \"p_lvl\": history[\"pLvl\"].flatten(),\n", + " }\n", + "\n", + " sim_data = pd.DataFrame(raw_data)\n", + " sim_data[\"Wealth\"] = sim_data.b_nrm * sim_data.p_lvl\n", + "\n", + " sim_data[\"Age_grp\"] = pd.cut(\n", + " sim_data.age,\n", + " bins=range(birth_age + 1, 97, 5),\n", + " labels=age_labels,\n", + " right=False,\n", + " )\n", + "\n", + " sim_data = sim_data.dropna()\n", + "\n", + " return sim_data.groupby(\"Age_grp\", observed=False)[\"Wealth\"].median()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "simulate_moments({}, agent=LifeCycleAgent).plot()\n", + "empirical_moments.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Estimate the model parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "init_params = {\n", + " \"CRRA\": 3.78,\n", + " \"DiscFac\": 0.97,\n", + " # Warm Glow bequest parameters\n", + " \"BeqFac\": 70.76,\n", + " \"BeqShift\": 1.63,\n", + "}\n", + "lower_bounds = {\n", + " \"CRRA\": 1.0,\n", + " \"DiscFac\": 0.9,\n", + " \"BeqFac\": 50.0,\n", + " \"BeqShift\": 0.0,\n", + "}\n", + "upper_bounds = {\n", + " \"CRRA\": 5.0,\n", + " \"DiscFac\": 1.0,\n", + " \"BeqFac\": 100.0,\n", + " \"BeqShift\": 10.0,\n", + "}\n", + "\n", + "\n", + "# res = estimate_msm(\n", + "# LifeCycleAgent,\n", + "# init_params,\n", + "# empirical_moments,\n", + "# moments_cov,\n", + "# simulate_moments,\n", + "# optimize_options={\n", + "# \"algorithm\": \"scipy_lbfgsb\",\n", + "# \"error_handling\": \"continue\",\n", + "# \"numdiff_options\": {\"n_cores\": 24},\n", + "# \"multistart\": True,\n", + "# },\n", + "# estimagic_options={\n", + "# \"lower_bounds\": lower_bounds,\n", + "# \"upper_bounds\": upper_bounds,\n", + "# \"numdiff_options\": {\"n_cores\": 24},\n", + "# },\n", + "# )\n", + "\n", + "# res.to_pickle(\"fullbeq_results.pkl\")\n", + "\n", + "res = read_pickle(\"fullbeq_results.pkl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "pd.concat(res.summary()).to_html(\"../../content/slides/tables/fullbeq_results.html\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'CRRA': 2.155237966339853,\n", + " 'DiscFac': 0.9709864400317447,\n", + " 'BeqFac': 75.56667334503045,\n", + " 'BeqShift': 2.0564862750710926}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res.params" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " CRRA DiscFac \\\n", + "CRRA 0.027012086812560916 -0.0003065328377841059 \n", + "DiscFac -0.0003065328377840978 4.018372320846756e-06 \n", + "BeqFac 5.5229633674520535 -0.06347799171495538 \n", + "BeqShift 0.0895509671892782 -0.0005304126778984375 \n", + "\n", + " BeqFac BeqShift \n", + "CRRA 5.522963367452015 0.08955096718927386 \n", + "DiscFac -0.06347799171495323 -0.0005304126778983582 \n", + "BeqFac 1172.3053247589396 23.070730960554826 \n", + "BeqShift 23.07073096055556 1.5773004121385747 " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame(res.cov())" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "LifeCycleAgent.assign_parameters(**res.params)\n", + "LifeCycleAgent.LivPrb = liv_prb\n", + "LifeCycleAgent.update()\n", + "LifeCycleAgent.solve()\n", + "LifeCycleAgent.LivPrb = [1.0] * LifeCycleAgent.T_cycle\n", + "LifeCycleAgent.initialize_sim()\n", + "history = LifeCycleAgent.simulate()\n", + "\n", + "raw_data = {\n", + " \"Age\": history[\"t_age\"].flatten() + birth_age,\n", + " \"pIncome\": history[\"pLvl\"].flatten(),\n", + " \"nrmM\": history[\"mNrm\"].flatten(),\n", + " \"nrmC\": history[\"cNrm\"].flatten(),\n", + "}\n", + "\n", + "sim_data = pd.DataFrame(raw_data)\n", + "sim_data[\"Cons\"] = sim_data.nrmC * sim_data.pIncome\n", + "sim_data[\"M\"] = sim_data.nrmM * sim_data.pIncome\n", + "\n", + "# Find the mean of each variable at every age\n", + "AgeMeans = sim_data.groupby([\"Age\"]).median().reset_index()\n", + "\n", + "plt.figure()\n", + "plt.plot(AgeMeans.Age, AgeMeans.pIncome, label=\"Permanent Income\")\n", + "plt.plot(AgeMeans.Age, AgeMeans.M, label=\"Market resources\")\n", + "plt.plot(AgeMeans.Age, AgeMeans.Cons, label=\"Consumption\")\n", + "plt.legend()\n", + "plt.xlabel(\"Age\")\n", + "plt.ylabel(f\"Thousands of {adjust_infl_to} USD\")\n", + "plt.title(\"Variable Medians Conditional on Survival\")\n", + "plt.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "simulate_moments(res.params, agent=LifeCycleAgent).plot()\n", + "empirical_moments.plot(figsize=(9, 5))\n", + "\n", + "plt.legend([\"Simulated\", \"Empirical\"])\n", + "plt.xlabel(\"Age Brackets\")\n", + "plt.ylabel(f\"Thousands of {adjust_infl_to} USD\")\n", + "plt.title(\"Variable Medians Conditional on Survival\")\n", + "plt.grid()\n", + "\n", + "plt.savefig(figs_dir / \"fullbeq_results.svg\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Assuming df is your DataFrame and 'A' and 'B' are columns in df\n", + "sensitivity = res.sensitivity(return_type=\"dataframe\").T\n", + "\n", + "fig, ax1 = plt.subplots(figsize=(9, 5))\n", + "\n", + "color = \"tab:blue\"\n", + "ax1.set_xlabel(\"Age Bracket\")\n", + "ax1.set_ylabel(\"CRRA Sensitivity\", color=color)\n", + "ax1.plot(sensitivity.index, sensitivity[\"CRRA\"], color=color, marker=\"o\")\n", + "ax1.tick_params(axis=\"y\", labelcolor=color)\n", + "\n", + "# Add a horizontal dashed line at y=0 on first axis\n", + "ax1.axhline(0, color=\"black\", linestyle=\"--\")\n", + "\n", + "ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis\n", + "\n", + "color = \"tab:red\"\n", + "ax2.set_ylabel(\n", + " \"DiscFac Sensivitity\",\n", + " color=color,\n", + ") # we already handled the x-label with ax1\n", + "ax2.plot(sensitivity.index, sensitivity[\"DiscFac\"], color=color, marker=\"o\")\n", + "ax2.tick_params(axis=\"y\", labelcolor=color)\n", + "\n", + "# Make sure both y-axes have the same limits\n", + "ax1.set_ylim(ax1.get_ylim())\n", + "ax2.set_ylim(ax2.get_ylim())\n", + "\n", + "# Reduce the number of x-ticks\n", + "plt.xticks(sensitivity.index[::2])\n", + "\n", + "fig.tight_layout() # otherwise the right y-label is slightly clipped\n", + "plt.grid()\n", + "plt.savefig(figs_dir / \"fullbeq_sensitivity.svg\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "hark-dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/msm_notebooks/MSM LCIM model.ipynb b/src/msm_notebooks/MSM LCIM model.ipynb new file mode 100644 index 0000000..6c464db --- /dev/null +++ b/src/msm_notebooks/MSM LCIM model.ipynb @@ -0,0 +1,638 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Method of Simulated Moments (MSM) for Structural Estimation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Steps of MSM estimation\n", + "1. Load empirical data\n", + "2. Define a function to calculate empirical moments from the data\n", + "3. Calculate the covariance matrix of the empirical moments (for the weighting matrix)\n", + "4. Define a `HARK` agent type with the model parameters to be estimated\n", + "5. Define a function to simulate the model and calculate the simulated moments\n", + "6. Estimate the model parameters by minimizing the distance between the empirical and simulated moments" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import annotations\n", + "\n", + "from copy import copy\n", + "from pathlib import Path\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from estimagic.utilities import read_pickle\n", + "from HARK.Calibration.Income.IncomeTools import (\n", + " Cagetti_income,\n", + " parse_income_spec,\n", + " parse_time_params,\n", + ")\n", + "from HARK.Calibration.life_tables.us_ssa.SSATools import parse_ssa_life_table\n", + "from HARK.Calibration.SCF.WealthIncomeDist.SCFDistTools import (\n", + " income_wealth_dists_from_scf,\n", + ")\n", + "from HARK.ConsumptionSaving.ConsIndShockModel import (\n", + " IndShockConsumerType,\n", + " init_lifecycle,\n", + ")\n", + "from HARK.utilities import plot_funcs\n", + "\n", + "figs_dir = Path(\"../../content/slides/figures/\")\n", + "figs_dir.mkdir(parents=True, exist_ok=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Load empirical data\n", + "\n", + "To avoid the expensive calculation and recalculation of the empirical moments and the covariance matrix, we calculate these in a separate notebook and save them to be loaded here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Calculate Moments\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "empirical_moments = pd.read_pickle(\"networth_mom.pkl\")\n", + "empirical_moments.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Calculate the covariance matrix of empirical moments" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "moments_cov = pd.read_pickle(\"networth_cov.pkl\")\n", + "plt.imshow(moments_cov)\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Define an agent type to simulate data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "birth_age = 25\n", + "death_age = 100\n", + "adjust_infl_to = 1992\n", + "income_calib = Cagetti_income\n", + "education = \"HS\"\n", + "\n", + "# Income specification\n", + "income_params = parse_income_spec(\n", + " age_min=birth_age,\n", + " age_max=death_age,\n", + " adjust_infl_to=adjust_infl_to,\n", + " **income_calib[education],\n", + " SabelhausSong=True,\n", + ")\n", + "\n", + "# Initial distribution of wealth and permanent income\n", + "dist_params = income_wealth_dists_from_scf(\n", + " base_year=adjust_infl_to,\n", + " age=birth_age,\n", + " education=education,\n", + " wave=1995,\n", + ")\n", + "\n", + "# We need survival probabilities only up to death_age-1, because survival\n", + "# probability at death_age is 0.\n", + "liv_prb = parse_ssa_life_table(\n", + " female=True,\n", + " cross_sec=True,\n", + " year=2004,\n", + " min_age=birth_age,\n", + " max_age=death_age - 1,\n", + ")\n", + "\n", + "# Parameters related to the number of periods implied by the calibration\n", + "time_params = parse_time_params(age_birth=birth_age, age_death=death_age)\n", + "\n", + "# Update all the new parameters\n", + "params = copy(init_lifecycle)\n", + "params.update(time_params)\n", + "params.update(dist_params)\n", + "params.update(income_params)\n", + "params[\"LivPrb\"] = liv_prb\n", + "params[\"AgentCount\"] = 1_000\n", + "params[\"T_sim\"] = 75\n", + "params[\"track_vars\"] = [\"aNrm\", \"bNrm\", \"cNrm\", \"pLvl\", \"t_age\", \"mNrm\"]\n", + "params[\"PermGroFacAgg\"] = 1.0" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "LifeCycleAgent = IndShockConsumerType(**params)\n", + "LifeCycleAgent.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Consumption functions\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "LifeCycleAgent.unpack(\"cFunc\")\n", + "# Plot the consumption functions\n", + "print(\"Consumption functions\")\n", + "plot_funcs(LifeCycleAgent.cFunc, 0, 5)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Turn off death for simulation\n", + "LifeCycleAgent.LivPrb = [1.0] * LifeCycleAgent.T_cycle\n", + "\n", + "# Run the simulations\n", + "LifeCycleAgent.initialize_sim()\n", + "history = LifeCycleAgent.simulate()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "raw_data = {\n", + " \"Age\": history[\"t_age\"].flatten() + birth_age,\n", + " \"pIncome\": history[\"pLvl\"].flatten(),\n", + " \"nrmM\": history[\"mNrm\"].flatten(),\n", + " \"nrmC\": history[\"cNrm\"].flatten(),\n", + "}\n", + "\n", + "sim_data = pd.DataFrame(raw_data)\n", + "sim_data[\"Cons\"] = sim_data.nrmC * sim_data.pIncome\n", + "sim_data[\"M\"] = sim_data.nrmM * sim_data.pIncome\n", + "\n", + "# Find the mean of each variable at every age\n", + "AgeMeans = sim_data.groupby([\"Age\"]).median().reset_index()\n", + "\n", + "plt.figure()\n", + "plt.plot(AgeMeans.Age, AgeMeans.pIncome, label=\"Permanent Income\")\n", + "plt.plot(AgeMeans.Age, AgeMeans.M, label=\"Market resources\")\n", + "plt.plot(AgeMeans.Age, AgeMeans.Cons, label=\"Consumption\")\n", + "plt.legend()\n", + "plt.xlabel(\"Age\")\n", + "plt.ylabel(f\"Thousands of {adjust_infl_to} USD\")\n", + "plt.title(\"Variable Medians Conditional on Survival\")\n", + "plt.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "age_groups = [\n", + " list(range(start, start + 5)) for start in range(birth_age + 1, 95 + 1, 5)\n", + "]\n", + "\n", + "# generate labels as (25,30], (30,35], ...\n", + "age_labels = [f\"({group[0]-1},{group[-1]}]\" for group in age_groups]\n", + "\n", + "# Generate mappings between the real ages in the groups and the indices of simulated data\n", + "age_mapping = dict(zip(age_labels, map(np.array, age_groups)))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'(25,30]': array([26, 27, 28, 29, 30]),\n", + " '(30,35]': array([31, 32, 33, 34, 35]),\n", + " '(35,40]': array([36, 37, 38, 39, 40]),\n", + " '(40,45]': array([41, 42, 43, 44, 45]),\n", + " '(45,50]': array([46, 47, 48, 49, 50]),\n", + " '(50,55]': array([51, 52, 53, 54, 55]),\n", + " '(55,60]': array([56, 57, 58, 59, 60]),\n", + " '(60,65]': array([61, 62, 63, 64, 65]),\n", + " '(65,70]': array([66, 67, 68, 69, 70]),\n", + " '(70,75]': array([71, 72, 73, 74, 75]),\n", + " '(75,80]': array([76, 77, 78, 79, 80]),\n", + " '(80,85]': array([81, 82, 83, 84, 85]),\n", + " '(85,90]': array([86, 87, 88, 89, 90]),\n", + " '(90,95]': array([91, 92, 93, 94, 95])}" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "age_mapping" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Define a function to calculate simulated moments" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def simulate_moments(params, agent=None):\n", + " agent.assign_parameters(**params) # new guess\n", + " agent.LivPrb = liv_prb # perceived mortality\n", + "\n", + " agent.update()\n", + " agent.solve()\n", + "\n", + " agent.LivPrb = [1.0] * agent.T_cycle # ignore mortality\n", + " agent.initialize_sim()\n", + " history = agent.simulate()\n", + "\n", + " raw_data = {\n", + " \"age\": history[\"t_age\"].flatten() + birth_age,\n", + " \"b_nrm\": history[\"bNrm\"].flatten(),\n", + " \"p_lvl\": history[\"pLvl\"].flatten(),\n", + " }\n", + "\n", + " sim_data = pd.DataFrame(raw_data)\n", + " sim_data[\"Wealth\"] = sim_data.b_nrm * sim_data.p_lvl\n", + "\n", + " sim_data[\"Age_grp\"] = pd.cut(\n", + " sim_data.age,\n", + " bins=range(birth_age + 1, 97, 5),\n", + " labels=age_labels,\n", + " right=False,\n", + " )\n", + "\n", + " sim_data = sim_data.dropna()\n", + "\n", + " return sim_data.groupby(\"Age_grp\", observed=False)[\"Wealth\"].median()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "simulate_moments({}, agent=LifeCycleAgent).plot()\n", + "empirical_moments.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Estimate the model parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "init_params = {\"CRRA\": 3.04, \"DiscFac\": 0.97}\n", + "lower_bounds = {\"CRRA\": 2.0, \"DiscFac\": 0.95}\n", + "upper_bounds = {\"CRRA\": 5.0, \"DiscFac\": 1.0}\n", + "\n", + "# res = estimate_msm(\n", + "# LifeCycleAgent,\n", + "# init_params,\n", + "# empirical_moments,\n", + "# moments_cov,\n", + "# simulate_moments,\n", + "# optimize_options={\n", + "# \"algorithm\": \"scipy_lbfgsb\",\n", + "# \"error_handling\": \"continue\",\n", + "# \"numdiff_options\": {\"n_cores\": 24},\n", + "# \"multistart\": True,\n", + "# },\n", + "# estimagic_options={\n", + "# \"lower_bounds\": lower_bounds,\n", + "# \"upper_bounds\": upper_bounds,\n", + "# \"numdiff_options\": {\"n_cores\": 24},\n", + "# },\n", + "# )\n", + "\n", + "# res.to_pickle(\"lcim_results.pkl\")\n", + "\n", + "res = read_pickle(\"lcim_results.pkl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "None\n" + ] + } + ], + "source": [ + "pd.concat(res.summary()).to_html(\"../../content/slides/tables/lcim_results.html\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "LifeCycleAgent.assign_parameters(**res.params)\n", + "LifeCycleAgent.LivPrb = liv_prb\n", + "LifeCycleAgent.update()\n", + "LifeCycleAgent.solve()\n", + "LifeCycleAgent.LivPrb = [1.0] * LifeCycleAgent.T_cycle\n", + "LifeCycleAgent.initialize_sim()\n", + "history = LifeCycleAgent.simulate()\n", + "\n", + "raw_data = {\n", + " \"Age\": history[\"t_age\"].flatten() + birth_age,\n", + " \"pIncome\": history[\"pLvl\"].flatten(),\n", + " \"nrmM\": history[\"mNrm\"].flatten(),\n", + " \"nrmC\": history[\"cNrm\"].flatten(),\n", + "}\n", + "\n", + "sim_data = pd.DataFrame(raw_data)\n", + "sim_data[\"Cons\"] = sim_data.nrmC * sim_data.pIncome\n", + "sim_data[\"M\"] = sim_data.nrmM * sim_data.pIncome\n", + "\n", + "# Find the mean of each variable at every age\n", + "AgeMeans = sim_data.groupby([\"Age\"]).median().reset_index()\n", + "\n", + "plt.figure()\n", + "plt.plot(AgeMeans.Age, AgeMeans.pIncome, label=\"Permanent Income\")\n", + "plt.plot(AgeMeans.Age, AgeMeans.M, label=\"Market resources\")\n", + "plt.plot(AgeMeans.Age, AgeMeans.Cons, label=\"Consumption\")\n", + "plt.legend()\n", + "plt.xlabel(\"Age\")\n", + "plt.ylabel(f\"Thousands of {adjust_infl_to} USD\")\n", + "plt.title(\"Variable Medians Conditional on Survival\")\n", + "plt.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "simulate_moments(res.params, agent=LifeCycleAgent).plot()\n", + "empirical_moments.plot(figsize=(9, 5))\n", + "\n", + "plt.legend([\"Simulated\", \"Empirical\"])\n", + "plt.xlabel(\"Age Brackets\")\n", + "plt.ylabel(f\"Thousands of {adjust_infl_to} USD\")\n", + "plt.title(\"Variable Medians Conditional on Survival\")\n", + "plt.grid()\n", + "\n", + "plt.savefig(figs_dir / \"lcim_results.svg\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Assuming df is your DataFrame and 'A' and 'B' are columns in df\n", + "sensitivity = res.sensitivity(return_type=\"dataframe\").T\n", + "\n", + "fig, ax1 = plt.subplots(figsize=(9, 5))\n", + "\n", + "color = \"tab:blue\"\n", + "ax1.set_xlabel(\"Age Bracket\")\n", + "ax1.set_ylabel(\"CRRA Sensitivity\", color=color)\n", + "ax1.plot(sensitivity.index, sensitivity[\"CRRA\"], color=color, marker=\"o\")\n", + "ax1.tick_params(axis=\"y\", labelcolor=color)\n", + "\n", + "# Add a horizontal dashed line at y=0 on first axis\n", + "ax1.axhline(0, color=\"black\", linestyle=\"--\")\n", + "\n", + "ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis\n", + "\n", + "color = \"tab:red\"\n", + "ax2.set_ylabel(\n", + " \"DiscFac Sensivitity\",\n", + " color=color,\n", + ") # we already handled the x-label with ax1\n", + "ax2.plot(sensitivity.index, sensitivity[\"DiscFac\"], color=color, marker=\"o\")\n", + "ax2.tick_params(axis=\"y\", labelcolor=color)\n", + "\n", + "# Make sure both y-axes have the same limits\n", + "ax1.set_ylim(ax1.get_ylim())\n", + "ax2.set_ylim(ax2.get_ylim())\n", + "\n", + "# Reduce the number of x-ticks\n", + "plt.xticks(sensitivity.index[::2])\n", + "\n", + "fig.tight_layout() # otherwise the right y-label is slightly clipped\n", + "plt.grid()\n", + "plt.savefig(figs_dir / \"lcim_sensitivity.svg\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "hark-dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/msm_notebooks/MSM TRP model.ipynb b/src/msm_notebooks/MSM TRP model.ipynb new file mode 100644 index 0000000..9d6bc3d --- /dev/null +++ b/src/msm_notebooks/MSM TRP model.ipynb @@ -0,0 +1,637 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Method of Simulated Moments (MSM) for Structural Estimation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Steps of MSM estimation\n", + "1. Load empirical data\n", + "2. Define a function to calculate empirical moments from the data\n", + "3. Calculate the covariance matrix of the empirical moments (for the weighting matrix)\n", + "4. Define a `HARK` agent type with the model parameters to be estimated\n", + "5. Define a function to simulate the model and calculate the simulated moments\n", + "6. Estimate the model parameters by minimizing the distance between the empirical and simulated moments" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import annotations\n", + "\n", + "from copy import copy\n", + "from pathlib import Path\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from estimagic.utilities import read_pickle\n", + "from HARK.Calibration.Income.IncomeTools import (\n", + " Cagetti_income,\n", + " parse_income_spec,\n", + " parse_time_params,\n", + ")\n", + "from HARK.Calibration.life_tables.us_ssa.SSATools import parse_ssa_life_table\n", + "from HARK.Calibration.SCF.WealthIncomeDist.SCFDistTools import (\n", + " income_wealth_dists_from_scf,\n", + ")\n", + "from HARK.ConsumptionSaving.ConsIndShockModel import init_lifecycle\n", + "from HARK.ConsumptionSaving.ConsPortfolioModel import portfolio_constructor_dict\n", + "from HARK.ConsumptionSaving.ConsWealthPortfolioModel import WealthPortfolioConsumerType\n", + "from HARK.utilities import plot_funcs\n", + "\n", + "figs_dir = Path(\"../../content/slides/figures/\")\n", + "figs_dir.mkdir(parents=True, exist_ok=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Load empirical data\n", + "\n", + "To avoid the expensive calculation and recalculation of the empirical moments and the covariance matrix, we calculate these in a separate notebook and save them to be loaded here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Calculate Moments\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "empirical_moments = pd.read_pickle(\"networth_mom.pkl\")\n", + "empirical_moments.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Calculate the covariance matrix of empirical moments" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "moments_cov = pd.read_pickle(\"networth_cov.pkl\")\n", + "plt.imshow(moments_cov)\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Define an agent type to simulate data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "birth_age = 25\n", + "death_age = 100\n", + "adjust_infl_to = 1992\n", + "income_calib = Cagetti_income\n", + "education = \"HS\"\n", + "\n", + "# Income specification\n", + "income_params = parse_income_spec(\n", + " age_min=birth_age,\n", + " age_max=death_age,\n", + " adjust_infl_to=adjust_infl_to,\n", + " **income_calib[education],\n", + " SabelhausSong=True,\n", + ")\n", + "\n", + "# Initial distribution of wealth and permanent income\n", + "dist_params = income_wealth_dists_from_scf(\n", + " base_year=adjust_infl_to,\n", + " age=birth_age,\n", + " education=education,\n", + " wave=1995,\n", + ")\n", + "\n", + "# We need survival probabilities only up to death_age-1, because survival\n", + "# probability at death_age is 0.\n", + "liv_prb = parse_ssa_life_table(\n", + " female=True,\n", + " cross_sec=True,\n", + " year=2004,\n", + " min_age=birth_age,\n", + " max_age=death_age - 1,\n", + ")\n", + "\n", + "# Parameters related to the number of periods implied by the calibration\n", + "time_params = parse_time_params(age_birth=birth_age, age_death=death_age)\n", + "\n", + "# Update all the new parameters\n", + "params = copy(init_lifecycle)\n", + "params.update(time_params)\n", + "params.update(dist_params)\n", + "params.update(income_params)\n", + "params[\"LivPrb\"] = liv_prb\n", + "params[\"AgentCount\"] = 1_000\n", + "params[\"T_sim\"] = 75\n", + "params[\"track_vars\"] = [\"aNrm\", \"bNrm\", \"cNrm\", \"pLvl\", \"t_age\", \"mNrm\"]\n", + "params[\"PermGroFacAgg\"] = 0.9\n", + "\n", + "params[\"constructors\"] = portfolio_constructor_dict\n", + "params[\"RiskyAvg\"] = 1.031\n", + "params[\"RiskyStd\"] = 0.001\n", + "\n", + "### Define some initial constraints\n", + "params[\"WealthShare\"] = 0.0\n", + "params[\"WealthShift\"] = 0.0" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "LifeCycleAgent = WealthPortfolioConsumerType(**params)\n", + "LifeCycleAgent.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Consumption functions\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "LifeCycleAgent.unpack(\"ShareFuncAdj\")\n", + "# Plot the consumption functions\n", + "print(\"Consumption functions\")\n", + "plot_funcs(LifeCycleAgent.ShareFuncAdj, 0, 5)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Turn off death for simulation\n", + "LifeCycleAgent.LivPrb = [1.0] * LifeCycleAgent.T_cycle\n", + "\n", + "# Run the simulations\n", + "LifeCycleAgent.initialize_sim()\n", + "history = LifeCycleAgent.simulate()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "raw_data = {\n", + " \"Age\": history[\"t_age\"].flatten() + birth_age,\n", + " \"pIncome\": history[\"pLvl\"].flatten(),\n", + " \"nrmM\": history[\"mNrm\"].flatten(),\n", + " \"nrmC\": history[\"cNrm\"].flatten(),\n", + "}\n", + "\n", + "sim_data = pd.DataFrame(raw_data)\n", + "sim_data[\"Cons\"] = sim_data.nrmC * sim_data.pIncome\n", + "sim_data[\"M\"] = sim_data.nrmM * sim_data.pIncome\n", + "\n", + "# Find the mean of each variable at every age\n", + "AgeMeans = sim_data.groupby([\"Age\"]).median().reset_index()\n", + "\n", + "plt.figure()\n", + "plt.plot(AgeMeans.Age, AgeMeans.pIncome, label=\"Permanent Income\")\n", + "plt.plot(AgeMeans.Age, AgeMeans.M, label=\"Market resources\")\n", + "plt.plot(AgeMeans.Age, AgeMeans.Cons, label=\"Consumption\")\n", + "plt.legend()\n", + "plt.xlabel(\"Age\")\n", + "plt.ylabel(f\"Thousands of {adjust_infl_to} USD\")\n", + "plt.title(\"Variable Medians Conditional on Survival\")\n", + "plt.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "age_groups = [\n", + " list(range(start, start + 5)) for start in range(birth_age + 1, 95 + 1, 5)\n", + "]\n", + "\n", + "# generate labels as (25,30], (30,35], ...\n", + "age_labels = [f\"({group[0]-1},{group[-1]}]\" for group in age_groups]\n", + "\n", + "# Generate mappings between the real ages in the groups and the indices of simulated data\n", + "age_mapping = dict(zip(age_labels, map(np.array, age_groups)))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'(25,30]': array([26, 27, 28, 29, 30]),\n", + " '(30,35]': array([31, 32, 33, 34, 35]),\n", + " '(35,40]': array([36, 37, 38, 39, 40]),\n", + " '(40,45]': array([41, 42, 43, 44, 45]),\n", + " '(45,50]': array([46, 47, 48, 49, 50]),\n", + " '(50,55]': array([51, 52, 53, 54, 55]),\n", + " '(55,60]': array([56, 57, 58, 59, 60]),\n", + " '(60,65]': array([61, 62, 63, 64, 65]),\n", + " '(65,70]': array([66, 67, 68, 69, 70]),\n", + " '(70,75]': array([71, 72, 73, 74, 75]),\n", + " '(75,80]': array([76, 77, 78, 79, 80]),\n", + " '(80,85]': array([81, 82, 83, 84, 85]),\n", + " '(85,90]': array([86, 87, 88, 89, 90]),\n", + " '(90,95]': array([91, 92, 93, 94, 95])}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "age_mapping" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Define a function to calculate simulated moments" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def simulate_moments(params, agent=None):\n", + " agent.assign_parameters(**params) # new guess\n", + " agent.LivPrb = liv_prb # perceived mortality\n", + "\n", + " agent.update()\n", + " agent.solve()\n", + "\n", + " agent.LivPrb = [1.0] * agent.T_cycle # ignore mortality\n", + " agent.initialize_sim()\n", + " history = agent.simulate()\n", + "\n", + " raw_data = {\n", + " \"age\": history[\"t_age\"].flatten() + birth_age,\n", + " \"b_nrm\": history[\"bNrm\"].flatten(),\n", + " \"p_lvl\": history[\"pLvl\"].flatten(),\n", + " }\n", + "\n", + " sim_data = pd.DataFrame(raw_data)\n", + " sim_data[\"Wealth\"] = sim_data.b_nrm * sim_data.p_lvl\n", + "\n", + " sim_data[\"Age_grp\"] = pd.cut(\n", + " sim_data.age,\n", + " bins=range(birth_age + 1, 97, 5),\n", + " labels=age_labels,\n", + " right=False,\n", + " )\n", + "\n", + " sim_data = sim_data.dropna()\n", + "\n", + " return sim_data.groupby(\"Age_grp\", observed=False)[\"Wealth\"].median()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "simulate_moments({}, agent=LifeCycleAgent).plot()\n", + "empirical_moments.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Estimate the model parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "init_params = {\"CRRA\": 2.0, \"DiscFac\": 0.97, \"WealthShare\": 0.3, \"WealthShift\": 1.0}\n", + "lower_bounds = {\"CRRA\": 1.0, \"DiscFac\": 0.95, \"WealthShare\": 0.0, \"WealthShift\": 0.0}\n", + "upper_bounds = {\"CRRA\": 5.0, \"DiscFac\": 1.0, \"WealthShare\": 1.0, \"WealthShift\": 100.0}\n", + "\n", + "# res = estimate_msm(\n", + "# LifeCycleAgent,\n", + "# init_params,\n", + "# empirical_moments,\n", + "# moments_cov,\n", + "# simulate_moments,\n", + "# optimize_options={\n", + "# \"algorithm\": \"scipy_lbfgsb\",\n", + "# \"error_handling\": \"continue\",\n", + "# \"numdiff_options\": {\"n_cores\": 24},\n", + "# \"multistart\": True,\n", + "# },\n", + "# estimagic_options={\n", + "# \"lower_bounds\": lower_bounds,\n", + "# \"upper_bounds\": upper_bounds,\n", + "# \"numdiff_options\": {\"n_cores\": 24},\n", + "# },\n", + "# )\n", + "\n", + "# res.to_pickle(\"trp_results.pkl\")\n", + "\n", + "res = read_pickle(\"trp_results.pkl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "pd.concat(res.summary()).to_html(\"../../content/slides/tables/trp_results.html\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "LifeCycleAgent.assign_parameters(**res.params)\n", + "LifeCycleAgent.LivPrb = liv_prb\n", + "LifeCycleAgent.update()\n", + "LifeCycleAgent.solve()\n", + "LifeCycleAgent.LivPrb = [1.0] * LifeCycleAgent.T_cycle\n", + "LifeCycleAgent.initialize_sim()\n", + "history = LifeCycleAgent.simulate()\n", + "\n", + "raw_data = {\n", + " \"Age\": history[\"t_age\"].flatten() + birth_age,\n", + " \"pIncome\": history[\"pLvl\"].flatten(),\n", + " \"nrmM\": history[\"mNrm\"].flatten(),\n", + " \"nrmC\": history[\"cNrm\"].flatten(),\n", + "}\n", + "\n", + "sim_data = pd.DataFrame(raw_data)\n", + "sim_data[\"Cons\"] = sim_data.nrmC * sim_data.pIncome\n", + "sim_data[\"M\"] = sim_data.nrmM * sim_data.pIncome\n", + "\n", + "# Find the mean of each variable at every age\n", + "AgeMeans = sim_data.groupby([\"Age\"]).median().reset_index()\n", + "\n", + "plt.figure()\n", + "plt.plot(AgeMeans.Age, AgeMeans.pIncome, label=\"Permanent Income\")\n", + "plt.plot(AgeMeans.Age, AgeMeans.M, label=\"Market resources\")\n", + "plt.plot(AgeMeans.Age, AgeMeans.Cons, label=\"Consumption\")\n", + "plt.legend()\n", + "plt.xlabel(\"Age\")\n", + "plt.ylabel(f\"Thousands of {adjust_infl_to} USD\")\n", + "plt.title(\"Variable Medians Conditional on Survival\")\n", + "plt.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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88Qe3b99mzpw5ODg48O6775rKLFy4kDp16jB9+vRkz02tj3la53RfuHAh3bt3Z9y4ccn237t3D2dn5+fKh4WFpbgvpRuaJ9zc3AD466+/0vTN+8u0bduWjz76iPHjx/P++++nOhuKm5sbGo2GPXv2mG68/+vJviexp/baXrb2QXrfw7QoU6YMS5cuRVEUTpw4QWBgIP/73/+wsbHhiy++SPE5lSpVwtXVlTVr1vD999+/9GcgT548HDx4EEVRkpUNDw9Hr9eb6i2tXFxc0Gg0qb6PGelJnd2+ffu5WYlu3bqV7tjBWI/+/v4sW7Ys2fvxbN//9MSo1+u5e/dussRAURTCwsJMA6szy9tvv02jRo1YvXo14eHhuLu7Y21tneLrSS0xT+1nqFOnTgwePJjAwEC+++47FixYQOvWrZ9rVUhJr169GDBgAGfPnuXKlSvcvn072TiEqKgo1q9fz+jRo5P9rD8Z8yGEMJKWAiFyqD59+pCUlMQPP/zAxo0b6dixI7a2tqbjGo3muRvbEydOsH///te6bkrn3bBhAzdv3kyx/JIlS1AUxfT42rVr7Nu3L9kCX89q3Lgx5ubmXL58mUqVKqW4pYeNjQ1ff/01LVq0oH///qmWa968OYqicPPmzRSvWaZMGQCqVauGtbU1ixYtSvb8ffv2panrRnrfw/TQaDSUK1eOX375BWdnZ44cOZJqWQsLC4YPH865c+f45ptvUiwTHh7Ov//+C0D9+vWJjY1l9erVyco8GVRbv379dMVqZ2dHlSpVWLlyZbJv1mNiYli3bt1Ln/9si8mL1KtXDyDZQGGAw4cPc/bs2XTHDsb32tLSMtmNcFhY2CvPPvQkhmdjXLFiBQ8fPnylGFNy586dFKcdTUpK4uLFi9ja2pqSUz8/P8LDw5MNsk9MTGTz5s3puqaLiwutW7dm/vz5rF+/nrCwsJd2HXqiU6dOWFtbExgYSGBgIPny5aNRo0am4xqNBkVRnvtMzZo1yzQYXwghLQVC5FiVKlWibNmyTJo0CUVRnluboHnz5nzzzTeMHj2a2rVrc/78ef73v//h7++PXq9/5es2b96cwMBAihcvTtmyZQkODuaHH35IdU748PBw2rRpw/vvv09UVBSjR4/G2tqaESNGpHoNPz8//ve//zFq1CiuXLlCkyZNcHFx4c6dOxw6dAg7OzvGjh2brrgHDx5smvowNTVr1uSDDz6gV69eBAUF8fbbb2NnZ8ft27fZu3cvZcqUoX///ri4uDB06FC+/fZb+vbty7vvvsuNGzcYM2ZMmrq9pPc9fJn169czbdo0WrduTcGCBVEUhZUrVxIZGUnDhg1f+NzPP/+cs2fPMnr0aA4dOkTnzp1Ni5ft3r2b33//nbFjx1KzZk26d+/O1KlT6dGjByEhIZQpU4a9e/cybtw4mjVrRoMGDdId+zfffEOTJk1o2LAhQ4YMISkpiQkTJmBnZ/fSb3kLFSqEjY0NixYtokSJEtjb2+Pt7Y23t/dzZYsVK8YHH3zAlClT0Gq1NG3alJCQEL766it8fHz47LPP0h178+bNWblyJQMGDKB9+/bcuHGDb775Bi8vLy5evJju8zVs2JDGjRszfPhwoqOjqVmzJidOnGD06NEEBASkOK3nq1iwYAEzZ86kc+fOVK5cGScnJ0JDQ5k1axanT5/m66+/xtLSEjCu9fH111/TsWNHPv/8c+Lj4/n1119f6Wa7d+/eLFu2jI8//pj8+fOn+efF2dmZNm3aEBgYSGRkJEOHDk02baqjoyNvv/02P/zwA25ubvj5+bFr1y5mz579yi1vQuRIqg1xFkJkusmTJyuAUrJkyeeOJSQkKEOHDlXy5cunWFtbKxUqVFBWr1793GwiT2b8+OGHH547R0qzgTx48EDp06eP4u7urtja2ipvvfWWsmfPHqV27drJZt15MsvHggULlE8++UTJmzevYmVlpdSqVUsJCgpKdp2UZjJRFEVZvXq1UrduXcXR0VGxsrJSfH19lfbt2yv//PPPC9+X/84+9CLPzj70xJw5c5SqVasqdnZ2io2NjVKoUCGle/fuyeI2GAzK999/r/j4+CiWlpZK2bJllXXr1j33PmTEe/js63j2nOfOnVM6deqkFCpUSLGxsVGcnJyUKlWqKIGBgS98/f+1Zs0a5Z133lHy5s2rmJubKy4uLkrdunWVGTNmKAkJCaZyERERyocffqh4eXkp5ubmiq+vrzJixAglPj4+2fkA5aOPPnruOinNwrN27VqlbNmyiqWlpVKgQAFl/PjxKf5MpPTcJUuWKMWLF1csLCwUQBk9erSiKCn/TCUlJSkTJkxQihYtqlhYWChubm5K165dlRs3biQrV7t2baVUqVLPxZ7STDzjx49X/Pz8FCsrK6VEiRLKH3/8kebYUxIXF6cMHz5c8fX1VSwsLBQvLy+lf//+yoMHD5473zvvvPPc85/9GUrJmTNnlCFDhiiVKlVKVt+1a9dWFixY8Fz5jRs3KuXLl1dsbGyUggULKr/99luqsw+lVOdPJCUlKT4+PgqgjBo16rnjL5qpa8uWLaaZklKaYS00NFRp166d4uLiojg4OChNmjRRTp069dz7LrMPidxMoyj/abcXQgghhBBC5DoypkAIIYQQQohcTpICIYQQQgghcjlJCoQQQgghhMjlJCkQQgghhBAil5OkQAghhBBCiFxOkgIhhBBCCCFyOVm8DDAYDNy6dQsHB4dUl2AXQgghhBAiO1EUhZiYGLy9vZMt6pcSSQqAW7du4ePjo3YYQgghhBBCZLgbN26QP3/+F5aRpABwcHAAjG+Yo6PjG7++Tqdjy5YtNGrUCAsLizd+fSF1kFVIPahP6iBrkHpQn9RB1iD18Hqio6Px8fEx3eu+iCQFYOoy5OjoqFpSYGtri6Ojo/zAq0TqIGuQelCf1EHWIPWgPqmDrEHqIWOkpXu8DDQWQgghhBAil5OkQAghhBBCiFxOkgIhhBBCCCFyORlTIIQQQgiRCyiKgl6vJykpSe1Q0kyn02Fubk58fHy2ivtNsrCwwMzM7LXPI0mBEEIIIUQOl5iYyO3bt3n06JHaoaSLoih4enpy48YNWUsqFRqNhvz582Nvb/9a55GkQAghhBAiBzMYDFy9ehUzMzO8vb2xtLTMNjfYBoOB2NhY7O3tX7r4Vm6kKAp3794lNDSUIkWKvFaLgSQFQgghhBA5WGJiIgaDAR8fH2xtbdUOJ10MBgOJiYlYW1tLUpCKvHnzEhISgk6ne62kQN5dIYQQQohcQG6qc6aMavWRnw4hhBBCCCFyOUkKhBBCCCGEyOUkKRBCCCGEENmWRqNh9erVmX4dPz8/Jk2alOnXSUlgYCDOzs6Zeg1JCoQQQgghRJZ19+5dPvzwQwoUKICVlRWenp40btyY/fv3A3D79m2aNm2qcpTPexM38hlJZh8SQgghhBBZVvfu3VEUhXnz5lGwYEHu3LnDtm3buH//PgCenp4qR5gzSEuBEEIIkUVcvfeQmbsu023OYdZc05KgN6gdksihFEXhUaJelU1RlDTHGRkZyYEDB/j++++pW7cuvr6+VKlShREjRvDOO+8AybsPhYSEoNFoWL58ObVq1cLGxobKlStz4cIFDh8+TKVKlbC3t6dJkybcvXvXdJ06deowaNCgZNdu3bo1PXv2TDW2n3/+mTJlymBnZ4ePjw8DBgwgNjYWgJ07d9KrVy+ioqLQaDRoNBrGjBkDGKeIHTZsGPny5cPOzo6qVauyc+fOZOcODAykQIEC2Nra0qZNGyIiItL8nr0qaSkQQgghVKIoCqduRrP5dBhbzoRx4U7sf45q6TTrEFM7V6RAnuw1t7zI+uJ0SZT8erMq1z7zv8bYWqbtFtTe3h57e3vWrFlDjRo1sLKyStPzRo8ezaRJkyhQoAC9e/emU6dOODo6MnnyZGxtbenQoQNff/0106dPf+XXodVq+fXXX/Hz8+Pq1asMGDCAYcOGMW3aNGrUqMGkSZP4+uuvOX/+vOm1APTq1YuQkBCWLl2Kt7c3q1atokmTJpw8eZIiRYpw8OBBevfuzbhx42jbti2bNm1i9OjRrxxnWklSIIQQQrxB+iQDh67eZ8uZO2w5HcatqHjTMXOthuqF8lDBx4k/dl/i5M1o3pmyhx/al6VJaS8VoxZCHebm5kydOpVBgwYxc+ZMKlSoQO3atenYsSNly5ZN9XlDhw6lcePGAHz66ad06tSJbdu2UbNmTQD69OlDYGDga8X235YFf39/vvnmG/r378+0adOwtLTEyckJjUaTrHvT5cuXWbJkCaGhoXh7e5ti3bRpE3PnzmXcuHFMnjyZxo0b88UXXwBQtGhR9u3bx6ZNm14r3peRpEAIIYTIZHGJSey+eJfNp8PYfi6cyEc60zFbSzPqFMtLo5Ke1C3ujpONBTqdDpfI86y958aR65F8uPAIPWv4MaJZcazMX33FUiGesLEw48z/Gqt27fRo2bIl7du3599//2X//v1s2rSJiRMnMmvWrFS79/w3YfDw8ACgTJkyyfaFh4enP/j/2LFjB+PGjePMmTNER0ej1+uJj4/n4cOH2NnZpficI0eOoCgKRYsWTbY/ISGBPHnyAHD27FnatGmT7Hj16tUlKRBCCCGyo8hHiWw7G87m02HsvniXeN3T8QGudpY0KOFO41Ke1CzshnUKN0kuVrCwdyUm77jCzF1XCNwXwpHrD/itUwXpTiRem0ajSXMXnqzA2tqahg0b0rBhQ77++mv69u3L6NGjU00KLCwsTP9/suLvs/sMhqefSa1W+9xYB51OR2quXbtGs2bN+PDDD/nmm29wdXVl79699OnT54XPMxgMmJmZERwcjJlZ8s/9k+5F6RlzkZGyz0+DEEIIkcXdioxjy+kwtpy5w8Gr90kyPP3jnt/FhkYlPWlcyoOKvi6Ym718rg8LMy0jmpagmn8ePlt+jBOhUdKdSAigZMmSGbo2Qd68ebl9+7bpcVJSEqdOnaJu3boplg8KCkKv1/PTTz+h1Ro/y8uXL09WxtLSkqSkpGT7AgICSEpKIjw8nFq1aqV47pIlS3LgwIFk+559nBkkKRBCCCFekaIoXAyPZcvpMDafvsPJm1HJjhf3dKBxKU8alfKgpJej6RvL9Kpb3J2Nn9Ri4JKjBF97IN2JRK4RERFBu3bt6Nu3L+XLl8fBwYGgoCAmTpxIq1atMuw69erVY/DgwWzYsIFChQrxyy+/EBkZmWr5QoUKodfrmTJlCi1atODff/9lxowZycr4+fkRGxvLtm3bKFeuHLa2thQtWpQuXbrQvXt3fvrpJwICArh37x7bt2+nTJkyNGvWjE8++YQaNWowceJEWrduzZYtWzK96xBIUiCEEEKki8GgcPRGpKlF4Oq9h6ZjGg1U9nWlUSkPGpX0zNBuPt7ONiz9oBo/bjkv3YlErmFvb0/FihWZPHkyly9fRqfT4ePjw/vvv8/IkSMz7Dq9e/fm+PHjdO/eHXNzcz777LNUWwkAypcvz88//8yECRMYMWIEb7/9Nt9//z3du3c3lalRowYffvgh7733HhEREYwePZoxY8Ywd+5cvv32W4YMGcLNmzfJkycP1atXp1mzZgBUq1aNWbNmmco3aNCAL7/8km+++SbDXm9KNIpaHZeykOjoaJycnIiKisLR0fGNX1+n07Fx40aaNWuWrL+beHOkDrIGqQf1SR2kLFFvYN/le2w5c4etZ+5wNybBdMzSTMtbRdxoXMqD+iU8cLNP25SJL/KyethxLpzPlh8j8pEOB2tz6U6UCXLSZyE+Pp6rV6/i7++PtbW12uGki8FgIDo6GkdHR1M3HZHci+o3Pfe40lIghBBCpCA2Qc/O8+FsPn2HnefCiUnQm445WJlTr4Q7jUp6UrtYXuyt3uyfU+lOJITIaJIUCCGEEI/djUngn7PG9QP+vRRBYtLT2UncHaxoWNKDRqU8qV4wD5bm6n5r+aQ70U9bLjBj12UC94UQfO0BUztLdyIhRPpJUiCEECJXuxbxkC2n77D5dBjB1x/w3061/m52NCrlQeNSnpTP74xW+2oDhTOLhZmWL5oWp6q/K58tP8bJm1G88+seJrYvS9My0p1ICJF2khQIIYTIVRRF4fStaNNA4XNhMcmOl83vZJwxqKQHhd3tX3nGoDfp2e5E/RdJdyIhRPpIUiCEECLH0ycZOBzygC1nwthy+g43I+NMx8y0GqoVdKVRSU8alvTA29lGxUhfnXQnEkK8DkkKhBBC5EjxuiT2XLzH5tNhbDt7hwePnq4yam2hpXbRvDQu5Um94u4421qqGGnG+W93osHSnUgIkQ6SFAghhMhRdp4PZ+mhG+y6cJc43dPVRJ1tLahf3IPGpTyoVSQvNpY5t1tN3eLubJDuREKIdJCkQAghRI6gKArTdl7mh83nTfvyOds8njHIgyp+rpib5Z55zqU7kRAiPSQpEEIIke3pkwx8teY0Sw5dB6BTFR+6VPWllLdjthgonFmkO5EQIq1U/cpk+vTplC1bFkdHRxwdHalevTp///236XjPnj3RaDTJtmrVqiU7R0JCAgMHDsTNzQ07OztatmxJaGjom34pQgghVPIwQc/784NYcug6Gg2MbVmK79uWpXQ+p1ydEPzXk+5EFX1diEnQ03/REcasPU2CPunlTxYiF+rZsyetW7d+aTmNRsPq1asz7Lp+fn5MmjQpw86XHqomBfnz52f8+PEEBQURFBREvXr1aNWqFadPnzaVadKkCbdv3zZtGzduTHaOQYMGsWrVKpYuXcrevXuJjY2lefPmJCXJLzohhMjpwqPjee/3/ew4fxdrCy0zu1akRw0/tcPKkp50J/qwdiEAAveF0H76fq5HPFI5MiFS16tXL1xcXDAzM0v2JXGTJk0y9bqTJ08mMDDwpeVu375N06ZNMzWWN0XV7kMtWrRI9vi7775j+vTpHDhwgFKlSgFgZWWFp6dnis+Piopi9uzZLFiwgAYNGgCwcOFCfHx8+Oeff2jcuHHmvgAhhBCquXgnhp5zD3MzMo48dpbM6lGJgAIuaoeVpUl3IpEd1a9fn/nz56PVPv0u28rKKlOv6eTk9MLjiYmJWFpapnqPmh1lmTEFSUlJ/Pnnnzx8+JDq1aub9u/cuRN3d3ecnZ2pXbs23333He7u7gAEBwej0+lo1KiRqby3tzelS5dm3759qSYFCQkJJCQkmB5HR0cDoNPp0Ol0KT4nMz25phrXFkZSB1mD1IP6sksdHLx6n/6LjxETr8cvjy2zulfA19U2y8edVpldD28VcmHNgOoMWn6CI9cj6b/oCN2qFWB446JYmeeewdgvkl0+C2mh0+lQFAWDwYDBYDDuVBTQqdRKZGEL6ejaZ2VlhYeHx3PdAQ0GA2ZmZkybNo3169ezY8cOfH19mTVrFnnz5uWDDz7g8OHDlC1blvnz51OokLGVbOzYsaxZs4Z+/foxbtw4IiIiaNasGb///jvOzs6AsYUiMjKSVatWAVCvXj1KlSqFpaUlCxYsoFSpUuzYsQMzMzNWrFhh6moUGhrK559/ztatW0lISKBEiRJMmTKFqlWrcvnyZYYMGcLBgwd5+PAhJUqU4LvvvjN9sf3Ek7pKK4PBgKIo6HQ6zMySzy6Wnp9f1ZOCkydPUr16deLj47G3t2fVqlWULFkSgKZNm/Luu+/i6+vL1atX+eqrr6hXrx7BwcFYWVkRFhaGpaUlLi7Jvxny8PAgLCws1Wt+//33jB079rn9W7ZswdZWvRkZtm7dqtq1hZHUQdYg9aC+rFwHQXc1LL6sJUnR4O+g8L5/NKcP7OT0y5+a7WR2PXT1Ahe9lm23tCw4cJ2dJ6/Rs2gSbtaZetlsJSt/FtLK3NwcT09PYmNjSUxMNO7UPcJ5aglV4on86KwxMUiDJze1MTExqZb59ttv+fbbbxk7dixjxoyhc+fO+Pn58cknn5A/f34GDhxI//79+euvvwDjl8OXLl1i6dKlLF68mOjoaD755BP69evHH3/8YbquXq83fXGs1+uZP38+vXr14u+//0ZRFNOxuLg4oqOjiY2NpXbt2nh5ebFo0SI8PDw4fvw4MTExREdHExYWRt26dRk+fDjW1tYsWbKEVq1acejQIXx8fADjDX58fLzp3GmRmJhIXFwcu3fvRq/XJzv26FHaEz/Vk4JixYpx7NgxIiMjWbFiBT169GDXrl2ULFmS9957z1SudOnSVKpUCV9fXzZs2EDbtm1TPaeiKC8cXDZixAgGDx5sehwdHY2Pjw+NGjXC0dExY15YOuh0OrZu3UrDhg2xsLB449cXUgdZhdSD+rJyHSiKwszdV1mw/xIATUp58EO70lhb5Lx5999kPbQAdl64y7AVp7jxUMcvZ6z5vk0pmpTyyNTrZnVZ+bOQXvHx8dy4cQN7e3usrR9nfInqfW4cHRzA0i5NZS0sLNi8eTP58+dPtn/YsGF8+eWXgPFb/R49egAwcuRIatasyVdffUWbNm0A4/jTPn36mO7xrKysiI+PZ8GCBabzTpkyhRYtWjB58mQ8PT2xsLDA3Nzc9Bxzc3MKFy6c4iBgGxsbHB0dWbp0KRERERw+fBhXV1cAypcvbypXs2ZNatasaXocEBDA33//zc6dO/noo48A0Gq1WFtbp+t+ND4+HhsbG95+++2n9ftYepIL1ZMCS0tLChcuDEClSpU4fPgwkydPZubMmc+V9fLywtfXl4sXLwLg6elJYmIiDx48SNZaEB4eTo0aNVK9ppWVVYp90SwsLFT94Kt9fSF1kFVIPagvq9WBPsnA6LWnWXzQOOXo+7X8GdG0BFptzp5d6E3VQ8NS3mzI52Ja7Gzg0uP0qO7LyHdK5PrFzrLaZ+FVJCUlodFo0Gq1T/vlW9nDyFuqxKNNZ/ehWrVqMXPmzGRjClxdXU2Py5UrZ/q/l5dXivvi4+OJjY3F0dE4TXGBAgUoUKCA6Xw1a9bEYDBw8eJFvL29TQOa/3vNSpUqJXtsej2P39cTJ04QEBCAm5tbiq/j4cOHjB07lvXr13Pr1i30ej1xcXHcuHEj2Xmfve7LaLVaNBpNij+r6fnZVT0peJaiKMn6+/9XREQEN27cMFV4xYoVsbCwYOvWrXTo0AEwjgI/deoUEydOfGMxCyGEyDwPE/R8vPgIO87fRaOB0c1L0rOmv9ph5TjPLnY2b/81jlyP5LfOAfjmSdu3uiIb0WjS/G292mxtbSlcuHCqN8r/vfF90lMkpX0v6qf/pMyLeprY2b34/bKxsXnh8c8//5zNmzfz448/UrhwYWxsbGjfvv3TLl0qU3U00ciRI9mzZw8hISGcPHmSUaNGsXPnTrp06UJsbCxDhw5l//79hISEsHPnTlq0aIGbm5upOcjJyYk+ffowZMgQtm3bxtGjR+natStlypR5btCGEEKI7Cc8JvmUozO6VpSEIBM9mZ1obs/KuNhacPJmFM1/3cvfJ2+rHZoQGer69evcuvW0pWT//v1otVqKFi36yucsW7Ysx44d4/79+yke37NnDz179qRNmzaUKVMGT09PQkJCXvl6GU3VpODOnTt069aNYsWKUb9+fQ4ePMimTZto2LAhZmZmnDx5klatWlG0aFF69OhB0aJF2b9/Pw4ODqZz/PLLL7Ru3ZoOHTpQs2ZNbG1tWbdu3XOjr4UQQmQvF+/E0GbqPk7djCaPnSVL3q9G41I5Z/q/rOzJYmeV/rPY2eg1p2SxM6GKhIQEwsLCkm337t17rXNaW1vTo0cPjh8/zp49e/jkk0/o0KHDa00x2qlTJzw9PWndujX//vsvV65cYcWKFezfvx+AwoULs3LlSo4dO8bx48fp3LlzumYZymyqdh+aPXt2qsdsbGzYvHnzS89hbW3NlClTmDJlSkaGJoQQQkUHrkTwwfwgouP1+LvZEdirsnRhecO8nW1Y8kE1ft56gek7pTuRUM+2bdvIly9fsn3FihXj3Llzr3zOwoUL07ZtW5o1a8b9+/dp1qwZ06ZNe604LS0t2bJlC0OGDKFZs2bo9XpKlizJ1KlTAeMX2b1796ZGjRq4ubkxfPjwdA0EzmxZbkyBEEKI3G3NsZt8/ucJEpMMVPR14Y/ulXC1s1Q7rFzJwkzL8CbFqeL3dLGz5r/uZUL7sjSTxc7EGzB37lwmT56Mo6NjimMKFEVJ9tjPz++5fXXq1HluH0D//v3p379/itd9djXjnTt3plju2fP6+vqapj59lp+fH9u3b0+278msQ0+o2Z1IVigRQgiRJSiKwrSdl/h06TESkww0Le3Jor5Vc19CoE9Ec249HlHHIDFW7WiA57sTDZDuRELkONJSIIQQQnX6JANf/2fK0b5v+TOyWc6fcjSZJD2cWAq7JmAeeZ1qgPLTFChQDQrVM26eZSEdUxVmJOlOJETOJi0FQgghVPUwQc/784NYfPC6ccrRFiX5snnJ3JMQGAxw8i+YVhXWfASR11Hs3Hlo6YbGoIOQPbBtLPxeG34sAiv6wrHFEP3mZwR60p3o2dmJNsrsRCIbGTNmDMeOHVM7jCxHWgqEEEKoJjwmnj6BQZy8GYWVuZbJHQNoUjqXzDCkKHBuA+z4DsLPGPfZuMJbn6EP6ME/W3bQrHpxLK7tgcvb4epueHQPTv5p3ADcSz5tRfCtARYvnic9o9Qt7s7GT2sxcPFRgq49YMCiI7LYmRDZnCQFQgghVHEpPIYecw5zMzIOVztLZvWoRIUCLi9/YnanKHBpG+z4Fm4dNe6zcoIaA6Hah2DlADqdcXEp10LgURyqvA/6RAg9bEwQLm83Pjf8jHHb/xuYWxsTgydJgnvJdK0am15eTtKdKLtJabCtyP4yql4lKRBCCPHGHbwSwfv/mXJ0bs/K+LnlghvJq3tg+7dw44DxsYUdVOsPNT4Gm5ckROaW4FfTuNX/Ch5GwNWdxgTh0naIufU0YQCw93yaIBSsA/Z5M/zlyOxE2cOT1X0fPXr00lV3RfbzZEXk112jS5ICIYQQb9R/pxytUMCZWT0q5/wZhm4cNrYMXNlpfGxuDZX7wlufgZ3bq53TLg+UbmfcFAXunn+aFITshdgwOL7YuAF4lXuaJPhUMyYZGUS6E2VtZmZmODs7Ex4eDoCtrS2aTGxFykgGg4HExETi4+NTnJI0tzMYDNy9exdbW1vMzV/vtl6SAiGEEG+EoijM2HWFCZuMCw41KeXJpI7lsbbIwTeNt4/DjnFwYZPxsdYCKvaAWkPA0TvjrqPRgHtx41Z9AOjija0RT5KEsJPGWG4fh72/GFso/N56miS4FXntrkYpdSe6GvGIWd0rYWkuN3Nqe7JS75PEILtQFIW4uDhsbGyyTSLzpmm1WgoUKPDa748kBUIIITKdPsnA6LWnWfR4ytE+j6ccNcupMwyFn4Od4+DMGuNjjRbKdYbaw8DFN/Ovb2Ft7DJUsA40/B/E3DG2UjxJEh6Gw8XNxg3AyQcK1TUmCP61wdb11S77n+5EAxYdYfeFu3y27Bi/dgrIuXWdTWg0Gry8vHB3d0en06kdTprpdDp2797N22+/beoGJZKztLTMkFYUSQqEEEJkqocJegYuOcr2c+FoNPDVOyXp/Za/2mFljvtXYOd4OLEcUACNsXtPnRHgVli9uBw8oNx7xk1R4M6ppwnCtf0QdQOOzDduGi14V3jaipC/Epil72asbnF3fu9ekd6Bh9lw8jb2VuaMb1dGvunNAszMzF677/mbZGZmhl6vx9raWpKCTCZJgRBCiEyTa6YcjbwBu3+AowtBebzKb/HmUHckeJRSN7ZnaTTgWca41fwUEh/BtX1Pk4S7Z+FmkHHbPRGsHMH/7cctCfXBNW0JXa0iefm1YwAfLT7CsqAbONlaMKJpcUkMhMiiJCkQQgiRKS6Fx9Bz7mFCH+TgKUdjwmDPzxA8F5KMM4BQuKExGchXQd3Y0srSFoo0MG4AUTfhyo7HScIOiLsP59YbNwAX/6etCP5vg7VjqqduWsaL8W3LMmzFCX7ffQUnGws+qqtii4kQIlWSFAghhMhwB69E8MGCYKLidPjlsSWwV5WcNeXowwj4dxIc+gP0ccZ9frWg3pdQoJqqob02p3wQ0NW4GQxw+9jTBOHGAXhwFYJmGzeNGfhUeZwk1Afv8qBN3jWlQ2UfouN1fLvhLD9sPo+jjQXdqr2BcRVCiHSRpEAIIUSGWnv8FkOXH8+ZU47GRcL+qXBgGiTGGvflr2xMBvxrZ+piYarQao0tHvkqwNtDISHGON3pk65GEZfg+n7jtuM7sHY2Dm5+0pLg7ANA31oFiY7T8ev2S3y95hQOVua0Dsin6ksTQiQnSYEQQogMoSgKM3dfYfzfxilHG5fyYHLHgJwx5WhCLByaCf/+CvGRxn2eZaDeV1CkUc5LBlJj5QDFmho3gAfXniYIV3YZ35szq40bQLWPoPF3oNHwWcOiRMfrCdwXwpA/j2NvZU6Dkh4qvRAhxLMkKRBCCPHa9EkGxqw7zcIDxilHe9f0Z9Q7OWDKUV0cBM0xjht4dM+4L29x45iB4i2M36TnZi6+UKmXcUvSw62jcHmbMUm4cRAOTAWDDppORKPR8HXzkkTH6Vh59CYDFh9hXq8qVC+UR+1XIYRAkgIhhBCv6VGinoGLj7Lt8ZSjX75Tkj7ZfcpRfSIcnQ+7f4SY28Z9Lv7GZKB0u+f6zQvAzBx8Khu3Ol/AkQWwdiAc+t14vOlEtFoNE9qXJTpezz9n79B33mGWfFCNsvmdVQ1dCAG5/CsOIYQQryM8Jp6Ovx9g27lwrMy1TO9SIXsnBEl647Siv1WEDUOMCYFjfmjxK3x8GMp2kIQgrSp0g5ZTAI0xMfh7GCgKFmZafuscQPWCeXiYmESPOYe4eCdG7WiFyPUkKRBCCPFKLoXH0nbaPk6ERuFia8Hi96vRpLSX2mG9GoMBTv4F06rCmo8g8jrYe0DTH+CTI1CxR7oX8BKkmhhYW5jxR49KlMvvxINHOrrNPsSN+4/UjlaIXE2SAiGEEOl26Op92k3fR+iDOPzy2LJyQE0q+mbDNQgUBc6uhxk1YUUf42w6Nq7Q8H/wyTGo+gGYW6kdZfaWSmJgb2VOYK8qFHG3Jyw6nq6zDxIeE692tELkWpIUCCGESJd1x2/RddZBouJ0BBRwZkX/GvhntzUIFAUu/gN/1IVlXSD8jHHl3rqj4NPjxpV+LW3VjjLnSCUxcLGzZEGfquR3seFaxCO6zz5E1COd2tEKkStJUiCEECJNFEVh5q7LDFxylMQkA41LebC4bzXy2Gezb9Kv7oE5TWBRO+NsORZ2UGuIMRmoPeyFK/SK15BKYuDpZM2ivlXJ62DFubAYegUe4lGiXu1ohch1JCkQQgjxUvokA1+vOc33j9cg6FXTj2ldKmJjmY0G3d44DPNbwbzmxpV5zayM8+h/ehzqfw22rmpHmPOlkhj45rFjQZ8qONlYcOR6JP0WBJOgT1I7WiFyFUkKhBBCvNCjRD0fLgxmwYFraDTwVfOSjG5RKvusQXD7OCx+D2Y3gCs7QWsBlfrAp8egyTiwz6t2hLnLs4nBxs9BUSju6cjcXpWxtTRjz8V7DFp6DH2SQe1ohcg1JCkQQgiRqrsxCXT8/QD/nDVOOTqtczaacjT8HCzvDjPfhgubQKOF8l1hYBA0/xkcvdWOMPeq0A1a/QZo4PAfpsSgQgEXfu9WCUszLX+fCmPkqpMoiqJ2tELkCrJ4mRBCiBRdCo+l59xDhD6Iw8XWglk9KlHRNxt0sbl3CXZPhBPLAQXQGBccq/MFuBVROzrxREBX479rPjYmBgDNfuCtIm782qk8AxYdYXlQKA7WFnz5Tgk0mmzSMiVENiVJgRBCiOccDnlA/8XHiIrT4ZvHlsBeVbL2DEPx0XBmNRxbDNf3P91fvLlxFWKPUqqFJl4glcSgSWkvJrQry+d/nWD23qs42VjwSX1J6ITITJIUCCGESObIPQ2LA4PQJSmU93Fmdo9KWXOGIUMSXN1tTATOrgN9nHG/RgtFGkHt4ZCvgroxipdLJTF4t5IPMfF6/rf+DD9vvYCjtTk9a2aTrmtCZEOSFAghhDAJ3H+NeRfNAIVGJT2Y3DEg680wFHHZmAgcXwrRoU/3uxWF8p2h7HsyXiC7SSUx6P2WP1FxOiZvu8iYdWdwtLGgbYX86sUpRA4mSYEQQggA5u8P4buN5wHoXq0Ao1uWzjozDMVHwelVxmTgxsGn+62doHR7YzKQryJIv/PsK5XEYFCDIkTF6QjcF8Lnf53A3sqcRqU81YtTiBxKkgIhhBAsP3yDr9ecBqBBPgNfNiumfkJgSDJOIXp8yePuQfHG/RotFG4A5TpBsWZgYa1qmCIDpZAYaJr9wNfNSxITr2fFkVA+XnKUwF6VqVHITcVAhch5JCkQQohcbs2xmwxfeQKAntULUF65ou5ML/cuPu0eFHPr6f68xY0tAmU6gKOXevGJzPVcYqCgbfYjE9qVISZex5Yzd3h/XhCL3q9GeR9nNSMVIkeRpEAIIXKxTaduM3j5cRQFulQtwMimxfj77ytvPpC4SDi90pgMhB5+ut/aGcq8C+U7gXcF6R6UWwR0BTSw5iM4PAsA82Y/8munAHoHHmbf5Qh6zj3E8n7VKerhoG6sQuQQkhQIIUQuteNcOAOXHCXJoNCuQn6+aVWapCT9mwvAkASXd8CxRXBuAyQlGPdrzIzdg8p3hmJNwTwLznwkMl9AF+O//0kMrJv9yO/dK9Fl1kGO34ik2+yD/PVhDXxcbVUMVIicQZICIYTIhfZevEe/hcHokhSal/ViYvuyaLUakpLewMXvnje2CJxYBjG3n+53L/m0e5CDxxsIRGR5KSQG9s1+ZF6vynSYuZ8Ld2LpMusgf31YHXdHGVsixOuQpEAIIXKZQ1fv8/78IBL1BhqV9OCX98pn/qDiuAdwaoUxGbgZ/HS/jYsxCSjfGbzKSfcg8bwUEgPnZj+yoE9V3p2xn+v3H9Ft9iGW9auGs62lioEKkb1JUiCEELnIsRuR9A48TJwuidpF8zKlcwAWZtrMuViSHi5vh+OLH3cPSjTu15gZFxcr3xmKNpbuQeLlUkgMPJr9yMI+VWk/Yx/n78TQc+5hFvWtip2V3NqYxNwxruXhUQbMJWESLyafHCGEyCVO3Yyi++yDxCboqV4wDzO7VcTKPBMWJgs/+7R7UOydp/vdSxlv7sq8C/buGX9dkbOlkBgUeNxi0GHmfo7diOSDBUHM7lEZa4sstuDem/bwHuz5yfg+JSWChR341YSCdYybe0lplRPPkaRACCFygQt3Yug+5xDR8Xoq+bowq0eljL1xenT/cfegRXDr6NP9tnkedw/qBJ5l5UZEvJ4UEoNizX4ksFdlusw6yL+XIvhkyVGmdamAeWa1gGVlCTGwfxrsmwKJMcZ9lg7G/1/cYtwA7NyhYG3wr21MEpx9VAtZZB2qfmKmT59O2bJlcXR0xNHRkerVq/P333+bjiuKwpgxY/D29sbGxoY6depw+vTpZOdISEhg4MCBuLm5YWdnR8uWLQkNDX32UkIIkWtdvfeQLrMOcv9hImXzOzGnV+WM6WKRpIfzm2B5d/ipGGwcakwItOZQ7B14bxEMPgdNx8t4AZFxArpAq6mAxpgYbBhCgI8zf3SvhKWZli1n7jB8xUkMBkXtSN8cfQIcmAGTy8POccYkwKscdFsFX1yHD/dCo2+Ns3pZ2MLDcDj5J6z9GCaVhl8rwPrBcGatcfyPyJVUbSnInz8/48ePp3DhwgDMmzePVq1acfToUUqVKsXEiRP5+eefCQwMpGjRonz77bc0bNiQ8+fP4+BgnJd40KBBrFu3jqVLl5InTx6GDBlC8+bNCQ4OxswslzcfCiFyvRv3H9H5jwPcjUmguKcD83tXwdHa4vVOeuf04+5By403F094loHyXaB0e7DP+3rXEOJFAroYk8zVAyBoNgA13/mJKZ0DGLDoCCuOhOJoY87XzUuquxBfZjMkGW/ud3wHkdeN+1wLQb0voWRr0D7+7tezjHGrMRD0ica1QK7sNG43g+H+ZeMWNBvQgHeAsSWhYB3wqSarhucSqiYFLVq0SPb4u+++Y/r06Rw4cICSJUsyadIkRo0aRdu2bQFj0uDh4cHixYvp168fUVFRzJ49mwULFtCgQQMAFi5ciI+PD//88w+NGzd+469JCCGyittRcXSedYDbUfEUdrdnYd+qrz47y8MIOPWXsXvQ7eNP99u6QdkOUK4TeJXNmMCFSIvynY3//icxaPzOT0xsV5Yhfx5n7r8hONlYMKhBURWDzCSKAhc2w7b/QfjjHhT2nlBnOAR0A7MXJP7mlsbxBX41od4oiI+CkH+NCcLVXXD3HNw6Ytz2/gLm1lCg2tPxCJ5lQStfuuZEWWZMQVJSEn/++ScPHz6kevXqXL16lbCwMBo1amQqY2VlRe3atdm3bx/9+vUjODgYnU6XrIy3tzelS5dm3759qSYFCQkJJCQkmB5HR0cDoNPp0Ol0mfQKU/fkmmpcWxhJHWQNUg8Z525MAl1mH+bG/TgKuNoQ2KMCTlbal763yeogSYfm8j9oTyxFc3ELGoPxmKK1QCnSCEPZjiiFGjy9AZF6yzDyWUijUu+iSUrCbN1ANEGzSTIYaNlkIg8eFuPbjeeZ9M9F7Cy19Kzum+5TZ9U60Nw4iHbHN2hvHABAsXbCUP1TDJX7GrsGGQBDOmI2s4VCDY0bQMxtNCF70F7dhebqLjSxYU9bFQDFxgXF9y0Uv7cx+NcGF/9M7RqYVeshu0jP+6ZRFEXVTncnT56kevXqxMfHY29vz+LFi2nWrBn79u2jZs2a3Lx5E29vb1P5Dz74gGvXrrF582YWL15Mr169kt3gAzRq1Ah/f39mzpyZ4jXHjBnD2LFjn9u/ePFibG1lVUQhRPYWq4Mpp80Ii9PgYqnwSekkXNMx66dj3HV8Ivbg82AfVvoY0/5IGz+u53mLmy7VSTR3yITIhXg1PhF7CLg+Cw0KV93qcSJ/DzaFavk71PiNdpdCSVRxz95jDBziblDi1l94RRsH8idpLLiStxEXPZqjM7fLnIsqCvYJt8gbc4a8MadxizmLhSEuWZFHlm7cdShl3OxLkmjhmDmxiFfy6NEjOnfuTFRUFI6OL64b1VsKihUrxrFjx4iMjGTFihX06NGDXbt2mY4/2xdQUZSX9g98WZkRI0YwePBg0+Po6Gh8fHxo1KjRS9+wzKDT6di6dSsNGzbEwuI1+/qKVyJ1kDVIPby+6Dgd3eYGERYXg4eDFYv6VsbXNY1fduji0KwZgPn5daZdil1eDKXbYyjTETuPUpQASmRO6OI/5LOQXs1IOlEOs3UD8b+3nQIFfGn6/gQ8Nl0gcP91ll41p2aVcjQsmfapcLNMHURex2z3eDTn/kSDgqIxw1C+C4a3PsfP0Qu/NxmLQY/+9jFjC8LVXWhCD2ObeA/fiF34Rhjv3RT30hj8a6H41UYpUB0sXy9hyTL1kE096Q2TFqonBZaWlqaBxpUqVeLw4cNMnjyZ4cOHAxAWFoaXl5epfHh4OB4eHgB4enqSmJjIgwcPcHFxSVamRo0aqV7TysoKK6vnvzazsLBQ9QdO7esLqYOsQurh1cQm6Om78ChnbseQx86SRe9Xo7C7fdqeHPcAlnaG6/swYAbFm6EN6IqmcH3MzCyQHsTqkM9COlTsBmZmsHoAZkfmYqbV8nXzH4lNNPBXcCiDlp9gbq/K1Czslq7TqlYHsXdhz49wePbT7kAlW6Op9yVmbkVU+kxagF9141b3C0h8CNf3P+1eFHYSTfgpzMJPwcHpoLUAnypPxyN4VwCzV7v1lM/Cq0nPe6Z6UvAsRVFISEjA398fT09Ptm7dSkBAAACJiYns2rWLCRMmAFCxYkUsLCzYunUrHTp0AOD27ducOnWKiRMnqvYahBDiTYtLTKJ34GGOXo/E2daChX2rpj0hiL4FC9tB+BkUKwf2+QykarvBaOUPsMhunhl8rAXGt5lIbLyeTafDeH9+EIv6ViWggMsLT6Oq+GjYPxX2/waJscZ9BetA/dGQr4KqoT3H0s44zWlh42QvPLxnHKz8JEmIvA7X/jVuO74zrpngX+vp+gh5i8lUxVmIqknByJEjadq0KT4+PsTExLB06VJ27tzJpk2b0Gg0DBo0iHHjxlGkSBGKFCnCuHHjsLW1pXNn44feycmJPn36MGTIEPLkyYOrqytDhw6lTJkyptmIhBAip4vXJfHBgiAOXb2Pg5U5C3pXpYRXGrtC3j0PC9pCdCjYe6LvuIyI4GuZG7AQmemZxMAcmNxxAn3m6dl76R495x5meb/qFPPMYuNi9AkQNAd2/wCPIoz7vMpDgzFQqK6akaWdnRuUbmfcAO5ffZogXN1lbJE8v9G4gXHGpCetCAVrg6N3yucVb4SqScGdO3fo1q0bt2/fxsnJibJly7Jp0yYaNjSOgB82bBhxcXEMGDCABw8eULVqVbZs2WJaowDgl19+wdzcnA4dOhAXF0f9+vUJDAyUNQqEELlCot7AR4uOsOfiPWwtzQjsXZky+Z3S9uQbh2BxB+Mf6jyFoetKsPcGJCkQ2dwziYEVMLPreLrOMbamdZt9kL8+rEGBPFlgchFDknHNjx3jIOrxWgN5CkO9r6Bkq+z9Tbqrv3Gr1AsMBgg78TRJuL4fYsPgxFLjBuBW7On6CH5vgXUaf5eJDKFqUjB79uwXHtdoNIwZM4YxY8akWsba2popU6YwZcqUDI5OCCGyNn2SgU+XHmXbuXCszLXM7lGZir6uaXvy+U3wZ0/Qx0G+itD5T7DLI9OKipyjfGdAA6v7Q9Bs7IC5Pcbx3u+HOH8nhi6zD/DXhzXwcFRpYS5FgQubHq81cMa4z8EL6nwB5bu+ct/7LEurBe/yxu2tQaCLh9BDT5OEW0fh3nnjduh30GghX0W0vm/j8tDe+H6JTJXDfuKEECJ3SDIoDP3zOH+fCsPSTMvv3StRvVCetD356EJY+wkoSVC4IXSY99ozhAiRJZXvZPz3cWLgDCzo/S3v/n6QaxGP6DrrIMv7VcfF7hUX9XtV1/bDP2Pg8VoDWDvBW4OhygdgmQVaL94EC2vwf9u41f/a2GIZsvdxkrALIi5C6GHMQg/zNqD8scy4MFu5jsZuSiLDSVIghBDZjMGgMGrVSVYfu4W5VsPULhWoXTTvy5+oKLD3Z+M3kwDlOkPLX1+8+qkQ2d0ziYE7sLD3/2g/8wAXw2PpOfcQi96vhr3VG7glCjtl/Pxd3Gx8bG4D1T6Emp+CTRYe/Pwm2LhAiRbGDSAqFK7swnBxK4azGzC/ew62jDImU8WaQoXuUKierK6cgSQpEEKIbERRFP63/gxLD99Aq4FJHcvTsKTHy59oMMCmL+DQ40Udaw4yDmDMzv2VhUirZxIDH2BB79F0+P0gx0OjeH9eEHN7VcbaIpNuMB+EGMcMnFgOKKAxM97U1h4Ojl4ve3bu5JQfArqQVLoDW8z/okm+WMyOL4ZbR+DsWuPmmM/YTSygK7j4qR1xtqdVOwAhhBBpoygK4zedI3BfCBoN/NC+HM3LpmG2Dn0CrOj9NCFo/D00HCsJgchdyneC1tMBDQTNpmjQWOb1rISdpRn7r0QwcMlR9EmGjL1mbDhsHAZTKsGJZYACpdrCx4ehxSRJCNJIb2aLoUJP+GAH9N8HVfsbWxaibxpna5pcDua1gBN/GscqiFciSYEQQmQTk/65yMxdVwD4rnUZ2lXM//InxUfDovZwepVxIaF2s6H6gEyOVIgs6pnEoNyJb/ijewUszbVsPXOHYX+dwGDIgAGt8dHGloHJ5Y3JuEFn7OrywU54dy7kKfT618itPEpB0/Ew5Dy0nwMF6wIauLobVvaFn4rChqFw+7jakWY70n1ICCGygek7LzN520UAvm5eks5VC7z8STF3YFE7CDsJlvbw3sLsM9+5EJklWVeiOdQApnYaxoeLjrLy6E0cbSwY3aLkq51bFw9Bs2H3jxB337jPu4Kxq17B2hkRvXjC3OrpmgiR1+HoIji2CKJuwOE/jJtnWWM3rTLtZcxGGkhLgRBCZHFz/73KhE3nABjWpBi93/J/+ZMiLsPshsaEwC4v9NwgCYEQTyRrMZhDw6sT+bF9aQAC94Xwyz8X03c+Q5LxpvS3SrB5pDEhyFMEOsyH97dLQpDZnAtA3RHw6XHjeiul2oCZpXFdhI1D4afisKKvcVYjQwZ3EctBpKVACCGysMUHrzN2nXEO80/qF2FAncIvf9LNI7DoXXh0D1z8odtKcC2YyZEKkc2U72QcV7PqQwiaQ5tKEN1iIKPXneXXbRext9Ty0iH8imJcnXfb/+CuMXHHwfvxWgNdct5aA1md1gwK1zduj+4bx3EcWQDhp+Hkn8bNxc84MLl8F1lB+RnSUiCEEFnUyiOhjFp9EoAP3i7IZw2KvPxJl7ZBYHNjQuBVDvpskYRAiNSU6whtZvCkxaDHgykMbmBMvMf9fZ4D4S8YjB/yL8xuBEs7GxMCa2do+A18cgQq9pCEQG22rlCtP/T/19haU7EXWDkaZ4La/i38Usr45cmZtaBPVDvaLEF+YoUQIgvacOI2Q/88jqJAj+q+jGhaHM3LZgs6sdzYT9qgh4J1jGMIrBzeSLxCZFvlOhr/fdxiMLAiRNfsw6x/r7H0spa65+/SqPR/vlEOOwn/jIVLW42PzW2Mg/drfAI2zm88fPESGo1x1fZ8FaHxODizBo4ugGv/wsUtxs3WzfhzUKE75C2mdsSqkaRACCGymK1n7vDp0qMYFHivkg+jW5R6eUKw7zfjwj4Apdsb+0ubv+FVWoXIrv6TGGiC5zCqIkQGdOWvo2F89ucJ1rg7Utj8Luz4Dk7+BSigNYcKPaD2MHDwVDV8kUaWtsZuY+U7GcddHV0AxxZD7B3Y/5txy18FKnQzjkvIZV+qSFIghBBZyO4Ld/lo0RH0BoVW5b0Z17YMWu0LEgKDAf75GvZNMT6uNgAafQda6R0qRLo8kxh8Xz6Jo/YNiIqN4tQf71MoaQsag95YpnQ7qDtKphbNzvIUMs4KVfdLY6vPkQVwYROEHjJuf38BpdtAQHfwqZIr1nWRpEAIIbKIA1ci+GBBEIlJBpqW9uSnd8th9qKEIEkHaz56vCgS0GAs1Pw0V/zxEiJT/CcxsDg2j0V2h3DUX8X28YJYSqH6aBqMNo7XETmDmTkUa2rcYu7A8SXGFoSIS3B0oXFzKwoB3aBcJ7DPq3bEmUa+ShJCiCwg+NoDegceJl5noF5xdyZ3DMDc7AW/ohNiYfF7xoRAYwatZ8BbgyQhEOJ1PR58rKDB8+FZbInnuFKITomjmOA2ThKCnMzBw/h79OMg6PU3lOsMFrZw7wJs/Qp+Lg5Lu8CFzZCkVzvaDPdKLQWKohAREYFGoyFPnjwZHZMQQuQqJ0Oj6DnnEI8Sk3irsBvTuhhXWE3Vw3vGWTNuHTH+weowH4o0fHMBC5HTletIksaCe/9Mxq3hYEL0ldi/7Dj7d12mhJcDrcrnUztCkZk0GvCtYdyaToBTK4ytBzeD4dx64+bgBeU7G6c3zSEzvKWrpSAsLIzu3bvj4uKCh4cH7u7uuLi40Lt3b+7cuZNZMQohRI51LiyabnMOEpOgp4qfK793r4i1hVnqT3gQYpwG8dYRsHGFHuskIRAiEyglWnKw0BCU4u/QKiA/H9Y2jh8Y9tcJTt2MUjk68cZYO0KlXsZpTfvvN47bsnGFmNuw5yf4NcA4DfTxZaCLUzva15LmloLo6Ghq1KhBbGwsvXr1onjx4iiKwpkzZ1iyZAl79+7lyJEj2NvbZ2a8QgiRY1wKj6XrrINEPtJR3seZOb0qY2v5gl/Lt0/AovbGmTKcChgXJXNLw9oFQojX9nnjYpwLi2bn+bt8MD+ItQPfws3eSu2wxJvkURKafG8coHx+o3Fw8uXtELLHuG38HMq0N85e5FU+23XnTHNSMHnyZMzMzDh9+jR58yYfZPHll19Ss2ZNfv31V0aOHJnhQQohRE5zLeIhXWYd4F5sIiW9HJnXqwr2Vi/4lXx1t7Eva0I0uJeCrivA0evNBSxELmem1TC5YwCtp/7L1XsPGbDwCAv7Vn1xVz+RM5lbGacsLdUGIm8YpzU9uhCirkPQbOPmUcaYHJR517iQWjaQ5p/kDRs2MHLkyOcSAgB3d3dGjBjBunXrMjQ4IYTIiW5GxtH5j4PciU6gqIc9C/tWxcnWIvUnnF4FC9sZEwLfmtBroyQEQqjAycaCP7pXwsHKnEMh9xm77rTaIQm1OftAneHw6XHotto4Xa2ZJdw5CX8Pg5+Kw1+94fIOUBS1o32hNCcFFy5coEaNGqker1GjBufPn8+QoIQQIqcKj46nyx8HuBkZh7+bHQv7VsXV7gWLjB36A/7sBUmJUKIFdF0pq6YKoaLC7vZM6lgejQYWHbzOooPX1A5JZAVaLRSqC+3nwJDz0HSisbUgKcE4UHnTF2pH+FJpTgqio6NxdnZO9bizszPR0dEZEZMQQuRIEbEJdJl1kJCIR+R3sWFR36q4O1inXFhRYNs3sHEooEClPvDuPLBIpbwQ4o2pX8KDoY2KATB6zWkOXb2vckQiS7F1har94MM98MFO4+/vqv2y/BiDNCcFiqKgfcEKmRqNBiWLN4sIIYRaIh8l0nX2IS6Gx+LlZM2S96vh7WyTcuEkPaz9GPb8aHxcdxS88xNoXzArkRDijRpQpxDvlPVCb1DovzCYm5HZe+YZkQk0GvAOgOY/Q6XeakfzUmkeaKwoCkWLFkWTSpYjCYEQQqQsJl5HjzmHOHs7Gjd7Kxb1rYqPq23KhRMfGfufXvgbNFpo/gtU7PlG4xVCvJxGo+GH9mW5cvchZ29H029BEH/2q4GNpSTvIntKc1Iwd+7czIxDCCFypEeJenoHHuZ4aBQuthYs6luVgnlTmbr50X3jKsWhh8Dc2tg3tfg7bzZgIUSa2Vqa83u3irSa+i+nbkYzfMUJJncsn+oXqEJkZWlOCnr06JGZcQghRI4Tr0ui77wgDoc8wNHanAV9qlLM0yHlwlGhsKAt3DsP1k7QaRn4Vn+zAQsh0s3H1ZZpXSrQddZB1h6/RUlvR9NCZ0JkJ681uW58fDzz5s1j2rRpXLx4MaNiEkKIbC9Bn8SHC4PZdzkCO0sz5vWuQul8TikXDj8LsxoaEwLHfNB7syQEQmQj1QrmYXSLkgBM2HSOnefDVY5IiPRLc1Lw+eef8+mnn5oeJyYmUr16dd5//31GjhxJQEAA+/fvz5QghRAiO9ElGfhkyVF2nr+LtYWWOT0rE1DAJeXC1/bDnMYQcwvcikGfLeBe4s0GLIR4bV2r+dKxsg+KAgOXHOXK3Vi1QxIiXdKcFPz999/Ur1/f9HjRokVcu3aNixcv8uDBA959912+/fbbTAlSCCGyiySDwuDlx9l8+g6W5lpmda9M1YJ5Ui58bgMsaA3xUZC/CvTeBE7532i8QoiModFoGNuqFBV9XYiJ1/P+/CCi43VqhyVEmqU5Kbh+/TolS5Y0Pd6yZQvt27fH19cXjUbDp59+ytGjRzMlSCGEyA4MBoXhK06w7vgtLMw0zOhagbeKuKVcODgQlnUFfTwUbQrd1xjnthZCZFtW5mZM71oBT0drLt99yGdLj2EwyOyMIntIc1Kg1WqTTTt64MABqlWrZnrs7OzMgwcPMjY6IYTIRiZsOsdfwaGYaTX82jGAesU9ni+kKLBrIqz7FBQDBHSD9xaCZSpTlAohshV3B2t+714RK3Mt286F8/PWC2qHJESapDkpKF68OOvWrQPg9OnTXL9+nbp165qOX7t2DQ+PFP4ACiFELrDySCgzd18B4If2ZWlaxuv5QoYk2DAEdnxnfPz259ByCpileSI4IUQ2UDa/M+PblQHgtx2XWH/ilsoRCfFyaf5L9Pnnn9OpUyc2bNjA6dOnadasGf7+/qbjGzdupEqVKpkSpBBCZGVHrz/gi5UnARhYrzBtK6QwLkAXDyv7wtl1gAaa/QBV3n+zgQoh3pg2Afk5cyuaP/Zc5fM/T+DvZkcp71RmIBMiC0hzS0G7du3YuHEjZcuW5bPPPmPZsmXJjtva2jJgwIAMD1AIIbKysKh4+i0IJlFvoGFJDz5rUPT5QnGRsLCdMSEws4R350pCIEQuMLxJcWoVcSNOl8QH84OJiE1QOyQhUpWuNusGDRrQoEGDFI+NHj06QwISQojsIl6XRL8FQYTHJFDMw4Ff3iuPVvvMSqbRt40JQfhpsHKEjovA/211AhZCvFHmZlp+61SBVlP3EhLxiAGLjrCwb1UszF5rmSghMkWak4Ldu3enuN/JyYnChQtjZ2eXYUEJIURWpygKX6w4wfHQKFxsLZjVoxL2Vs/8Sr130bhKcdR1sPeArivAs4w6AQshVOFka8Ef3SvReuq/HLx6n2/Xn2Fsq9JqhyXEc9KcFNSpUyfVY2ZmZvTv35+ffvoJCwuLjIhLCCGytJm7r7D62C3MtRqmdamIj+szsweFBsGidyHuPrgWgm4rwcVPlViFEOoq4uHApI4BvD8/iHn7r1HCy5GOVQqoHZYQyaS5/erBgwcpblevXmXx4sWsXbuWH374ITNjFUKILGH7uTtM2HQOgNEtS1G90DOLk13cCvNaGBMC7wrGVYolIRAiV2tY0oPBDY1jjr5ac4rga/dVjkiI5NKcFDg5OaW4+fr68u677zJ58mQWLVqUmbEKIYTqLoXH8MmSYygKdKlagG7VfJMXOLYYFr8HukdQqD70WAd2qSxgJoTIVT6uW5impT3RJSn0W3CE21FxaockhEmGjXQpV64c165dy6jTCSFElhP5KJG+84KITdBT1d+V0S1KJS9w6A9Y3R+UJCjbETovAyt7dYIVQmQ5Wq2GH98tR3FPB+7FJtBvQTDxuiS1wxICyMCk4NatW7i7u2fU6YQQIkvRJxn4ePFRQiIekd/FhmldKmBp/p9foSf/go2fG/9f/WNoPR3MZIyVECI5Oytz/uheCWdbC06ERjFi5UkURVE7LCEyJikIDw/nyy+/pF69eul63vfff0/lypVxcHDA3d2d1q1bc/78+WRlevbsiUajSbZVq1YtWZmEhAQGDhyIm5sbdnZ2tGzZktDQ0Nd+XUII8cR3G8+y99I9bC3N+KN7JfLYWz09eGkbrPoQUKDKB9DoW9DKlINCiJT5uNoyrXMFzLQaVh29yey9V9UOSYi0zz4UEBCARqN5bn9UVBShoaGUKFGCpUuXpuviu3bt4qOPPqJy5cro9XpGjRpFo0aNOHPmTLIpTps0acLcuXNNjy0tLZOdZ9CgQaxbt46lS5eSJ08ehgwZQvPmzQkODsbMzCxdMQkhxLOWHb7O3H9DAPi5Q3lKeDk+PRgaDMu6gUEHpdtBkwmQwu9KIYT4rxqF3fjqnRKMWXeGcRvPUtTDgbeL5lU7LJGLpTkpaN26dYr7HR0dKV68OI0aNUr3DfimTZuSPZ47dy7u7u4EBwfz9ttPF/exsrLC09MzxXNERUUxe/ZsFixYYFpYbeHChfj4+PDPP//QuHHjdMUkhBD/FRRyny9XnwLgswZFaVL6P7+L7p6HRe1B9xAK1YPWM6SFQAiRZj1q+HH6VjR/Bofy8eIjrP34LfzcZN0noY40JwVvYsXiqKgoAFxdXZPt37lzJ+7u7jg7O1O7dm2+++470/iF4OBgdDodjRo1MpX39vamdOnS7Nu3L8WkICEhgYSEp0uNR0dHA6DT6dDpdBn+ul7myTXVuLYwkjrIGrJaPdyKjKPfgmB0SQpNSnnwYS3fp7FF38R8QRs0cfcxeFcgqe0cUDSQRWJ/VVmtDnIrqQf1vak6GN28OBfDYzh2I4q+8w7zZ7+qzy+EmIvJZ+H1pOd90yhZZHSLoii0atWKBw8esGfPHtP+ZcuWYW9vj6+vL1evXuWrr75Cr9cTHByMlZUVixcvplevXslu8gEaNWqEv78/M2fOfO5aY8aMYezYsc/tX7x4Mba2ts/tF0LkPolJMPm0GaEPNeSzVfi0dBJWjxtDLfQx1Lr4HQ7xt4ix8mJv0S9JNHdQN2AhRLYVlQg/nTAjSqehjIuB3sUMaKUXosgAjx49onPnzkRFReHo6PjCslkmFf344485ceIEe/fuTbb/vffeM/2/dOnSVKpUCV9fXzZs2EDbtm1TPZ+iKCmOgQAYMWIEgwcPNj2Ojo7Gx8eHRo0avfQNyww6nY6tW7fSsGFDWRFaJVIHWUNWqQdFURi0/AShD+/gamfBog+rkc/Zxngw8SFmi9qijb+F4uCFdY+/aeCUX7VYM1pWqYPcTupBfW+6DkpWiqLz7MOcfACXrQvzaf3CmX7N7EA+C6/nSW+YtMgSScHAgQNZu3Ytu3fvJn/+F/9x9fLywtfXl4sXLwLg6elJYmIiDx48wMXFxVQuPDycGjVqpHgOKysrrKysnttvYWGh6g+c2tcXUgdZhdr18Nv2i2w8dQcLMw0zu1XCL+/jLwv0ibCyN9wKBmtnNN1WY+Hmr1qcmUntOhBGUg/qe1N1UMnfje/blGHIn8f5becVSuVzpmkZr0y/bnYhn4VXk573TNURcYqi8PHHH7Ny5Uq2b9+Ov//L/7hGRERw48YNvLyMH5SKFStiYWHB1q1bTWVu377NqVOnUk0KhBAiNVtOh/HjlgsAfNOqNJX9Ho9xMhhgzQC4vA0sbKHLn+BeXMVIhRA5TbuK+eld03gvNOTP45wLS/u3vEK8LlWTgo8++oiFCxeyePFiHBwcCAsLIywsjLg447LfsbGxDB06lP379xMSEsLOnTtp0aIFbm5utGnTBgAnJyf69OnDkCFD2LZtG0ePHqVr166UKVPGNBuREEKkxbmwaD5bdgyAnjX86FilgPGAosDmEXDyT9CaQ4cF4FNFvUCFEDnWyGbFqVk4D48Sk3h/fhAPHiaqHZLIJdKVFMTFxbF3717OnDnz3LH4+Hjmz5+frotPnz6dqKgo6tSpg5eXl2lbtmwZAGZmZpw8eZJWrVpRtGhRevToQdGiRdm/fz8ODk8H9f3yyy+0bt2aDh06ULNmTWxtbVm3bp2sUSCESLP7DxN5f34QDxOTqFk4D1++U+LpwT0/wcEZxv+3ngFF5AsHIUTmMDfT8lunChRwteXG/Tg+WnwEfZJB7bBELpDmMQUXLlygUaNGXL9+HY1GQ61atViyZImpG09UVBS9evWie/fuab74yyY+srGxYfPmzS89j7W1NVOmTGHKlClpvrYQQjyhSzIwYFEwN+7H4ZvHlt86VcDc7PF3JsGBsP0b4/+bTICy76oWpxAid3Cxs+SP7pVoM+1f9l2O4LuNZxndopTaYYkcLs0tBcOHD6dMmTKEh4dz/vx5HB0dqVmzJtevX8/M+IQQItP9b90ZDly5j72VOX90r4SL3eNV08+shfWfGf9fayhU+1C9IIUQuUoxTwd+7lAegLn/hrA86Ia6AYkcL81Jwb59+xg3bhxubm4ULlyYtWvX0rRpU2rVqsWVK1cyM0YhhMg0Cw9cY8GBa2g0MOm98hT1eNw18epuWNEHFANU6AH1vlQ3UCFErtOktCef1i8CwJerTnH0+gOVIxI5WZqTgri4OMzNk/c2mjp1Ki1btqR27dpcuHAhw4MTQojMdOBKBGPWngbg88bFaFDSw3jg9nFY0hmSEqFEC2j+C6Sy7okQQmSmT+sXoVFJDxKTDPRbEMyd6Hi1QxI5VJqTguLFixMUFPTc/ilTptCqVStatmyZoYEJIURmunH/Ef0XBqM3KLQs503/2oWMByIuw8J2kBgDfrWg7SzQyqQFQgh1aLUafn6vPEU97AmPSaDfgmDidUlqhyVyoDQnBW3atGHJkiUpHvvtt9/o1KnTSwcOCyFEVvAwQW+c6u+RjjL5nJjQrqxxBfSYMFjQBh7eBc+y0HExWFirHa4QIpd7Mt7JycaCYzciGbXqlNxziQyX5qRgxIgRbNy4MdXj06ZNw2CQKbOEEFmbwaAwePkxzoXF4GZvxe/dK2JjaQZxkcYWgshr4OIPXVeAtaPa4QohBAC+eeyY2rkCWg2sOBLK3H9D1A5J5DCqLl4mhBBv2uRtF9l8+g6WZlpmdquIl5MN6OJgSSe4cwrsPaDbKrB3VztUIYRI5q0iboxsZlxD5buNZ/n30j2VIxI5SbqSguPHj9O9e3cKFiyIjY0N9vb2lClThq+++oroaFmKWwiRtW08eZvJ2y4CMK5tGSr6ukCSHv7qDdf3gZUTdF0Jrv4qRyqEECnr85Y/bSvkI8mg8NHiI1yPeKR2SCKHSHNSsHnzZqpXr05MTAzVqlVDq9XSq1cv3nnnHZYuXUqFChUICwvLzFiFEOKVnb4VxZDlxwHo+5Y/7SvmB0WBdZ/C+Y1gbg2dl4JnaZUjFUKI1Gk0Gsa1KUO5/E5EPtIZV2JP0KsdlsgB0pwUfPHFF/z888+sWrWKxYsXs3r1av755x/Gjx/PmTNn8PPzY8SIEZkZqxBCvJJ7sQl8MD+YOF0SbxfNyxdNixsP/DMaji0EjRm0nwu+NdQNVAgh0sDawoyZ3SqR18GK83diGLL8OAaDDDwWryfNScG5c+do0qSJ6XGDBg24fPkyt2/fxsLCgtGjR7Nhw4ZMCVIIIV5Vot5A/4XB3IyMo6CbHVM6BWBupoV/f4V/JxsLtfwVijdTN1AhhEgHTydrZnStiKWZlk2nw/htxyW1QxLZXJqTgnz58nH+/HnT48uXL2MwGMiTJw8A+fPnJzY2NuMjFEKIV6QoCl+vOcXhkAc4WJvzRw/jlH4cWwxbvzIWajAWArqqG6gQQryCir4ufNva2OXx560X2HJaunGLV5fmpKB79+707duXGTNmMHfuXNq0aUPLli2xtLQE4NixY/j7y+A8IUTWMX//NZYevoFWA792CqBQXns4vwnWfGwsUP1jqPmpukEKIcRr6FDZh541/AD4bNkxLtyJUTcgkW2Zp7XgyJEjefjwId988w0JCQk0btyYyZMnm47ny5eP6dOnZ0qQQgiRXv9eusf/1p8BYETTEtQt5g7X9sOfPUBJgnKdoOE3oNGoHKkQQryeUe+U4HxYDPuvRPD+/CDWfFQTZ1tLtcMS2UyaWwrMzc2ZMGECN2/e5N69eyxatAg3NzfT8SpVqvD2229nSpBCCJEe1yIeMmDREZIMCm0r5KNvLX+4cxqWvAf6eCjSGFpOAa0s1SKEyP4szLRM7VKB/C42XIt4xMAlR9EnyYKyIn1e6S9iUlISd+7c4d49WTRDCJG1xMTr6DsviKg4HeV9nBnXpgyayOuwoC3ER4FPNXg3EMws1A5VCCEyjKudJX90r4SNhRl7Lt5jwqZzaockspl0JQUbNmzg7bffxs7ODm9vbzw8PHB2dqZbt25cv349s2IUQog0STIoDFp6jIvhsXg4WvF7t4pYJ9yHBW0gNgzcSxrXIrC0VTtUIYTIcCW8HPmpQzkA/thzlZVHQlWOSGQnaU4KFixYQKdOnahYsSKfffYZefPmZdiwYYwfP54bN25QsWJFLl68mJmxCiHEC/205TzbzoVjaa7l926VcLdMhEXt4P5lcCpgXK3YxkXtMIUQItM0K+PFwHqFAfhi5UmO34hUNyCRbaQ5KRg3bhx//PEHv/zyC99//z3r169n4cKF9OvXj507d1K/fn2GDx+embEKIUSq1hy7ybSdlwGY2K4s5bxsYFkXuH0cbN2g2ypw9FI5SiGEyHyfNShKgxLuJOoN9FsQzL3YBLVDEtlAmpOCa9euUbVqVdPjSpUqERYWxu3btwEYPHgwO3bsyPgIhRDiJU6ERjLsrxMAfFi7EK3LecLK9+HqbrC0h65/gVthlaMUQog3Q6vV8Mt75Snsbk9YdDyDZcVjkQZpTgr8/PwICgoyPT5y5AharRYPDw8AXF1d0el0GR+hEEK8QHhMPB/MDyZBb6BecXc+b1QUNgyBM2vAzBI6LgbvALXDFEKIN8rB2oJpXSpgbaFl94W7zNx9Re2QRBaX5nUKPvroI/r27cvhw4extrZm1qxZdOvWDTMzMwAOHjxI0aJFMy1QIYR4VoI+iX4LggmLjqewuz2TO5bHbPd4CJ4LaKDtH1CwttphCiGEKop6ODC2ZSmGrzjJj1vOU8XfhYq+rmqHJbKodCUFWq2WhQsXkpCQQM+ePfnqq69Mx6tUqcLixYszJUghhHiWoiiMWnWKo9cjcbKxYFb3Sjgcnwu7JhgLvPMTlGqtaoxCCKG2DpV8+PdSBGuP3+KTJcfY8MlbsrCZSFGakwKA/v37079//xSPFSlSJEMCEkKItJi99yp/BYdiptUwtXMF/G7/DX8PMx6sOwoq91E3QCGEyAI0Gg3ftSnNidBIQiIeMeyvE8zsVhGNrOYuniHLeQohsp1dF+4ybuNZAL58pwRvaY7Dqg8BBap8AG9/rm6AQgiRhThYW/Bb5wpYmmnZcuYO8/aFqB2SyIIyLCk4fvy4aXyBEEJklit3Y/l48REMCrxXyYeeBe7Bsm5g0EHpdtBkAsg3YEIIkUzpfE6MbFYcgHEbz3HqZpTKEYmsJkNbChRFprsSQmSeqDgdfecHEROvp5KvC9+8ZY5mcQfQPYRC9aD1DNBKA6gQQqSkRw0/GpX0IDHJwMeLjxATL7NGiqfSPKagbdu2LzweFRUl/dOEEJkmyaDwyZKjXLn7EG8na2a29MRycQuIuw/5KkKHBWAug+eEECI1Go2Gie3LcvrXvYREPGLUqlNM7lhe7t8EkI6WgnXr1hEfH4+Tk1OKm729fWbGKYTI5SZsOseuC3exttAyu0Mh8qzqCNGhkKcIdP4TrOR3kBBCvIyzrSW/dgrATKth7fFbLA+6oXZIIotIc0tBiRIlaNeuHX36pDyjx7Fjx1i/fn2GBSaEEE+sCA7l98cL70xqU5QS2/vAvfPg4A3dVoFdHpUjFEKI7KOirwtDGxVjwqZzjF57moACLhT1cFA7LKGyNLcUVKxYkSNHjqR63MrKigIFCmRIUEII8cSR6w8YsfIkAIPq+NLk9OdwMwhsXIwJgbOPyhEKIUT20+/tgrxdNC/xOgMfLTpCXGKS2iEJlaU5KZgxYwY//PBDqsdLlCjB1atXMyQoIYQACIuKp9+CYBKTDDQukZdPYyfB5W1gYWvsMuReXO0QhRAiW9JqNfzcoRzuDlZcDI9lzNrTaockVJbmpMDKygpbW9vMjEUIIUzidUl8sCCIuzEJFHO3Z0qeP9Gc+hO05sZBxT6V1Q5RCCGyNTd7KyZ1LI9GA8uCbrDm2E21QxIqkrn7hBBZjqIoDF9xghOhUbjYWrCs1D4sg343Hmw9A4o0UDdAIYTIIWoUcmNgvSIAjFx5kqv3HqockVCLJAVCiCznj70hrDl2C3Othr+qXMR5/3jjgSYToOy76gYnhBA5zKf1i1DV35WHiUl8vPgICXoZX5AbSVIghMhSTj/Q8OPWiwDMqnKbQge/NB6oNRSqfahiZEIIkTOZaTVM7hiAq50lp29F8/3Gc2qHJFSQpqTgxIkTGAyGzI5FCJHLXQqPZd5FLYoCX5a6R52TX4BigAo9oN6XaocnhBA5lqeTNT+9Ww6AwH0hbDoVpnJE4k1LU1IQEBDAvXv3AChYsCARERGZGpQQIveJfJTIh4uOkZCkoUO+CPrcGAVJiVCiBTT/BWTFTSGEyFR1i7vT7+2CAAz76zihDx6pHJF4k9KUFDg7O5umGw0JCZFWAyFEhtInGfh48VGu3X9EOavbfB83Fk1iDPjVgrazQGumdohCCJErDG1cjPI+zkTH6xm45Ci6JLnnyy3StKJxu3btqF27Nl5eXmg0GipVqoSZWcp/pK9cuZKhAQohcr5vN5xl76V7FLCMZr7leMwe3QPPstBxMVhYqx2eEELkGhZmWqZ0CqDZr3s4ej2SH7ecZ0TTEmqHJd6ANCUFv//+O23btuXSpUt88sknvP/++zg4vP5y2N9//z0rV67k3Llz2NjYUKNGDSZMmECxYsVMZRRFYezYsfz+++88ePCAqlWrMnXqVEqVKmUqk5CQwNChQ1myZAlxcXHUr1+fadOmkT9//teOUQiRuZYeuk7gvhAceMR6559xjL6L4uKPpusKsHZUOzwhhMh1fFxt+aF9WT5ceISZu65QvWAe6hRzVzsskcnSlBQANGnSBIDg4GA+/fTTDEkKdu3axUcffUTlypXR6/WMGjWKRo0acebMGezs7ACYOHEiP//8M4GBgRQtWpRvv/2Whg0bcv78eVMMgwYNYt26dSxdupQ8efIwZMgQmjdvTnBwcKotGkII9R0Ouc9Xa06hwcBa73k43r9AvLkTZp3+xMJe/gAJIYRampT2ont1X+bvv8bg5cf5+9NaeDhKy21Olu4pSefOnWu6GQ8NDeXmzVdf/W7Tpk307NmTUqVKUa5cOebOncv169cJDg4GjK0EkyZNYtSoUbRt25bSpUszb948Hj16xOLFiwGIiopi9uzZ/PTTTzRo0ICAgAAWLlzIyZMn+eeff145NiFE5gp98IgPFwSjS1KY6r0F//t7UMytOVjoM3DxUzs8IYTI9UY2K0FJL0fuP0zk06VHSTIoaockMlGaWwqeMBgMfPvtt/z000/ExsYC4ODgwJAhQxg1ahRa7asvfRAVFQWAq6srAFevXiUsLIxGjRqZylhZWVG7dm327dtHv379CA4ORqfTJSvj7e1N6dKl2bdvH40bN37uOgkJCSQkJJgeR0dHA6DT6dDpdK8c/6t6ck01ri2MpA7erEeJevrOCyLiYSJ98pyi2f35ACQ2nkjkLWepBxXJZyFrkHpQn9QBmAGTOpSh9fQDHLhyn8lbzzOwXqE3GoPUw+tJz/uW7qRg1KhRzJ49m/Hjx1OzZk0UReHff/9lzJgxxMfH891336X3lICxVWDw4MG89dZblC5dGoCwMOMcuR4eHsnKenh4cO3aNVMZS0tLXFxcnivz5PnP+v777xk7duxz+7ds2YKtre0rxZ8Rtm7dqtq1hZHUQeYzKBB4Qcu5+1rKWtzgi7ifAbictxGnbjkDUg9ZgdRB1iD1oD6pA2hXQMOCS2ZM2XEJw50LFHF68y0GUg+v5tGjtE8rm+6kYN68ecyaNYuWLVua9pUrV458+fIxYMCAV04KPv74Y06cOMHevXufO6Z5Zn5yRVGe2/esF5UZMWIEgwcPNj2Ojo7Gx8eHRo0a4ej45gc26nQ6tm7dSsOGDbGwsHjj1xdSB2/SlO2XOX7/Mq5mj1jmPB2LmHgMvjUp0GkeXgakHlQmn4WsQepBfVIHTzUDHq06xYojt1h+w5a1LauTx87yjVxb6uH1POkNkxbpTgru379P8eLFn9tfvHhx7t+/n97TATBw4EDWrl3L7t27k80Y5OnpCRhbA7y8vEz7w8PDTa0Hnp6eJCYm8uDBg2StBeHh4dSoUSPF61lZWWFlZfXcfgsLC1V/4NS+vpA6yGx/n7zNrzsuo8HA+nzzsQkPAScftB3mo7W2hcfNnFIP6pM6yBqkHtQndWD0TesyHA+N5lJ4LMNXnmZuz8potW9uUUmph1eTnvcs3QMAypUrx2+//fbc/t9++41y5cql61yKovDxxx+zcuVKtm/fjr+/f7Lj/v7+eHp6JmsySkxMZNeuXaYb/ooVK2JhYZGszO3btzl16lSqSYEQ4s07cyuawcuPAzDP7x+8w3eDuTW8txDs3FSOTgghxIvYWpoztXMFrMy17Lpwlz/2yLpUOU26WwomTpzIO++8wz///EP16tXRaDTs27ePGzdusHHjxnSd66OPPmLx4sWsWbMGBwcH0xgAJycnbGxs0Gg0DBo0iHHjxlGkSBGKFCnCuHHjsLW1pXPnzqayffr0YciQIeTJkwdXV1eGDh1KmTJlaNCgQXpfnhAiE9yLTeD9+UHE6ZL4LN853g4LNB5o8St4l1czNCGEEGlUzNOBMS1LMWLlSX7YfJ7K/q5UKODy8ieKbCHdLQW1a9fmwoULtGnThsjISO7fv0/btm05f/48tWrVSte5pk+fTlRUFHXq1MHLy8u0LVu2zFRm2LBhDBo0iAEDBlCpUiVu3rzJli1bkq2T8Msvv9C6dWs6dOhAzZo1sbW1Zd26dbJGgRBZQKLeQP+FwdyMjKOOawSfRP9kPFBtAJR7T93ghBBCpEvHyj40L+uF3qAwcPFRoh7JrEA5RbpbCsA45eerDij+L0V5+eh1jUbDmDFjGDNmTKplrK2tmTJlClOmTHntmIQQGUdRFL5ec4rDIQ/wtk7gd4uf0Tx6CH61oOE3aocnhBAinTQaDd+3LcPJm1Fci3jEsBXHmdG14ksngBFZ36svKiCEEC8xb18ISw/fwFxjYJ13IJZRV8HJB94NBLNX+k5CCCGEyhysLfitUwUszDRsPn2HBQeuqR2SyACSFAghMsXei/f4ZsNZAP4supM8t3bJwGIhhMghyuR3YkTTEgB8u/4sp25GqRyReF2SFAghMlzIvYd8tPgISQaF0YUvE3BtlvFAyykysFgIIXKIXjX9aFDCg8QkAx8vPkJsgl7tkMRrkKRACJGhouN19J0fRFScjpbeUfS8M954oNpHULaDusEJIYTIMBqNhh/fLYu3kzUhEY8YtepkmsaLiqwp3UlBXFxcsiWTr127xqRJk9iyZUuGBiaEyH6SDAqDlh7jUngsRRz0/GyYiEb3EPzfhob/Uzs8IYQQGczZ1pJfOwVgptWw5tgt/gwKVTsk8YrSnRS0atWK+fPnAxAZGUnVqlX56aefaNWqFdOnT8/wAIUQ2ccPm8+z/Vw4NuawwmMO5pFXwakAtA+UgcVCCJFDVfJzZXDDogB8vfYUF+/EqByReBXpTgqOHDliWo/gr7/+wsPDg2vXrjF//nx+/fXXDA9QCJE9rDoayoxdlwFYU3IHjqE7wdwGOi4EuzzqBieEECJT9a9diFpF3IjXGfho8RHiEpPUDkmkU7qTgkePHpkWDtuyZQtt27ZFq9VSrVo1rl2TKamEyI2O3Yhk+IqTAEwuG0LRC38YD7T6DbzKqRiZEEKIN0Gr1fBzh/LkdbDiwp1Y/rf+tNohiXRKd1JQuHBhVq9ezY0bN9i8eTONGjUCIDw8HEdHxwwPUAiRtd2JjueD+UEk6g10L/SQllcfL2xY/WMo017d4IQQQrwxeR2smPReeTQaWHLoBmuO3VQ7JJEO6U4Kvv76a4YOHYqfnx9Vq1alevXqgLHVICAgIMMDFEJkXfG6JD6YH0R4TAIV3GHMw3GPBxbXhgZj1Q5PCCHEG1azsBsD6xYGYOTKk4Tce6hyRCKt0p0UtG/fnuvXrxMUFMSmTZtM++vXr88vv/ySocEJIbIuRVH4YsUJjodG4WqjZZHz72gjr4JzAVmxWAghcrFP6hehip8rDxOT+HjJERL0Mr4gO3ildQo8PT0JCAhAq3369CpVqlC8ePEMC0wIkbXN3H2F1cduYabVsLbkTmyu73w8sHgx2LqqHZ4QQgiVmJtpmdypPC62Fpy6Gc33G8+pHZJIgzR9lde2bds0n3DlypWvHIwQInvYfu4OEzYZf8nPqRxK/uMzjAda/QaeZVSMTAghRFbg5WTDTx3K0TswiMB9IVQvlIfGpTzVDku8QJpaCpycnEybo6Mj27ZtIygoyHQ8ODiYbdu24eTklGmBCiGyhot3YvhkyTEUBQaX1VH7zGjjgRoDZWCxEEIIk3rFPXi/lj8Aw/46wc3IOJUjEi+SppaCuXPnmv4/fPhwOnTowIwZMzAzMwMgKSmJAQMGyOxDQuRwkY8S6Ts/iNgEPfV8LRgYPhJ0j6BgHag/Ru3whBBCZDGfNy7OoZAHHL8RySdLjrL0g2pYmL1S73WRydJdK3PmzGHo0KGmhADAzMyMwYMHM2fOnAwNTgiRdeiTjAvSXIt4RAFnS2bYTEUTGQLOvtB+rgwsFkII8RxLcy2/dQrAwdqc4GsP+HnrBbVDEqlId1Kg1+s5e/bsc/vPnj2LwWDIkKCEEFnPtxvO8u+lCGwtzVhZfDuWITsfDyxeJAOLhRBCpMrH1ZYJ7coCMH3nZXZduKtyRCIl6f5qr1evXvTu3ZtLly5RrVo1AA4cOMD48ePp1atXhgcohFDfkkPXCdwXAsDiGrdwOzDNeEAGFgshhEiDZmW86FqtAAsPXGfwsmP8/Wkt3B2t1Q5L/Ee6k4Iff/wRT09PfvnlF27fvg2Al5cXw4YNY8iQIRkeoBBCXYeu3ufrNacAGFdDQ/ngUcYDNT6RgcVCCCHS7Mt3ShIU8oBzYTF8uvQYC/tWxUyrUTss8Vi6uw9ptVqGDRvGzZs3iYyMJDIykps3bzJs2LBk4wyEENlf6INH9F8YjC5J4d1SdnS68sXjgcV1ocEYtcMTQgiRjVhbmDG1SwVsLc3YfyWCqTsuqR2S+I/XGv7t6OgoMw4JkUM9TNDTd14QEQ8TKeNlx3jDL2girz0eWDwHtPIlgBBCiPQplNeeb1uXBmDSPxc4cCVC5YjEE+lOCu7cuUO3bt3w9vbG3NwcMzOzZJsQIvszGBSG/nmcc2ExuNlbsqTgZsyu7gQLW1mxWAghxGtpWyE/7Srkx6DAp0uPEhGboHZIglcYU9CzZ0+uX7/OV199hZeXFxqN9AUTIqeZvO0if58Kw9JMy/Kat7Df9WRg8VTwLK1ucEIIIbK9/7UqxbEbD7h89yFD/jzOnB6V0cr4AlWlOynYu3cve/bsoXz58pkQjhBCbRtP3mbytosATKlvTsF/hxsP1BwEpduqF5gQQogcw87KnN86V6D11H/Zef4us/Ze4YO3C6kdVq6W7u5DPj4+KIqSGbEIIVR2+lYUQ5YfB+Djaq40PjEY9HFQqD7U/1rl6IQQQuQkJbwc+bpFSQAmbjrP0esPVI4od0t3UjBp0iS++OILQkJCMiEcIYRa7sUm8MH8YOJ0SdQu7MLgqPEQeR1c/KDdLBlYLIQQIsN1rlKAd8p6oTcoDFxylKg4ndoh5Vrp7j703nvv8ejRIwoVKoStrS0WFhbJjt+/fz/DghNCvBmJegP9FwZzMzKOgm52/O61Hu3hnWBhJwOLhRBCZBqNRsP3bctwMjSK6/cf8cWKE0zrUkHGrKog3UnBpEmTMiEMIYRaFEXhq9WnOBzyAAdrc5ZUD8Vq61TjwdZTwaOUugEKIYTI0RytLZjSKYD2M/bx96kwFh64RrfqfmqHleukOyno0aNHZsQhhFDJvH0hLAu6gVYDc5pY4fHPUOOBtz6DUm3UDU4IIUSuUM7HmeFNivPthrN8s/4sFXxdKOXtpHZYucprLV4WFxdHdHR0sk0IkX3svXiPbzacBWBMAy8qHxhoHFhcuAHU+0rl6IQQQuQmfd7yp35xdxKTDAxcfJTYBL3aIeUq6U4KHj58yMcff4y7uzv29va4uLgk24QQ2cPVew/5aPERkgwK7wZ40i10zOOBxf4ysFgIIcQbp9Fo+PHdcng5WXPl3kO+Wn1KZrx8g9KdFAwbNozt27czbdo0rKysmDVrFmPHjsXb25v58+dnRoxCiAwWHa/j/flBRMXpCCjgzPeOK9Bc3fV0YLGNJPhCCCHePBc7S37tFICZVsOqozdZefSW2iHlGulOCtatW8e0adNo37495ubm1KpViy+//JJx48axaNGizIhRCJGBkgwKny45yqXwWDwdrQmsGIL5wScDi6eBR0l1AxRCCJGrVfZzZXDDogCMXX+WsEcqB5RLpDspuH//Pv7+/gA4OjqapiB966232L17d8ZGJ4TIcBM3n2PH+btYmWtZ0Mwap62DjQfeGgylWqsamxBCCAHQv3Yh3irsRpzOQOAFM+J1SWqHlOOlOykoWLCgaeGykiVLsnz5csDYguDs7JyRsQkhMtiqo6HM3HUFgMktC1Bkx4egj4fCDaHelypHJ4QQQhhptRp+fq8ceewsuR2n4YctF9UOKcdLd1LQq1cvjh8/DsCIESNMYws+++wzPv/88wwPUAiRMY5ef8DwFScB+LiOL03OfgFRTwYW/yEDi4UQQmQp7g7WTGhrXCtn/oHr7DgfrnJEOVu61yn47LPPTP+vW7cu586dIygoiEKFClGuXLkMDU4IkTHCouLptyCYRL2BhiU9GKIsgqu7jQOLOy2RgcVCCCGypNpF8/K2p4HdYVo+//MEmwbVws3eSu2wcqTXWqcAoECBAtSrV08SAiGyqHhdEv0WBBEek0BRD3umlLqA5uA048E2M8C9hLoBCiGEEC/Q0tdAUXd77sUmMOyvEzJNaSZJd1IwYcIEli1bZnrcoUMH8uTJQ758+UzdioQQWYOiKAxfcYLjoVE421owr4kV1n8/bu2rNRRKtlQ3QCGEEOIlLLTw87tlsDTXsv1cOAsOXFM7pBwp3UnBzJkz8fHxAWDr1q1s3bqVv//+m6ZNm8qYAiGymBm7rrDm2C3MtRp+b+eL16a+xoHFRRpB3ZFqhyeEEEKkSTFPB0Y0LQ7AdxvOcuFOjMoR5TzpTgpu375tSgrWr19Phw4daNSoEcOGDePw4cPpOtfu3btp0aIF3t7eaDQaVq9enex4z5490Wg0ybZq1aolK5OQkMDAgQNxc3PDzs6Oli1bEhoamt6XJUSOs+3sHSZuPgfAmObFqHJ4CETdANdC0FYGFgshhMheetbwo3bRvCToDXyy5KhMU5rB0p0UuLi4cOPGDQA2bdpEgwYNAGM3haSk9FXOw4cPKVeuHL/99luqZZo0acLt27dN28aNG5MdHzRoEKtWrWLp0qXs3buX2NhYmjdvnu5YhMhJLt6J4dOlx1AU6FK1AF2jZ0HIHrC0f7xisbPaIQohhBDpotFo+OHdsuSxs+RcWAw/bD6vdkg5SrpnH2rbti2dO3emSJEiRERE0LRpUwCOHTtG4cKF03Wupk2bmp6fGisrKzw9PVM8FhUVxezZs1mwYIEpOVm4cCE+Pj78888/NG7cOMXnJSQkkJCQYHocHR0NgE6nQ6fTpes1ZIQn11Tj2sIoJ9XBg0eJ9Jl3mNgEPVX8XPja5xisNw4s1reYiuJSCLLo68xJ9ZBdSR1kDVIP6pM6yBqerQcXazPGtSlFv4VHmb33KjULuVCrsJuaIWZp6fn5TXdS8Msvv+Dn58eNGzeYOHEi9vb2gLFb0YABA9J7upfauXMn7u7uODs7U7t2bb777jvc3d0BCA4ORqfT0ahRI1N5b29vSpcuzb59+1JNCr7//nvGjh373P4tW7Zga2ub4a8hrbZu3aratYVRdq+DJANMP6vlerQWVyuFnnaHMd/wDQDnPVpy7ooGrmx8yVnUl93rISeQOsgapB7UJ3WQNTxbD7U8tOy5o2XQ4mCGl0vC3kKlwLK4R48epbmsRski8zppNBpWrVpF69atTfuWLVuGvb09vr6+XL16la+++gq9Xk9wcDBWVlYsXryYXr16JfvWH6BRo0b4+/szc+bMFK+VUkuBj48P9+7dw9HRMVNe34vodDq2bt1Kw4YNsbCQn2o15JQ6+N+Gcyw4cP3/7d13dBTl28bx76YXQigBkkAIvYNBkBapSlFBEJQmClZUQFEU9UUUEFFRBETBAlJUmkhTVJqi0lsihBIJEDpGIBBCQurz/hHZHyHUUGaTvT7n5Bx25pnde/fKbvZmZp7Bx8OV73uUp8qPHbAlHCSzQksyOn8Ltuuehfimyi855GXKwDEoB+spA8dwqRzOpmXwwIS1xPx7hhaVi/HZw2HYbDYLK3VMCQkJBAQEcOrUqSt+x73mPQXTpk277PpHH330Wu/ykrp06WL/d40aNahbty6hoaEsWrSIjh07XnI7Y8xlfzE8PT3x9Mx54Qt3d3dL3/hWP77k7QxmrN/P12v3AzDmoepUXf0sJByEIuVx6TQRF4+8c7GXvJxDfqEMHINysJ4ycAwX5uDu7s7H3W6nw6er+DX6X2ZvPkKPBqEWVuiYruV395qbghdeeCHb7bS0NJKSkvDw8MDHx+eGNgUXCgoKIjQ0lF27dgEQGBhIamoq8fHxFC78vyuyxsXF0ahRo5tWh4ijWRB5iDfmRwHwcqtKtDr4qU4sFhGRfK1acEEGtqnM8EU7GL5oOw3KFaFCcT+ry8qzrvlYgvj4+Gw/iYmJREdHc+eddzJjxoybUaPd8ePHOXDgAEFBQQDUqVMHd3f3bMeZHTlyhKioKDUF4jS+XruP/rMiycg0dLq9FH2KbIB1E7JWPvA5FK9ibYEiIiI3yePhZWlcMYCzaZk8PyOSlHTNPplbN+QA44oVK/Lee+/l2ItwJYmJiURGRhIZGQnA3r17iYyMZP/+/SQmJvLyyy+zZs0aYmNjWbFiBe3atSMgIIAHHngAAH9/f5544gkGDBjA8uXLiYiIoEePHtSsWdM+G5FIfjZ+RQyD50dhDDzaMJQPGmVg+6F/1somA6FqW0vrExERuZlcXGyMeug2Cvu4s/1IAh9qmtJcu2FnHbq6unL48OFr2mbjxo3Url2b2rVrA/DSSy9Ru3Zt3nzzTVxdXdm6dSvt27enUqVK9OzZk0qVKrFmzRr8/P63a2j06NF06NCBzp07Ex4ejo+PDz/88AOurrowk+Rfxhje+3knI3/J+vDr27wCQ+8qjsvsHpCRApXaQLPXLa5SRETk5ite0IuRD94GwJd/7mXlrmMWV5Q3XfM5BQsXLsx22xjDkSNH+OSTTwgPD7+m+2rWrBmXm/xo8eLFV7wPLy8vxo0bx7hx467psUXyqoxMw+AFUUxfl3VS8ev3VKF3w0D45kFIOARFK0DHL8DFsWcaEhERuVFaVivBw/VL8+26/bw0O5LF/ZtQ2NfD6rLylGtuCs6fMhSyphItVqwYLVq0YNSoUTeqLhG5iLSMTAbM/ouFfx3GZoMRD9SkW3VfmHo/HNoIHn5ZJxZ7+VtdqoiIyC31xn3VWLvnOLv/PcOr32/h80fqaJrSa3DNTUFmZubNqENEruBsWgbPfbuZX3fG4eZiY3SXMNqFpsNXreH4LvAuDN1nQ7HKVpcqIiJyy3l7uDK2a20eGL+KJdv/YeaGA3SrV9rqsvKM6zq+wBhz2cN/ROTGOH02jZ5frefXnXF4urnw5aN1aVfiBExsmdUQFCwFjy+GkHpWlyoiImKZGiX9eaV11n+ODfthO7v/TbS4orwjV03BtGnTqFmzJt7e3nh7e1OrVi2+/vrrG12biAAnzqTy8MR1rNt7ggKebkx7vB7Nvf6GyfdC4lEoXg2eXKo9BCIiIsCTd5YjvEJRktMyeGFmBKnpOsrlalxzU/DRRx/x7LPPcu+99zJ79mxmzZpFmzZteOaZZxg9evTNqFHEaR09dZYun69hy8FTFPH1YMZTDah/dhV83RFSTkHpRvDYT1Aw2OpSRUREHELWNKVhFPJxJ+pQAqOWaprSq3HN5xSMGzeOCRMmZLtycfv27alevTpDhgzhxRdfvKEFijirfcfP0GPSOg6cSCawoBffPFmPCvtmwaKXAQNV2kKnieDubXWpIiIiDiXQ34v3OtbimW828cUfe2hasRiNKgRYXZZDu+Y9BUeOHLno1YIbNWrEkSNHbkhRIs4u+uhpHvxsDQdOJBNa1IfvejegQtTHsGgAYKDOY9B5mhoCERGRS2hTI5Bu9UIwBl6a/RfxZ1KtLsmhXXNTUKFCBWbPnp1j+axZs6hYseINKUrEmUUeOEnnz9fw7+kUqgT68d3TdxCy6nX4Y2TWgGavQ9vR4KIL9ImIiFzO4LbVKBfgy9GEs/zfvK2aIOcyrvnwoaFDh9KlSxf++OMPwsPDsdlsrFy5kuXLl1+0WRCRq7d69zGemrqRM6kZ1C5diMkP16DQT09D9CKwucB9o6Du41aXKSIikif4eLgxtmttOk5Yxc9RR5m98QBd7tA0pRdzzXsKOnXqxLp16wgICGD+/PnMnTuXgIAA1q9fzwMPPHAzahRxCku3/0OvyRs4k5pBeIWifNO9EoXmdM5qCFw9ofPXaghERESuUc1S/gxolTVD35CF29mjaUov6pr3FADUqVOHb7755kbXIuK05kccYsB3f5GRaWhZrQTj7iuG17dt4d+dWVcn7jYTQnOeyyMiIiJX9nTjcvwe/S9r9hyn/6xI5jzTCA+367pcV76Tq6YgMzOTmJgY4uLiclzhuEmTJjekMBFn8fWaWN5cuA1joGPtkoxs6oHb1Hsg4RD4BUOP76FENavLFBERybNcXGx81OU22oz5ky0HTzFm2d8MbFPF6rIcyjU3BWvXrqV79+7s27cvx8kaNpuNjIyMG1acSH5mjGH8it18sDhr/uSeDUN5KywRlynt4exJCKgEPeZCoRBrCxUREckHgvy9ea9jTZ79djMTft9N44rFaFi+qNVlOYxr3m/yzDPPULduXaKiojhx4gTx8fH2nxMnTtyMGkXyHWMM7/2y094Q9GtRgSGV9+Hy9X8NQal68PhiNQQiIiI30D01g+hct9R/05RGciopzeqSHMY17ynYtWsXc+bMoUKFCjejHpF8LyPT8Mb8KGas3w/AoHur8pTvnzCrP5hMqNQGHpwMHj7WFioiIpIPvdWuOuv3niD2eBL/N28rn3Svjc1ms7osy13znoL69esTExNzM2oRyffSMjLpPyuSGev3Y7PBew/U4CnzPfzwfFZDULsHdPlWDYGIiMhN4uuZNU2pm4uNRVuPMGfTQatLcghXtadgy5Yt9n/369ePAQMGcPToUWrWrIm7u3u2sbVq1bqxFYrkE2fTMnju2838ujMOd1cbozvXpO3BMbBhYtaAxi9DizdA/1shIiJyU90WUogXW1big8XRDFm4jTvKFKFMgK/VZVnqqpqCsLAwbDZbthOLH3/8f/Oln1unE41FLu702TSemLqR9XtP4OXuwufdatA0ahBsXwDY4J6RUP9pq8sUERFxGs80Lc8ff//Lur0neGFWJHOeaYi7q/NOU3pVTcHevXtvdh0i+daJM6n0/Go9Ww+dws/TjSndK1Nn9TOwbyW4ekDHL6C6LvwnIiJyK7m62BjdJYw2Y/7grwMn+Xj5LvtFzpzRVTUFoaGhPP7444wdOxY/P7+bXZNIvnH01Fl6TFpHTFwiRXw9mN6lNFWWPwz/RIGHH3SbDmV1bQ8RERErBBfyZkTHmvSdHsGnv8XQuGIx6pUtYnVZlrjqfSRTp04lOTn5ZtYikq/EHjvDg5+tJiYukSB/L+Z1LkaVRQ9mNQQFSsBjP6khEBERsVjbWsE8WKcUmQZenBXJqWTnnKb0qpuCCy9UJiKXtvNoAg99voaD8cmUKerDgg6ehM5/AE7thyLl4YklEKST8kVERBzBkPurE1rUh0Mnk3ljfpRTfu+9prMpNIeryJVF7I+ny+dr+fd0ClUC/ZjfOonicx+C5BMQfHtWQ1C4jNVlioiIyH8KeLoxpksYri42fvjrMPMiDlld0i13TRcvq1Sp0hUbA13VWJzZ6phjPDltI0mpGdQuXYhv6+7BZ94LYDKg/F3QeRp4FrC6TBEREblA7dKF6X9XRUYt/Zs3F2yjbmgRShd1nusGXVNTMHToUPz9/W9WLSJ52pJtR+k7I4LU9EzCyxdhcsXVePw0LGtlra7Q/hNwdb/8nYiIiIhlnmtegT92/cuG2HhemBXBd70b4uYk05ReU1PQtWtXihcvfrNqEcmz5kUc5OXvtpCRaWhdtRifBszBbcXnWSsbPQ93DwUX5/hQERERyavOTVN6z5g/idh/ko9/jeGllpWsLuuWuOpvKTqfQOTipq2J5cVZf5GRaXgorBgTfCbgtuG/hqD1CGj1thoCERGRPKJUYR+GP1ADgE9+3cXGWOc4NF6zD4nkkjGGT3+L4c0F2wDoXb8YI1OG47JtLri4Q8eJ0LCPxVWKiIjItWofVpKOtUuSaeCFmZEknM3/05RedVOQmZmpQ4dE/mOM4b2fd/LB4mgAXmtchNf+GYBt7+/gUQAeng21HrK4ShEREcmtoe2rE1LEm0Mnk3lzfpTV5dx0OqZB5BplZBr+b95WPv9jDwAjm/vyTMwz2I5uAZ8A6PUjlG9hcZUiIiJyPfy83BnTpTauLjbmRx5mfj6fplRNgcg1SE3P5IWZEcxYfwAXG3xxtwudtzwJ8bFZ1x54YgkE17a6TBEREbkB6oQWpl+LCgAMnh/FgRNJFld086gpELlKyakZ9P56Iz9uOYK7q40ZLZJptf5JOPMvBNaCJ5ZC0fJWlykiIiI3UN/mFagTWpjTKem8OCuS9IxMq0u6KdQUiFyF02fT6Dl5Pb9F/4uXuwsLmhym/ppnIDURyjaFXouggM65ERERyW/cXF0Y0yUMP083Nu6L59Pfdltd0k2hpkDkCk6cSaX7l+tYv/cEfp5uLGkQRbU1AyAzDWp0goe/A6+CVpcpIiIiN0lIER/e7pA1TenHv+5i0754iyu68dQUiFzGkVPJdP58DVsPnSLAx41fb1tO6Q3Ds1Y2eC5r2lE3T2uLFBERkZuuQ+2StA8LJiPT0H9WBKfz2TSlagpELiH22BkenLCGmLhEShV047eKsyi25b+Lkt09NOvCZLoomYiIiNN4u0MNShby5sCJZN5auM3qcm4ofaMRuYidRxN48LM1HDqZTNWiLiwLGo9f9Pdgc4UOE+DO/qCrfIuIiDiVgl7ujO0ahosN5m4+xMK/Dltd0g2jpkDkApv3x9Pl87UcS0yhfvFMFvq9j9e+FeDuA91nQVh3q0sUERERi9QtU4S+LSoCMGjeVg7G549pStUUiJxn9e7j9Ji4jlPJabQpeZbprm/ifjQCvItAzx+gYkurSxQRERGLPd+iArVLF+L02XRemvUXGZnG6pKum6VNwR9//EG7du0IDg7GZrMxf/78bOuNMQwZMoTg4GC8vb1p1qwZ27ZlP34rJSWFfv36ERAQgK+vL/fffz8HDx68hc9C8outJ2w8+fVmklIz6F76FOPPvo5r/B7wL511UbJSda0uUURERByAm6sLY7vUxtfDlfWxJ5iwIsbqkq6bpU3BmTNnuO222/jkk08uun7kyJF89NFHfPLJJ2zYsIHAwEBatmzJ6dOn7WP69+/PvHnzmDlzJitXriQxMZG2bduSkZFxq56G5APfbTrEV9EupGUYni93hHdODcTlzD9QokZWQxBQ0eoSRURExIGULurDsPZZ05SOXraLiP15e5pSS5uCe+65h+HDh9OxY8cc64wxjBkzhkGDBtGxY0dq1KjB1KlTSUpKYvr06QCcOnWKSZMmMWrUKO6++25q167NN998w9atW1m2bNmtfjqSB50+m8ZLsyL5v/nbyMTG0PJ/8+I/r2NLOQ2hd2ZdlKxgkNVlioiIiAPqeHtJ2tYK+m+a0kgSU9KtLinX3Kwu4FL27t3L0aNHadWqlX2Zp6cnTZs2ZfXq1fTu3ZtNmzaRlpaWbUxwcDA1atRg9erVtG7d+qL3nZKSQkpKiv12QkICAGlpaaSl3fo5Z889phWP7cz+OniKF2dv4UB8Mi42GFHkF7oc+hobhswq7choPwHcvEC53DJ6L1hPGTgG5WA9ZeAY8kIOQ9tWYfO+ePYdT+LN+Vt5v2MNq0uyu5bXzWGbgqNHjwJQokSJbMtLlCjBvn377GM8PDwoXLhwjjHntr+Yd999l6FDh+ZYvmTJEnx8fK639FxbunSpZY/tTDINLD9s46cDLmQaG0U8MphQZDb1T/4AwN6Au9ji1QmW/Gpxpc5L7wXrKQPHoByspwwcg6Pn0KkUfHLKlbkRhymYeIDaAY5x4nFS0tXPjOSwTcE5tgvmgjfG5Fh2oSuNef3113nppZfstxMSEggJCaFVq1YULFjw+grOhbS0NJYuXUrLli1xd3e/5Y/vTI4mnOWVOVtZ+99xf+2r+TPSdQKeu34EIPXOgZRq8gqldA0CS+i9YD1l4BiUg/WUgWPISzmkL9vFhN/3MveAJ4/d35DgQt5Wl2Q/GuZqOGxTEBgYCGTtDQgK+t8x3XFxcfa9B4GBgaSmphIfH59tb0FcXByNGjW65H17enri6emZY7m7u7ulv3BWP35+t2TbUQZ+v4WTSWn4eLjyXqvitNv+ErbDERgXdyJK9aJm04HKwAHovWA9ZeAYlIP1lIFjyAs5vNSqCqv3xPPXgZMMnLuN6U81wNXF2v9kvJbXzGGvU1C2bFkCAwOz7S5KTU3l999/t3/hr1OnDu7u7tnGHDlyhKioqMs2BeJcklMzeGP+Vp7+ehMnk9KoUbIgS7oW4v71PbAdzroGQcbDczlQtLHVpYqIiEge5e7qwtguYfh4uLJu7wk+/2O31SVdE0v3FCQmJhIT8795Xffu3UtkZCRFihShdOnS9O/fnxEjRlCxYkUqVqzIiBEj8PHxoXv3rCvK+vv788QTTzBgwACKFi1KkSJFePnll6lZsyZ33323VU9LHMiOIwk8PyOCXXGJADzdpByvhO7Gfd5TkJYEAZWg+yyMXwhE/WRxtSIiIpKXlQnwZcj91Rk4ZwsfLfmb8PIB3BZSyOqyroqlTcHGjRtp3ry5/fa54/x79uzJlClTGDhwIMnJyTz33HPEx8dTv359lixZgp+fn32b0aNH4+bmRufOnUlOTuauu+5iypQpuLq63vLnI47DGMPU1bGM+HknqemZBBTw5KOHatHk2Az47i3AQLnm8NAU8C6kGYZERETkhnioTil+j/6XRVuP0H9WJD/2uxNfT4c9Yt/O0gqbNWuGMZc+O9tmszFkyBCGDBlyyTFeXl6MGzeOcePG3YQKJS86npjCK3O28OvOOABaVCnOyAeqELDiNYj4JmvQHU9Cm/fB1fHfpCIiIpJ32Gw2RjxQk83749l77AzDftjO+w/WsrqsK3LYcwpEcuPPXf/SZuyf/LozDg83F4a0q8akh8oRMLdrVkNgc4F7RsJ9o9QQiIiIyE3h7+POR53DsNlg1sYD/Lz1iNUlXZGaAskXUtMzGfHTDh6ZtJ5/T6dQsXgBFvQJp1fldGyT7oZ9K8HDD7rPhvq9rS5XRERE8rmG5YvyTNPyAAz9YTsp6RkWV3R5+q9SyfP2/JvICzMj2XroFAAP1y/NG/dVw/vgnzD1UTh7CgqVhm6zoEQ1i6sVERERZ/Hi3ZX493QKvZuUw9PNsc93VVMgeZYxhu82HWTIwm0kpWZQyMed9zvVonX1QNg4GX56GTLTIaQ+dPkWChSzumQRERFxIh5uLnz40G1Wl3FV1BRInnQqOY1B87by45asY/QalCvC6C5hBPl5wC//B2s/zRpYszPcPw7cvSysVkRERMSxqSmQPGdj7AlemBnJoZPJuLrYeKllJZ5pWh7XtESY2Qv+/iVrYPM3oMnLYLP2aoIiIiIijk5NgeQZ6RmZfPJbDB8v30WmgdJFfBjbNYzapQvDyf0wvSvEbQM3L+gwAWp0tLpkERERkTxBTYHkCQfjk3hxViQbYuMB6Fi7JEPbV8fPyx0OboQZ3eBMHPgWh24zoVQdiysWERERyTvUFIjDW7TlCK/N3cLps+kU8HRjeIcadKhdMmvl1jkw/znISIESNaHbDCgUYm3BIiIiInmMmgJxWEmp6QxZuI3ZGw8CEBZSiI+71qZ0UR8wBn5/H1a8mzW40j3QaSJ4FrCwYhEREZG8SU2BOKSoQ6d4fkYEe46dwWaDPs0q8MLdFXF3dYG0s7CgD0TNyRrcsC+0HAYujj3/r4iIiIijUlMgDiUz0zBp5V5GLt5JWoYhsKAXo7uE0bB80awBiXEwszsc3AAubnDfR1Cnp7VFi4iIiORxagrEYcSdPsuA2X/x565jALSqVoL3O9WisK9H1oB/tsH0LnDqAHgVgi5fQ9km1hUsIiIikk+oKRCH8NvOOF7+7i+On0nFy92FwW2r0b1eaWznrjHw92KY8zikJkKR8tB9NgRUsLZoERERkXxCTYFY6mxaBu//spPJq2IBqBLox7hutalYwi9rgDGw7jNY/H9gMqFMY+g8DXyKWFe0iIiISD6jpkAss+uf0zw/M5IdRxIAeCy8DK+2qYKX+38nDGekwc8DYeNXWbdvfxTuHQVuHhZVLCIiIpI/qSmQW84Yw/T1+3n7x+2cTcukqK8HHz50G82rFP/foOST8F1P2LMCsEGrt7NmGTp3OJGIiIiI3DBqCuSWij+Tymtzt7B42z8ANK4YwKjOt1Hcz+t/g47vhhld4djf4O6bdf2BKvdaVLGIiIhI/qemQG6ZNbuP8+KsSI4mnMXd1carbarweHhZXFzO+9//2FUw62FIjoeCJaH7LAisaV3RIiIiIk5ATYHcdGkZmYxZ9jfjV+zGGCgX4MvH3WpTo6R/9oER38IPL0BmGgTfDt1mgF+gNUWLiIiIOBE1BXJT7T+exPMzI4g8cBKALnVDeLNdNXw9z/vVy8yE5UNh1Zis29U6wAOfgbv3rS5XRERExCmpKZCbZn7EId6YH0ViSjoFvdx4t2Mt7qsVlH1Q6hmY+zTs/DHrdpOB0Ox1cHG59QWLiIiIOCk1BXLDnT6bxlsLtjE34hAAd5QpzJiutSlZ6IL/+U84nHWF4qNbwNUD2n8KtTpbULGIiIiIc1NTIDdU5IGTPD8jgv0nknCxwQt3VaJP8/K4uV7wP/+HI2BGNzh9BHwCoOt0KF3fmqJFREREnJyaArkhMjINn/2+m9FL/yY901CykDdju4ZRt8xFrjy8fWHWIUPpyVCsatYMQ4VDb33RIiIiIgKoKZAb4J+Es/SfGcmaPccBaFsriHceqIm/t3v2gcbAyo9g+bCs2xXuhgcng1fBW1yxiIiIiJxPTYFcl8gDJ3l62kbiTqfg4+HK0Pur82CdUtguvPJwekrWdKN/zci6Xf8ZaPUOuOpXUERERMRq+kYmubYg8hCvzNlCanomlUv4MaHH7ZQrViDnwDPHsy5Itn8N2Fzhnveh3lO3vmARERERuSg1BXLNMjMNHyyJZsKK3QDcXbU4Y7rWpoDnRX6d/o2G6Z0hPhY8C8JDU6DCXbe0XhERERG5PDUFck0SU9LpPzOSZTv+AeC5ZuV5uVVlXFxsOQfHLIfvHoOUU1C4DHSbBcWr3NqCRUREROSK1BTIVTtwIoknp24k+p/TeLi5MLJTLTrULnnxwRsmwk8DwWRA6YbQ5VvwLXprCxYRERGRq6KmQK7K2j3HefabTcQnpVHcz5MvHq1LWEihnAMz0mHx/8H6z7Nu39YN2o0FN89bWq+IiIiIXD01BXJF09ft580FUaRnGmqV8ueLR+oS6O+Vc+DZUzDncYhZlnX7rrfgzhfhwpmIRERERMShqCmQS0rPyGT4oh1MWR0LQLvbgvngwVp4ubvmHBwfC9O7wr87wM0bOn4O1drf0npFREREJHfUFMhFnUpKo8/0zayMOQbAy60q0ad5hZzXHwDY+yd81wuSjkGBQOg+E4Jr39qCRURERCTX1BRIDjFxiTw1bSN7j53Bx8OVjzqH0aZGYM6BqWeyrk687rOs24G1oNtM8L/EycciIiIi4pDUFEg2K6Lj6DcjgtNn0ylZyJuJPetSNahgzoH71sCC5+DEnqzbtz8Kbd4DD99bW7CIiIiIXDc1BQKAMYZJK/cy4qcdZBq4o0xhJvSoQ0CBC2YNSkuGX4fDmk8BA37B0H4cVLjbkrpFRERE5PqpKRBS0jN4Y14U3206CECXuiG83aEGHm4u2Qce2ADzn4Xju7Juhz0MrUeAd6FbW7CIiIiI3FBqCpzcscQUnvl6Exv3xeNigzfuq8Zj4WWyn1CcdhZWvAurPwaTmXUycbuxULmNdYWLiIiIyA3jcuUh1hkyZAg2my3bT2Dg/054NcYwZMgQgoOD8fb2plmzZmzbts3CivOW7YcTaP/JKjbui8fPy43Jj9Xj8TvLZm8IDm2CL5rCqjFZDUGtLvDcGjUEIiIiIvmIw+8pqF69OsuWLbPfdnX93xz5I0eO5KOPPmLKlClUqlSJ4cOH07JlS6Kjo/Hz87Oi3Dzjl6ijvDgrkuS0DMoG+DKxZ13KFyvwvwHpKfD7SFg5GkwG+BaDtmOgalvLahYRERGRm8PhmwI3N7dsewfOMcYwZswYBg0aRMeOHQGYOnUqJUqUYPr06fTu3ftWl5onGGMY92sMHy39G4DGFQP4pNvt+Pu4/2/Qkb9g3rMQ999elxqd4J4PwLeoBRWLiIiIyM3m8E3Brl27CA4OxtPTk/r16zNixAjKlSvH3r17OXr0KK1atbKP9fT0pGnTpqxevfqyTUFKSgopKSn22wkJCQCkpaWRlpZ2857MJZx7zJv92MmpGbw+bxuLoo4C0LNhaV5rXQk31/8eOyMNl1WjcVn1EbbMdIxPUTLafICpev+5Qm9qfVa6VRnI5SkH6ykDx6AcrKcMHINyuD7X8rrZjDHmJtZyXX7++WeSkpKoVKkS//zzD8OHD2fnzp1s27aN6OhowsPDOXToEMHBwfZtnn76afbt28fixYsveb9Dhgxh6NChOZZPnz4dHx+fm/JcrHYyBSZGu3LgjA1Xm+Ghspk0LPG/6Asm76f2vi8olLwfgMOF7uCvUj1Jdb/INQpERERExOElJSXRvXt3Tp06RcGCl/9O59BNwYXOnDlD+fLlGThwIA0aNCA8PJzDhw8TFBRkH/PUU09x4MABfvnll0vez8X2FISEhHDs2LErvmA3Q1paGkuXLqVly5a4u7tfeYNrFHngJM9Nj+TfxFQK+7jzSbfbqFemSNbKzHRcVn+My58fYMtMw3gXJqP1+5hqD8D5Jxznczc7A7k6ysF6ysAxKAfrKQPHoByuT0JCAgEBAVfVFDj84UPn8/X1pWbNmuzatYsOHToAcPTo0WxNQVxcHCVKlLjs/Xh6euLp6Zljubu7u6W/cDfj8edFHOTV77eSmp5JlUA/vny0LiFF/tsbErcD5j0DRyKzble+F1vbMbj5Xf71y8+s/h2QLMrBesrAMSgH6ykDx6AccudaXjOHnpL0QikpKezYsYOgoCDKli1LYGAgS5cuta9PTU3l999/p1GjRhZW6RgyMg3v/byTF2f9RWp6Ji2rlWDOs42yGoKM9KxZhT5vktUQePnDA19A1+ngxA2BiIiIiLNy6D0FL7/8Mu3ataN06dLExcUxfPhwEhIS6NmzJzabjf79+zNixAgqVqxIxYoVGTFiBD4+PnTv3t3q0i11+mwa/WdGsnxnHAB9mpdnQMvKuLjY4N+/s65KfGhj1uCKraDdx1Aw6DL3KCIiIiL5mUM3BQcPHqRbt24cO3aMYsWK0aBBA9auXUtoaCgAAwcOJDk5meeee474+Hjq16/PkiVLnPoaBfuPJ/HktA38/U8inm4ujHywFu3DSkJmBqweD8vfhowU8CwIbd6DsO5Ode6AiIiIiOTk0E3BzJkzL7veZrMxZMgQhgwZcmsKcnBrdh/nuW83EZ+URnE/T758tC63hRSC47th/nNwYG3WwPIt4P5x4F/K0npFRERExDE4dFMgV+/bdft4a8E20jMNt5Xy54tH61KigAes/QyWDYH0ZPAoAK3fgdt7au+AiIiIiNipKcjj0jIyefvH7Uxbsw+A+28LZuSDtfA6vR+m9oF9q7IGlm0C7T+FQqUtrFZEREREHJGagjzsZFIqfaZvZlXMcQBeaV2Z55qWxbbxK1j6FqSdAXdfaDUM6jwOLnlqsikRERERuUXUFORRMXGneXLqRmKPJ+Hj4cqYLmG0Ck6BrzvA3j+yBoXeCe0/gSJlLa1VRERERBybmoI86LfoOJ6fHsHplHRKFfZm4qN1qHJ4HkwYBKmJ4OYNdw+Bek9r74CIiIiIXJGagjzEGMPEP/fy7s87yDRQr0wRPr+/BIWX9YLdv2YNCmkAHcZD0fKW1ioiIiIieYeagjwiJT2DQfOimLPpIABd65ZieJktuE3tDCkJ4OYFLQZDg2fBxdXiakVEREQkL1FTkAf8ezqFZ77ZxKZ98bjYYMTdAXQ5Ohzbj0uyBpSsCx0mQLFK1hYqIiIiInmSmgIHF3XoFE9P28jhU2cp6OXKdw33U3nDs3D2FLh6QPNB0Kif9g6IiIiISK6pKXBgP289wkuz/yI5LYM6RVKZVnw6vmv+2zsQXDtr70DxqtYWKSIiIiJ5npoCB2SM4ePlMYxe9jdgGFgyimfOfIZLbDy4uEOz1yC8P7gqPhERERG5fvpW6WCSUzN4+bu/WLT1CEU5xdeBs6l2/LeslYG1svYOBNawtkgRERERyVfUFDiQI6fO8uz0SLYdTqCd23o+8JmK18l4cHGDJq9A4wHg6m51mSIiIiKSz6gpcBCxp+Htz9aSkXiMz72n0dqshlSgeHV4YAIE3WZ1iSIiIiKST6kpcADzIg7z8TZX7rKt4n3vyRQ2J8HmCo1fgiYDwc3D6hJFREREJB9TU2Cxj5fvYtLSzXzoPpUHXFeBAYpVyTp3oOTtVpcnIiIiIk5ATYHFWrhG0MVzICVsJzE2F2yNnodmr4O7l9WliYiIiIiTUFNgsRpuh8B2ktOeQXh3m4xbmYZWlyQiIiIiTkZNgdUa9iPD5saKuEDalKxrdTUiIiIi4oRcrC7A6bm6kVnvGTJddDKxiIiIiFhDTYGIiIiIiJNTUyAiIiIi4uTUFIiIiIiIODk1BSIiIiIiTk5NgYiIiIiIk1NTICIiIiLi5NQUiIiIiIg4OTUFIiIiIiJOTk2BiIiIiIiTU1MgIiIiIuLk1BSIiIiIiDg5NQUiIiIiIk5OTYGIiIiIiJNTUyAiIiIi4uTcrC7AERhjAEhISLDk8dPS0khKSiIhIQF3d3dLanB2ysAxKAfrKQPHoByspwwcg3K4Pue+2577rns5agqA06dPAxASEmJxJSIiIiIiN9bp06fx9/e/7BibuZrWIZ/LzMzk8OHD+Pn5YbPZbvnjJyQkEBISwoEDByhYsOAtf3xRBo5COVhPGTgG5WA9ZeAYlMP1McZw+vRpgoODcXG5/FkD2lMAuLi4UKpUKavLoGDBgvqFt5gycAzKwXrKwDEoB+spA8egHHLvSnsIztGJxiIiIiIiTk5NgYiIiIiIk1NT4AA8PT1566238PT0tLoUp6UMHINysJ4ycAzKwXrKwDEoh1tHJxqLiIiIiDg57SkQEREREXFyagpERERERJycmgIRERERESenpuAqHT9+nOLFixMbG2t1KVelWbNm2Gw2bDYbkZGRVpdzwygH6ykDx6AcrKcMHINysJ4yyB/UFFyld999l3bt2lGmTBn++usvunXrRkhICN7e3lStWpWxY8fm2MYYw4cffkilSpXw9PQkJCSEESNGXPZx5s6dS926dSlUqBC+vr6EhYXx9ddf5xg3fvx4ypYti5eXF3Xq1OHPP//McT/r16+/viftgM7P4fjx47Rp04bg4GD769u3b18SEhKybZObHM43c+ZMbDYbHTp0yLHOGXM4P4PzHT9+nFKlSmGz2Th58mS2dbnJYMqUKfYP7fN/zp49m22cM2YAOXO42Gv12WefZdsmt++FkydP0qdPH4KCgvDy8qJq1ar89NNP2cY4Yw4Xey9MmTKFWrVq4eXlRWBgIH379s22TW4yOP8LzPk/9913X7ZxzpgBZM/hUp8bNpuNuLg4+za5fS+MGTOGypUr4+3tTUhICC+++KI+k8j5XtiwYQN33XUXhQoVonDhwrRq1SrHl+/cZJCWlsawYcMoX748Xl5e3Hbbbfzyyy85xjljBjeEkStKSkoyhQoVMqtXrzbGGDNp0iTTr18/s2LFCrN7927z9ddfG29vbzNu3Lhs2/Xr189UrlzZLFiwwOzZs8dERESYpUuXXvaxfvvtNzN37lyzfft2ExMTY8aMGWNcXV3NL7/8Yh8zc+ZM4+7ubr788kuzfft288ILLxhfX1+zb9++bPe1d+9eA5iIiIgb80JY7MIcTpw4YcaPH282bNhgYmNjzbJly0zlypVNt27dsm2XmxzOiY2NNSVLljSNGzc27du3z7bOGXO4MIPztW/f3txzzz0GMPHx8dnW5SaDyZMnm4IFC5ojR45k+zmfM2ZgzMVzAMzkyZOzvVZJSUnZtstNDikpKaZu3brm3nvvNStXrjSxsbHmzz//NJGRkfYxzpjDxTIYNWqUCQ4ONt9++62JiYkxUVFRZuHChdm2y00Gx48fz5ZrVFSUcXV1NZMnT7aPccYMjMmZQ1JSUo7PjNatW5umTZtm2y43OXzzzTfG09PTfPvtt2bv3r1m8eLFJigoyPTv398+xhlzuDCDhIQEU7hwYdOrVy+zc+dOExUVZTp16mSKFy9uUlNT7dvlJoOBAwea4OBgs2jRIrN7924zfvx44+XlZTZv3mwf44wZ3ChqCq7C999/bwICAi475rnnnjPNmze3396+fbtxc3MzO3fuvO7Hr127tnnjjTfst+vVq2eeeeaZbGOqVKliXnvttWzL8tsv/NXkMHbsWFOqVCn77evJIT093YSHh5uJEyeanj175mgKnDGHS2Uwfvx407RpU7N8+fIcTUFuM5g8ebLx9/e/7BhnzMCYi+cAmHnz5l1ym9zmMGHCBFOuXLlsf8wv5Iw5XJjBiRMnjLe3t1m2bNklt7lRfxdGjx5t/Pz8TGJion2ZM2ZgzJX/LsTFxRl3d3czbdo0+7Lc5tCnTx/TokWLbMteeuklc+edd9pvO2MOF2awYcMGA5j9+/fbl23ZssUAJiYmxhiT+wyCgoLMJ598km1Z+/btzcMPP2y/7YwZ3Cg6fOgq/PHHH9StW/eyY06dOkWRIkXst3/44QfKlSvHjz/+SNmyZSlTpgxPPvkkJ06cuOrHNcawfPlyoqOjadKkCQCpqals2rSJVq1aZRvbqlUrVq9efQ3PKu+5Ug6HDx9m7ty5NG3a1L7senIYNmwYxYoV44knnsixzllzuFgG27dvZ9iwYUybNg0Xl5wfKdeTQWJiIqGhoZQqVYq2bdsSERFhX+esGcCl3wt9+/YlICCAO+64g88++4zMzEz7utzmsHDhQho2bEifPn0oUaIENWrUYMSIEWRkZADOm8OFGSxdupTMzEwOHTpE1apVKVWqFJ07d+bAgQP2MTfi7wLApEmT6Nq1K76+voDzZgBX/rswbdo0fHx8ePDBB+3LcpvDnXfeyaZNm+yHnezZs4effvrJfhiXs+ZwYQaVK1cmICCASZMmkZqaSnJyMpMmTaJ69eqEhoYCuc8gJSUFLy+vbMu8vb1ZuXIl4LwZ3ChqCq5CbGwswcHBl1y/Zs0aZs+eTe/eve3L9uzZw759+/juu++YNm0aU6ZMYdOmTdk+mC7l1KlTFChQAA8PD+677z7GjRtHy5YtATh27BgZGRmUKFEi2zYlSpTg6NGjuXyGecOlcujWrRs+Pj6ULFmSggULMnHiRPu63OawatUqJk2axJdffnnR9c6aw4UZpKSk0K1bNz744ANKly590W1ym0GVKlWYMmUKCxcuZMaMGXh5eREeHs6uXbsA580ALv5eePvtt/nuu+9YtmwZXbt2ZcCAAdmOz81tDnv27GHOnDlkZGTw008/8cYbbzBq1CjeeecdwHlzuDCDPXv2kJmZyYgRIxgzZgxz5szhxIkTtGzZktTUVPuY3P5dOGf9+vVERUXx5JNP2pc5awZw5b/PX331Fd27d8fb29u+LLc5dO3albfffps777wTd3d3ypcvT/PmzXnttdcA583hwgz8/PxYsWIF33zzDd7e3hQoUIDFixfz008/4ebmBuQ+g9atW/PRRx+xa9cuMjMzWbp0KQsWLODIkSOA82Zwo6gpuArJyck5OtNztm3bRvv27XnzzTftX9wBMjMzSUlJYdq0aTRu3JhmzZoxadIkfvvtN6Kjo9m/fz8FChSw/5z/x9vPz4/IyEg2bNjAO++8w0svvcSKFSuyPa7NZst22xiTY1l+c6kcRo8ezebNm5k/fz67d+/mpZdesq/LTQ6nT5+mR48efPnllwQEBFy2JmfL4cIMXn/9dapWrUqPHj0uuU1u3wsNGjSgR48e3HbbbTRu3JjZs2dTqVIlxo0bl+3+nS0DuPh74Y033qBhw4aEhYUxYMAAhg0bxgcffGBfn9scMjMzKV68OF988QV16tSha9euDBo0iAkTJmR7fGfL4cIMMjMzSUtL4+OPP6Z169Y0aNCAGTNmsGvXLn777Tf7mNz+XThn0qRJ1KhRg3r16uVY52wZwOX/Pq9Zs4bt27fn2Nub2xxWrFjBO++8w/jx49m8eTNz587lxx9/5O233852/86Ww4UZJCcn8/jjjxMeHs7atWtZtWoV1atX59577yU5ORnIfQZjx46lYsWKVKlSBQ8PD/r27ctjjz2Gq6trtpqcLYMbxc3qAvKCgIAA4uPjcyzfvn07LVq04KmnnuKNN97Iti4oKAg3NzcqVapkX1a1alUA9u/fT/PmzbOdiX/+oUcuLi5UqFABgLCwMHbs2MG7775Ls2bNCAgIwNXVNUfHGxcXl6Mzzm8ulUNgYCCBgYFUqVKFokWL0rhxYwYPHkxQUFCucti9ezexsbG0a9fOvvzcYRhubm5ER0cTEhLilDlcmMGvv/7K1q1bmTNnDpD1wXtu3KBBgxg6dOh1vRfO5+Liwh133GHfU6D3Qs73wvkaNGhAQkIC//zzDyVKlMh1DkFBQbi7u2f7o1u1alWOHj1Kamqq0+ZwYQZBQUEAVKtWzb6sWLFiBAQEsH//fvuY63kvJCUlMXPmTIYNG5ajFmfMAC7/Xpg4cSJhYWHUqVMn2/Lc5jB48GAeeeQR+16amjVrcubMGZ5++mkGDRrktDlcmMH06dOJjY1lzZo19kNKp0+fTuHChVmwYAFdu3bNdQbFihVj/vz5nD17luPHjxMcHMxrr71G2bJl7bU4YwY3ivYUXIXatWuzffv2bMu2bdtG8+bN6dmzp303+vnCw8NJT09n9+7d9mV///03AKGhobi5uVGhQgX7z6W+CEHWF62UlBQAPDw8qFOnDkuXLs02ZunSpTRq1CjXzzEvuFgOFzr3pfTc65WbHKpUqcLWrVuJjIy0/9x///32D6mQkBCnzeHCDL7//nv++usv++t07tCtP//8kz59+gA37r1gjCEyMtL+5ctZM4Crey9ERETg5eVFoUKFgNznEB4eTkxMTLbzE/7++2+CgoLw8PBw2hwuzCA8PByA6Oho+7ITJ05w7Ngx+3HU1/temD17NikpKTn2zDlrBnDp90JiYiKzZ8++6Dlhuc0hKSkpx3lTrq6umKxJW5w2hwszOPc6nf8/8+dun/scud73gpeXFyVLliQ9PZ3vv/+e9u3bA879XrghrDi7Oa/ZsmWLcXNzMydOnDDGGBMVFWWKFStmHn744WzTnsXFxdm3ycjIMLfffrtp0qSJ2bx5s9m4caOpX7++admy5WUfa8SIEWbJkiVm9+7dZseOHWbUqFHGzc3NfPnll/Yx56bbmjRpktm+fbvp37+/8fX1NbGxsdnuK7+dWX9hDosWLTJfffWV2bp1q9m7d69ZtGiRqV69ugkPD7dvk9scLnSx2YecMYcLM7jQb7/9lmP2odxmMGTIEPPLL7+Y3bt3m4iICPPYY48ZNzc3s27dOvsYZ8zAmJw5LFy40HzxxRdm69atJiYmxnz55ZemYMGC5vnnn7dvk9sc9u/fbwoUKGD69u1roqOjzY8//miKFy9uhg8fbh/jjDlc7L3Qvn17U716dbNq1SqzdetW07ZtW1OtWjX7zE3X+3l05513mi5dulx0nTNmYMylP5MmTpxovLy8LvpZldsc3nrrLePn52dmzJhh9uzZY5YsWWLKly9vOnfubB/jjDlcmMGOHTuMp6enefbZZ8327dtNVFSU6dGjh/H39zeHDx82xuQ+g7Vr15rvv//e7N692/zxxx+mRYsWpmzZstn+5jhjBjeKmoKr1KBBA/PZZ58ZY7I+GIAcP6Ghodm2OXTokOnYsaMpUKCAKVGihOnVq5c5fvz4ZR9n0KBBpkKFCsbLy8sULlzYNGzY0MycOTPHuE8//dSEhoYaDw8Pc/vtt5vff/89x5j8+At/fg6//vqradiwofH39zdeXl6mYsWK5tVXX80xR35ucrjQxZoCY5wzh/MzuNDFmgJjcpdB//79TenSpY2Hh4cpVqyYadWq1UWvj+CMGRiTPYeff/7ZhIWFmQIFChgfHx9To0YNM2bMGJOWlpZtm9y+F1avXm3q169vPD09Tbly5cw777xj0tPTs41xxhwufC+cOnXKPP7446ZQoUKmSJEi5oEHHsg2LaMxuc8gOjraAGbJkiWXHOOMGRhz8c+khg0bmu7du19ym9zkkJaWZoYMGWLKly9vvLy8TEhIiHnuuedyfN45Yw4XZrBkyRITHh5u/P39TeHChU2LFi3MmjVrsm2TmwxWrFhhqlatajw9PU3RokXNI488Yg4dOpRjnDNmcCOoKbhKixYtMlWrVjUZGRlWl3LV8uMvvHKwnjJwDMrBesrAMSgH6ymD/EEnGl+le++9l127dnHo0CFCQkKsLueK7rnnHv744w+ry7jhlIP1lIFjUA7WUwaOQTlYTxnkDzZj/jszU/KVQ4cO2af+Kl26NB4eHhZX5JyUg/WUgWNQDtZTBo5BOVhPGVycmgIRERERESenKUlFRERERJycmgIRERERESenpkBERERExMmpKRARERERcXJqCkREREREnJyaAhERybPKlCnDmDFjrC5DRCTPU1MgIpJPrF69GldXV9q0aXPLHnPKlCnYbDb7T4ECBahTpw5z5869ZTVcj2bNmtG/f3+ryxARsZyaAhGRfOKrr76iX79+rFy5kv3799+yxy1YsCBHjhzhyJEjRERE0Lp1azp37kx0dPQlt0lNTb1l9YmIyJWpKRARyQfOnDnD7NmzefbZZ2nbti1TpkzJMWbhwoVUrFgRb29vmjdvztSpU7HZbJw8edI+ZvXq1TRp0gRvb29CQkJ4/vnnOXPmzGUf22azERgYSGBgIBUrVmT48OG4uLiwZcsW+5gyZcowfPhwevXqhb+/P0899RQAr776KpUqVcLHx4dy5coxePBg0tLSctRdt25dvLy8CAgIoGPHjpesZfLkyfj7+7N06VIAtm/fzr333kuBAgUoUaIEjzzyCMeOHQOgV69e/P7774wdO9a+pyM2Npb4+HgefvhhihUrhre3NxUrVmTy5MmXfQ1ERPI6NQUiIvnArFmzqFy5MpUrV6ZHjx5MnjyZ8y9YHxsby4MPPkiHDh2IjIykd+/eDBo0KNt9bN26ldatW9OxY0e2bNnCrFmzWLlyJX379r3qOjIyMpg6dSoAt99+e7Z1H3zwATVq1GDTpk0MHjwYAD8/P6ZMmcL27dsZO3YsX375JaNHj7Zvs2jRIjp27Mh9991HREQEy5cvp27duhd97A8//JCXX36ZxYsX07JlS44cOULTpk0JCwtj48aN/PLLL/zzzz907twZgLFjx9KwYUOeeuop+56OkJAQBg8ezPbt2/n555/ZsWMHEyZMICAg4KpfAxGRPMmIiEie16hRIzNmzBhjjDFpaWkmICDALF261L7+1VdfNTVq1Mi2zaBBgwxg4uPjjTHGPPLII+bpp5/ONubPP/80Li4uJjk5+aKPO3nyZAMYX19f4+vra1xcXIynp6eZPHlytnGhoaGmQ4cOV3weI0eONHXq1LHfbtiwoXn44YcvOT40NNSMHj3avPbaayYoKMhs2bLFvm7w4MGmVatW2cYfOHDAACY6OtoYY0zTpk3NCy+8kG1Mu3btzGOPPXbFWkVE8hM3i3sSERG5TtHR0axfv95+cq+bmxtdunThq6++4u6777aPueOOO7JtV69evWy3N23aRExMDN9++619mTGGzMxM9u7dS9WqVS/6+H5+fmzevBmApKQkli1bRu/evSlatCjt2rWzj7vY//DPmTOHMWPGEBMTQ2JiIunp6RQsWNC+PjIy0n6o0aWMGjWKM2fOsHHjRsqVK5ft+fz2228UKFAgxza7d++mUqVKF72/Z599lk6dOrF582ZatWpFhw4daNSo0WVrEBHJ69QUiIjkcZMmTSI9PZ2SJUvalxljcHd3Jz4+nsKFC2OMwWazZdvOnHd4EUBmZia9e/fm+eefz/EYpUuXvuTju7i4UKFCBfvtWrVqsWTJEt5///1sTYGvr2+27dauXUvXrl0ZOnQorVu3xt/fn5kzZzJq1Cj7GG9v7ys8e2jcuDGLFi1i9uzZvPbaa9meT7t27Xj//fdzbBMUFHTJ+7vnnnvYt28fixYtYtmyZdx111306dOHDz/88Iq1iIjkVWoKRETysPT0dKZNm8aoUaNo1apVtnWdOnXi22+/pW/fvlSpUoWffvop2/qNGzdmu3377bezbdu2bF/wc8vV1ZXk5OTLjlm1ahWhoaHZzm3Yt29ftjG1atVi+fLlPPbYY5e8n3r16tGvXz9at26Nq6srr7zyCpD1fL7//nvKlCmDm9vF/9x5eHiQkZGRY3mxYsXo1asXvXr1onHjxrzyyitqCkQkX9OJxiIiediPP/5IfHw8TzzxBDVq1Mj28+CDDzJp0iQAevfuzc6dO3n11Vf5+++/mT17tn2GonN7EF599VXWrFlDnz59iIyMZNeuXSxcuJB+/fpdtgZjDEePHuXo0aPs3buXL774gsWLF9O+ffvLblehQgX279/PzJkz2b17Nx9//DHz5s3LNuatt95ixowZvPXWW+zYsYOtW7cycuTIHPfVsGFDfv75Z4YNG2Y/UblPnz6cOHGCbt26sX79evbs2cOSJUt4/PHH7Y1AmTJlWLduHbGxsRw7dozMzEzefPNNFixYQExMDNu2bePHH3+85KFTIiL5hZoCEZE8bNKkSdx99934+/vnWNepUyciIyPZvHkzZcuWZc6cOcydO5datWoxYcIE+//Qe3p6Aln/K//777+za9cuGjduTO3atRk8ePBlD7UBSEhIICgoiKCgIKpWrcqoUaMYNmxYjtmNLtS+fXtefPFF+vbtS1hYGKtXr7bPSnROs2bN+O6771i4cCFhYWG0aNGCdevWXfT+wsPDWbRoEYMHD+bjjz8mODiYVatWkZGRQevWralRowYvvPAC/v7+uLhk/fl7+eWXcXV1pVq1ahQrVoz9+/fj4eHB66+/Tq1atWjSpAmurq7MnDnzss9FRCSvs5kLDyoVERGn8M477/DZZ59x4MABq0sRERGL6ZwCEREnMX78eO644w6KFi3KqlWr+OCDD67pGgQiIpJ/qSkQEXESu3btYvjw4Zw4cYLSpUszYMAAXn/9davLEhERB6DDh0REREREnJxONBYRERERcXJqCkREREREnJyaAhERERERJ6emQERERETEyakpEBERERFxcmoKREREREScnJoCEREREREnp6ZARERERMTJ/T/5A7yxIqG5QwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "simulate_moments(res.params, agent=LifeCycleAgent).plot()\n", + "empirical_moments.plot(figsize=(9, 5))\n", + "\n", + "plt.legend([\"Simulated\", \"Empirical\"])\n", + "plt.xlabel(\"Age Brackets\")\n", + "plt.ylabel(f\"Thousands of {adjust_infl_to} USD\")\n", + "plt.title(\"Variable Medians Conditional on Survival\")\n", + "plt.grid()\n", + "\n", + "plt.savefig(figs_dir / \"trp_results.svg\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Assuming df is your DataFrame and 'A' and 'B' are columns in df\n", + "sensitivity = res.sensitivity(return_type=\"dataframe\").T\n", + "\n", + "fig, ax1 = plt.subplots(figsize=(9, 5))\n", + "\n", + "color = \"tab:blue\"\n", + "ax1.set_xlabel(\"Age Bracket\")\n", + "ax1.set_ylabel(\"CRRA Sensitivity\", color=color)\n", + "ax1.plot(sensitivity.index, sensitivity[\"CRRA\"], color=color, marker=\"o\")\n", + "ax1.tick_params(axis=\"y\", labelcolor=color)\n", + "\n", + "# Add a horizontal dashed line at y=0 on first axis\n", + "ax1.axhline(0, color=\"black\", linestyle=\"--\")\n", + "\n", + "ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis\n", + "\n", + "color = \"tab:red\"\n", + "ax2.set_ylabel(\n", + " \"DiscFac Sensivitity\",\n", + " color=color,\n", + ") # we already handled the x-label with ax1\n", + "ax2.plot(sensitivity.index, sensitivity[\"DiscFac\"], color=color, marker=\"o\")\n", + "ax2.tick_params(axis=\"y\", labelcolor=color)\n", + "\n", + "# Make sure both y-axes have the same limits\n", + "ax1.set_ylim(ax1.get_ylim())\n", + "ax2.set_ylim(ax2.get_ylim())\n", + "\n", + "# Reduce the number of x-ticks\n", + "plt.xticks(sensitivity.index[::2])\n", + "\n", + "fig.tight_layout() # otherwise the right y-label is slightly clipped\n", + "plt.grid()\n", + "plt.savefig(figs_dir / \"trp_sensitivity.svg\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "hark-dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/msm_notebooks/MSM Term Bequest model.ipynb b/src/msm_notebooks/MSM Term Bequest model.ipynb new file mode 100644 index 0000000..a8aab4a --- /dev/null +++ b/src/msm_notebooks/MSM Term Bequest model.ipynb @@ -0,0 +1,811 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Method of Simulated Moments (MSM) for Structural Estimation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Steps of MSM estimation\n", + "1. Load empirical data\n", + "2. Define a function to calculate empirical moments from the data\n", + "3. Calculate the covariance matrix of the empirical moments (for the weighting matrix)\n", + "4. Define a `HARK` agent type with the model parameters to be estimated\n", + "5. Define a function to simulate the model and calculate the simulated moments\n", + "6. Estimate the model parameters by minimizing the distance between the empirical and simulated moments" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import annotations\n", + "\n", + "from copy import copy\n", + "from pathlib import Path\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from estimagic.utilities import read_pickle\n", + "from HARK.Calibration.Income.IncomeTools import (\n", + " Cagetti_income,\n", + " parse_income_spec,\n", + " parse_time_params,\n", + ")\n", + "from HARK.Calibration.life_tables.us_ssa.SSATools import parse_ssa_life_table\n", + "from HARK.Calibration.SCF.WealthIncomeDist.SCFDistTools import (\n", + " income_wealth_dists_from_scf,\n", + ")\n", + "from HARK.ConsumptionSaving.ConsBequestModel import BequestWarmGlowConsumerType\n", + "from HARK.ConsumptionSaving.ConsIndShockModel import init_lifecycle\n", + "from HARK.utilities import plot_funcs\n", + "\n", + "figs_dir = Path(\"../../content/slides/figures/\")\n", + "figs_dir.mkdir(parents=True, exist_ok=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Load empirical data\n", + "\n", + "To avoid the expensive calculation and recalculation of the empirical moments and the covariance matrix, we calculate these in a separate notebook and save them to be loaded here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Calculate Moments\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "empirical_moments = pd.read_pickle(\"networth_mom.pkl\")\n", + "empirical_moments.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Calculate the covariance matrix of empirical moments" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "moments_cov = pd.read_pickle(\"networth_cov.pkl\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Define an agent type to simulate data" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "birth_age = 25\n", + "death_age = 100\n", + "adjust_infl_to = 1992\n", + "income_calib = Cagetti_income\n", + "education = \"HS\"\n", + "\n", + "# Income specification\n", + "income_params = parse_income_spec(\n", + " age_min=birth_age,\n", + " age_max=death_age,\n", + " adjust_infl_to=adjust_infl_to,\n", + " **income_calib[education],\n", + " SabelhausSong=True,\n", + ")\n", + "\n", + "# Initial distribution of wealth and permanent income\n", + "dist_params = income_wealth_dists_from_scf(\n", + " base_year=adjust_infl_to,\n", + " age=birth_age,\n", + " education=education,\n", + " wave=1995,\n", + ")\n", + "\n", + "# We need survival probabilities only up to death_age-1, because survival\n", + "# probability at death_age is 0.\n", + "liv_prb = parse_ssa_life_table(\n", + " female=True,\n", + " cross_sec=True,\n", + " year=2004,\n", + " min_age=birth_age,\n", + " max_age=death_age - 1,\n", + ")\n", + "\n", + "# Parameters related to the number of periods implied by the calibration\n", + "time_params = parse_time_params(age_birth=birth_age, age_death=death_age)\n", + "\n", + "# Update all the new parameters\n", + "params = copy(init_lifecycle)\n", + "params.update(time_params)\n", + "params.update(dist_params)\n", + "params.update(income_params)\n", + "params[\"LivPrb\"] = liv_prb\n", + "params[\"AgentCount\"] = 1_000\n", + "params[\"T_sim\"] = 75\n", + "params[\"track_vars\"] = [\"aNrm\", \"bNrm\", \"cNrm\", \"pLvl\", \"t_age\", \"mNrm\"]\n", + "params[\"PermGroFacAgg\"] = 1.0\n", + "\n", + "\n", + "### Define some initial constraints\n", + "params[\"BeqCRRA\"] = 0.0\n", + "params[\"BeqCRRATerm\"] = 0.0\n", + "params[\"BeqFac\"] = 0.0\n", + "params[\"BeqFacTerm\"] = 0.0\n", + "params[\"BeqShift\"] = 0.0\n", + "params[\"BeqShiftTerm\"] = 0.0" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "LifeCycleAgent = BequestWarmGlowConsumerType(**params)\n", + "LifeCycleAgent.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Consumption functions\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "LifeCycleAgent.unpack(\"cFunc\")\n", + "# Plot the consumption functions\n", + "print(\"Consumption functions\")\n", + "plot_funcs(LifeCycleAgent.cFunc, 0, 5)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Turn off death for simulation\n", + "LifeCycleAgent.LivPrb = [1.0] * LifeCycleAgent.T_cycle\n", + "\n", + "# Run the simulations\n", + "LifeCycleAgent.initialize_sim()\n", + "history = LifeCycleAgent.simulate()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "raw_data = {\n", + " \"Age\": history[\"t_age\"].flatten() + birth_age,\n", + " \"pIncome\": history[\"pLvl\"].flatten(),\n", + " \"nrmM\": history[\"mNrm\"].flatten(),\n", + " \"nrmC\": history[\"cNrm\"].flatten(),\n", + "}\n", + "\n", + "sim_data = pd.DataFrame(raw_data)\n", + "sim_data[\"Cons\"] = sim_data.nrmC * sim_data.pIncome\n", + "sim_data[\"M\"] = sim_data.nrmM * sim_data.pIncome\n", + "\n", + "# Find the mean of each variable at every age\n", + "AgeMeans = sim_data.groupby([\"Age\"]).median().reset_index()\n", + "\n", + "plt.figure()\n", + "plt.plot(AgeMeans.Age, AgeMeans.pIncome, label=\"Permanent Income\")\n", + "plt.plot(AgeMeans.Age, AgeMeans.M, label=\"Market resources\")\n", + "plt.plot(AgeMeans.Age, AgeMeans.Cons, label=\"Consumption\")\n", + "plt.legend()\n", + "plt.xlabel(\"Age\")\n", + "plt.ylabel(f\"Thousands of {adjust_infl_to} USD\")\n", + "plt.title(\"Variable Medians Conditional on Survival\")\n", + "plt.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "age_groups = [\n", + " list(range(start, start + 5)) for start in range(birth_age + 1, 95 + 1, 5)\n", + "]\n", + "\n", + "# generate labels as (25,30], (30,35], ...\n", + "age_labels = [f\"({group[0]-1},{group[-1]}]\" for group in age_groups]\n", + "\n", + "# Generate mappings between the real ages in the groups and the indices of simulated data\n", + "age_mapping = dict(zip(age_labels, map(np.array, age_groups)))" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'(25,30]': array([26, 27, 28, 29, 30]),\n", + " '(30,35]': array([31, 32, 33, 34, 35]),\n", + " '(35,40]': array([36, 37, 38, 39, 40]),\n", + " '(40,45]': array([41, 42, 43, 44, 45]),\n", + " '(45,50]': array([46, 47, 48, 49, 50]),\n", + " '(50,55]': array([51, 52, 53, 54, 55]),\n", + " '(55,60]': array([56, 57, 58, 59, 60]),\n", + " '(60,65]': array([61, 62, 63, 64, 65]),\n", + " '(65,70]': array([66, 67, 68, 69, 70]),\n", + " '(70,75]': array([71, 72, 73, 74, 75]),\n", + " '(75,80]': array([76, 77, 78, 79, 80]),\n", + " '(80,85]': array([81, 82, 83, 84, 85]),\n", + " '(85,90]': array([86, 87, 88, 89, 90]),\n", + " '(90,95]': array([91, 92, 93, 94, 95])}" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "age_mapping" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Define a function to calculate simulated moments" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "def simulate_moments(params, agent=None):\n", + " agent.assign_parameters(**params) # new guess\n", + " agent.LivPrb = liv_prb # perceived mortality\n", + "\n", + " agent.update()\n", + " agent.solve()\n", + "\n", + " agent.LivPrb = [1.0] * agent.T_cycle # ignore mortality\n", + " agent.initialize_sim()\n", + " history = agent.simulate()\n", + "\n", + " raw_data = {\n", + " \"age\": history[\"t_age\"].flatten() + birth_age,\n", + " \"b_nrm\": history[\"bNrm\"].flatten(),\n", + " \"p_lvl\": history[\"pLvl\"].flatten(),\n", + " }\n", + "\n", + " sim_data = pd.DataFrame(raw_data)\n", + " sim_data[\"Wealth\"] = sim_data.b_nrm * sim_data.p_lvl\n", + "\n", + " sim_data[\"Age_grp\"] = pd.cut(\n", + " sim_data.age,\n", + " bins=range(birth_age + 1, 97, 5),\n", + " labels=age_labels,\n", + " right=False,\n", + " )\n", + "\n", + " sim_data = sim_data.dropna()\n", + "\n", + " return sim_data.groupby(\"Age_grp\", observed=False)[\"Wealth\"].median()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "simulate_moments({}, agent=LifeCycleAgent).plot()\n", + "empirical_moments.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Estimate the model parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "init_params = {\n", + " \"CRRA\": 1.0,\n", + " \"DiscFac\": 0.97,\n", + " # Terminal bequest parameters\n", + " \"BeqCRRATerm\": 1.56,\n", + " \"BeqFacTerm\": 85.94,\n", + " \"BeqShiftTerm\": 28.91,\n", + "}\n", + "lower_bounds = {\n", + " \"CRRA\": 1.0,\n", + " \"DiscFac\": 0.9,\n", + " \"BeqCRRATerm\": 1.0,\n", + " \"BeqFacTerm\": 50.0,\n", + " \"BeqShiftTerm\": 0.0,\n", + "}\n", + "upper_bounds = {\n", + " \"CRRA\": 5.0,\n", + " \"DiscFac\": 1.0,\n", + " \"BeqCRRATerm\": 5.0,\n", + " \"BeqFacTerm\": 100.0,\n", + " \"BeqShiftTerm\": 50.0,\n", + "}\n", + "\n", + "\n", + "# res = estimate_msm(\n", + "# LifeCycleAgent,\n", + "# init_params,\n", + "# empirical_moments,\n", + "# moments_cov,\n", + "# simulate_moments,\n", + "# optimize_options={\n", + "# \"algorithm\": \"scipy_lbfgsb\",\n", + "# \"error_handling\": \"continue\",\n", + "# \"numdiff_options\": {\"n_cores\": 24},\n", + "# \"multistart\": True,\n", + "# },\n", + "# estimagic_options={\n", + "# \"lower_bounds\": lower_bounds,\n", + "# \"upper_bounds\": upper_bounds,\n", + "# \"numdiff_options\": {\"n_cores\": 24},\n", + "# },\n", + "# )\n", + "\n", + "# res.to_pickle(\"termbeq_results.pkl\")\n", + "\n", + "res = read_pickle(\"termbeq_results.pkl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " value standard_error ci_lower ci_upper p_value \\\n", + "CRRA 0 3.039525 0.193870 2.659547 3.419503 2.133078e-55 \n", + "DiscFac 0 0.972220 0.002772 0.966788 0.977652 0.000000e+00 \n", + "BeqCRRATerm 0 1.125000 0.000000 1.125000 1.125000 0.000000e+00 \n", + "BeqFacTerm 0 67.187500 0.000000 67.187500 67.187500 0.000000e+00 \n", + "BeqShiftTerm 0 17.187500 0.000000 17.187500 17.187500 0.000000e+00 \n", + "\n", + " free stars \n", + "CRRA 0 True *** \n", + "DiscFac 0 True *** \n", + "BeqCRRATerm 0 True *** \n", + "BeqFacTerm 0 True *** \n", + "BeqShiftTerm 0 True *** \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/alujan/micromamba/envs/estimatingmicrodsops/lib/python3.12/site-packages/estimagic/utilities.py:200: UserWarning: Standard matrix inversion failed due to LinAlgError described below. A pseudo inverse was calculated instead. Taking the inverse of the information matrix failed. Only ever use this covariance matrix or standard errors based on it for diagnostic purposes, not for drawing conclusions.\n", + " warnings.warn(header + msg)\n" + ] + } + ], + "source": [ + "print(pd.concat(res.summary()))" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'CRRA': 3.0395247792711992,\n", + " 'DiscFac': 0.9722198123437832,\n", + " 'BeqCRRATerm': 1.125,\n", + " 'BeqFacTerm': 67.1875,\n", + " 'BeqShiftTerm': 17.1875}" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res.params" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/alujan/micromamba/envs/estimatingmicrodsops/lib/python3.12/site-packages/estimagic/utilities.py:200: UserWarning: Standard matrix inversion failed due to LinAlgError described below. A pseudo inverse was calculated instead. Taking the inverse of the information matrix failed. Only ever use this covariance matrix or standard errors based on it for diagnostic purposes, not for drawing conclusions.\n", + " warnings.warn(header + msg)\n" + ] + }, + { + "data": { + "text/html": [ + "
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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "LifeCycleAgent.assign_parameters(**res.params)\n", + "LifeCycleAgent.LivPrb = liv_prb\n", + "LifeCycleAgent.update()\n", + "LifeCycleAgent.solve()\n", + "LifeCycleAgent.LivPrb = [1.0] * LifeCycleAgent.T_cycle\n", + "LifeCycleAgent.initialize_sim()\n", + "history = LifeCycleAgent.simulate()\n", + "\n", + "raw_data = {\n", + " \"Age\": history[\"t_age\"].flatten() + birth_age,\n", + " \"pIncome\": history[\"pLvl\"].flatten(),\n", + " \"nrmM\": history[\"mNrm\"].flatten(),\n", + " \"nrmC\": history[\"cNrm\"].flatten(),\n", + "}\n", + "\n", + "sim_data = pd.DataFrame(raw_data)\n", + "sim_data[\"Cons\"] = sim_data.nrmC * sim_data.pIncome\n", + "sim_data[\"M\"] = sim_data.nrmM * sim_data.pIncome\n", + "\n", + "# Find the mean of each variable at every age\n", + "AgeMeans = sim_data.groupby([\"Age\"]).median().reset_index()\n", + "\n", + "plt.figure()\n", + "plt.plot(AgeMeans.Age, AgeMeans.pIncome, label=\"Permanent Income\")\n", + "plt.plot(AgeMeans.Age, AgeMeans.M, label=\"Market resources\")\n", + "plt.plot(AgeMeans.Age, AgeMeans.Cons, label=\"Consumption\")\n", + "plt.legend()\n", + "plt.xlabel(\"Age\")\n", + "plt.ylabel(f\"Thousands of {adjust_infl_to} USD\")\n", + "plt.title(\"Variable Medians Conditional on Survival\")\n", + "plt.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "simulate_moments(res.params, agent=LifeCycleAgent).plot()\n", + "empirical_moments.plot(figsize=(9, 5))\n", + "\n", + "plt.legend([\"Simulated\", \"Empirical\"])\n", + "plt.xlabel(\"Age Brackets\")\n", + "plt.ylabel(f\"Thousands of {adjust_infl_to} USD\")\n", + "plt.title(\"Variable Medians Conditional on Survival\")\n", + "plt.grid()\n", + "\n", + "plt.savefig(figs_dir / \"termbeq_results.svg\")" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/alujan/micromamba/envs/estimatingmicrodsops/lib/python3.12/site-packages/estimagic/utilities.py:200: UserWarning: Standard matrix inversion failed due to LinAlgError described below. A pseudo inverse was calculated instead. Taking the inverse of the information matrix failed. Only ever use this covariance matrix or standard errors based on it for diagnostic purposes, not for drawing conclusions.\n", + " warnings.warn(header + msg)\n", + "/home/alujan/micromamba/envs/estimatingmicrodsops/lib/python3.12/site-packages/estimagic/utilities.py:200: UserWarning: Standard matrix inversion failed due to LinAlgError described below. A pseudo inverse was calculated instead. Taking inverse failed during the calculation of sensitvity measures. Interpret them with caution.\n", + " warnings.warn(header + msg)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Assuming df is your DataFrame and 'A' and 'B' are columns in df\n", + "sensitivity = res.sensitivity(return_type=\"dataframe\").T\n", + "\n", + "fig, ax1 = plt.subplots(figsize=(9, 5))\n", + "\n", + "color = \"tab:blue\"\n", + "ax1.set_xlabel(\"Age Bracket\")\n", + "ax1.set_ylabel(\"Sensitivity\", color=color)\n", + "ax1.plot(sensitivity.index, sensitivity[\"CRRA\"], color=color, marker=\"o\")\n", + "ax1.tick_params(axis=\"y\", labelcolor=color)\n", + "\n", + "# Add a horizontal dashed line at y=0 on first axis\n", + "ax1.axhline(0, color=\"black\", linestyle=\"--\")\n", + "\n", + "ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis\n", + "\n", + "color = \"tab:red\"\n", + "ax2.set_ylabel(\"Sensivitity\", color=color) # we already handled the x-label with ax1\n", + "ax2.plot(sensitivity.index, sensitivity[\"DiscFac\"], color=color, marker=\"o\")\n", + "ax2.tick_params(axis=\"y\", labelcolor=color)\n", + "\n", + "# Make sure both y-axes have the same limits\n", + "ax1.set_ylim(ax1.get_ylim())\n", + "ax2.set_ylim(ax2.get_ylim())\n", + "\n", + "# Reduce the number of x-ticks\n", + "plt.xticks(sensitivity.index[::2])\n", + "\n", + "fig.tight_layout() # otherwise the right y-label is slightly clipped\n", + "plt.grid()\n", + "plt.savefig(figs_dir / \"termbeq_sensitivity.svg\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "hark-dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/msm_notebooks/MSM Warm Glow Bequest model.ipynb b/src/msm_notebooks/MSM Warm Glow Bequest model.ipynb new file mode 100644 index 0000000..919822f --- /dev/null +++ b/src/msm_notebooks/MSM Warm Glow Bequest model.ipynb @@ -0,0 +1,775 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Method of Simulated Moments (MSM) for Structural Estimation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Steps of MSM estimation\n", + "1. Load empirical data\n", + "2. Define a function to calculate empirical moments from the data\n", + "3. Calculate the covariance matrix of the empirical moments (for the weighting matrix)\n", + "4. Define a `HARK` agent type with the model parameters to be estimated\n", + "5. Define a function to simulate the model and calculate the simulated moments\n", + "6. Estimate the model parameters by minimizing the distance between the empirical and simulated moments" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import annotations\n", + "\n", + "from copy import copy\n", + "from pathlib import Path\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from estimagic.utilities import read_pickle\n", + "from HARK.Calibration.Income.IncomeTools import (\n", + " Cagetti_income,\n", + " parse_income_spec,\n", + " parse_time_params,\n", + ")\n", + "from HARK.Calibration.life_tables.us_ssa.SSATools import parse_ssa_life_table\n", + "from HARK.Calibration.SCF.WealthIncomeDist.SCFDistTools import (\n", + " income_wealth_dists_from_scf,\n", + ")\n", + "from HARK.ConsumptionSaving.ConsBequestModel import BequestWarmGlowConsumerType\n", + "from HARK.ConsumptionSaving.ConsIndShockModel import init_lifecycle\n", + "from HARK.utilities import plot_funcs\n", + "\n", + "figs_dir = Path(\"../../content/slides/figures/\")\n", + "figs_dir.mkdir(parents=True, exist_ok=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Load empirical data\n", + "\n", + "To avoid the expensive calculation and recalculation of the empirical moments and the covariance matrix, we calculate these in a separate notebook and save them to be loaded here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Calculate Moments\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "empirical_moments = pd.read_pickle(\"networth_mom.pkl\")\n", + "empirical_moments.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Calculate the covariance matrix of empirical moments" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "moments_cov = pd.read_pickle(\"networth_cov.pkl\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Define an agent type to simulate data" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "birth_age = 25\n", + "death_age = 100\n", + "adjust_infl_to = 1992\n", + "income_calib = Cagetti_income\n", + "education = \"HS\"\n", + "\n", + "# Income specification\n", + "income_params = parse_income_spec(\n", + " age_min=birth_age,\n", + " age_max=death_age,\n", + " adjust_infl_to=adjust_infl_to,\n", + " **income_calib[education],\n", + " SabelhausSong=True,\n", + ")\n", + "\n", + "# Initial distribution of wealth and permanent income\n", + "dist_params = income_wealth_dists_from_scf(\n", + " base_year=adjust_infl_to,\n", + " age=birth_age,\n", + " education=education,\n", + " wave=1995,\n", + ")\n", + "\n", + "# We need survival probabilities only up to death_age-1, because survival\n", + "# probability at death_age is 0.\n", + "liv_prb = parse_ssa_life_table(\n", + " female=True,\n", + " cross_sec=True,\n", + " year=2004,\n", + " min_age=birth_age,\n", + " max_age=death_age - 1,\n", + ")\n", + "\n", + "# Parameters related to the number of periods implied by the calibration\n", + "time_params = parse_time_params(age_birth=birth_age, age_death=death_age)\n", + "\n", + "# Update all the new parameters\n", + "params = copy(init_lifecycle)\n", + "params.update(time_params)\n", + "params.update(dist_params)\n", + "params.update(income_params)\n", + "params[\"LivPrb\"] = liv_prb\n", + "params[\"AgentCount\"] = 1_000\n", + "params[\"T_sim\"] = 75\n", + "params[\"track_vars\"] = [\"aNrm\", \"bNrm\", \"cNrm\", \"pLvl\", \"t_age\", \"mNrm\"]\n", + "params[\"PermGroFacAgg\"] = 1.0\n", + "\n", + "\n", + "### Define some initial constraints\n", + "params[\"BeqCRRA\"] = 0.0\n", + "params[\"BeqCRRATerm\"] = 0.0\n", + "params[\"BeqFac\"] = 0.0\n", + "params[\"BeqFacTerm\"] = 0.0\n", + "params[\"BeqShift\"] = 0.0\n", + "params[\"BeqShiftTerm\"] = 0.0" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "LifeCycleAgent = BequestWarmGlowConsumerType(**params)\n", + "LifeCycleAgent.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Consumption functions\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "LifeCycleAgent.unpack(\"cFunc\")\n", + "# Plot the consumption functions\n", + "print(\"Consumption functions\")\n", + "plot_funcs(LifeCycleAgent.cFunc, 0, 5)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Turn off death for simulation\n", + "LifeCycleAgent.LivPrb = [1.0] * LifeCycleAgent.T_cycle\n", + "\n", + "# Run the simulations\n", + "LifeCycleAgent.initialize_sim()\n", + "history = LifeCycleAgent.simulate()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "raw_data = {\n", + " \"Age\": history[\"t_age\"].flatten() + birth_age,\n", + " \"pIncome\": history[\"pLvl\"].flatten(),\n", + " \"nrmM\": history[\"mNrm\"].flatten(),\n", + " \"nrmC\": history[\"cNrm\"].flatten(),\n", + "}\n", + "\n", + "sim_data = pd.DataFrame(raw_data)\n", + "sim_data[\"Cons\"] = sim_data.nrmC * sim_data.pIncome\n", + "sim_data[\"M\"] = sim_data.nrmM * sim_data.pIncome\n", + "\n", + "# Find the mean of each variable at every age\n", + "AgeMeans = sim_data.groupby([\"Age\"]).median().reset_index()\n", + "\n", + "plt.figure()\n", + "plt.plot(AgeMeans.Age, AgeMeans.pIncome, label=\"Permanent Income\")\n", + "plt.plot(AgeMeans.Age, AgeMeans.M, label=\"Market resources\")\n", + "plt.plot(AgeMeans.Age, AgeMeans.Cons, label=\"Consumption\")\n", + "plt.legend()\n", + "plt.xlabel(\"Age\")\n", + "plt.ylabel(f\"Thousands of {adjust_infl_to} USD\")\n", + "plt.title(\"Variable Medians Conditional on Survival\")\n", + "plt.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "age_groups = [\n", + " list(range(start, start + 5)) for start in range(birth_age + 1, 95 + 1, 5)\n", + "]\n", + "\n", + "# generate labels as (25,30], (30,35], ...\n", + "age_labels = [f\"({group[0]-1},{group[-1]}]\" for group in age_groups]\n", + "\n", + "# Generate mappings between the real ages in the groups and the indices of simulated data\n", + "age_mapping = dict(zip(age_labels, map(np.array, age_groups)))" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'(25,30]': array([26, 27, 28, 29, 30]),\n", + " '(30,35]': array([31, 32, 33, 34, 35]),\n", + " '(35,40]': array([36, 37, 38, 39, 40]),\n", + " '(40,45]': array([41, 42, 43, 44, 45]),\n", + " '(45,50]': array([46, 47, 48, 49, 50]),\n", + " '(50,55]': array([51, 52, 53, 54, 55]),\n", + " '(55,60]': array([56, 57, 58, 59, 60]),\n", + " '(60,65]': array([61, 62, 63, 64, 65]),\n", + " '(65,70]': array([66, 67, 68, 69, 70]),\n", + " '(70,75]': array([71, 72, 73, 74, 75]),\n", + " '(75,80]': array([76, 77, 78, 79, 80]),\n", + " '(80,85]': array([81, 82, 83, 84, 85]),\n", + " '(85,90]': array([86, 87, 88, 89, 90]),\n", + " '(90,95]': array([91, 92, 93, 94, 95])}" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "age_mapping" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Define a function to calculate simulated moments" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "def simulate_moments(params, agent=None):\n", + " agent.assign_parameters(**params) # new guess\n", + " agent.LivPrb = liv_prb # perceived mortality\n", + "\n", + " agent.update()\n", + " agent.solve()\n", + "\n", + " agent.LivPrb = [1.0] * agent.T_cycle # ignore mortality\n", + " agent.initialize_sim()\n", + " history = agent.simulate()\n", + "\n", + " raw_data = {\n", + " \"age\": history[\"t_age\"].flatten() + birth_age,\n", + " \"b_nrm\": history[\"bNrm\"].flatten(),\n", + " \"p_lvl\": history[\"pLvl\"].flatten(),\n", + " }\n", + "\n", + " sim_data = pd.DataFrame(raw_data)\n", + " sim_data[\"Wealth\"] = sim_data.b_nrm * sim_data.p_lvl\n", + "\n", + " sim_data[\"Age_grp\"] = pd.cut(\n", + " sim_data.age,\n", + " bins=range(birth_age + 1, 97, 5),\n", + " labels=age_labels,\n", + " right=False,\n", + " )\n", + "\n", + " sim_data = sim_data.dropna()\n", + "\n", + " return sim_data.groupby(\"Age_grp\", observed=False)[\"Wealth\"].median()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "simulate_moments({}, agent=LifeCycleAgent).plot()\n", + "empirical_moments.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Estimate the model parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "init_params = {\n", + " \"CRRA\": 4.23,\n", + " \"DiscFac\": 0.92,\n", + " # Warm Glow bequest parameters\n", + " \"BeqCRRA\": 2.43,\n", + " \"BeqFac\": 70.76,\n", + " \"BeqShift\": 1.63,\n", + "}\n", + "lower_bounds = {\n", + " \"CRRA\": 2.0,\n", + " \"DiscFac\": 0.9,\n", + " \"BeqCRRA\": 2.0,\n", + " \"BeqFac\": 50.0,\n", + " \"BeqShift\": 0.0,\n", + "}\n", + "upper_bounds = {\n", + " \"CRRA\": 5.0,\n", + " \"DiscFac\": 1.0,\n", + " \"BeqCRRA\": 5.0,\n", + " \"BeqFac\": 100.0,\n", + " \"BeqShift\": 10.0,\n", + "}\n", + "\n", + "\n", + "# res = estimate_msm(\n", + "# LifeCycleAgent,\n", + "# init_params,\n", + "# empirical_moments,\n", + "# moments_cov,\n", + "# simulate_moments,\n", + "# optimize_options={\n", + "# \"algorithm\": \"scipy_lbfgsb\",\n", + "# \"error_handling\": \"continue\",\n", + "# \"numdiff_options\": {\"n_cores\": 24},\n", + "# \"multistart\": True,\n", + "# },\n", + "# estimagic_options={\n", + "# \"lower_bounds\": lower_bounds,\n", + "# \"upper_bounds\": upper_bounds,\n", + "# \"numdiff_options\": {\"n_cores\": 24},\n", + "# },\n", + "# )\n", + "\n", + "# res.to_pickle(\"wgbeq_results.pkl\")\n", + "\n", + "res = read_pickle(\"wgbeq_results.pkl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "pd.concat(res.summary()).to_html(\"../../content/slides/tables/wgbeq_results.html\")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'CRRA': 4.233599768665493,\n", + " 'DiscFac': 0.9230085869771656,\n", + " 'BeqCRRA': 2.4272113515446527,\n", + " 'BeqFac': 70.763871892437,\n", + " 'BeqShift': 1.6301132761646395}" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res.params" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CRRADiscFacBeqCRRABeqFacBeqShift
CRRA0.0420316696604428-0.0015148271456938140.0055230630180335590.693158676961249-0.037273690105313506
DiscFac-0.00151482714569393467.317648714288826e-050.0102789821233584792.7619511841455840.06992026554728135
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BeqShift-0.037273690103986640.069920265547756445.3003759458050512115.359307872828300.9630983426005
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" + ], + "text/plain": [ + " CRRA DiscFac BeqCRRA \\\n", + "CRRA 0.0420316696604428 -0.001514827145693814 0.005523063018033559 \n", + "DiscFac -0.0015148271456939346 7.317648714288826e-05 0.010278982123358479 \n", + "BeqCRRA 0.005523063018579608 0.010278982123317532 6.8358029160650915 \n", + "BeqFac 0.6931586770356484 2.7619511841536126 1827.0760473917653 \n", + "BeqShift -0.03727369010398664 0.0699202655477564 45.30037594580505 \n", + "\n", + " BeqFac BeqShift \n", + "CRRA 0.693158676961249 -0.037273690105313506 \n", + "DiscFac 2.761951184145584 0.06992026554728135 \n", + "BeqCRRA 1827.0760473809892 45.300375945366476 \n", + "BeqFac 488441.45454020856 12115.359307827153 \n", + "BeqShift 12115.359307872828 300.9630983426005 " + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame(res.cov())" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "LifeCycleAgent.assign_parameters(**res.params)\n", + "LifeCycleAgent.LivPrb = liv_prb\n", + "LifeCycleAgent.update()\n", + "LifeCycleAgent.solve()\n", + "LifeCycleAgent.LivPrb = [1.0] * LifeCycleAgent.T_cycle\n", + "LifeCycleAgent.initialize_sim()\n", + "history = LifeCycleAgent.simulate()\n", + "\n", + "raw_data = {\n", + " \"Age\": history[\"t_age\"].flatten() + birth_age,\n", + " \"pIncome\": history[\"pLvl\"].flatten(),\n", + " \"nrmM\": history[\"mNrm\"].flatten(),\n", + " \"nrmC\": history[\"cNrm\"].flatten(),\n", + "}\n", + "\n", + "sim_data = pd.DataFrame(raw_data)\n", + "sim_data[\"Cons\"] = sim_data.nrmC * sim_data.pIncome\n", + "sim_data[\"M\"] = sim_data.nrmM * sim_data.pIncome\n", + "\n", + "# Find the mean of each variable at every age\n", + "AgeMeans = sim_data.groupby([\"Age\"]).median().reset_index()\n", + "\n", + "plt.figure()\n", + "plt.plot(AgeMeans.Age, AgeMeans.pIncome, label=\"Permanent Income\")\n", + "plt.plot(AgeMeans.Age, AgeMeans.M, label=\"Market resources\")\n", + "plt.plot(AgeMeans.Age, AgeMeans.Cons, label=\"Consumption\")\n", + "plt.legend()\n", + "plt.xlabel(\"Age\")\n", + "plt.ylabel(f\"Thousands of {adjust_infl_to} USD\")\n", + "plt.title(\"Variable Medians Conditional on Survival\")\n", + "plt.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "simulate_moments(res.params, agent=LifeCycleAgent).plot()\n", + "empirical_moments.plot(figsize=(9, 5))\n", + "\n", + "plt.legend([\"Simulated\", \"Empirical\"])\n", + "plt.xlabel(\"Age Brackets\")\n", + "plt.ylabel(f\"Thousands of {adjust_infl_to} USD\")\n", + "plt.title(\"Variable Medians Conditional on Survival\")\n", + "plt.grid()\n", + "\n", + "plt.savefig(figs_dir / \"wgbeq_results.svg\")" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Assuming df is your DataFrame and 'A' and 'B' are columns in df\n", + "sensitivity = res.sensitivity(return_type=\"dataframe\").T\n", + "\n", + "fig, ax1 = plt.subplots(figsize=(9, 5))\n", + "\n", + "color = \"tab:blue\"\n", + "ax1.set_xlabel(\"Age Bracket\")\n", + "ax1.set_ylabel(\"CRRA Sensitivity\", color=color)\n", + "ax1.plot(sensitivity.index, sensitivity[\"CRRA\"], color=color, marker=\"o\")\n", + "ax1.tick_params(axis=\"y\", labelcolor=color)\n", + "\n", + "# Add a horizontal dashed line at y=0 on first axis\n", + "ax1.axhline(0, color=\"black\", linestyle=\"--\")\n", + "\n", + "ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis\n", + "\n", + "color = \"tab:red\"\n", + "ax2.set_ylabel(\n", + " \"DiscFac Sensivitity\",\n", + " color=color,\n", + ") # we already handled the x-label with ax1\n", + "ax2.plot(sensitivity.index, sensitivity[\"DiscFac\"], color=color, marker=\"o\")\n", + "ax2.tick_params(axis=\"y\", labelcolor=color)\n", + "\n", + "# Make sure both y-axes have the same limits\n", + "ax1.set_ylim(ax1.get_ylim())\n", + "ax2.set_ylim(ax2.get_ylim())\n", + "\n", + "# Reduce the number of x-ticks\n", + "plt.xticks(sensitivity.index[::2])\n", + "\n", + "fig.tight_layout() # otherwise the right y-label is slightly clipped\n", + "plt.grid()\n", + "plt.savefig(figs_dir / \"wgbeq_sensitivity.svg\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "hark-dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/msm_notebooks/NetWorth_Cov.ipynb b/src/msm_notebooks/NetWorth_Cov.ipynb new file mode 100644 index 0000000..0dd53ab --- /dev/null +++ b/src/msm_notebooks/NetWorth_Cov.ipynb @@ -0,0 +1,616 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Method of Simulated Moments (MSM) for Structural Estimation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Steps of MSM estimation\n", + "1. Load empirical data\n", + "2. Define a function to calculate empirical moments from the data\n", + "3. Calculate the covariance matrix of the empirical moments (for the weighting matrix)\n", + "4. Define a `HARK` agent type with the model parameters to be estimated\n", + "5. Define a function to simulate the model and calculate the simulated moments\n", + "6. Estimate the model parameters by minimizing the distance between the empirical and simulated moments" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import annotations\n", + "\n", + "import warnings\n", + "\n", + "warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n", + "\n", + "\n", + "from pathlib import Path\n", + "\n", + "import estimagic as em\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from statsmodels.stats.weightstats import DescrStatsW\n", + "\n", + "figs_dir = Path(\"../../content/slides/figures/\")\n", + "figs_dir.mkdir(parents=True, exist_ok=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Load empirical data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 197277 entries, 56740 to 290734\n", + "Columns: 85 entries, wgt to networthwssrinc\n", + "dtypes: category(1), float64(35), int8(40), object(9)\n", + "memory usage: 75.4+ MB\n" + ] + } + ], + "source": [ + "scf_data = pd.read_stata(\"../data/scf_pooled.dta\")\n", + "\n", + "scf_data = scf_data[scf_data[\"age\"] >= 26]\n", + "scf_data[\"networththou\"] = scf_data[\"networth\"] / 1000\n", + "scf_data[\"wssrinc\"] = scf_data[\"wageinc\"] + scf_data[\"ssretinc\"]\n", + "scf_data[\"networthwssrinc\"] = scf_data[\"networth\"] / scf_data[\"wssrinc\"]\n", + "\n", + "scf_data = scf_data.replace([np.inf, -np.inf], np.nan)\n", + "scf_data = scf_data.dropna()\n", + "\n", + "scf_data.info(verbose=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Calculate Moments" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['(26-30]', '(31-35]', '(36-40]', '(41-45]', '(46-50]', ..., '(71-75]', '(76-80]', '(81-85]', '(86-90]', '(91-95]']\n", + "Length: 14\n", + "Categories (15, object): ['(21-25]' < '(26-30]' < '(31-35]' < '(36-40]' ... '(76-80]' < '(81-85]' < '(86-90]' < '(91-95]']" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "indices = scf_data[\"age_lbl\"].unique().sort_values()\n", + "indices" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def calculate_weighted_median(data, var=\"networththou\", weights=\"wgt\"):\n", + " stats = DescrStatsW(data[var], weights=data[weights])\n", + " return stats.quantile(0.5, return_pandas=False)[0]\n", + "\n", + "\n", + "def calculate_moments(data, var=\"networththou\", weights=\"wgt\", groupby=\"age_lbl\"):\n", + " medians = data.groupby(groupby, observed=True).apply(\n", + " calculate_weighted_median,\n", + " include_groups=False,\n", + " var=var,\n", + " weights=weights,\n", + " )\n", + " return medians.reindex(indices, fill_value=0.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "age_lbl (26-30] (31-35] (36-40] (41-45] (46-50] (51-55] \\\n", + "0 26.224575 58.486449 98.9523 142.238501 202.137328 254.618662 \n", + "\n", + "age_lbl (56-60] (61-65] (66-70] (71-75] (76-80] (81-85] \\\n", + "0 297.932667 315.878976 340.648307 327.475497 279.517 305.089339 \n", + "\n", + "age_lbl (86-90] (91-95] \n", + "0 292.36975 273.808343 \n" + ] + } + ], + "source": [ + "empirical_moments = calculate_moments(scf_data)\n", + "empirical_moments.to_pickle(\"networth_mom.pkl\")\n", + "print(empirical_moments.to_frame().T)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "empirical_moments.plot()\n", + "plt.title(\"Median Net Worth by Age\")\n", + "plt.ylabel(\"Median Net Worth (in thousands)\")\n", + "plt.xlabel(\"Age Bracket\")\n", + "plt.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Calculate the covariance matrix of empirical moments" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "age_lbl (26-30] (31-35] (36-40] (41-45] (46-50] (51-55] \\\n", + "age_lbl \n", + "(26-30] 1.138775 0.019539 0.064554 -0.030823 -0.052405 -0.159678 \n", + "(31-35] 0.019539 2.298721 -0.141000 -0.095465 0.107037 -0.134604 \n", + "(36-40] 0.064554 -0.141000 4.188192 0.181477 0.420562 0.107629 \n", + "(41-45] -0.030823 -0.095465 0.181477 6.994191 0.282429 -0.549976 \n", + "(46-50] -0.052405 0.107037 0.420562 0.282429 10.501479 0.030212 \n", + "(51-55] -0.159678 -0.134604 0.107629 -0.549976 0.030212 16.671975 \n", + "(56-60] 0.252333 -0.267676 -0.165513 -0.583933 0.216019 -0.167664 \n", + "(61-65] -0.217663 -0.229395 0.049796 -1.152797 -0.757894 -0.568752 \n", + "(66-70] 0.421039 -0.080862 0.499413 0.100408 -0.560151 -1.817446 \n", + "(71-75] 0.039465 -0.021901 -0.850859 -1.240737 -1.725575 -1.414565 \n", + "(76-80] -0.215562 0.315986 -0.090836 0.489720 0.149084 -2.091930 \n", + "(81-85] 0.147581 -0.134546 0.581648 -0.000474 -0.322253 -0.556007 \n", + "(86-90] 0.139354 -0.942070 -0.051136 0.370804 -0.624413 3.502248 \n", + "(91-95] -0.624135 0.390411 -0.072405 -0.986377 -2.565575 0.414367 \n", + "\n", + "age_lbl (56-60] (61-65] (66-70] (71-75] (76-80] (81-85] \\\n", + "age_lbl \n", + "(26-30] 0.252333 -0.217663 0.421039 0.039465 -0.215562 0.147581 \n", + "(31-35] -0.267676 -0.229395 -0.080862 -0.021901 0.315986 -0.134546 \n", + "(36-40] -0.165513 0.049796 0.499413 -0.850859 -0.090836 0.581648 \n", + "(41-45] -0.583933 -1.152797 0.100408 -1.240737 0.489720 -0.000474 \n", + "(46-50] 0.216019 -0.757894 -0.560151 -1.725575 0.149084 -0.322253 \n", + "(51-55] -0.167664 -0.568752 -1.817446 -1.414565 -2.091930 -0.556007 \n", + "(56-60] 29.768258 -1.343071 -0.182760 -0.967008 0.539660 -0.919362 \n", + "(61-65] -1.343071 46.091555 -0.898380 -2.215345 0.535793 0.295031 \n", + "(66-70] -0.182760 -0.898380 73.407375 2.457866 1.041616 -0.829654 \n", + "(71-75] -0.967008 -2.215345 2.457866 69.449185 1.447970 -2.957618 \n", + "(76-80] 0.539660 0.535793 1.041616 1.447970 28.782637 1.771822 \n", + "(81-85] -0.919362 0.295031 -0.829654 -2.957618 1.771822 52.428265 \n", + "(86-90] 4.291696 -2.165120 -0.599134 -3.667929 -0.347411 -0.757689 \n", + "(91-95] 3.544736 -7.249572 -5.049652 4.086360 2.567470 1.384539 \n", + "\n", + "age_lbl (86-90] (91-95] \n", + "age_lbl \n", + "(26-30] 0.139354 -0.624135 \n", + "(31-35] -0.942070 0.390411 \n", + "(36-40] -0.051136 -0.072405 \n", + "(41-45] 0.370804 -0.986377 \n", + "(46-50] -0.624413 -2.565575 \n", + "(51-55] 3.502248 0.414367 \n", + "(56-60] 4.291696 3.544736 \n", + "(61-65] -2.165120 -7.249572 \n", + "(66-70] -0.599134 -5.049652 \n", + "(71-75] -3.667929 4.086360 \n", + "(76-80] -0.347411 2.567470 \n", + "(81-85] -0.757689 1.384539 \n", + "(86-90] 143.722185 0.096852 \n", + "(91-95] 0.096852 204.649155 \n" + ] + } + ], + "source": [ + "moments_cov = em.get_moments_cov(\n", + " scf_data,\n", + " calculate_moments,\n", + " bootstrap_kwargs={\n", + " \"seed\": 11323,\n", + " \"n_cores\": 24,\n", + " \"error_handling\": \"continue\",\n", + " },\n", + ")\n", + "\n", + "moments_cov.to_pickle(\"networth_cov.pkl\")\n", + "\n", + "moments_cov = pd.read_pickle(\"networth_cov.pkl\")\n", + "\n", + "print(moments_cov)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(26-30] 1.067134\n", + "(31-35] 1.516153\n", + "(36-40] 2.046507\n", + "(41-45] 2.644653\n", + "(46-50] 3.240599\n", + "(51-55] 4.083133\n", + "(56-60] 5.456030\n", + "(61-65] 6.789076\n", + "(66-70] 8.567810\n", + "(71-75] 8.333618\n", + "(76-80] 5.364945\n", + "(81-85] 7.240736\n", + "(86-90] 11.988419\n", + "(91-95] 14.305564\n", + "dtype: float64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "moments_std = pd.Series(np.sqrt(np.diag(moments_cov)), index=indices)\n", + "moments_std" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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meanstd
(26-30]26.2245751.067134
(31-35]58.4864491.516153
(36-40]98.9523002.046507
(41-45]142.2385012.644653
(46-50]202.1373283.240599
(51-55]254.6186624.083133
(56-60]297.9326675.456030
(61-65]315.8789766.789076
(66-70]340.6483078.567810
(71-75]327.4754978.333618
(76-80]279.5170005.364945
(81-85]305.0893397.240736
(86-90]292.36975011.988419
(91-95]273.80834314.305564
\n", + "
" + ], + "text/plain": [ + " mean std\n", + "(26-30] 26.224575 1.067134\n", + "(31-35] 58.486449 1.516153\n", + "(36-40] 98.952300 2.046507\n", + "(41-45] 142.238501 2.644653\n", + "(46-50] 202.137328 3.240599\n", + "(51-55] 254.618662 4.083133\n", + "(56-60] 297.932667 5.456030\n", + "(61-65] 315.878976 6.789076\n", + "(66-70] 340.648307 8.567810\n", + "(71-75] 327.475497 8.333618\n", + "(76-80] 279.517000 5.364945\n", + "(81-85] 305.089339 7.240736\n", + "(86-90] 292.369750 11.988419\n", + "(91-95] 273.808343 14.305564" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "moments = pd.DataFrame({\"mean\": empirical_moments, \"std\": moments_std})\n", + "moments" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "empirical_moments.plot(figsize=(9, 5))\n", + "\n", + "# Add shaded standard deviation\n", + "plt.fill_between(\n", + " moments.index,\n", + " moments[\"mean\"] - moments[\"std\"],\n", + " moments[\"mean\"] + moments[\"std\"],\n", + " color=\"b\",\n", + " alpha=0.2,\n", + ")\n", + "\n", + "plt.title(\"Median Net Worth by Age\")\n", + "plt.ylabel(\"Median Net Worth (in thousands)\")\n", + "plt.xlabel(\"Age Bracket\")\n", + "plt.grid()\n", + "\n", + "plt.savefig(figs_dir / \"median_net_worth_by_age.svg\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "medians = scf_data.groupby([\"age_lbl\", \"edcl_lbl\"]).apply(\n", + " calculate_weighted_median,\n", + " include_groups=False,\n", + ")\n", + "\n", + "moments_by_educ = medians.reset_index().pivot(\n", + " index=\"age_lbl\",\n", + " columns=\"edcl_lbl\",\n", + " values=0,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "moments_by_educ.plot(figsize=(9, 5))\n", + "\n", + "plt.title(\"Median Net Worth by Age\")\n", + "plt.ylabel(\"Median Net Worth (in thousands)\")\n", + "plt.xlabel(\"Age Bracket\")\n", + "plt.grid()\n", + "\n", + "plt.savefig(figs_dir / \"median_net_worth_by_educ.svg\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "norm_moments = calculate_moments(scf_data, \"networthwssrinc\")\n", + "norm_moments.to_pickle(\"norm_networth_mom.pkl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "norm_moments.plot(figsize=(9, 5))\n", + "\n", + "plt.title(\"Median Normalized Net Worth by Age\")\n", + "plt.ylabel(\"Median Normalized Net Worth\")\n", + "plt.xlabel(\"Age Bracket\")\n", + "plt.grid()\n", + "\n", + "plt.savefig(figs_dir / \"median_norm_net_worth_by_age.svg\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "norm_moments_cov = em.get_moments_cov(\n", + " scf_data,\n", + " calculate_moments,\n", + " moment_kwargs={\n", + " \"var\": \"networthwssrinc\",\n", + " },\n", + " bootstrap_kwargs={\n", + " \"seed\": 11323,\n", + " \"n_cores\": 24,\n", + " \"error_handling\": \"continue\",\n", + " },\n", + ")\n", + "\n", + "norm_moments_cov.to_pickle(\"norm_networth_cov.pkl\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "hark-dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/msm_notebooks/beq_results.pkl b/src/msm_notebooks/beq_results.pkl new file mode 100644 index 0000000..5675dfa Binary files /dev/null and b/src/msm_notebooks/beq_results.pkl differ diff --git a/src/msm_notebooks/cis_results.pkl b/src/msm_notebooks/cis_results.pkl new file mode 100644 index 0000000..5ad0286 Binary files /dev/null and b/src/msm_notebooks/cis_results.pkl differ diff --git a/src/msm_notebooks/finassets_cov.pkl b/src/msm_notebooks/finassets_cov.pkl new file mode 100644 index 0000000..fc59a61 Binary files /dev/null and b/src/msm_notebooks/finassets_cov.pkl differ diff --git 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= scf_data[\"age\"] >= 71" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([35, 40, 51, 28, 32, 33, 52, 62, 63, 21, 27, 43, 30, 23, 56, 70, 50,\n", + " 75, 44, 55, 22, 64, 38, 41, 20, 69, 89, 31, 47, 59, 71, 77, 68, 49,\n", + " 57, 25, 36, 34, 65, 79, 60, 73, 26, 48, 54, 80, 37, 67, 46, 42, 61,\n", + " 45, 39, 58, 78, 76, 81, 29, 82, 72, 66, 24, 85, 19, 53, 74, 83, 18,\n", + " 84, 87, 90, 86, 88, 91, 95, 92, 93, 94, 17], dtype=int8)" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scf_data[\"age\"].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [], + "source": [ + "def weighted_mean(data, var, weights):\n", + " stats = DescrStatsW(data[var], weights=data[weights])\n", + " return stats.mean\n", + "\n", + "\n", + "def to_percent(y, position):\n", + " return f\"{100 * y:.2f}%\"\n", + "\n", + "\n", + "formatter = FuncFormatter(to_percent)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_356464/1201100060.py:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " scf_data.groupby([\"year\", \"edcl_lbl\"]).apply(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "scf_data.groupby([\"year\", \"edcl_lbl\"]).apply(\n", + " weighted_mean,\n", + " \"savres1\",\n", + " \"wgt\",\n", + ").unstack().plot()\n", + "plt.title(\"Reason for saving: can't save\")\n", + "plt.gca().yaxis.set_major_formatter(formatter)" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_356464/2384399675.py:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " scf_data.groupby([\"year\", \"edcl_lbl\"]).apply(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "scf_data.groupby([\"year\", \"edcl_lbl\"]).apply(\n", + " weighted_mean,\n", + " \"savres2\",\n", + " \"wgt\",\n", + ").unstack().plot()\n", + "plt.title(\"Reason for saving: education\")\n", + "plt.gca().yaxis.set_major_formatter(formatter)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_356464/2346591443.py:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " scf_data.groupby([\"year\", \"edcl_lbl\"]).apply(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "scf_data.groupby([\"year\", \"edcl_lbl\"]).apply(\n", + " weighted_mean,\n", + " \"savres3\",\n", + " \"wgt\",\n", + ").unstack().plot()\n", + "plt.title(\"Reason for saving: family\")\n", + "plt.gca().yaxis.set_major_formatter(formatter)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_356464/173237029.py:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " scf_data.groupby([\"year\", \"edcl_lbl\"]).apply(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "scf_data.groupby([\"year\", \"edcl_lbl\"]).apply(\n", + " weighted_mean,\n", + " \"savres4\",\n", + " \"wgt\",\n", + ").unstack().plot()\n", + "plt.title(\"Reason for saving: home\")\n", + "plt.gca().yaxis.set_major_formatter(formatter)" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_356464/3279881744.py:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " scf_data.groupby([\"year\", \"edcl_lbl\"]).apply(\n" + ] + }, + { + "data": { + "image/png": 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aGujfv79c+2bmzJnYv38/unXrhhUrVsDQ0BBHjx7F8+fPAUBmhOX27dvo2LEjlixZUik/IV1dXfz8889ISkqCra0tLl++jD179uDnn3+Gubk5APFUWadOnbBmzRpoa2vDwsICN2/exL///lvpaxg6dCh8fX0xadIkxMbGwtvbGyKRCCEhIbC3t8eQIUOq1Cc9e/aEn58fGjduDCcnJ4SHh2PDhg1o0KCBTL5evXpJ43Tp6+sjMTERW7duhYWFBWxsbAAA27ZtQ9u2bdGuXTv8/PPPsLS0RHZ2Nl6+fIkLFy7g1q1bla6LQikVRXtrUyh1FclKorCwsBLn8vPzibm5ObGxsSECgYAQQkhkZCQZNGgQMTAwIGw2mxgZGZEOHTqQXbt2ScsVFhaSOXPmEFNTU6KiokKaN29Ozp49S0aPHk0sLCyk+Q4cOEC8vb2JoaEhUVZWJiYmJmTQoEHkyZMnMjqePn1KevXqRTQ1NYmysjJxdnaWWSVESOmrvggpfVVRaZRVnhBCzp49S9zd3YmKigpRVVUlHTt2JPfu3ZPJI1k19uHDh3LbkbBgwQLi5uZGtLW1CYfDIdbW1mTWrFkkPT1dmufNmzdkwIABRFtbm6irq5OuXbuSqKgoYmFhQUaPHl2izri4OAKAACDXr18vcb6sVWNNmjQpkbf4e0UIIVFRUaRTp05ERUWF6OjokPHjx5MDBw4QACQyMlKaT9KXS5curbAfJO0HBgYSNzc3wuFwiLGxMVm0aFGJ1YkpKSnkhx9+IDo6OkRTU5OMGDGCPHz4sNRVY6qqqqW2l5+fT5YsWUJsbGyIsrIy0dXVJR06dCD379+X5gFApkyZUqJs8X7/9OkTGT9+PDEwMCA8Ho+0bduW3L17l3h6ehJPT09pvk2bNpHWrVsTPT09oqysTMzNzcn48eNJQkKCTP3x8fFk3LhxxNTUlLDZbKKvr09at25NVq1aVeW6KJTiMAj5YlybQqFQKHLhxx9/xLFjx5CRkQFlZeUql/fy8kJ6ejqioqJqQB2FQpFAp8YoFAqlmqxYsQImJiawtrZGTk4OLl68iL179+LXX3/9KiOIQqHUHtQQolAolGrCZrOxYcMGvHnzBgKBADY2Nti8eTNmzJihaGkUCqUC6NQYhUKhUCiU7xa6fJ5CoVAoFMp3CzWEKBQKhUKhfLdQQ4hCoVAoFMp3C3WWrgCRSIR3795BXV290lsEUCgUCoVCUSyEEGRnZ1e4jx41hCrg3bt3JXb3plAoFAqFUj9ITk4uEdX8S6ghVAGSPaKSk5NL7NhMoVAoFAqlbpKVlQUzMzPpfbwsqCFUAZLpMA0NDWoIUSgUCoVSz6jIrYU6S1MoFAqFQvluoYYQhUKhUCiU7xZqCFEoFAqFQvluoT5ClO8WkUiEoqIiRcugUCgKhs1mQ0lJSdEyKAqCGkKU75KioiLEx8dDJBIpWgqFQqkDaGlpwcjIiMaL+w6hhhDlu4MQgpSUFCgpKcHMzKzcQFsUCuXbhhCCvLw8pKWlAQCMjY0VrIhS21BDiPLdIRAIkJeXBxMTE/B4PEXLoVAoCobL5QIA0tLSYGBgQKfJvjPoozDlu0MoFAIAlJWVFayEQqHUFSQPRXw+X8FKKLUNNYQo3y3UF4BCoUigvwffL9QQolAoFAqF8t1CDSEKhfLNEBgYCAaDgc+fP1cqv5eXF2bOnFmpvJaWlti6dav0NYPBwNmzZ8vMn5CQAAaDgYiIiErVT6FQFAM1hCgUCoVCoXy3UEOIUq8RERH4QurcSKFQKJSvgxpClHrN8gfL0eZ4G8RnxitaCqUGIIRg/fr1sLa2BpfLhbOzM/755x/p+cuXL8PW1hZcLhfe3t5ISEgoUce9e/fg6ekJHo8HbW1t+Pj44NOnT3LRl5KSgm7duoHL5cLKygqnTp2SS70UCqX2oIYQpd7yseAjzr08h3xBPi68uqBoOZQa4Ndff4Wvry927tyJZ8+eYdasWRgxYgRu376N5ORk9O/fH927d0dERAQmTJiABQsWyJSPiIhAx44d0aRJEzx48ABBQUHo1auXNIRCdfntt98wYMAAREZGYsSIERg6dChiYmLkUjeFQqkdaEBFSr3lWsI1CIn4hnb7zW1Mbz5dwYoo8iQ3NxebN2/GrVu34OHhAQCwtrZGUFAQ/v77b1haWsLa2hpbtmwBg8GAnZ0dnj59inXr1knrWL9+Pdzc3PDXX39J05o0aSI3jQMHDsSECRMAACtXrsT169fxxx9/yLRHoVDqNtQQotRbLr++LP0/7lMc3ua8hamaqQIVUeRJdHQ0CgoK0LlzZ5n0oqIiNGvWDPn5+WjVqpVM/BeJwSQhIiICAwcOrDGNxdvz8PCgq8QolHoGNYQo9ZI32W8Q8SECTAYTVhpWeJX5CoHJgRhuP1zR0ihyQrIh7qVLl2BqKmvgcjgcTJs2rcI6JFsn1CY0MB+FUr+gPkKUesnlePFoUEujlujbqC8A4HbybQUqosgbBwcHcDgcJCUloVGjRjKHmZkZHBwcEBwcLFOm+GsnJyfcvHmzxjSW1n7jxo1rrD0KhSJ/6IgQpd5BCMGl15cAAD2se8BZ3xmbwjch7H0YcopyoKaspmCFFHmgrq6OOXPmYNasWRCJRGjbti2ysrJw//59qKmpYdKkSdi0aRNmz56Nn376CeHh4fDz85OpY+HChWjatCkmT56MSZMmQVlZGQEBARg4cCD09PSqrfHUqVNwc3ND27ZtceTIEYSGhmLfvn3VrpdCodQedESIUu94/vE5Xme+hjJTGR3NO8JK0wqWGpYQiAS49+6eouVR5MjKlSuxZMkSrFmzBvb29vDx8cGFCxdgZWUFc3NznD59GhcuXICzszN27dqF1atXy5S3tbWFv78/IiMj0bJlS3h4eODcuXNgseTzDLh8+XIcP34cTk5OOHDgAI4cOQIHBwe51E2hUGoHBiGEKFpEXSYrKwuamprIzMyEhoaGouVQAGx6uAl+z/zQ2aIzNnttBgBsDNuIA9EH0NO6J9a0W1Nu+YKCAsTHx8PKygoqKiq1IZlCodRx6O/Ct0dl7990RIhSrxCKhFL/oB7WPaTpXmZeAIC7b+9CIBIoQhqFQqFQ6iHUEKLUK8LfhyMtLw3qyupoZ9pOmu5i4AJNjiYyCzMRkRahOIGUesndu3ehpqZW5kGhUL5dqLM0pV5xKV7sJN3FoguUlZSl6SwmC+1M2+Hi64sITA6Em5GbghRS6iNubm40/g+F8p1CDSFKvaFIWITrCdcByE6LSfAy88LF1xdx+81tzGkxp7blUeoxXC4XjRo1UrQMCoWiAOjUGKXecPfNXWTzs2HIM4SroWuJ821M2oDFZCEhK4FuwkqhUCiUSkENIUq9QTIt1t2qO5iMkh9dNWU1tDBsAYAGV6RQKBRK5aCGEKVekF2ULTVuult3LzOfp5knACAgOaBWdFEoFAqlfkMNIUq94EbiDRSJitBQsyHstO3KzCdZRh/xIQKfCz7XjjgKhUKh1FuoIUSpF0imxXpY9yh3U0tTNVPYattCRES4+/ZubcmjUCgUSj2FGkKUOk9aXhpCU0IBAN2sulWY37MBnR6rDpaWlti6dWu16hgzZgz69u0rFz3VhcFg4OzZs4qWoXACAwPBYDDw+fPnMvMsW7YMLi4uVarXy8sLM2fOrJY2CkWRVNkQunPnDnr16gUTE5NSf2BycnIwdepUNGjQAFwuF/b29ti5c2eF9Z4+fVq627SDgwPOnDlTIs9ff/0lDX/u6uqKu3dln/g3btwIQ0NDGBoaYsuWLTLnQkJC4OrqCqFQWNVLpiiYK/FXQEDgou+CBuoNKszvbeYNALj/7j6KhEU1La/WGDNmDBgMhvTQ1dVF165d8eTJE0VLo3wjzJkzBzdv3lS0DAqlVqmyIZSbmwtnZ2fs2LGj1POzZs3C1atXcfjwYcTExGDWrFmYNm0azp07V2adDx48wODBgzFy5EhERkZi5MiRGDRoEEJCQqR5Tpw4gZkzZ2Lx4sV4/Pgx2rVrh27duiEpKQkA8PTpUyxZsgTHjh3D0aNHsWjRIkRFRQEA+Hw+Jk2ahF27dkFJSamql0xRMKVtqVEeTfSaQI+rh1x+Lh6mPqxJabVO165dkZKSgpSUFNy8eRMsFgs9e/ZUtCy5QwiBQFB/tkopKqo7BrdQKIRIJPqqsmpqatDV1ZWzoqrD5/MVLYHyHVFlQ6hbt25YtWoV+vfvX+r5Bw8eYPTo0fDy8oKlpSV+/PFHODs74+HDsm9IW7duRefOnbFw4UI0btwYCxcuRMeOHWWG5zdv3ozx48djwoQJsLe3x9atW2FmZiYdbYqJiYGTkxM6dOiAjh07wsnJCTExMQCADRs2oH379mjRokVVL5eiYOIz4xGdEQ0WgwUfS59KlWEymNLpscA3gTWorvbhcDgwMjKCkZERXFxcMH/+fCQnJ+PDhw/SPPPnz4etrS14PB6sra3x22+/lbixnD9/Hm5ublBRUYGenl6J73NeXh7GjRsHdXV1mJubY/fu3TLn3759i8GDB0NbWxu6urro06cPEhISytRdWFiI6dOnw8DAACoqKmjbti3CwsKk5yXTNteuXYObmxs4HA7u3r2LyMhIeHt7Q11dHRoaGnB1dS33t+TFixdo3749VFRU4ODggOvXr5fIU5F2gUCA6dOnQ0tLC7q6upg/fz5Gjx4tM9Xn5eWFqVOnYvbs2dDT00Pnzp0BANHR0ejevTvU1NRgaGiIkSNHIj09XVqOEIL169fD2toaXC4Xzs7O+Oeff8q8HgD49OkTRo0aBW1tbfB4PHTr1g0vXryQnvfz84OWlhYuXrwoHVVPTEwss77w8HC4ubmBx+OhdevWiI2NlZ4rPjVWmb4AAJFIhHnz5kFHRwdGRkZYtmyZzPnMzEz8+OOPMDAwgIaGBjp06IDIyMgS7e7fvx/W1tbgcDig+4FTagu5+wi1bdsW58+fx9u3b0EIQUBAAOLi4uDjU/ZN7MGDB+jSpYtMmo+PD+7fvw9A/LQVHh5eIk+XLl2keZo2bYq4uDgkJSUhMTERcXFxcHR0xMuXL+Hn54dVq1bJ+UoptcGl12In6damraGtol3pclJDKDmwwh9UQgjyigQKOarzY5+Tk4MjR46gUaNGMk/x6urq8PPzQ3R0NLZt24Y9e/bITBVfunQJ/fv3R48ePfD48WPcvHkTbm6yW5Js2rQJbm5uePz4MSZPnoyff/4Zz58/ByA2kry9vaGmpoY7d+4gKCgIampq6Nq1a5kjI/PmzcPp06dx4MABPHr0CI0aNYKPjw8+fvxYIt+aNWukDzbDhw9HgwYNEBYWhvDwcCxYsABsNrvUNkQiEfr37w8lJSUEBwdj165dmD9/vkyeymhft24djhw5Al9fX9y7dw9ZWVml+hgdOHAALBYL9+7dw99//42UlBR4enrCxcUFDx8+xNWrV/H+/XsMGjRIWubXX3+Fr68vdu7ciWfPnmHWrFkYMWIEbt8uO+7VmDFj8PDhQ5w/fx4PHjwAIQTdu3eXMW7z8vKwZs0a7N27F8+ePYOBgUGZ9S1evBibNm3Cw4cPwWKxMG7cuDLzVqUvVFVVERISgvXr12PFihVSI5QQgh49eiA1NRWXL19GeHg4mjdvjo4dO8q8/y9fvsTJkydx+vRput0JpXYh1QAAOXPmjExaYWEhGTVqFAFAWCwWUVZWJgcPHiy3HjabTY4cOSKTduTIEaKsrEwIIeTt27cEALl3755Mnt9//53Y2tpKX+/cuZPY2toSW1tbsnPnTkIIIR07diRnzpwhp06dIk2aNCEuLi7k9u3bZWopKCggmZmZ0iM5OZkAIJmZmRX2B0W+iEQi0vWfrsTRz5FcenWpSmXz+HnE9ZArcfRzJM8znsucy8/PJ9HR0SQ/P58QQkhuIZ9YzL+okCO3kF/paxo9ejRRUlIiqqqqRFVVlQAgxsbGJDw8vNxy69evJ66urtLXHh4eZPjw4WXmt7CwICNGjJC+FolExMDAQPqd2rdvH7GzsyMikUiap7CwkHC5XHLt2jWp1j59+hBCCMnJySnxHS8qKiImJiZk/fr1hBBCAgICCABy9uxZGS3q6urEz8+v3OuTcO3aNaKkpESSk5OlaVeuXJH5naqMdkNDQ7JhwwbpeYFAQMzNzaXXQwghnp6exMXFRab93377jXTp0kUmTfL7ERsbS3JycoiKigq5f/++TJ7x48eToUOHlnpNcXFxJX770tPTCZfLJSdPniSEEOLr60sAkIiIiHL7R9LHN27ckKZdunSJAJB+F5YuXUqcnZ2l5yvbF23btpVpq0WLFmT+/PmEEEJu3rxJNDQ0SEFBgUyehg0bkr///lvaLpvNJmlpaeVeQ01S/HeBUv/JzMys1P1b7nuNbd++HcHBwTh//jwsLCxw584dTJ48GcbGxujUqVOZ5YoviSaElEirKM+kSZMwadIk6Ws/Pz+oq6vDw8MDdnZ2CAsLw5s3bzBkyBDEx8eDw+GU0LFmzRosX768StdMqRmepD/Bm5w34LK40vhAlYXL4sLD2AOBbwIRmBwIO52yYw/VJ7y9vaXTwR8/fsRff/2Fbt26ITQ0FBYWFgCAf/75B1u3bsXLly+Rk5MDgUAADQ0NaR0RERGYOHFiue04OTlJ/2cwGDAyMkJaWhoA8dTKy5cvoa6uLlOmoKAAr169KlHXq1evwOfz0aZNG2kam81Gy5YtpdPXEoqPTM2ePRsTJkzAoUOH0KlTJwwcOBANGzYsVXNMTAzMzc3RoMF/DvUeHh4yeSrSnpmZiffv36Nly5bSc0pKSnB1dS3hd1Nca3h4OAICAkrdrV5Sd0FBgXQaTUJRURGaNWtW5jWxWCy4u7tL03R1dWFnZyfTd8rKyjLvWXl8mc/Y2BgAkJaWBnNzc5l8VemL4m0bGxvLfF5ycnJK+B7l5+fLfF4sLCygr69fqWugUOSJXA2h/Px8LFq0CGfOnEGPHmLHVicnJ0RERGDjxo1lGkJGRkZITU2VSUtLS4OhoSEAQE9PD0pKSuXmKU56ejpWrFiBO3fuICQkBLa2trCxsYGNjQ34fD7i4uLQtGnTEuUWLlyI2bNnS19nZWXBzMys8p1AkRuXX4udpDuYdwCPzatyeU8zT6kh9JPzT2Xm47KVEL2icv5H8obLrprzvqqqqszmoK6urtDU1MSePXuwatUqBAcHY8iQIVi+fDl8fHygqamJ48ePY9OmTf+1yeVW2E7x6ScGgyG9+YlEIri6uuLIkSMlypV2IyP/n/6rzMOOqqqqzOtly5Zh2LBhuHTpEq5cuYKlS5fi+PHj6NevX5ntFNf9JZXVXprW4hTXKhKJ0KtXL6xbt65EXmNjY+nijUuXLsHU1FTmfGkPZWW1K0n/UiOXyy03vtaXfPneSsqU51xdmb6o6PNibGyMwMDAEuW0tLSk/xfvTwqltpCrjxCfzwefzweTKVutkpJSuV80Dw+PEk6N/v7+aN26NQDx046rq2uJPNevX5fmKc7MmTMxa9YsNGjQAEKhUGY+XSAQlLmMnsPhQENDQ+ag1D4CkQBXE64CAHpYVW61WHEkfkJRGVH4kPehzHwMBgM8ZZZCjsrevMrTzmQykZ+fDwC4d+8eLCwssHjxYri5ucHGxqaE46yTk1O1lkg3b94cL168gIGBARo1aiRzaGpqlsjfqFEjKCsrIygoSJrG5/Px8OFD2NvbV9iera0tZs2aBX9/f/Tv3x++vr6l5nNwcEBSUhLevXsnTXvw4EGVtGtqasLQ0BChoaHSMkKhEI8fP65Uvzx79gyWlpYl6lZVVZU6MiclJZU4X9bDloODAwQCgcwK2oyMDMTFxVWq76pDdfriS5o3b47U1FSwWKwS162npydv2RRKlamyIZSTk4OIiAipM1t8fDwiIiKQlJQEDQ0NeHp6Yu7cuQgMDER8fDz8/Pxw8OBBmSe4UaNGYeHChdLXM2bMgL+/P9atW4fnz59j3bp1uHHjhkyQrtmzZ2Pv3r3Yv3+/dFl+UlKSzFSYhOvXr+PFixeYMmUKAKBly5Z4/vw5rly5gt27d0NJSQl2dt/GVMm3SnBKMD4WfISOig5ambT6qjr0efpw1HUEANx+821swlpYWIjU1FSkpqYiJiYG06ZNQ05ODnr16gVAbHQkJSXh+PHjePXqFbZv314iJtfSpUtx7NgxLF26FDExMXj69CnWr19faQ3Dhw+Hnp4e+vTpg7t37yI+Ph63b9/GjBkz8ObNmxL5VVVV8fPPP2Pu3Lm4evUqoqOjMXHiROTl5WH8+PFltpOfn4+pU6ciMDAQiYmJuHfvHsLCwso0ADp16gQ7OzuMGjUKkZGRuHv3LhYvXlxl7dOmTcOaNWtw7tw5xMbGYsaMGfj06VOFRuuUKVPw8eNHDB06FKGhoXj9+jX8/f0xbtw4CIVCqKurY86cOZg1axYOHDiAV69e4fHjx/jzzz9x4MCBUuu0sbFBnz59MHHiRAQFBSEyMhIjRoyAqakp+vTpU64eefC1ffElnTp1goeHB/r27Ytr164hISEB9+/fx6+//lruCkAKpdaoqvORxOGu+DF69GhCCCEpKSlkzJgxxMTEhKioqBA7OzuyadMmGedET09PaX4Jp06dInZ2doTNZpPGjRuT06dPl2j7zz//JBYWFkRZWZk0b968VKfnvLw8YmtrSx4/fiyTvmfPHmJoaEjMzc3JxYsXK329lXW2osiXBXcWEEc/R7Lqwapq1bMrYhdx9HMkU29MlabVV6fI0aNHy3zn1NXVSYsWLcg///wjk2/u3LlEV1eXqKmpkcGDB5MtW7YQTU1NmTynT58mLi4uRFlZmejp6ZH+/ftLz1lYWJAtW7bI5Hd2diZLly6Vvk5JSSGjRo0ienp6hMPhEGtrazJx4kTp9+RLZ2lCxH0+bdo0af42bdqQ0NBQ6XnJ78qnT5+kaYWFhWTIkCHEzMyMKCsrExMTEzJ16tRy37fY2FjStm1boqysTGxtbcnVq1dLLOqoSDufzydTp04lGhoaRFtbm8yfP58MHDiQDBkyRFqHp6cnmTFjRon24+LiSL9+/YiWlhbhcrmkcePGZObMmdLfP5FIRLZt2yb9rdPX1yc+Pj7lLuD4+PEjGTlyJNHU1CRcLpf4+PiQuLg46XlfX98S729plNbHjx8/JgBIfHw8IaSks/TX9kWfPn1kfuOzsrLItGnTiImJCWGz2cTMzIwMHz6cJCUlldquIqivvwuUsqns/ZtBCA3WUB5ZWVnQ1NREZmYmnSarJfIF+fA84Yl8QT4OdTsEFwOXr64r9mMsfrjwAzhKHNwdchdcFhcFBQWIj4+XRimnUMpDJBLB3t4egwYNwsqVKxUtR6F8y31Bfxe+PSp7/5b7qjEKpboEJgciX5CPBmoN4KzvXK26bLVtYaxqjJTcFISkhFR59Rnl+yMxMRH+/v7w9PREYWEhduzYgfj4eAwbNkzR0mod2heU7wG66SqlziEJotjdurtcnIklxk9gcmD1hFG+C5hMJvz8/NCiRQu0adMGT58+xY0bN2rcObkuQvuC8j1AR4QodYpPBZ9w7+09AF+/Wqw4Xg28cOz5MQQmB0JEvm4PJsr3g5mZGe7du6doGXUC2heU7wE6IkSpU1xPvA4BEcBexx7WWtZyqdPNyA2qbFVkFGTgWfozudRJoVAolG8DaghR6hSSabHK7jRfGZSVlNHaRBxvKiA5QG71UigUCqX+Qw0hSp3hXc47PEp7BAYY6GrZVa51e5t5A/h24glRKBTFEp0RjXMvz1Vr42RK3YD6CFHqDJfjxVtqtDBqAUPV0rdO+VrambYDk8FE3Kc4pOWmybVuCoXyfSEiIky/NR3v895DREToZ1NyyxdK/YGOCFHqDDUxLSZBS0ULLvouAIDQ1NDyM1MoFEo5PE57jPd57wEAOx7vQL4gX8GKKNWBGkKUOkHsx1i8/PwSbCYbnSxK35y3ukimx0JTqCFEoVC+nqvxV6X/p+Wn4VD0IQWqoVQXaghR6gSX4sWjQZ4NPKGhXDMRvD3NxJuwPs14Wi+X0Xt5ecnsv1caDAYDZ8+erXSdgYGBYDAY+Pz5c7W0VRY/Pz+ZHcdrijFjxqBv375VKlNc27Jly+Di4lKlOirzHlHqN0KRENcTxRuA927YGwCw7+k+pOenK1IWpRpQQ4iicEREhCvxVwCIgyjWFFaaVrDUsIRQJEShsLDG2lEkKSkp6Natm6JlfBPMmTMHN2/eVLSMOsvp06fRoUMHaGtrg8fjwc7ODuPGjZPZnd7Pzw8MBqPE8eUWFmPGjJGms9lsGBoaonPnzti/fz9Eorr3wPIo7REyCjKgoayBpR5L4ajriDxBHnZG7FS0NMpXQg0hisIJfx+O1NxUqLPV0b5B+xpty7OBeFSoQFBQo+0oCiMjI3A4HEXL+CZQU1ODrq6uomUoDKFQWKYhMn/+fAwePBguLi44f/48nj17ht27d6Nhw4ZYtGiRTF4NDQ2kpKTIHImJiTJ5unbtipSUFCQkJODKlSvw9vbGjBkz0LNnTwgEghq7xq9BMi3WyaITlJWU8YvbLwCA0y9O4/Xn14qURvlKqCFEUTiS1WKdLDqBo1SzN3HJdhuFwsJ6uexVJBJh3rx50NHRgZGREZYtWyZzvvjU2P379+Hi4gIVFRW4ubnh7NmzYDAYiIiIkCkXHh4ONzc38Hg8tG7dGrGxsWVqKCoqwtSpU2FsbAwVFRVYWlpizZo10vOfP3/Gjz/+CENDQ6ioqMDR0REXL16UqePatWuwt7eHmpqa9Cb45TWuWLECDRo0AIfDgYuLC65evSpT/unTp+jQoQO4XC50dXXx448/Iicnp5K9KMbPzw/m5ubg8Xjo168fMjIyZM4XnxqTTLctX74cBgYG0NDQwE8//YSioqIy2/j06RNGjRolHTXp1q0bXrx4IaNBS0sLFy9ehJ2dHXg8Hn744Qfk5ubiwIEDsLS0hLa2NqZNmwahUCgtd/jwYbi5uUFdXR1GRkYYNmwY0tLKXw1ZFS0ODg7gcDglDBYACA4Oxvr167F582Zs3rwZ7dq1g5WVFTw9PbF48WJcvnxZJj+DwYCRkZHMYWgouyqUw+HAyMgIpqamaN68ORYtWoRz587hypUr8PPzK/e6ahOBSIAbSTcAAD4WPgDEAVu9zbwhJEJsCd+iSHmUr4QaQhSFwhfy4Z/gD6BmVosVx8XABWrKahAR0X8rPQgBinIVc1TRGDtw4ABUVVUREhKC9evXY8WKFbh+/XqpebOzs9GrVy80bdoUjx49wsqVKzF//vxS8y5evBibNm3Cw4cPwWKxMG7cuDI1bN++HefPn8fJkycRGxuLw4cPw9LSEoDYiOnWrRvu37+Pw4cPIzo6GmvXroWSkpK0fF5eHjZu3IhDhw7hzp07SEpKwpw5c6Tnt23bhk2bNmHjxo148uQJfHx80Lt3b+lNOy8vD127doW2tjbCwsJw6tQp3LhxA1OnTq10P4aEhGDcuHGYPHkyIiIi4O3tjVWrVlVY7ubNm4iJiUFAQACOHTuGM2fOYPny5WXmHzNmDB4+fIjz58/jwYMHIISge/fu4PP5Mv2xfft2HD9+HFevXkVgYCD69++Py5cv4/Llyzh06BB2796Nf/75R1qmqKgIK1euRGRkJM6ePYv4+HiMGTOmXO2V1bJmzRrs3bsXz549g4GBQYl6jh07BjU1NUyePLnUdqq7P6CEDh06wNnZGf/++69c6pMHD98/xMeCj9DiaKGFcQtp+izXWVBiKCHwTSBdjFEPoXGEKArl7tu7yCrKgj5XH26GbjXeHovJkraTy8+FDnQAfh6w2qTG2y6VRe8AZdVKZ3dycsLSpUsBADY2NtixYwdu3ryJzp07l8h75MgRMBgM7NmzByoqKnBwcMDbt28xceLEEnl///13eHqKpw0XLFiAHj16oKCgQMaXQ0JSUhJsbGzQtm1bMBgMWFhYSM/duHEDoaGhiImJga2tLQDA2lp2qxQ+n49du3ahYcOGAICpU6dixYoV0vMbN27E/PnzMWTIEADAunXrEBAQgK1bt+LPP//EkSNHkJ+fj4MHD0JVVdx3O3bsQK9evbBu3boSow2lsW3bNvj4+GDBggUAAFtbW9y/f7/EyFNxlJWVsX//fvB4PDRp0gQrVqzA3LlzsXLlSjCZss+VL168wPnz53Hv3j20bi2ObH7kyBGYmZnh7NmzGDhwoLQ/du7cKe2PH374AYcOHcL79++hpqYGBwcHeHt7IyAgAIMHDwYAGUPV2toa27dvR8uWLZGTkwM1NbUSuqui5a+//oKzs3OZfRAXFwdra2uwWP/dPjZv3owlS5ZIX799+xaampoAgMzMzBKaWrduDX9///K6GgDQuHFjPHnypMJ8tcWX02JsJluabqVphYG2A3E89jg2PtyI4z2Pg8mg4wz1BfpOURSKJHZQN6tuUGIqVZBbPrQ0bglAbAjVN5ycnGReGxsblzklEhsbCycnJxljpmXLlhXWa2xsDABl1jtmzBhERETAzs4O06dPl7mhRUREoEGDBlIjqDR4PJ70pl/8GrKysvDu3Tu0adNGpkybNm0QExMDAIiJiYGzs7PUCJKcF4lE5U7pfUlMTAw8PDxk0oq/Lg1nZ2fweDyZMjk5OUhOTi61DRaLBXd3d2marq4u7OzspNcClOwPQ0NDWFpayhgPhoaGMu/H48eP0adPH1hYWEBdXR1eXl4AxEZqWddbGS3KysolPmOlUXzUZ9y4cYiIiMDff/+N3NxcmWlndXV1REREyBy+vr4VtgEAhBC5jTBVF76Ij5tJYud5H0ufEud/dvkZqmxVxHyMkf6uUeoHdESIojByinKkW17UxrSYhGb6zRD5MRJ8ER+FgkJw2DzxyIwiYPMqzvNldjZb5jWDwSjTobW0m0hZflFf1ispU1a9zZs3R3x8PK5cuYIbN25g0KBB6NSpE/755x9wudyvuobiukrTLUkr7+ZY2ZumvP3DSmu3rDaK6y+tP8p7n3Nzc9GlSxd06dIFhw8fhr6+PpKSkuDj41Omv1JltXC53Ar70MbGBkFBQeDz+VKdWlpa0NLSwps3b0rkZzKZaNSoUbl1lkVMTAysrKy+qqy8CUsJw+fCz9BR0Sl19FpHRQcTmk7AtkfbsP3xdnS26AwVVskRVUrdg44IURTGzaSbKBQWwkrTCvY69rXWrqqyKpSVlAEA2fxsgMEQT08p4qjBp13JtEJh4X+hAh4+fCiXujU0NDB48GDs2bMHJ06cwOnTp/Hx40c4OTnhzZs3iIuL++p6TUxMEBQUJJN+//592NuLPyMODg6IiIhAbu5/I3r37t0Dk8ksdyTqSxwcHBAcHCyTVvx1aURGRiI//78owsHBwVBTU0ODBg1KbUMgECAkJESalpGRgbi4OOm1fA3Pnz9Heno61q5di3bt2qFx48YVOkrLU8vQoUORk5ODv/7666v0V5Zbt27h6dOnGDBgQI22U1muJV4DAHS26AwWs/QxhBH2I2DIM0RqbiqOxBypTXmUakANIYrCkAwfd7fqXuvD35Inteyi7FpttzYZNmwYRCIRfvzxR8TExODatWvYuHEjgOo5tG7ZsgXHjx/H8+fPERcXh1OnTsHIyAhaWlrw9PRE+/btMWDAAFy/fl06clSR782XzJ07F+vWrcOJEycQGxuLBQsWICIiAjNmzAAADB8+HCoqKhg9ejSioqIQEBCAadOmYeTIkZXyDwKA6dOn4+rVq1i/fj3i4uKwY8eOSmksKirC+PHjER0djStXrmDp0qWYOnVqCf8gQDxy0qdPH0ycOBFBQUGIjIzEiBEjYGpqij59+lS6P4pjbm4OZWVl/PHHH3j9+jXOnz+PlStXlltGnlo8PDzwyy+/4JdffsHs2bMRFBSExMREBAcHY9++fWAwGDL9QQhBampqiePLEcfCwkKkpqbi7du3ePToEVavXo0+ffqgZ8+eGDVqVNU6qAbgC/m4kfj/1WKlTItJUGGpYEZz8ed079O9+FjwsVb0UaoHNYQoCiE9Px0hqeKn0x5WtTctJkGyTD+PnweBqG7FKZEXGhoauHDhAiIiIuDi4oLFixdLHVpLc4KuLGpqali3bh3c3NzQokULJCQk4PLly9Kb3+nTp9GiRQsMHToUDg4OmDdvnszS74qYPn269EbbtGlTXL16FefPn4eNjQ0AsU/NtWvX8PHjR7Ro0QI//PADOnbsiB07dlS6jVatWmHv3r34448/4OLiAn9/f/z6668VluvYsSNsbGzQvn17DBo0CL169SoRwuBLfH194erqip49e8LDwwOEEFy+fLnE1FdV0NfXh5+fH06dOgUHBwesXbtWauCWhzy1bNy4EUePHsXjx4/Rs2dP2NjYYODAgRCJRHjw4AE0NP6LDp+VlQVjY+MSx5ejWFevXoWxsTEsLS3RtWtXBAQEYPv27Th37pzMikNFEZwSjKyiLOhx9dDcoHm5eXtY94C9jj1y+DnYFbmrlhRSqgOD1MdgKrVIVlYWNDU1kZmZKfPlplSPw9GHsS5sHZz0nXCke+0OIRcUFCA+Ph5MXSYETAFM1UyhpaJVqxoUxZEjRzB27FhkZmZWyp+H8h9jxozB58+fq7SFCaX+IPldsLKyKvGg8GvQrzj36hyGNh6KRe6LyqjhP0JSQjDBfwJYDBbO9DkDS03LGlJd/zn/6jySspLwk/NPMivx5EFl7990RIiiEKQ7zStgNEiCKku86iib/+1Ojx08eBBBQUGIj4/H2bNnMX/+fAwaNIgaQRRKJSkSFuFW0i0A5U+LfYm7sTvaN2gPARFg66OtNaiufvMm+w1Wh6zG30/+xvmX5xWmgxpClFonMSsRURlRUGIoVfqHpSZQZYsNoZyinHq5CWtlSE1NxYgRI2Bvb49Zs2Zh4MCB2L17t6JlUSj1hgfvHiCbnw0DrgGaGTSrdLnZrrPBZDBxM+kmwt+H16DC+olQJMTioMXI5eeimUEz9G3UV2FaqCFEqXUko0GtTFpBl6u4vZxUWCpgMVkQERHy+HkK01GTzJs3DwkJCdJh/y1btsjEwaFUHj8/Pzot9h1yLUG8WqyLZZcqBUlsqNUQA2zEK942hm38Zh+2vpYD0QfwKO0ReCwefm/7e63FkSsNaghRahVCSJ2YFgPEK6fUldUBfNurxygUytdRKCzEreSqTYt9yWSXyeCxeIjKiJIaVBQg9mMs/nj8BwBgQcsFMFM3U6geaghRapVnGc+QlJ0ELouLjuYdFS1HxhCi6wYoFMqX3Ht7D7n8XBjyDOGkX3HE7eLocfUw1nEsAGDbo20oEpa9Qe/3QqGwEAuDFkIgEsDbzFuhU2ISqCFEqVUko0FeZl7gVTGqck2gylYFg8EQR5kWFlZcgEKhfDdIRnF8LH2+eu+wUQ6jYMA1wNuctzj2/Jg85dVLdjzegRefXkBHRQdLPZbWiS1UqCFEqTUEIgGuxF8BoPhpMQlMBhNqbPGeTnR6jEKhSCgQFCAwORDA102LSeCxeZjabCoA4O8nf+Nzwefqi6unhKWG4cCzAwCAZR7LFOoj+iXUEFIgfCFf0RJqldCUUGQUZECLo4XWpq0VLUeKmjI1hCgUiixBb4OQJ8iDsaoxmuo1rVZdvRv2hq22LbKLsvH3k7/lpLB+kV2UjcVBi0FAMMBmALzNvRUtSQo1hBQAIQRHYo6g99ne+FTwSdFyao1L8eJpMR9LH7kHzqoO6myxn1C+IP+7M04pFErpfDktVt3pGyWmEn5x+wUAcDz2OJKzkqutr76xNnQtUnJT0ECtAea2mKtoOTJQQ0gBFAoLcSL2BN7kvMGSe0u+CyfdAkEBbibdBFC7O81XBrYSG1yWOMBgDj9HwWpql4SEBDAYDERERJSZx8/PD1paWlWqd8yYMejbt2+1tFUFLy8vzJw5s8bbYTAYVV5CX1ybpaUltm7dWunylXmPKPIlj5+H229uAwC6WnaVS52tTVqjjUkbCETfX5BF/wR/nH91HkwGE6vbrZbGcKsrUENIAaiwVLCh/QawmWwEvgnE0edHFS2pxgl8E4hcfi5MVE3grO+saDkloMvoy2bw4MFfvZs8pSRhYWH48ccfFS2jWixbtgxDhgyRvn78+DEGDx4MY2NjcDgcWFhYoGfPnrhw4YL0QU9i0JV2BAcHAxAb3ZI0JSUlaGtrw93dHStWrEBmZmatXd/dt3eRL8iHqZopHHQd5FbvbDdxkEX/RH9EpEXIrd66zIe8D1gRvAIAMN5xfJWCUtYW1BBSEHY6dtKh0k0PN+H5x+cKVlSzSHeat+7+1asvahKJIZTD/3ajTH8tXC4XBgYGipbxzaCvr1/vg1qeP39eumv9uXPn0KpVK+Tk5ODAgQOIjo7GqVOn0LdvX/z6668lDJgbN24gJSVF5nB1dZWe19DQQEpKCt68eYP79+/jxx9/xMGDB+Hi4oJ3797VyvXJc1rsS2y1baXLxTc+3PjNzwYQQrDk/hJkFmbCXscePzv/rGhJpVL37kjfEcMaD4NXAy/wRXzMvT33m41unFmYiaC3QQDqzmqx4nCUOGAz2SCEIJefq2g5peLl5YXp06dj3rx50NHRgZGRUYmdz5OSktCnTx+oqalBQ0MDgwYNwvv37yus+/Xr1/D29gaPx4OzszMePHggPVfa1NiqVatgYGAAdXV1TJgwAQsWLICLi0uJejdu3AhjY2Po6upiypQp4PPL9sGKjIyEt7c31NXVoaGhAVdXVzx8+FB6/t69e/D09ASPx4O2tjZ8fHzw6dN/PnYikajafbNz5040bNgQysrKsLOzw6FDhyrsuy/Jzc3FqFGjoKamBmNjY2zatKlEnuJTYwwGAzt37kS3bt3A5XJhZWWFU6dOldvO7du30bJlS3A4HBgbG2PBggUQCATS815eXpg2bRpmzpwJbW1tGBoaYvfu3cjNzcXYsWOhrq6Ohg0b4sqVK9IyQqEQ48ePh5WVFbhcLuzs7LBt27YSbScnJyMqKgrdunVDbm4uxo8fjx49euDSpUvo0qULGjZsiJYtW2LChAmIjIyEpqamTHldXV0YGRnJHGz2fz6DDAYDRkZGMDY2hr29PcaPH4/79+8jJycH8+bNq/A9qC75/HzcfXMXgPymxb5kissUcFlcRH6IxPXE63Kvvy5xKu4Ugt4GQZmpjDXt1oCtVHd8Q7+EGkIKhMFgYEWbFTDgGiAhKwFrQ9cqWlKN4J/oD4FIADttOzTSbqRoOSUghCBfkA8Wk4UCQQE+5H1AHj+vVo6qPhEeOHAAqqqqCAkJwfr167FixQpcv35deh19+/bFx48fcfv2bVy/fh2vXr3C4MGDK6x38eLFmDNnDiIiImBra4uhQ4fK3Fi/5MiRI/j999+xbt06hIeHw9zcHDt37iyRLyAgAK9evUJAQAAOHDgAPz8/+Pn5lalh+PDhaNCgAcLCwhAeHo4FCxZIb5ARERHo2LEjmjRpggcPHiAoKAi9evWCUCiUW9+cOXMGM2bMwC+//IKoqCj89NNPGDt2LAICAirsPwlz585FQEAAzpw5A39/fwQGBiI8vOJ9pn777TcMGDAAkZGRGDFiBIYOHYqYmJhS8759+xbdu3dHixYtEBkZiZ07d2Lfvn1YtWqVTL4DBw5AT08PoaGhmDZtGn7++WcMHDgQrVu3xqNHj+Dj44ORI0ciL0/8ACYSidCgQQOcPHkS0dHRWLJkCRYtWoSTJ0/K1Hv+/Hm0b98eWlpa8Pf3R0ZGRrkGijxGVAwMDDB8+HCcP39e5j2vCcLeh6FAWABzdXM01mks9/oNeAYY3WQ0AGDro63f7AKNhMwEbHy4EQAw03UmGmo1VLCiciCUcsnMzCQASGZmZo21EZoSSpr6NSWOfo7k8uvLNdaOohh9ZTRx9HMk+57uU7QUQggh+fn5JDo6muTn5xNCCMktyiWOfo4KOXKLciut29PTk7Rt21YmrUWLFmT+/PmEEEL8/f2JkpISSUpKkp5/9uwZAUBCQ0NLrTM+Pp4AIHv37i1RJiYmhhBCiK+vL9HU1JSed3d3J1OmTJGpp02bNsTZ2Vn6evTo0cTCwoIIBAJp2sCBA8ngwYPLvD51dXXi5+dX6rmhQ4eSNm3alFlWHn3TunVrMnHiRJk6Bg4cSLp37y59DYCcOXOmVA3Z2dlEWVmZHD9+XJqWkZFBuFwumTFjhjTNwsKCbNmyRabOSZMmydTl7u5Ofv75Z0LIf+/R48ePCSGELFq0iNjZ2RGRSCTN/+effxI1NTUiFApL7Q+BQEBUVVXJyJEjpWkpKSkEAHnw4EGp10MIIZMnTyYDBgyQSevcuTPZvn07IYSQtWvXEgDk48eP0vOhoaFEVVVVely4cEHmOrhcrsx5VVVV6eek+GftS3bu3EkAkPfv35eptzpIfhd+C/yNOPo5km3h22qkHULEvzmexz2Jo58jOfTsUI21oyj4Qj4ZenEocfRzJOOvjidCkVAhOip7/67yiNCdO3fQq1cvmJiYlLmCIiYmBr1794ampibU1dXRqlUrJCUllVvv6dOn4eDgAA6HAwcHB5w5c6ZEnr/++gtWVlZQUVGBq6sr7t69K3N+48aNMDQ0hKGhIbZs2SJzLiQkBK6urjX+NPE1tDBqgR+dxM6TKx6sQHL2t7O0MiUnBeHvw8EAA92tuitaTr3HyUk2zL+xsTHS0tIAiL93ZmZmMDP7b98eBwcHaGlplTm6UFq9xsbGACCttzixsbFo2bKlTFrx1wDQpEkTKCn9t5Hil1pLY/bs2ZgwYQI6deqEtWvX4tWrV9JzkhGhyl5D8fYq0zcxMTFo06aNTB1t2rSpsO8kvHr1CkVFRfDw8JCm6ejowM7OrsKyX5aRvC6r3ZiYGHh4eMiMtLRp0wY5OTl48+aNNO3L/lBSUoKuri6aNv0vHo6hoSEA2fd5165dcHNzg76+PtTU1LBnzx6Z3+6srCzcvn0bvXv3LvNanJycEBERgYiICOTm5pYYWTxx4oT0vOT48nNSFuT/o6c1GYlYREQITxWP4FUniGJFfBlkcdeTXcgsrD1H8Npgz9M9eJr+FOpsdaxqu6pO+oV+CauqBXJzc+Hs7IyxY8diwIABJc6/evUKbdu2xfjx47F8+XJoamoiJiYGKioqZdb54MEDDB48GCtXrkS/fv1w5swZDBo0CEFBQXB3dwcg/vLMnDkTf/31F9q0aYO///4b3bp1Q3R0NMzNzfH06VMsWbIEFy9eBCEEPXv2ROfOneHo6Ag+n49JkyZh9+7dlfrCKYJJzpMQmhqKx2mPseDOAvh186tTsXa+lisJYh8EV0NXGKkaKVhN6XBZXIQMCwEAvM15i6zCLOjydGHArXkHYcmy/crypS8FIL4piERi525CSKk3ibLSy6pXkldSb2kUr4+UMsVXntbSWLZsGYYNG4ZLly7hypUrWLp0KY4fP45+/fqBy624n+TRN6VdV2VvvKX1QXUoq93SNJVmJJTWH+W9zydPnsSsWbOwadMmeHh4QF1dHRs2bEBISIi0zJUrV2Bvbw8LCwsAgI2NDQCxcdyqVSsAAIfDQaNGZU+Bm5mZlXu+LGJiYqChoQFd3ZqLRlwoLARfxIelhiVstW1rrB0A6NuoL47EHMHLzy+x9+le6eKZ+k5UehT+jhQHjVzcanGd/d3/kiqbad26dcOqVavQv3//Us8vXrwY3bt3x/r169GsWTNYW1ujR48e5a462bp1Kzp37oyFCxeicePGWLhwITp27CjjULh582aMHz8eEyZMgL29PbZu3QozMzOpb0JMTAycnJzQoUMHdOzYEU5OTtInqg0bNqB9+/Zo0aJFVS+31mAxWVjbbi3UldXxJP0J/nz8p6IlyQXpTvN1LHbQlzAYDPDYPPDYPOhz9aHCUoFAJJCm1eQhz6dbBwcHJCUlITn5vxHF6OhoZGZmwt7eXm7t2NnZITQ0VCbtS6fm6mBra4tZs2bB398f/fv3h6+vLwDxKMPNmze/ut7K9I29vT2CgoJkyt2/f7/SfdeoUSOw2WzpUnAA+PTpU6VCD3xZRvK6cePS/VMcHBxw//59GcPr/v37UFdXh6mpaaW0lsbdu3fRunVrTJ48Gc2aNUOjRo1kRuUA8QqxL0eDunTpAh0dHaxbt+6r260MaWlpOHr0KPr27Qsms+ZGF/IF+QDkv1qsNFhMFma7zgYAHIk5grc5b2u0vdogX5CPhXcXQkiE6GrZtd7MAsj1EyUSiXDp0iXY2trCx8cHBgYGcHd3rzAA2YMHD9ClSxeZNB8fH9y/fx8AUFRUhPDw8BJ5unTpIs3TtGlTxMXFISkpCYmJiYiLi4OjoyNevnwJPz+/Eo6EZVFYWIisrCyZo7YwUTPB8tbLAQD7o/bjwbsHFZSo27z49AJxn+LAYrLQ2aKzouVUCsl2G4WCwnq3U3SnTp3g5OSE4cOH49GjRwgNDcWoUaPg6ekJNzc3ubUzbdo07Nu3DwcOHMCLFy+watUqPHnypFo3jvz8fEydOhWBgYFITEzEvXv3EBYWJjVCFi5ciLCwMEyePBlPnjzB8+fPsXPnTqSnp1eq/sr0zdy5c+Hn54ddu3bhxYsX2Lx5M/7991/MmTOnUm2oqalh/PjxmDt3Lm7evImoqCiMGTOmUjfuU6dOYf/+/YiLi8PSpUsRGhqKqVOnlpp38uTJSE5OxrRp0/D8+XOcO3cOS5cuxezZs6tlJDRq1AgPHz7EtWvXEBcXh99++w1hYWHS8wKBAFeuXJEum5dc8969e3Hp0iX06NED165dw+vXr/HkyROsX78eAEqMwmdkZCA1NVXmKCgokJ4nhCA1NRUpKSmIiYnB/v370bp1a2hqamLt2ppbUCIUCVEoEG+8XBOrxUqjrWlbuBu7gy/iY9ujkiv06htbwrcgISsBBlwD/Nrq1zqxoWplkKshlJaWhpycHKxduxZdu3aFv78/+vXrh/79++P27dtllktNTZXOV0swNDREamoqACA9PR1CobDcPPb29li9ejU6d+6MLl26YM2aNbC3t8ekSZOwfv16XLt2DY6OjmjWrBnu3LlTppY1a9ZAU1NTenzpU1AbdLbojIG2A0FAsChoETLyM2q1fXkiGQ1qZ9oOmhzNCnLXDVhMFnhscYyX+hZcUeKzp62tjfbt26NTp06wtrbGiRMn5NrO8OHDsXDhQsyZMwfNmzdHfHw8xowZU+70d0UoKSkhIyMDo0aNgq2tLQYNGoRu3bph+XLxg4GtrS38/f0RGRmJli1bwsPDA+fOnQOLVbnZ/cr0Td++fbFt2zZs2LABTZo0wd9//w1fX194eXlV+joko8+9e/dGp06d0LZtW5kYOWWxfPlyHD9+HE5OTjhw4ACOHDkCB4fSA/mZmpri8uXLCA0NhbOzMyZNmoTx48fj119/rbTO0pg0aRL69++PwYMHw93dHRkZGZg8ebL0/O3bt6Gmplbievr164f79++Dx+Nh1KhRsLOzQ4cOHXDr1i0cP34cPXv2lMnfqVMnGBsbyxxfPixnZWXB2NgYpqam8PDwwN9//43Ro0fj8ePHUv+1miCXnwsCAjN1s1pb3cpgMDDHbQ4YYOBK/BU8/fC0VtqtCe69vYdjz48BAFa2XVlvfvMBVG/VGIqtoHj79i0BQIYOHSqTr1evXmTIkCFl1sNms8nRo0dl0g4fPkw4HI5Mvffv35fJs2rVKmJnZ1dmvb6+vqRv374kNTWVaGpqkri4OHLr1i1ibGxMCgoKSi1TUFBAMjMzpUdycnKNrxorTj4/n/Q925c4+jmSn67/pDCP++ogFAlJ51OdiaOfI7kaf1XRcmQovmqsOB/yPpCoD1Ek/nN87Qqrx3Tq1ImMGDFC0TLqJcV/R+sq06ZNk65k+xZ5+eEluRF6g+x/tL/W2150dxFx9HMkoy6PklkNWF/4lP+JeJ/wJo5+jmR18GpFy5FSY6vGykNPTw8sFqvEk4y9vX25q8aMjIykIzsS0tLSpCNAenp6UFJSKjdPcdLT07FixQr88ccfCAkJga2tLWxsbODt7Q0+n1/mvD2Hw4GGhobMUduosFSwvv16cJQ4uPf2Hg5FVy2oW10gIi0CKbkpUGWrwrOBp6LlVAnJJqx5gjwIRXVvlaGiycvLw+bNm/Hs2TM8f/4cS5cuxY0bNzB69GhFS6PUII6Ojvj557oZGbi6CEQC5PPF/kFtTdvWevvTmk0DR4mDR2mPcCv5Vq23Xx0IIVgZvBIf8j/AUsMSM11nKlpSlZGrIaSsrIwWLVogNjZWJj0uLk66yqA0PDw8pIHPJPj7+6N169bSel1dXUvkuX79ujRPcWbOnIlZs2ahQYMGEAqFMhFtBQJBnVxG/yU22jaY10IcpGzro614lvFMwYqqhmRarKN5R6iwvn7KRBFwWBwoKymDEPLdbcJaGRgMBi5fvox27drB1dUVFy5cwOnTp9GpUydFS6PUID/++KPM8vtvieyibBAQsJlsmGnUrjsEABipGmGUwygAwNbwreCL6k+QxUvxl+Cf6A8WQ7zgp6orYesCVV4+n5OTg5cvX0pfx8fHIyIiAjo6OjA3N8fcuXMxePBgtG/fHt7e3rh69SouXLiAwMBAaZlRo0bB1NQUa9asAQDMmDED7du3x7p169CnTx+cO3cON27ckFnBMXv2bIwcORJubm7w8PDA7t27kZSUhEmTJpXQeP36dbx48QIHDx4EII5x8vz5c1y5cgXJyclQUlKqVGwPRTPQdiAevHuAG0k3MO/2PJzsdbLO7dpbGnwhH9cSxXv11OXVYuWhrqyOjPwMZBdl16+57lqAy+Xixo0bipbxzUC+8f2m6gNZReJFMYp8aBvnOA6nX5xGQlYC/on7B0MbD1WYlsqSkpOC1cGrAQA/Of+EJnpNFKzoK6nqnFtAQAABUOIYPXq0NM++fftIo0aNiIqKCnF2diZnz56VqcPT01MmPyGEnDp1itjZ2RE2m00aN25MTp8+XaLtP//8k1hYWBBlZWXSvHlzcvv27RJ58vLyiK2trTQKq4Q9e/YQQ0NDYm5uTi5evFjp662NyNLl8bngM+l0qhNx9HMkC+8sVIiGqhKQFEAc/RyJ1wkvIhAKKi5Qy1TkI0QIITlFOSTqQxSJyYipl3P2FAqlcvCFfPLswzPy5N0T8iTqSbm/CzXN8ZjjxNHPkbQ71o5kFWYpTEdlEIqEZNzVccTRz5EMuziM8IV8RUsqQWXv3wxC6ONIeWRlZUFTUxOZmZkK8RcCgEfvH2HstbEQERFWt12NXg17KURHZZl3ex6uJFzBCPsRmN9yvqLllKCgoADx8fHSKOWlQQhB7KdYCEVCWGpa1ouROAqFUnU+FXzCu5x3YIvYEGYIy/1dqGn4Ij4GnB+A+Mx4jHccX6f9bQ5FH8L6sPXgsrg41esULDTKdn9RFJW9f9ftuNcUAEBzw+aY5CyeAlwVvAqJWYkKVlQ2ufxcBCSLN6msr9NigNgPRo0tjilU35bRUyiUyiPZ3kLyfVckbCYbs5rPAgAcjjmMlJwUBSsqnZefXmJr+FYAwBy3OXXSCKoK1BCqJ/zY9Ee4GbohT5CHeXfm1dkdi28l3UKBsAAWGhZooltP54v/j7qyePUYNYQolG8TgUiAXH4ugP+CqSoaLzMvuBm6oVBYiD8e/6FoOSXgC/lYGLQQRaIitDNth4G2AxUtqdpQQ6ieoMRUwpp2a6DJ0UR0RnSdjUIq3VLDqke9iSpaFmpsNTAYDBQJi6QRZykUyrfDl07SykrKClYjRhJkEQAuvL6A6IxoBSuS5a/Iv/D843NocbSwos2Kev87D1BDqF5hpGqEFa1XAAAORB/A3Td3FaxIlvT8dASniPdM6m5dP/aYKQ8lphJ4rP9HmebTUSEK5Vsjq1BsCNW1laFN9JpIXQs2PdxUZ1YWPnr/CPuj9gMAlnoshR5XT8GK5AM1hOoZHcw7SJdV/nrvV6TnV26vpdrgWsI1CIkQjrqO9X7OWAKdHqt9xowZg759+0pfe3l5YebMmQrTQ6kcfn5+0NLSUrSMSsMX8aXTYhrKilkIUx7Tm02HMlMZoamhuPtW8Q+9ufxcLApaBBERoXfD3uhk8e3EDaOGUD3kF7dfYKtti48FH7Hw7kKIiEjRkgAAl19fBlC/naSLIzGE8vh5EIgEClZD+dZYvXo1lJSUanQzUUrpZBeKH264LG6dmRb7EhM1Ewx3GA5APCqk6N+f9WHr8TbnLUxUTbCg5QKFapE31BCqh3CUONjQfgO4LC6CU4LhG+WraElIykrCk/QnYDKY6GpVOzs31wbKSsrgsDgAgJwiGmWaUjW+jGhfGr6+vpg3bx72799fS4rE1JWplq+hoj6tLJlF4tViGpy6NxokYULTCdDiaOF15mv8++JfhekISArAvy/+BQMMrGq7SvqA+K1ADaF6irWWtdQq3/F4B558eKJQPZfixU7SrYxbfTPzxhIke48p2k/on3/+QdOmTcHlcqGrq4tOnTohN1c8tC8SibBixQo0aNAAHA4HLi4uuHr1qrRsQkICGAwGTp48iXbt2oHL5aJFixaIi4tDWFgY3NzcoKamhq5du+LDhw8y7fr6+sLe3h4qKipo3Lgx/vrrr3J1ikQirFu3Do0aNQKHw4G5uTl+//136fmnT5+iQ4cO0uv48ccfkZNTeSOzqKgI8+bNg6mpKVRVVeHu7i4TuR4A9uzZAzMzM/B4PPTr1w+bN28uMW1z4cIFuLq6QkVFBdbW1li+fDkEgrKfuqvSx15eXlBRUcHhw4fLrO/27dvIz8/HihUrkJubizt37sicj4yMhLe3N9TV1aGhoQFXV1c8fPgQAJCYmIhevXpBW1sbqqqqaNKkCS5fvixTd8uWLcHhcGBsbIwFCxaAz+cjszATLz69QExGDH5f83uZ71Fl8PPzg7m5ubSPMzIySuSpqI+fP3+Otm3bQkVFBQ4ODrhx4wYYDIZ0N/ry+rSiz+Xbt28xePBgaGtrQ1dXF3369EFCQgIA8cqnPH4egLo5LSZBQ1lDGjrlz4g/pVN5tUlGfgaWPVgGABjTZAxaGLWodQ01Ts3HdqzfKDqydHmIRCLyS+AvxNHPkfj846OwSKQikYj0/LcncfRzJOdenlOIhqpQPLK0SCQiwtzcMo/sz+kkKukhiU5+RPg52eXmrepR2ajV7969IywWi2zevJnEx8eTJ0+ekD///JNkZ2cTQgjZvHkz0dDQIMeOHSPPnz8n8+bNI2w2m8TFxRFCCImPjycASOPGjcnVq1dJdHQ0adWqFWnevDnx8vIiQUFB5NGjR6RRo0Zk0qRJ0nZ3795NjI2NyenTp8nr16/J6dOniY6ODvHz8ytT67x584i2tjbx8/MjL1++JHfv3iV79uwhhBCSm5tLTExMSP/+/cnTp0/JzZs3iZWVlUyk+dGjR5M+ffpIX3t6epIZM2ZIXw8bNoy0bt2a3Llzh7x8+ZJs2LCBcDgc6bUGBQURJpNJNmzYQGJjY8mff/5JdHR0iKamprSOq1evEg0NDeLn50devXpF/P39iaWlJVm2bFmZ11XZPra0tJT219u3b8usb+TIkWTOnDmEEEJ++eUXMmrUKJnzTZo0ISNGjCAxMTEkLi6OnDx5kkRERBBCCOnRowfp3LkzefLkCXn16hW5cOGCNNL+mzdvCI/HI5MnTyYxMTHk33//Jbp6umTavGkk6kMUifoQRcZNG0c0tTRLfY8qQ3BwMGEwGGTNmjUkNjaWbNu2jWhpaVWpj4VCIbGzsyOdO3cmERER5O7du6Rly5YEADlz5ky5fVrR5zI3N5fY2NiQcePGkSdPnpDo6GgybNgwYmdnRwoLC0l6XjqJ+hBFXn1+JdVbmYjziqBIUER6/NuDOPo5ku2Pttdq2yKRiEy9OZU4+jmSfuf6kUJBYa22X10qe/+mhlAF1GVDiBBCsgqziM8/PsTRz5HMDZyrkO0gotKjiKOfI3E95EqyC7Nrvf2qUvwHT5ibS6LtGivkEObmVkpzeHg4AUASEhJKPW9iYkJ+//13mbQWLVqQyZMnE0L+u6Hs3btXev7YsWMEALl586Y0bc2aNcTOzk762szMjBw9elSm3pUrVxIPD49SdWRlZREOh1PmTXX37t1EW1ub5OTkSNMuXbpEmEwmSU1NJYSUbwi9fPmSMBiMEgZGx44dycKF4i1oBg8eTHr06CFzfvjw4TI36Xbt2pHVq1fL5Dl06BAxNjYuVTchle/jrVu3llmHhMzMTMLj8aSGzePHjwmPx5P5nVFXVy/T4GzatGmZRtuiRYuInZ0dEQqFJLMgk7z49IL8uu5XwlPlkai0KPIi5QVR5iiTZZuXffX3dejQoaRr164yaYMHD65SH1+5coWwWCySkpIiPX/9+vVSDaHifVrR53Lfvn3Ezs5O5vewsLCQcLlccu3aNfL682sS9SGKpOelS8/XVUOIEEKuJ1wnjn6OxO2QG0nNSa21dk/HnSaOfo6k2cFm5HnG81prV15U9v5Np8YUBP99GjIvXIAoP79a9agrq2Nd+3VQYijhSsIVnH15Vj4Cq4AkdpCXmVedCUr2reHs7IyOHTuiadOmGDhwIPbs2YNPnz4BEIeRf/fuHdq0aSNTpk2bNoiJiZFJc3Jykv5vaGgIADI7ihsaGiItLQ0A8OHDByQnJ2P8+PFQU1OTHqtWrcKrV69K1RkTE4PCwkJ07NixzPPOzs5QVf1vy5I2bdpAJBIhNja2wn549OgRCCGwtbWV0XT79m2pptjYWLRs2VKmXPHX4eHhWLFihUwdEydOREpKCvLy8kq0W5U+dnNzq/A6jh49Cmtrazg7OwMAXFxcYG1tjePHj0vzzJ49GxMmTECnTp2wdu1amT6fPn06Vq1ahTZt2mDp0qV48uS/qfGYmBi4ubshPiseydnJKBQUorl7c+Tl5oGXy8PHpI8oKixCq/atkJqX+lX+QjExMfDw8JBJK/66oj6OjY2FmZkZjIyMpGWKv08SvuzTynwuw8PD8fLlS6irq0vP6+jooKCgAHEv4urFtNiXdDTviGYGzVAgLMCOiB210mZyVjLWhoqd+Kc1mwY7nbq/UfnXUuXd5ynyIWHoEAjepcBs316oFftxrSrO+s6Y2mwqtj3ahjWha+Bs4AxrTWs5KS0foUiIq/FiP4keVvVztRiDy4Xdo/By82QX5eBNdjJYTDYaaTWUWxAxBpdbqXxKSkq4fv067t+/D39/f/zxxx9YvHgxQkJCoKurK66rmCZCSIk0Npv9X9v/P1c8TSQSr0KU/N2zZw/c3d1L6CkNbgXXU5qm4nrKQyQSQUlJCeHh4SU0qKmpldlG8Zu9SCTC8uXL0b9//xJtlLfPVGX6+Esjryz279+PZ8+egcX67ydYJBJh3759+PHHHwEAy5Ytw7Bhw3Dp0iVcuXIFS5cuxfHjx9GvXz9MmDABPj4+uHTpEvz9/bFmzRps3LgRYyeNRU5RDgifoEBQACaDCR0VHeSrix+4WEos6XvEZDBRKCjE58LP0FbRrlBz8euuiIr6uLzPQnG+7NPKfC5FIhFcXV1x5MiREnUpqSkhD3ngsXlgK7FLnK+LSIIsDr88HOdensMI+xE1apgIRUIsClqEfEE+XA1dMcphVI21VRegI0IKQrWF+MknLzhELvWNcxwHd2N35AvyMe/2PBQKaycSctj7MHzI/wANZQ20NW1bK23KGwaDASaPV+6hrqkHBo8LAUcJfI5Shfkre1TFoGIwGGjTpg2WL1+Ox48fQ1lZGWfOnIGGhgZMTEwQFBQkk//+/fuwt7f/6n4xNDSEqakpXr9+jUaNGskcVlZWpZaxsbEBl8vFzZs3Sz3v4OCAiIgIqZM3ANy7dw9MJhO2trYVamrWrBmEQiHS0tJKaJKMLDRu3BihoaEy5SROxhKaN2+O2NjYEnU0atQITGbJn0V59vHTp0/x8OFDBAYGIiIiQnrcuXMHYWFhiIqKkua1tbXFrFmz4O/vj/79+8PX978VomZmZpg0aRJOnz6NaTOn4a+//0JSVhIsbSwRGRYJXRVd2GjbwFDVEKHBoVBXV4epqan0PYoKFreTlpcGoUhYpWtwcHBAcHCwTFrx1xX1cePGjZGUlIT3799Ly4SFhVXYdmU+l82bN8eLFy9gYGBQIg/hio24+jIaJMFJ3wldLbuCgGDTw0012pbvM19EfIiAKlsVv7f9HUrM0h98vhlqdoau/lNTPkKf/j1Dou0ak9eDBsmtzve570m7Y+2Io58jWROyRm71lsevQb8SRz9Hsux+2U6mdY2v9QVIzEwkUR+iSFpuWg0pK5vg4GDy+++/k7CwMJKYmEhOnjxJlJWVyeXLlwkhhGzZsoVoaGiQ48ePk+fPn5P58+eX6sj7+PFjaZ0BAQEEAPn06ZM0zdfXV8bPY8+ePYTL5ZKtW7eS2NhY8uTJE7J//36yadOmMrUuW7aMaGtrkwMHDpCXL1+SBw8eSH2TcnNzibGxMRkwYAB5+vQpuXXrFrG2tq6Ss/Tw4cNlnGdDQ0PJ2rVryaVLlwgh/zlLb9q0icTFxZFdu3YRXV1doqWlJa3j6tWrhMVikaVLl5KoqCgSHR1Njh8/ThYvXlzmdX1NH5fGjBkziLu7e6nnWrduTWbOnEny8vLIlClTSEBAAElISCBBQUGkYcOGZN68edI6rl69SqJio8j5wPOkafOmxKePD3mW/ow8intEeDwemTJlComJiSFnz54lenp6ZOnSpSXeo3V/rSOXQy+TSwGXZPzHOnToQP74448yr+HBgweEwWCQdevWkdjYWPLHH3+U6ixdXh8LBAJiZ2dHfHx8SGRkJAkKCiLu7u4EADl79my5fVrR51LiLO3l5UXu3LlDXr9+TQIDA8mUaVPIjcgbJOpDFCkSFMnUWZd9hCQkZyWTZgebEUc/RxL0JqhG2ohOjyYuB12Io58jOfvibI20UVtQZ2k5UVOGUNHbt2KHWYcmRJAtPwfj28m3iaOfI3H0cyQBSQFyq7c0CgQFpNWRVsTRz5GEpYTVaFvy5Gt/8DLyM8QrTT69qjiznImOjiY+Pj5EX1+fcDgcYmtrK3OjEgqFZPny5cTU1JSw2Wzi7OxMrly5Ij3/tYYQIYQcOXKEuLi4EGVlZaKtrU3at29P/v333zK1CoVCsmrVKmJhYUHYbDYxNzeXcZp98uQJ8fb2JioqKkRHR4dMnDhRuvqNkIoNoaKiIrJkyRJiaWlJ2Gw2MTIyIv369SNPnjyR5tm9ezcxNTUlXC6X9O3bl6xatYoYGRnJ6Lx69Spp3bo14XK5RENDg7Rs2ZLs3r273Ouqah8Xp7CwkOjq6pL169eXen7Tpk1ET0+PFBYWkiFDhhAzMzOirKxMTExMyNSpU6Wf2Z8m/0QsrCyIMkeZ6OjpkN6DepOoxP9u7oGBgaRFixZEWVmZGBkZkfnz5xM+ny9zLatWrSLm5uaExWYR4wbGZOWqldLzFhYWMoZTaezbt480aNCAcLlc0qtXL7Jx48YSn52K+jgmJoa0adOGKCsrk8aNG5MLFy4QAOTq1asV9mlFn8uUlBQyatQooqenRzgcDrG2tiYjx44kwa+DyevPr0vUVx8MIUIIWR+6njj6OZK+Z/sSgVAg17oLBAWkz5k+xNHPkcy8NVMhi2/kSWXv3wxC6nFkrVogKysLmpqayMzMhIaGfIdSX3buAn5yMhrs2gl1Ly+51bs+bD0ORR+CFkcL//T6B4aqhnKr+0v8E/zxy+1fYKRqhGsDroHJqB8zrQUFBYiPj4eVlVW5/iDF4Qv5iPsUBwCw1batN/4FFGDixIl4/vw57t5V/FYF1SGPn4e0vDRpPBkGgwFtjjb0uHpf9XkkhCAhKwF5/DxocbRgqm4qb8lV4t69e2jbti1evnyJhg0byr3+159fI1+QD2NVY+hwdWTOfe3vQm2TWZiJ7v92R1ZRFpa3Xo7+NiV9sL4Wyb1DV0UXZ/qcqbLvWF2jsvfv+nHn+kZRbSV29MsLCa0gZ9WY2Xwm7HXs8bnwMxYGLazy/H9luRwvDuDW3ap7vTGCqgNbiQ0uS+xomsOnUabrMhs3bkRkZCRevnyJP/74AwcOHMDo0aMVLeuryefnIzErEfGZ8cjl54IBBrRVtNFIqxGM1Yy/2ihnMBgw4ol9qz4Xfka+oHqrWKvKmTNncP36dSQkJODGjRv48ccf0aZNmxoxgoqERdLrU+fU38jImhxN/OT0EwBxMF3JCrjqEpwSjEPRhwAAK9qsqPdGUFX49u9edRheS7EhlBsSXEHOqqGspIz17deDy+IiLDUM+6L2ybV+QPxUcueNOBJud6v6v9N8ZaGbsNYPQkND0blzZzRt2hS7du3C9u3bMWHCBEXLqjL5gnwkZSXhdeZr6RYvWipaaKTdCCZqJnLZI4vL5kp3X0/N/brl9F9LdnY2Jk+ejMaNG2PMmDFo0aIFzp07VyNtZRWJd5pXZauCzazfo7lDGg9BA7UG+JD/AQeiD1S7vqyiLPwa9CsAYJDtILRv0L7addYnqCGkQHju4pVjhTHPIfz8Wa51W2pa4tdW4g/2XxF/ISItQq7130i8Ab6Ij0Zajb7p+BLFkRhCOfycOrPZLaUkJ0+eRFpaGvLz8/Hs2TNMmjRJ0ZKqRIGgQGwAfX4tNbq1OGIDyFTNVO6bhBrwDMBgMJDHz6tVI3/UqFF48eIFCgoK8ObNG/j5+UnDQcibzMK6v7dYZVFWUsYM1xkAAN8oX3zI+1BBifJZHbIa7/Pew1zdHL+4/SIPifUKaggpELaBAZStrQFCkFdsea886GXdCz2se0BIhJh3Z570h0AeSPYW+5Z2mq8MHCUO2Ew2CCEK2feH8m1TIChAcnYyXn1+JTVINDmaaKTVCKbqpuAocWqkXWUlZeiqiA2Q93nvvzkjv0hYhAJBAYD6t2y+LHwsfOCk54R8QT7+jPgTEPKBTwlA/B3g8REgcC1wdgpwfhrwOhAQlf6eXk24ikuvL4HJYGJ1u9XgsXm1eh11ARpQUcHw3Fui6PVr5AaHQL1TJ7nWzWAw8Kv7r3jy4QmSs5Ox/MFybPLcVO1ggKm5qXiYKjbc6vO02NdMATAYDKgrq+NjwUdkF2V/c7swUxRDoaAQH/I/yDysaHA0oM/Vhwqrdhx39bh6+FT4CUXCInwq+ARdbs2MzCgCSb+qslXBYpZ+26vz64b4+UDmG+BzIvA5GYzMZMzJFWIUgDNx/2B44J+wKSoqveyjg4CWBdBsBOAyDNBsAAB4n/seKx+sBABMbDoRzvrOtXQxdQtqCCkYVfdW+HzsOPJC5BNYsThqympY3349Rl4eieuJ1/HPi38w0HZgteq8Gn8VBATNDZrDRM1ETkprD0n02aKiogojIZfGl4YQqUJ0XAqlOIXCQnzIK2YAKWtAn1d7BpAEJaYSDHgGSMlJwYe8D9DkaJZpNNQ3JP5BEl+o0pBsrfJlpPVapSALyEwGPicBn5OBzP///ZwkTs8tOf3VDEBnAz1cV+Vhs7YmdqZnAVpmgKYZoGUu/j8rBXj6j9iACvgdCFgNNOwA0mwElry7gqyiLDjoOuAn559q/5rrCN/Gp7weI/UTevECgowMsGpgftxRzxEzms/ApvBNWBe6Ds30m6GRdqOvrq++T4uxWCzweDx8+PABbDa71EjC5cEkTIAPFKEIn3M+g8uuujFF+b6RjLpIbtCAeLRCR0VHbAAJIJ3KqU24hAuWiIUiYRFSPqdAn6df6xrkTZGgCHn5eWCAAbaIjYIC2X4lhCAvLw9paWnQ0tIqc/uYakEIkPdRbIxkJv/f0En+wtBJAgoq4bqgrP6FoSM2dmZweAiI/hNBPC4ejDsED1OPkuW6rAJiLgCPDwEJd4FXN3E8LQT39XTAARNr7MfXewfy6kDjCFVATcYRkvC6T18UxsbCdMtmaHTrViNtiIgIk29Mxr1399BIqxGO9Tj2VU+crz6/Qt9zfcFisBAwKABaKlryF1sLFBUVIT4+XrpvUVX5VPAJ+YJ8qCmrfTM+B5SaRygSIpufjXx+PgjEP70cFgfqbHW5O0B/LYWCQmQUZIABBvR5+vV+VCi7KBvZRdngsDhSP6jS0NLSgpGR0deN8IpEQM77/0ZvvvwrMXoqs8ydq1NsRMf8P6NH0wzgagOl6FsbuhZHYo7ATtsOJ3qeKH9LjI+v8Tp0JwanXkEBg4GF6R8xLDsHMGkunjpr+gOgUvbIWX2isvfv+v0J/0bgubdEYWwsckNCaswQYjKYWNV2FX44/wNefn6JjQ83SleVVQXJTvNtTdvWWyMIAJSVlWFjY4OisubUKyAhKQFbwrfAUtMS2ztsl7M6yrfGh7wP+CfuH/gn+kvjerkYuGCY/TA01mmsYHUlWXp/KR6/fwwPEw8sdF+oaDnVYtqtaUjMTMS0ZtPgZulWah42m131kaDYK0DwTrGxk/UWEFbit0TNsKRxo2Xx3/8ctapp+D8/Of2E8y/PI/ZTLC6+vog+jfqUmZevZYZFgmQUMBhorWWHIRwVIO4q8O6R+Li2GHDoIzaKLNuWanh9a1BDqA6g6u6OTwcPyW0D1rLQ4+phddvV+OnGTzgRewIexh7oaNGx0uUJIf8FUbSuv07SEphM5ldHkPUw98CC4AVI+ZCCj4KP9dJXilLzfMj7gH1R+3Aq9hSKROIbpbuROya7TEZzw+YKVlc2E5tNxIALA/Bvwr/o3bg3XA1dFS3pq3j9+TWCPwSDxWShvVV7qHDk5HeV8Qo4OUrW+GEwAQ3TUgyd/4/saJgC7Jrx+9JW0cZEp4nYHL4Z2x9vRxfLLtLgr8XZ/WQ3nmU8g4ayBlZ0+hNMVUMgNx14cgJ4dAj4EAM8OS4+tK3+c7DW+HZ/46ghVAfgtWgBMJkoSkgA/30a2IYGNdZWa9PWGNtkLHyf+WLJ/SVw0HWAsZpxpcpGfojE25y34LF48DLzqjGN9QEtFS246LvgUdojBCYHYpj9MEVLotQhMvIzsD9qP07EnkChsBAA0NygOaY2m4oWRi0UrK5iGmk3wgCbATgVdwobwjbgaI+j9TJ6/LXEawCA1iaty3WUrhKEAJfniI0gy3aA1wKxoaNuAigp7pY6zH4Yjj8/jne573A4+jAmOk0skefJhyfY82QPAOC3Vr/9t/2Sqh7gMQVoNRl4Gy5eZRb1L/ApHri1Uuxk3agT0GwkYNsVYNWNaVx5Uf8+2d8gShoaULG3BwDkhdbsqBAATGs2DY66jsgqysKCuwsgEAkqVe7i64sAgI7mHct82vie8DbzBgDcfnNbwUoodYXPBZ+xOXwzuv3bDQejD6JQWAhnfWfs7rwbfl396oURJGGyy2TwWDw8y3gmHQmub/gn+AMAfCx95Fdp9Dng1S1AiQP02iaePtIyV6gRBIhjnE1vPh0AsPfpXqTnp8ucz+PnYVHQIgiJEN2tuqOrVdeSlTAYQAM3oPd2YE4s0HcnYN4aICLghT9wciSw2V48fZb2vDYuq1aghlAdgff/fcdyg+W73UZpsJXYWN9+PVTZqniU9gi7n+yusAxfxJf+qNTX1WLyxtPMEwAQmhoq3f6A8v1yM/Em+pzrA98oX+QL8uGo64idnXbiULdD8DDxqHdhFvS4etJRhW2PtilkFVt1ePnpJV5+fgk2ky2/EezCbODq/32m2s4EdOW/J1p16GbVDU10myBPkIddkbtkzm0O34zErEQY8gyxyH1RxZUpq4qnxMZdAaaGA21nAWpGQF468GAH8Jc7sLcTEO4nXvpfj6GGUB1B1b1mNmAtCzMNM/zW6jcAwN9P/kZYali5+R+8e4BPhZ+go6IDd2P32pBY57HStIKlhiUEIgHuvbunaDkUBZFZmImFdxdiZuBMfCz4iEZajbCjww4c7XEUbU3b1jsD6EtG2I+AsaoxUnNTpRty1hck02JtTNrIb2Xn7XVA9juxg3PbWfKpU44wGUzpFhn/xP2D15mvAQB339zFidgTAIBVbVdVfZpQrxHQaRkw6xkw9DjQuCfAZAFvwoALM4BNdsDZyUDiffHUYT2DGkJ1BG5zV0BJCfw3b1D05m2ttNnDugf6NOwDERFhwd0F+Fzwucy8ktViXS271vvltPJE8qR5O5lOj32PBL0NQv9z/XHx9UUwGUyMdxyPEz1PwNPMs14bQBJUWCqY0Vy8p1Vp0y11FUIIriWIDSEfKzlNi6XFiFeJAUD3jUAdjR/WwqgFvM28ISRCbAnfgk8Fn7Dk/hIAYsO2lXGrr69ciQXYdQOGHAFmxwCdVwC6NuLQABFHAN9uwA43IGgLkJ0qpyuqeaghVEdQUlMFt2lTAKixKNOlsch9ESw1LJGWl4Yl95eUGmY+j5+HgOQAAHRarDieDcTTY3fe3qm0rxWl/pPLz8XyB8vx842fkZafBksNSxzsdhAzXWfWmXhA8qKbVTc46joiT5An3tOqHhD3KQ7xmfFQZirDq4FX9SskBLj0CyASiEdDbLtUv84aZJbrLCgxlBCYHIifrv+E9Px0NNRsKDVq5YKaAdBmBjA1DBjnL15dxlYFMl4CN5YBmx2Ao0OA55fE+6DVYaghVIfgSabHasFhWtomm4f17deDzWQjIDkAx2OPl8hzK/kW8gX5MFM3Q1O9prWmrT7gYuACTY4mMgszEfkhUtFyKLVAWGoYBpwfgH/i/gEADLcfjpO9Tn6z+zQxGUzMbTEXAPDvi3/x4tMLBSuqGMloUFvTtlBT/rrYPDI8OQkk3gNYXKDrmurXV8NYaVrhB9sfAAAxH2PAYrKwpt2amtm2hcEAzN2BPn+KHax7/wGYuQNECMRdAY4PExtF/r8BH+Lk374coIZQHUL1/9tt5AaH1OoGgPa69pjtOhsAsDFsI2I/xsqcv/z6/7GDrLp/E8P98oTFZKGdaTsAQGByoEK1UGqWAkEB1oWuw7hr4/A25y1MVE2wr8s+LGi54JtfRdncsDk6W3SGiIiw6eEmRcspF0II/BPFCztKXRlVVfI/A/6Lxf97zhWvEKsH/Oz8M1TZqgCAKS5TYK9rX/ONctSB5qOA8f7AlFCg9TRAVR/ITQPubwf+bAHs8xHHKyqsOwtMqCFUh+A2awYGmw3B+/fgJybWatvD7YejfYP2KBIVYd6decj7fzj4jwUfcf/dfQB0WqwsJH5C1BD6dnny4QkGXhiIwzGHAQADbAbgdO/TaGncUsHKao9ZzWeBxWTh3rt7CHobpGg5ZfL843MkZiWCo8SRTl1Xi4DfxRue6toAHtOqX18tocvVxR8d/sD8FvMxtsnY2hegbyfe42x2DDD4iDj+EIMJJAcD56eKHazPTQWSQhTuYF1lQ+jOnTvo1asXTExMwGAwcPbs2TLz/vTTT2AwGNi6dWuF9Z4+fRoODg7gcDhwcHDAmTNnSuT566+/YGVlBRUVFbi6uuLu3bsy5zdu3AhDQ0MYGhpiy5YtMudCQkLg6uoKoVBYqetUBEwuF1xn8fB6bg1HmS4Og8HAyjYroc/Vx+vM11gfth6AeIhZSIRw0HWAlaZVrWqqL7QxaQMWk4WErATEZ8YrWg5FjhQJi7D90XaMvDISCVkJ0Ofq48+Of2JZ62XymXKpR5hpmGFYY3Hg0E0PN9VZnzjJtFj7Bu3BY/OqV9m7CCBsr/j/HhvrXSDBFkYtMMJhRPl7j9U0SmzAvicw7AQwKxrouATQsQaKcsSbwO7vAvzpDsTfrbiuGqLKhlBubi6cnZ2xY8eOcvOdPXsWISEhMDGpOCz3gwcPMHjwYIwcORKRkZEYOXIkBg0ahJAvnIZPnDiBmTNnYvHixXj8+DHatWuHbt26ISkpCQDw9OlTLFmyBMeOHcPRo0exaNEiREVFAQD4fD4mTZqEXbt21czOwnKE10rs0V+bfkISdFR0sKbdGjDAwOkXp3E14ap0tVgPKzoaVBZqympoYSgOlEdXj307xH6MxdBLQ7Hn6R6IiAjdrbrjTJ8zaN+gvaKlKYwfnX6EJkcTLz+/xL8v/lW0nBLIrBarbhBFkUjsIE1EgOMAwNqr+gK/dzSMgXa/ANMeAWMuA85DxX5X6bFi52tFQaoBAHLmzJkS6W/evCGmpqYkKiqKWFhYkC1btpRbz6BBg0jXrl1l0nx8fMiQIUOkr1u2bEkmTZokk6dx48ZkwYIFhBBCTpw4Qdzd3WXynzx5khBCyO+//06mT59elUuTkpmZSQCQzMzMrypfVXJDQ0m0XWMS27oNEYlEtdJmcbaFbyOOfo6k5eGWxNHPkTT1a0rSctMUoqW+cCT6CHH0cySjr4xWtBRKNeEL+eTvyL+Jy0EX4ujnSNoda0f8E/wVLavOcDj6MHH0cyTtj7cn2YXZipYjQ9SHKOLo50haHG5Bcotyq1fZQ19ClmoQ8rspIZnv5KKPUgr5mYQ8O1sjVVf2/i13HyGRSISRI0di7ty5aNKkSaXKPHjwAF26yC5H9PHxwf37Yt+UoqIihIeHl8jTpUsXaZ6mTZsiLi4OSUlJSExMRFxcHBwdHfHy5Uv4+flh1apVldJSWFiIrKwsmaM2UXF2BoPDgTAjA0UvX9Zq2xJ+dvkZzvrOyBOI/YRaGreEPk9fIVrqCxI/ocdpj8uNx0Sp27zOfI2Rl0fij8d/QCASoINZB5zpcwadLTorWlqdYZDdIFhqWOJjwUfsi9qnaDkyyG1aLDdDvAQcALwXikcyKDWDioZ4t3sFIndDaN26dWCxWJg+fXqly6SmpsLQ0FAmzdDQEKmp4oBM6enpEAqF5eaxt7fH6tWr0blzZ3Tp0gVr1qyBvb09Jk2ahPXr1+PatWtwdHREs2bNcOfOnTK1rFmzBpqamtLDzMys0tchD5jKyuC5inelzq2lKNPFYTPZWNd+HdTZ6gDotFhlMFEzga22LUREhLtvFTfXTfk6RESEg88OYtCFQYjKiII6Wx2r267GVu+t0OXqKlpenYLNZGOWqziq8sFnB/Eu552CFYkhX0yLdbWs5mqxm8uA/E+AQROg5U/VF0ep08jVEAoPD8e2bdvg5+dX5WXWxfMTQkqkVZRn0qRJiI2NRWxsLCZNmgQ/Pz+oq6vDw8MDEyZMwJkzZ7B582YMGTIEhYWFpepYuHAhMjMzpUdycnKVrkMe8FpKttuofT8hCaZqpvi789+Y5ToLvRr2UpiO+oRkhQpdPVa/SM5Oxrhr47Dh4QYUCgvRxqQN/u3zL3o17EXDRZSBt5k3Whi1QJGoCNsebVO0HADA0/SneJf7DlwWF21N2359Rclh4t3XAaDHJoVvpkqpeeRqCN29exdpaWkwNzcHi8UCi8VCYmIifvnlF1haWpZZzsjISDqyIyEtLU06AqSnpwclJaVy8xQnPT0dK1aswB9//IGQkBDY2trCxsYG3t7e4PP5iIsrPbATh8OBhoaGzFHbqLaSBFYMBRGJar19CU31m2Kc4zi6pUYlkexGf+/dPfDreCRVivhB6mTsSQw4PwDh78PBZXGxxGMJdnbaCSNVI0XLq9MwGAzMcZsDBhi4HH8ZTz88VbQk6WiQl5nX1wcOFAqAS+KYanAZDlh4yEkdpS4jV0No5MiRePLkCSIiIqSHiYkJ5s6di2vXrpVZzsPDA9evX5dJ8/f3R+vWrQEAysrKcHV1LZHn+vXr0jzFmTlzJmbNmoUGDRpAKBSCz//vxiQQCOr0MnqVJk3A5PEgzMxEYWxsxQUodYImek2gx9VDLj8XYe/L38SWolhSc1Mx6cYkrAxeiXxBPlwNXXG692kMtB1IR4EqiYOug3S0eMPDDbUaBLY4IiL6L4hidabFHu4DUp8AKppAp+VyUkep61T5UT8nJwcvv3DijY+PR0REBHR0dGBubg5dXdn5dDabDSMjI9jZ2UnTRo0aBVNTU6xZIw5VPmPGDLRv3x7r1q1Dnz59cO7cOdy4cQNBQf8F7Zo9ezZGjhwJNzc3eHh4YPfu3UhKSsKkSZNKaLx+/TpevHiBgwfFw5stW7bE8+fPceXKFSQnJ0NJSUlGT12DwWaD6+aK3Dt3kRscAhX7WogISqk2TAYTng08cfrFaQQmB6K1SelGOkVxEEJw4fUFrA1Zi2x+NjhKHMxoPgPD7YeDyaDxZavK9GbT4Z/gj8dpj3Ej6YbCnMqffHiC1NxUqLJV0ca0zddVkv0euPX/RTUdlwBqdIHI90KVv/kPHz5Es2bN0KxZMwBiA6VZs2ZYsmRJpetISkpCSkqK9HXr1q1x/Phx+Pr6wsnJCX5+fjhx4gTc/7/3FgAMHjwYW7duxYoVK+Di4oI7d+7g8uXLsLCwkKk7Pz8fU6dOxd9//w0mU3x5pqam+OOPPzB27Fj8/vvvOHDgALjcuh0SX9X9//GEFOgnRKk6X+5Gr8gnZEpJ0vPTMTNgJhYHLUY2PxtN9ZriZK+TGOkwkhpBX4mhqiHGOI4BAGx+uBlFwiKF6JBMi3mbeYOjxPm6Sq7/BhRmASbNAFcFRGKmKAwGob/W5ZKVlQVNTU1kZmbWqr9QftQzJPzwA5hqarANfgAGi/rp1AfyBflod7wdCoWF+KfXP7DTqbsjj98T/gn+WBW8Cp8KP4HFZGGy82SMdRxL/d/kQB4/Dz3O9EB6fjrmuM3B6Caja7V9ERGh86nOSMtPw44OO+Bp9hXbaiQEAX49ADCAiTcBU1e566TUPpW9f9PHoDqKin1jMDU0IMrJQUF0tKLlUCoJl8WFh7HYwfL2GxplWtFkFmZi3p15+OX2L/hU+Am22rY43uM4JjpNpEaQnOCxeZjeTBwu5e8nf9d6HK2ItAik5adBna0OD5OvcG4W8sURpAHAbSw1gr5DqCFUR2EoKYHXQrxtQy6dHqtXSJ5I6TJ6xXLnzR30O9cPV+KvgMlgYmLTiTje4zgdpasBejfsDVttW2QXZWPXk1212vbVhKsAAG9zbygrfcVeYMF/AR+eAzxdoMNvclZHqQ9QQ6gOo+ou3tk6r5Y3YKVUD0k8oafpT5Gen65gNd8fOUU5WHZ/GabcnIIP+R9gqWGJQ90OYXrz6WArsRUt75tEiamEOW5zAAAnnp9AQmZCrbQrFAlxPVG8mvirVotlvgUC14n/77wC4OnIUR2lvkANoToMT+Iw/egRSJFinBApVUefp4+mek0B0E1Ya5uQlBD0P98fp1+cBgMMjHQYiVO9TsFJ30nR0r55PEw80L5BewiIAJvDN9dKm4/SHiE9Px0ayhpoZdyq6hVcWwjwcwEzd8B5mPwFUuoF1BCqw3BsGkFJWxskPx/5UVGKlkOpAtIo028CFSvkOyFfkI81IWswwX8CUnJTYKpmin0++zCvxbyvD65HqTK/uP4CJYYSApIDEJZa87G0JKvFOpp3rPpo38ubQPQ5gKEE9NgMMOnt8HuFvvN1GAaTCd7/QwjkBgcrWA2lKkiW0Qe/C0a+IF+xYr5xItIiMPDCQBx9fhQAMNB2IE73Po0WRi0UrOz7w1rLGj/Y/gAA2BC2ASJSc5HxBSLB10+L8QuAy+KpPLj/BBg5ylkdpT5BDaE6jtRPSEEbsFK+DlttWxirGqNAWICQFOrjVRMUCYuwJXwLRl8djcSsRBjwDLCr0y4s8VgCVbaqouV9t0x2mQw1thpiPsbgwqsLNdZO+PtwfCz4CC2OFloYV9Hovb8d+PgaUDMCvBbWjEBKvYEaQnUcyYhQ/uPHEJWxUSyl7sFgMKSjQnV59ZhQJERaXhqi0qPw4N0DRKRFIPZjLJKzk5GRn4E8fl6dDAwZnRGNwRcHY3/UfoiICL2se+Hf3v9+fVRhitzQUdHBRKeJAIDtj7cjj59XI+1IVot1NO8INrMK02KfEoC7m8T/+/wOqNT+fpKUugUNpFHHUbayAktfH4IPH5D/OEK6IStFDBEIICoogJKamqKllMCrgReOPT+G229uQ0REtR69mC/kIy0/De9z3+N93nuk5aUhNTcV7/P+e/0h7wOEpPx99xhggMvigsfmif+yeGX+z2X//+//85eXl6PEqfK+XnwRH3uf7sXuyN0QEAF0VHSwpNUSdLToWJ2uosiZ4fbDcTL2JN7mvMWB6AP42flnudYvEAlwI/EGAMDH0qdqha/MBwQFgFV7wHGAXHVR6ifUEKrjMBgM8NzdkXXxIvJCQ6ghVIx3Cxch298flieOQ6VxY0XLkcHNyA2qbFWk56cjOiMajnry80PI4+fJGDQSY0f6N+89PhZ8rFRdSgwl6HH1oMHRQIGgAHn8POQJ8qS+TQQEeQJxmjxhMpgljCYuiys1pko7dzn+Mp5lPAMAdDLvhN88foOOCl3yXNfgKHEws/lMzL0zF75RvhhgMwAGPAO51R+aGorPhZ+ho6JTNV+w55eBuKsAkw103wTQDXYpoIZQvYDn3hJZFy8iNzgE+tMVrabuIEhPR9alS4BIhE9HjsB45UpFS5JBWUkZbUzawD/RHwHJAZUyhAghyCrKQmpuqtjAyfvPwJG+zn2PbH525TQwlWGoaggDngEMeYYwVDUU/5UcqobQVdGFElOpRFkREYkNI0Ee8vn5UuMoj///v4I8GaNJ8v+X+UuU4eehQFggrT+Hn4Mcfg5QBX9ydWV1LHZfjO5W3elO8XUYH0sfHIo5hCcfnmDH4x1Y0WaF3OqWrBbrZN6p8hHCi/LEo0EA0HoqoG8rNz2U+g01hOoBqq3E8THynz6FKC8PTB5PwYrqBllXrgIi8aqUrEuXYbhgAZiqdctJ1svMC/6J/ridfBtTXKYgIz9DPEWVlypr3Hxh7EgMhYpQY6vJGDiS/41UjWDIE7/W4mh9tbHAZDDFozFsHiDHPYqFIiEKhAWlG1HFjKziBpemsiYmOk2U6+gCpWZgMBiY6zYXI6+MxNmXZzHcfrhconrzRfyvmxa7uxHITAI0zYD2c6utg/LtQA2hegC7QQOwTIwheJeCvEePodaWOoQCQNbFi9L/RXl5yLp6DVoD+itQUUnambYDk8FE7KdYuB5yhYAIKlVOm6MtO5LzhbFjxDOCAc8Aasp1zy+qMigxlaDKVKUru74DXAxc0NWyK64mXMWGhxuwp/Oeao/ihaSEIKsoC7oqunA1rOS+YOkvgHvbxf93XQMo088e5T+oIVQPYDAYUHVvhcwzZ5AXEkwNIQBFycnIj4wEmExoDx+OT4cO4fO//9Y5Q0hLRQvtTNvh9pvbEBABmAwm9Lh6JYyb4q85ShxFS6dQ5MKM5jNwM+kmQlJCcPftXbRv0L5a9V2NF68W62zRudQp3RIQIo4ZJOIDNl2Axj2r1T7l24MaQvUEnntLZJ45g1waTwgAxL5BEE8b6k6YgE9HjiA/PByFr+PBsbZSsDpZNnhuwOvM19BV0YUeV4/uek75rmig3gAjHEbAN8oXGx9uhIeJR9WWu38BX8jHraRbAKowLfbsDPA6EFDiAN3WUQdpSgloHKF6gur/4wkVREVBmF05R9lvFUIIMi+Ip8U0evYE29AAau3FT5mZ/55WpLRS4bK4aKLbBEaqRtQIonyXTGw6EdocbcRnxuN03Nd/Rx+kPEA2Pxv6XH00N2xecYHCbODaIvH/7WYDOtZf3Tbl24UaQvUEtrEx2BbmgEiEvIcPFS1HoRQ+f46iV6/AUFaGeudOAACtH8TxQD6fPQfC5ytSHoVCKYa6sjomu0wGAPwV8Reyi77uYU4yLdbFskvl4nIFrgWyUwBtK6DNzK9qk/LtQw2heoRqS/Go0Pe+3Ubm/52k1by8oKSuLv7f0xNKuroQpqcj584dRcqjUCil8IPtD7DStMKnwk/Y83RPlcsXCgsRkBwAoJLTYu+fAcE7xf933wCw6ea7lNKhhlA9gvf/YIq5Id/v3lVEJELWpcsAAI2ePaTpDDYbmn36AAA+n/5XIdooFErZsJgszHETb3R6OPow3mS/qVL5+2/vI4efA0OeIZz1ncvPTAhwaQ5AhIB9L8Cm89fKpnwHUEOoHqHaUrwBa+Hz5xB8+qRgNYohPzwcgtRUMNXVoebpKXNOMj2Wc/s2+GlpipBHoVDKoZ1pO7gbu4Mv4mPbo21VKivZW6xS02KRx4Gk+wCbB3Rd+7VyKd8J1BCqR7D09aHcsCFACPLCwhQtRyFkXhSvFlPv0hlMjuwSc461NbjNmgH/Y+++w5ssvwaOf5M03bulLQXasvcse++NiuytgoosAfVF3D8XoogoCMgSVJYsWbL3ngXZs3QAXXSvNON5/3jaaGmBAmmTtPfnunqZJs84iSU5uZ/7PkevJ2njRnOEJwjCY+QUWVSgYPud7ZyLOVeg/TJ1mcbmxU+8LJaRADs/km+3+T9wK/vM8Qolg0iErEzO6rGSOE9IysoiZbv8rdCtZ/61QHJGhZLWrbfIrumCUNJV9azKS5VeAuC7098V6N/pkbtHSNelU9qpNHW86zx+471fQnoceFeFpmNNELFQ3IlEyMo45iRCJ0vePKHUw0fQJyWhKuWNY/ZlQkIPwq5PIekuAC5duqJwdCTrzh0yzpwxY7SCIDzKuPrjcLBx4J/Yf4x9wx4nZ5suQV0eX5n6XgicWizf7jEDbGxNEa5QzIlEyMo4NpY7LWtu3EQXF2fmaIpWTksNt+7dUahUctn8FQPgyCyY0wiO/ITKwRbXbl0BMWlaECyVj6MPr9Z6FYBZZ2eh0WseuW2GLoP9kfuBJ1wWMxhg6zuABLX7Qfnnq2AtlBwiEbIyNh4e2FWrBkD6yZJzecyQlkbKXrmirGvPnqDTwNpXQZsOts6gTYNdH8P8Vri3kLtKJ2/fjj411ZxhC4LwCCNqjMDH0Ye7qXdZfmX5I7c7FHmIDF0GZZzLUNOr5qMPeHYZ3D0Ddq7Q+ctCiFgorkQiZIWcmsiXhdKOl5zLYyl79yJlZqIODMC+Vi35cljUBXD0gnGn4IU58u3YKzicmIBtKQekjAyS//7b3KELgpAPR7UjE+pPAGDhPwuJz4zPd7sCXRZLi4Pdn8m3230ALn6mDlcoxkQiZIUcmzQFIL0E1RPKKaLo1rMXius74ER2obSX5oGrPzQYBuNOQ/CrKBQK3MtEAZC4bB4Y9OYKWxCEx+hVsRfVPauTqk1l7rm5eR5P16ZzMFIukPrYy2K7P4XMRPCtDY1eL6RoheJKJEJWyLFRQ1AqyQoLQxsVZe5wCp0uPp60w0cAcG3bCDbKpfpp8hZU+c+bo6Mn9JoFo/bg1rgCKCQyb0WhmdYKIkt2WxJBsERKhZL3Gr0HwNrra7mdeDvX4wcjD5KpzyTAJYDqntXzP0j4CQj5Q77d43tQiX5+wtMRiZAVUrm4YF9TvlZeEkaFkrdvB70e+5o1sDv7JaQ/AL/a0Ol/+e9QNhibiQdwblAJgMSTEbCoI2x+G9LzH34XhJJAMhh4sORX7n/8CYa0NHOHA0Ajv0a0K9cOvaTn+zPf53rsiZfF9LrsCdJA/aEQ0KSwwxWKIZEIWSnjPKESUE8oObuIomsNF3m5vNoR+v4KNnaP3kmpwn2U/AaZFOmBpJfgzFKYHQxnf5NXmAhCCaJLSCDizdHEfPstiWvW8GDpUnOHZDQ5eDI2ChsORh7k2L1jAKRp0zh09xDwmMtipxZC9AWwd4eOj/hiJAhPIBIhK2WsJ1TMR4SyIu+ScfYsKBS4ZsnzhOj+HXhXfuK+zq1aYVOqFPo0LSlVv4BS1SEjHjaNhyVd4P4/hRy9IFiGjIuXuNOnL2mHDoFSftuPX7oMfXKymSOTBbkFMaDaAABmnJ6B3qBnf8R+NHoNQa5BVPGoknenlCjY+5V8u+On4ORddAELxYpIhKyUY4MGYGOD9u5dsiKfrnmhNclZ9eVYGtQOWqjVB+oNKdC+Chsb3Hr3BiDx4EUYfUheVmvrDJEnYUEb2PY+ZFrGh4EgFIaENWsIGzwY7b17qAMCKL92DXaVK2FISSF+6TJzh2c0us5oXGxduJ5wnU23Nj35stjOjyArBfwbQIMRRRytUJyIRMhKKZ2ccKhdGyjeo0LGIoplEsA9AHr+AI+rLPsQ95flRCjt8BG0sQ+g+XgYexJqvASSQV59NqchXFgrd6wWhGLCkJnJvQ8/JOrjT5CysnBu147ya9dgX6MG3mPHARD/22/oExPNG2g2d3t33qzzJgA/nv2Rw3cPA4+4LBZ6EC6sARTyBGmlqggjFYobkQhZMcdiXk8o89p1NNevo1BKuARkQZ8lYO/2VMewDQrCsWFDMBhI+usv+U63MtB/GQxdD54VITUa1o2EZb0g9prpn4ggFLGsyEjuDB5M0rr1oFRSatIkyv48B5WrKyA3LbarWhVDaqpFzRUaVG0QZZ3L8iDzAVqDlgpuFajkXin3Rros2PqufLvRSCjToOgDFYoVkQhZMaem/9YTKo4NRpPX/AaAU+lMVF0/gHKNnuk4btmNWBPXrUf67yTpSh1gzDFo9xHY2MOdQzCvhVysMcsyVtQIwtNKPXCA0D590Vy+gsrDg4BFC/F+8w0U2XODkCQUkh7vcXJD0oTffkeXkGDGiP9lq7JlcsPJxt+7BnXNe1ns+M8Qdw0cvaH9R0UcoXWR9HoMGRnmDsPiiUTIijnUq4dCrUYXE0PWnTvmDsekpKwMkjduAMCtUXloMemZj+XapQtKZ2e0ERGknzyV+0EbO2jzHow9AVW6gUEr9y77uQlc2SwulwlWQ9Lrif1pNhGj38KQlIR9nTqUX78Op+bN5Q0Sw2H/NzCrDnzpi0vYt9iV9cCQnk78ksXmDf4/OgZ0pFWZVjipnehVsVfuBxMj4MC38u3OX4CDR9EHaCX0iYmEDR7CtQbB3H75ZaKnTSNlzx6LuRRqSZ46ETp48CC9evXC398fhULBXzmXGwCtVsuUKVOoXbs2Tk5O+Pv7M3z4cO7du/fE465bt44aNWpgZ2dHjRo12LBhQ55t5s6dS/ny5bG3tyc4OJhDhw7lenzGjBn4+vri6+vLDz/8kOuxEydOEBwcjF5ffKoMK+3tcahfHyh+84QyFr2NNsWA0kbCefKvxpUuz0Lp4IBrjx4AJK5bl/9GHkEweBUMXAluAZAUAauHwor+EB/6zOcWhKKQszQ+bu5ckCQ8Bg8i8I/fUXt7yPPffntJToD2T4OkcJD0KCJPUqr8TQDif12EbmFfOPGLfHnYjF8AFAoFP7X/iQMDDlDWpWzuB3dMlfsLBjSDuoPME6AV0D14QNiIV8g4fx4kCc3lK8Qv+43IseO43qw5t1/qTdRXX5O8a5fFjAaa01N/uqSlpVG3bl3mzJmT57H09HTOnj3Lxx9/zNmzZ1m/fj3Xr1/nhRdeeOwxjx07xoABAxg2bBjnz59n2LBh9O/fnxP/+XBfvXo1EydO5MMPPyQkJIRWrVrRrVs3wsPDAbhw4QKffPIJK1euZMWKFXzwwQdcvHgRkBO00aNHM3/+fFSq4jWpzjhPqDglQtd3kPz3dgBcWjVG6RP03Id07/MyACk7dz5+yXC17vLoUKt3QamGGzvl0aH900Gb+dxxCIKpZVy4KC+NP3wYhb09/t98g9+oF1Du+gC+ryLPf7u9D5DkjuwvL4RxZ6DXjzh36oa9lwFJpyB+63HY9n/wc2P4oSb8NQb+WQOpMUX+nGyUNtipHqoTdmO3PEqrUMkTpJ9i0URJoo2OJmzoMDTXrqEq5U3g77/h//0M3AcOwLZCBTkxunqVhN9/5+74Cdxo1pzbL7xI1BdfkrxjJ7r4kld0ViE9x+QShULBhg0beOmllx65zalTp2jcuDFhYWEEBATku82AAQNITk5m27Ztxvu6du2Kh4cHK1euBKBJkyY0aNCAefPmGbepXr06L730EtOmTePPP/9k5syZHD9+3Lj9u+++S79+/fj666+Jjo7mxx9/fOrnmJycjJubG0lJSbhmTzS0JOmnTxM2dBgqT08qHzn86KaE1iL5PtLcFtxYaYNeo6LcokU4t2zx3IeVJInQF15Ec+MGfp9+gsegAnybjLsBf78Lt/fLv3uUh+4zoHLH545HEEwhYc0aoj//AkmrRV22DGXfbIN93A65yGAO17JQfwjUGyyPfD4kZd8+It8ag8LWhkqjy2Hz4DToNbk38q0FFdpChXYQ2BxsHQv1eeWhzYS5TSEhFJqNgy5fFe35rURW5F3CX30VbUQENqVLE/jrEmyDgnJto4uNJf30adJPnSLt5Emybt7Kcxy7ypVwbNQIx8aNcWzUCBsvryJ6BqZV0M/vQm/KkpSUJDfBdHd/5DbHjh1j0qTcc0C6dOnCrFmzAMjKyuLMmTO8//77ubbp3LkzR48eBaB27dpcv36d8PBwJEni+vXr1KpVi5s3b7J06VLOnDlToHg1Gg0azb9vAskWUnDsURzq1EFhb48+Ph7NjRvYV8mn8Ji1MOhhwxuk3U5Fr/FC5emJU1PTlMxXKBS49+1D9LRvSFy7rmCJkHdlGPYXXFoPOz6U34SX94HqvaDrN+BW9omHEITCYMjMJOqLL+RVYYBzNXf8a/+D6lL2HDiVHVTvKbedKN/mscvLndu2xb5OHTL/+YcHKW3xnbIWwo/JXwBu74OoCxB9Uf45NgdUtlCuiZwYVWwHpesV/vL1I7Pkf38upaHt+0/cvCTShIYS/upr6KKiUJcrR+DSX1GXKZNnO5tSpXDt1g3Xbt0A+TJa+ik5MUo/eRLNjRtobtxEc+MmCSvkgQjbihVxbNQQp5zEqFSpIn1uha1QE6HMzEzef/99Bg8e/NhsLCoqCl9f31z3+fr6EpXdUDQuLg69Xv/YbapXr87XX39Np06dAJg2bRrVq1enY8eOfPvtt+zYsYPPPvsMtVrNjz/+SOvWrfONZdq0afzvf9ZTql1ha4tjgwakHT1K+omT1p0IHfkRQg+SFCFXiHXt3h2Fjen+RF1feIHoGd+TeekSmVevYl+t2pN3UijkIo6VO8sTTY/Pk4fnb+6FtlOg6RhQqU0WoyA8SVZEBJFj30Jz/RYooFTtZLyq35OvFJWuC/WHyX+zjp4FOp5CoaDU+HFEvP4GCStX4vXaq9hU6iCvqgRIi/s3Kbq1H5Ij5RWWdw7B3i/k9hblW8tJUYV24FnetE84/jYcminf7vIV2LmY9vjFQOb164S/NhJ9XBy2FSoQ8OsS1A99Xj6KjZcXrl274NpVrtekS0iQk6JTp+XE6No1sm7dIuvWLRJXrQbAtnz5XCNGal+fQntuRaHQEiGtVsvAgQMxGAzMnTv3ids/fElHkqQ89z1pm9GjRzN69Gjj70uXLsXFxYVmzZpRtWpVTp06RWRkJAMHDiQ0NBQ7u7y9qqZOncrkyf8u30xOTqZcuXJPjN+cHJs2Je3oUdJOHMdz2FBzh/NsIk7B3i8x6BSk3HMCtLj17GHSU9h4eODSvj0pO3aQuHYdfh99WPCd7VzkN+F6g+Umj+HHYNcncG6FPF8hqKVJYxWEPLLSSV3xPXd/WIVBY0Blp6dMswScghyhzpvy6E/pOs90aKeWLXGoW5eM8+d5sGgRvlOn/udBb6jdV/6RJHhwKzsp2icnQ5mJcGWT/APy5becy2jlWxc4IcuXJMG2KfKlugptoebLz36sYirj0iUiRo5Cn5iIXbVqBCxe9FyXsmw8PHDt3BnXzp0BOTHKOHNGvpR26hSaK1fJCg0lKzSUxD//BMA2MBDHxv9JjPz8TPLcikqhJEJarZb+/fsTGhrK3r17nzi3xs/PzziykyMmJsY4AuTt7Y1KpXrsNg+Li4vj888/5+DBg5w4cYIqVapQuXJlKleujFar5fr169TOrsz8X3Z2dvkmSJbMqUljYoH0U6eR9HoU1jYhPDNJntAp6UlRtkHS3EBdtiz2deua/FTuffuQsmMHSZs34/Peuyif9v+1b014dRucXwk7P4bYq7C0B9QZAJ2+AJeCfQsThAKRJLh7BunMb8Qt30LcP/Lfq71XFmUH10Dd5lWo2h3U9s91GoVCgfeE8USMHEXCylV4vjYy/2/5CgV4V5J/Gr8ud3+/d1ZOim7vl1vXJNyRGxyfWQoowL+enBRVbCdfUntcs+SHXd0qL1hQquX5edY+B9LE0kNCiHjjTQwpKdjXrk3AwgWoHjMN5VnYeHjg0rEjLh3luZH6pCTSz5wl/eRJ0k+dIvPKFbLCwsgKCyNxzVoA1AEBuS6lqf39TRqTqZk8EcpJgm7cuMG+ffvwKkBm2qxZM3bt2pVrntDOnTtpnl3/wtbWluDgYHbt2kXv7N5RALt27eLFF1/M95gTJ05k0qRJlC1bllOnTqHVao2P6XS6YrWM3r5mTZROThiSksi8ehWHmjXNHVLBSRJsmQSJYeAeQPIVX+AGrj17FMrEb6fmzbEpXRrd/fuk7tmDa/fuT38QhUIeGaraDfZ8AaeXwD+r4do2ucBbw5GgKvTpd0JxlhoL/6yCkD/QRV7j3jEP0qLkZMejTTV8vvweZakKJj2lU/PmODRoQMbZszxYuLBgI6YqGyjXWP5pOwU0KXDnyL+X0mKvwr0Q+efwTLBxkCdb51xG86356OQmKw22Z88HajGhQI2WS5K0EyeJeOstpPR0HIKDKffLfFTOzoV+XpWbGy7t2+HSvh0A+pQU0s+cIf3kKTkxunQJbXg4SeHhxjls6rJlc11Ksy2bd+6SOT31u3Vqaio3b940/h4aGsq5c+fw9PTE39+fvn37cvbsWbZs2YJerzeO4nh6emJrawvA8OHDKVOmDNOmTQPg7bffpnXr1kyfPp0XX3yRjRs3snv3bg4fPmw8z+TJkxk2bBgNGzakWbNmLFiwgPDw8FyXwnLs2rWLGzdu8NtvcmXixo0bc/XqVbZt20ZERAQqlYqqVas+7VO3WAobGxwbNiT1wAHST5y0rkTo3HK4uA4UKnSdfiR10dsAuPXq9YQdn41CpcK990vEzZ1H4tp1z5YI5XDwgJ4z5UsSW9+Rvxlv+z8I+QN6zHzmSthCCaXXwc3dEPI7XN8OBh0Z8Woij/igS1OhsFNT+n9f4PZS/l/+npdCoaDUhPGEv/IqiX/+ideokU9/icPOBap2lX8Aku/B7QNyUnR7v9zO5tYe+QfAqdS/l9EqtJXb3+Q4+J1cz8stQC5nIRilHjpE5LjxSBoNTs2bUXbOHJSORbySL5vKxQWXtm1xadsWAH1qKhln5RGjtFOnyLx4CW1kJEmRkSRl1wdU+/v/mxg1boS6bFmzrnh+6uXz+/fvp127dnnuHzFiBJ999hnly+c/UW7fvn20zX6h2rZtS1BQEEv/0+Nm7dq1fPTRR9y+fZuKFSvy1Vdf8fLLua8Hz507l2+//Zb79+9Tq1YtfvjhhzyTnjMyMqhXrx6rV6+mXr16xvsXLVrERx99hJ2dHXPnzqVHj4LNP7H05fM5Hiz5lZhvv8W5TRvK/TLf3OEUTNwN+KW1XCCtwyck3C1L1GefYVe9OhU2rC+002ZFRnKrYydQKKi0e1e+KyuemkEvXwrY87k8ZwLkjtgdP3u+ORJC8Rd3Q06ez6+UEwXkgdLE+OpE70tF0ulRBwZQ9qefsC/kL3CSJBE+bDjpp0/jMXgQfp98YsqDQ8yVf+cXhR2R/+3/l3cVOSnyqy2PFBu0MHAFVDPtfEFrlrJ7N5GTJoNWi3PbtpT5cdbTX+IvQvrUNDJCQoyX0jIuXgSdLtc2NqVL4z/ta2PbKFMp6Of3c9URKgmsJRHKvHyZ0Jf7oHRyosqJ4yZdbVUodBpY1EFemlu+NQz7i7Dhr5B++jQ+772L18iRhXr6sFdeJf34cbzHjqXU+HGmO3BanNyr7Nwf8u8OnnIyVH/Yc1XHFooZTQpc+ktOgCKO/3u/ozeG6v2I2pdC0t97AXDu0AH/b6ahcima1VJpJ08SPnwECrWaiju2F978Dl2WPKfo1j45OboXApIh9zaVu8Dg1WJuULakrVu5939TQK/HpWtXynw7HUX2lRZrYUhPJz0kxHgpLePCBdBqqfD339hVMO2KQ5EImYi1JEKSwcD1Zs0xJCURtHoVDoUw0diktk+F43PB0QtGH0GbBjfbtQeg0r69qEuXLtTTJ23ewr333sPGvzSVdu/+tyGlqYQfhy2TIeaS/HvZRtB1OpQNNu15BOshSfLfRcgfcGkDaLMb+yqUcnmG+kPJsq9J5OR30Vy5IneNnzgRr1EjTf/3+QRhI14h/cQJ3AcMoPT/Piuak2YkQOihf+cXGXQwYnO+RSBLosR167n/0UcgSbi9+AKlv/rK8r/wFoAhI4OM8//g2KSxyS+PFfTzW3xFLSYUSiWOjRoC8iQ6i3Z9h5wEAbw0D1xLk/z33wA4NmxY6EkQgEunjihdXdHdu0/asWOmP0FAU3jzIHT5GmydIfIULGoP69+U500IJUfyfbkOzuxg+LWrPFqoTQOvSvJo4aTLMHg1KdEuhA4YhObKFVSengQsXoT3G68XeRIEGEdJE9evJyvybtGc1MEDarwgz7ubEAITL4gkKFv88uXc//BDkCTc+/en9LRpxSIJArkXpFPTJmadIyQSoWLEqYl8fTX9+PEnbGlGyffhr7fk203egipyEa+kLVsBcO3Zs0jCUNrb45Z9rqRHNWJ9XiobaDYWxp2GekPk+/5ZJX8g7p8OWemP31+wXrosufDm8v7wQw3Y8z+IvwVqJ3ly/Ws75L+LlpOQnHyI/eknIke/hSE5GYe6deWu8c2amS18x4YNcWreDLRaHljLnMNi6sHixUR/8SUAniOG4/e/z8ySHBdn4tLYE1jLpTGQq4uGvvAiCnt7qp48YXnXjg16+P0lCD0oT4YctQds7NDcvMntnr1ArabywQPYeHgUSTg586oUajWViuK8d8/Cjg/kYowArmWg4//kQnViDkTxkHwPjv0M51dBety/95drKidANV/KVRlZl5DAvXffI+3IEQA8Bg/G9/0pFvFvN/1sCGGDB4ONDRW3/Y2thReWLW4kSSLu57nEZTc49xr9JqXeftv6+0kWIXFprASyq1wZlacnUmYmGf/8Y+5w8spuoYHaEfosMRZWS9qyBQDnli2LLAkCsK9RA7vq1ZG0WpI3byn8E5ZpIBdj7LdUXhKcfBfWj4LFneTK2oJ102lgaU+5H1d6HDj7QouJ8sjPyB3QYFiuJCjjwgVC+/Qh7cgRuWv8d9/i98nHFpEEATg2qI9Ty5ag0xE3T4wKFSVJkoiZMcOYBJWaOBGfiRNFElRIRCJUjCgUChybNAYg7cQJM0fzkOwWGgB0+xZKyT3RJEki2XhZrOiXyLr36QNA4rp1FMngqEIBNXvDuFPQ4ZN/5w8t7gjrXoekyMKPQSgcx+fJl7+cfWHQannuT6f/5SkEKEkSCav/JGzwEHT37qMODCBo9epCq531PHLmCiVt3EhWWJiZoykZJIOB6C++JH7xEgB8p76P9+g3zRxV8SYSoWLGqYncrT3dkiZM/6eFBjVfli8R5Dx0/jzayEgUjo645FOfqrC59eqJwtYWzbVrZF66XHQnVttDq3dg/Jns10MBF/6E2Q1h3zS5qq5gPVKi5QKAIF/urNo13+rihsxM7n/wIVGffoqk1eLcsQPl167FvqplNkt2qFsXpzatQa8Xo0JFQNLruf/xxySsWAEKBX6ffYbniBHmDqvYE4lQMeOYnQhlhIRgyMw0czTkaaFBr1m55sPkTJJ26dDBLJVRVW5uuHTqBEDiurVFfn5c/ODFn+GN/RDYAnQZcOAbOSE6vxoMhiceQrAAez+HrFQoEyz3nctHVkQEdwYNlqvrKpWUemcyZWfPLrL6QM+q1LjsUaFNm9CEhpo5muJL0mq5939T5LYUSiX+30zDY2D+f0uCaYlEqJixDQrCxscHSasl49w5c4eTq4UGfZaAvZvxIUmnI3nbNgCTd5p/Gu595ctjyVu2mi959K8Hr2yF/r/JCWPKPdjwhnzJLMKCRveEvO6ehZDl8u2u3+RbODNl3z5C+/T9d2n8ksV4v/66Vcz5cKhdG+d27cBgIG7ePHOHUywZsrKInDSJ5K1bwcaGMjO/x+0RfTQF0xOJUDEjzxOSR4XSzL2MPu4G/P2efLv9h3l6b6UdP4H+wQNUHh44ZTfYNQfHJk1QlymDISWFlJ07zRYHCgXUeBHGnpLry9g6w90z8mTqtSMhMcJ8sQn5kyS5OCiSPBJUrnHuh/V6Yn78kci3xuReGm/iVgKFzXvcWED+sqC5fdvM0RQvhowMIseOI3X3HhS2tpSd/ROuXbuaO6wSRSRCxZBTUwuYJ6TTwNpX5V5C5VvLq2cekrx5MwCu3bqiUKuLOMB/KZRK3F7uDcjVW81ObQ8tJ8H4s9BgOKCAi2thTkN5wrkm1dwRCjkurpNbZKgd5eT1P3QJCUS88SYPsufWeAwZQuDvvz19I1ML4FCzJs4dO8ijQj/PNXc4xYY+NY2IN0eTdugQCnt7ys2fZ5a5kiWdSISKIeM8oQsXMKSZadLt7s/kPmIOntB7AShVuR42ZGaSsmsXUHRFFB/HvXdvUChIP3GCrPBwc4cjc/GFF2bLFaoDW4IuU56QOzsYzq0Q84fMLSsddmU3JW05GVz/7cmVa2m8g4O8NP7jjyxmafyzyJkrlPz332hu3DBzNNZPn5xMxMiRpJ88idLJiYBFC806Ml6SiUSoGLItW1buqK7TkX72bNEHkE8LjYel7t+PIT0dtb8/DvXqFW18+VD7++PUogUgtxWwKKXrwCtbYMAfcsuB1Ci5Ovei9hBWCO1BhII58qNcC8otAJrLSYIkSSSsWm1cGm8bGEjQ6lUWuTT+adlXqyYvLJAkYueKUaHnoUtIIOyVV8g4fx6lqysBvy7BsWFDc4dVYolEqJhyNC6jL+J6QilRuVtoVM3/WndOEUXXHj0sply8e5+XAUja8BeSXm/maB6iUED1XjD2JHT6HGxd5G7dv3aFNa9AgqjxUqQSI+RECKDz56B2ACB25kyiPvsMSavFpVNHgtauwb6KZS6Nfxbe2aNCKdu2k3ntupmjsU662FjChw9Hc1meOB/42zIc6tQxd1glmmV8Agkm55RTWPF4ESZCBgOsfwPSH8gtNDr9L9/N9ElJpB04CIBrL/NfFsvh3KEDKnd3dNHRpB0+bO5w8mdjBy3ehglnIfgVuXP5pQ0wpxHs+Rw0KeaOsGTY/alc6iCwBdR4CQDN7VAeZBfBK/XOZMr89JPFL41/WvZVq+CSPZE37uefzRyN9dHev0/Y0GFobtzExseHwN9/w75aNXOHVeKJRKiYyhkRyrx8GX1KEX04HpkFoQfytNB4WMquXUhaLXZVqljUt2WlrS2uL8iXMCxi0vTjOPtArx/l+UNBrUCvgUPfy/OHQv4Q84cKU9gxeZI0Cnm5fPYS+LiffwaDAef27a1mafyzKDV2DCgUpOzcSebVq+YOx2pkRUQQNmQoWWFh2PiXJvCP37GrWNHcYQmIRKjYUvv5YRsYCAYD6adOF/4JH9FCIz9J2X29LGGS9MPc+/QF5Lovuvh4M0dTAH61YcRmGLgCPMpDajRsHAsL28KdI+aOrvgxGGD7FPl2g+Hy/C3khsfJf/8NQKkJ480VXZGwq1wZ127dAIjN7oUlPJ7m9m3ChgxFe++e3FLljz+wDQgwd1hCNpEIFWOO2bVK0k8Ucj2hx7TQeJg2Opr0k/Kyftfu3Qs3rmdgX7UK9rVrg1ZL0sZN5g6nYBQKqNYDxp6Azl+CnSvcPw9Lu8OfwyHhjrkjLD7OLZdfWztXaP+x8e642XNAknDp2rVEXOrwHjcWlEpSd+8h49Ilc4dj0TKvXSNs6DB0MTHYVqpI4O+/o/b3f/KOQpERiVAxZpwnVJj1hJ7QQuNhyX9vA0nCoUEDbMuWKby4nkPOpOnEdWuLphGrqdjYQfPxMCEEGr4mzx+6vFGeP7T7M8hMNneE1i0zGfZkz3trMwWcS8l3X74sl4JQKCiVXXiwuLOrUAHXHnI1+Lg5Yq7Qo2RcuEjY8BHo4+Oxq16dwN9+Q+3jY+6whIeIRKgYc2wsJ0Kaq1fRJSQUzknOrXhkC438JOesFjNjS40nce3RA4W9PVk3b5F5/ry5w3l6Tt7Q8wcYfRgqtAV9Fhz+QZ4/dPY3MFjYijhrcWgGpMWCVyVo/Ibx7tifZgPypV67SpXMFV2R8x7zljwqtG8fGRcumjsci5N+9izhr76KISkJ+7p1CFz6KzaenuYOS8iHSISKMRtvb+wqy2/M6SdPmf4EcTfg73fl2/m00HiY5nYomZcugY2NcY6BJVK5uODapTNgBZOmH8e3Jgz7CwatBs+KkBYDm8bDgjYQesjc0VmXB7fgWHbtnC5fg41cGDHj/HlS9+8HlUqeRFyC2JUvb6yPFDtntpmjsSxpx44RPnIUhtRUHBs1ImDxElRuj/+SKJiPSISKOcfGhVRPqAAtNB6WMxrk1KI5Nh4epo3HxNz6ZDdi/ftvDOnpZo7mOSgUci2nMcehyzR5xC7qAizrCauHQrzoG1UgOz8CgxYqdYTKnY1354wGub34IrZBQWYKzny8x7wFKhVpBw6SYY2jp4UgZf9+It4cjZSRgVOLFpRb8AsqZydzhyU8hkiEijnH7L5jaaZOhJ7QQuNhkiSRtFVOhNwscLXYwxwbNUIdGIAhLY3k7TvMHc7zs7GFZmNgfAg0el2+lHllM/zcRG4TIeYPPdqtvXDtb/k16/K1cQ5c+unTpB05Amo13mNK1mhQDtvAQGOX9NjZYgVZ8o6dRI6fgJSVhXP79pSdNxelg4O5wxKeQCRCxZxTo0agUJB16xa62FjTHLQALTQelnnxItqwcBT29ri0b2+aOAqRQqHAvXfOpOl1Zo7GhJy8oMcMeOsIVGwvzx868iP8VB/OLJMnvwv/0uuyu8sjzwsqVRWQE/vYH38C5Mn1ljrxvyh4vzUabGxIO3yY9LMh5g7HbJI2beLupEmg1eLavRtlf5yF0op7y5UkIhEq5lTu7thlL+dNO2mC1WO5WmiMfmQLjYflXBZzad8epZN1DBO79X4JlEoyzpxBczvU3OGYlk91GLoeBq8Br8qQHgebJ8Ax8a0+l9NLIPaqPPLZdorx7vTjx0k/dQqFrS3eo0ebMUDzsy1XDvfeLwEQV0LnCiX8+Sf3prwPBgNuvXvj/913KNRqc4clFJBIhEoAp5y+Y8/bbiNPC43PC7SbpNeTlF1szhKLKD6K2tcX51atAEjaYMWTph9FoYAqnWHMMWjzvnzfrk/g1j7zxmUp0uNh31fy7fYfgoM8ry3XaNCAAaj9/MwVocXwejN7VOjoMdLPnDF3OEUq/rffiPrkU5AkPAYPovRXX6JQPX6qgGBZRCJUAjjm1BM6+ZyJUAFbaDws/eRJ9LFxqNzccG7Z4vliKGJufeVJ04l//YWk05k5mkKiUkPb96HeEJAM8iR4UYQR9k+DzETwqQkNXjHenXboEBnnzqGwt8f7jdfNFp4lsS1bBvfsBQYlaa5Q3C8LiP56GgCer76K78cfW0wTaaHgxP+xEsCxUSNQqdCGhaO9f//ZDvIULTQeltNSw6VrVxRWds3cpW1bVF5e6GPjSD140NzhFB6FAnrMBP8GkJEAq4ZAVpq5ozKfmCtwarF8u+s0UNkAuUeDPIYMxqZUKXNFaHG833wD1GrSjx83zWV4CyZJEjEzfyD2hx8A8B4zBp//e6/Y9pcr7kQiVAKonJ2xr1kTeMbVY0/RQuNhBo2GlJ07AXCz4CKKj6JQq3F74QUAEtcWo0nT+VHbw4A/wKkURF+EjeNK5uRpSZInSEt6qNYTKrQxPpS6Zw+Zly6hdHTEa9QoMwZpedT+/nj0k3v1xf0027qqsj8Fg0bDvXfe5cGCBQCUemcypSaMF0mQFROJUAmR024j/WnbbTzcQqPnD49tofGw1AMHMKSmYuPnh0Nw8NOd20K4Z18eSz1wAG1MjJmjKWRuZaD/76C0gUvr4ehP5o6o6F3bBrf3gcpW7t2WTTIYjHWDPIYPs/haWObg9cYbKNRq0k+fNn3tMgugi48n/JVX5Qa7NjaU/vILvF8Xl0etnUiESgjHJnID1rQTx5/um1quFhqLwcH9qc6bvGUrAK49ulvttXO7ihVxqFcP9HqSN1lJI9bnEdgMuk2Xb+/+DG7uMWs4RUqngZ0fyrebjQPP8saHUnbsQHP9OkoXF7xefdVMAVo2tZ8f7gMGAPJcoeI0KqS5fZs7AwaSERKC0sWFgIULcO/b19xhCSZgnZ9MwlNzbFAf1Gp09+6jjYx88g4GA0Sehr/fk39v9wGUa/xU59SnpMjtB7COIoqPkzMqlLh2XbF6c3+khiOh/rDsydOvlZwK1Cfmy8/V2RdaTTbeLen1xknAnq+MEO0SHsPr9ddR2NmRceYMaUePmjsck0g7fpw7AwehjYhAXbYsQatW4tSsmbnDEkxEJEIlhNLREYc6dQD5H3UumhQIPw4nF8Lmt2FhB5hWBhZ1AG2a3EKj5aSnPmfKrt1IWVnYVqporGVkrVy6dkPh6EjWnTtknD1r7nAKn0IBPb6Hso3klVOrhoAm1dxRFa7UGDjwnXy742dg52J8KHnLFrJu30bl5obniBHmic9KqH198BgojwrFFYNRocR16wkf9TqG5GQc6tUjaPUq7CpWNHdYggnZmDsAoeg4NW5MxpkzpO/agEepW3KLjOiLj14qrbKDwOZy9egntNDIT/KWzYA8GmTtEwlVzk64du1K0vr1JK5dh6OVznd6KjZ28nyhBW0g5jJsHAP9lj3VHDGrsudzyEqRV87VGWi8W9Jqif1ZrqTuOWokKmdnc0VoNbxGjSJh9Z9knDtH2uHDxnpc1kQyGIid9aNxUrRr926UnjYNpV3ByoYI1kMkQsVVVrq8BDj6AkRdhOiLON66CjiQfvI0UumtuT/PXEqDby3wq5X939pyx3LVs/2JaGNiSMsu4Ojaw/pWi+XHvW8fktavJ3n7dnw//KBkfCC6lpaToaU94PJGOPxDrktGxca9EAj5Q77dbTr8Zz5b0saNaMPDUXl54TlkiJkCtC42pUrhMWgQ8b/+SuzsOTi1bGlVX4YMmZnce38qKdu3A+A1+k1KTZhgtfMchcd76v+rBw8epFevXvj7+6NQKPjrr79yPS5JEp999hn+/v44ODjQtm1bLl269MTjrlu3jho1amBnZ0eNGjXYsGFDnm3mzp1L+fLlsbe3Jzg4mEOHDuV6fMaMGfj6+uLr68sP2fUdcpw4cYLg4GD0ev3TPmXLJkmQdFfu/3VwBqx5BWY3zL601V6+1HVqIYQfw8EtAYVSQpepIsv/Rej8FQzfCO/dhneuwtC18iWB2n3lnkrPmAQB8huIwYBD3brYlitnsqdrTg7162NbvjxSRgbJ27aZO5yiE9AEumdfMtrzOdzYZd54TE2SYNv7gAS1++eaCydlZRE3dx4AXq+PQunoaKYgrY/XqJEoHBzI/Ocf0qyoBpfuwQPCR7wiv4ep1ZSeNg2fiRNFElSMPfX/2bS0NOrWrcucOflXD/3222+ZOXMmc+bM4dSpU/j5+dGpUydSUlIeecxjx44xYMAAhg0bxvnz5xk2bBj9+/fnxH+WX65evZqJEyfy4YcfEhISQqtWrejWrRvh4eEAXLhwgU8++YSVK1eyYsUKPvjgAy5evAiAVqtl9OjRzJ8/H5U1lz7XaeD+eQhZLtc5WdoTvi0PP9SAFf1h7xdwaQM8uCFPcnX0hgpt5dUvvX9BOfYIDg2zl9G7doPm4+THnbxMHmpSzmoxK58k/V8KhcI4aTqpuNcUeljDVyH4FUCSa0o9uGXuiEzn0nqIOC5XTO/4Wa6HEtetQ3vvHjY+PngMHJj//kK+bLy88Bg8CLCeFWSamze5038AGefPo3RzI2DRImMfNaH4UkjP8depUCjYsGEDL730EiCPBvn7+zNx4kSmTJEbFGo0Gnx9fZk+fTpvvvlmvscZMGAAycnJbPvPt+yuXbvi4eHBypUrAWjSpAkNGjRg3rx5xm2qV6/OSy+9xLRp0/jzzz+ZOXMmx7MnAjdp0oR3332Xfv368fXXXxMdHc2PP/741M8xOTkZNzc3kpKScHV1fer9n1lKdK7LWkRfgrjrYMinzYNCBd6V/3Npq7b8X2ffPPM5YufOJe6n2bh06ULZH2cVSuhZYWHc6tIVVCoqH9iPjbd3oZzHHHSxsdxo2w70eips2YxdpUrmDqno6DRy8h15EkpVg1G7c00otkpZ6TCnESRHQrsPoc3/GR8yZGZyq3MXdDEx+H78kbgs9gx08fHc7NgJKT2dsnPn4tK+nblDeqS0o0eJnPA2htRU1AEBlJs/H7sK5Z+8o2CxCvr5bdI5QqGhoURFRdG5c2fjfXZ2drRp04ajR48+MhE6duwYkyblXpXUpUsXZs2aBUBWVhZnzpzh/fffz7VN586dOZq9PLN27dpcv36d8PBwJEni+vXr1KpVi5s3b7J06VLOFLARoEajQaPRGH9PTk4u0H7PTK+VE5yoi7kTn7TY/Le3d5fn7/x3Pk+panJV4AJwatqUuJ9mk37yJJLBUCjDvUlb5dEgp2bNilUSBPLcB+e2bUnds4fEtevwfX/Kk3cqLmzsYMDv8EsbuSP7X2/J84esaO5HHkd/kpMgt3LQfHyuhxJXr0YXE4ONf2nc+/UzU4DWzcbTE88hQ3iwcCGxc2bj3K6tRc4VSlizhqj/fQ46HQ4NGlD25zmiYGYJYtJEKCoqCgBfX99c9/v6+hIWFvbY/fLbJ+d4cXFx6PX6x25TvXp1vv76azp16gTAtGnTqF69Oh07duTbb79lx44dfPbZZ6jVan788Udat26dbyzTpk3jf//731M862e0bQqEHYHYa6DPymcDBXhVzDvK41rmuT54HGrVQuHggD4hAc2Nm9hXLXjPsIKQJInk7N5irlbYUqMg3Pv0IXXPHpI2bsRn8iSr65/2XFz85DYcS7vDlc1waAa0fs/cUT2bpEg4PEu+3elzUDsYHzKkpxO3YCEA3m+9hbIk/T82Mc/XXiVh+XI0l6+QumcPLh07mjskI8lgIHbmTB4skvvKufbsSemvvhQrw0qYQlk19nDGL0nSE78FFGSfJ20zevRoRo8ebfx96dKluLi40KxZM6pWrcqpU6eIjIxk4MCBhIaGYpfPH/vUqVOZPPnfVTHJycmUK4zJvvf/kZevA9i6gG/N3Cu2fKqDrZPJT6uwtcUxOJi0w4dJP3Hc5IlQ5uXLZIWGorCzs6g3PFNybt0Km1Kl0MXGkrJ/P67/GQEtEco1gu4zYPME2PsV+NWBKl3MHdXT2/Up6DIgoDnU7J3roYQVK9A/eIC6XDncsy/9C8/GxsMDj+HDeDD/F2Jnz8G5fXuLmHhsyMjg3pT3jb0QvceOxXvcWIscsRIKl0n/Gv38/IB/R4ZyxMTE5BnNeXi/x+3j7e2NSqV6quPGxcXx+eefM3v2bE6cOEGVKlWoXLky7dq1Q6vVcv369Xz3s7Ozw9XVNddPoWg5Sf5mPeEcvB8OI3fIBewavgplGxZKEpTDMbvvWNrT9h0rgJyWGs7t2hXb5eUKGxvcsj8cE9eVsEnTOYJHyNWnkWDdKIi7ae6Ink74cbi4FlBAt29yjbLqU1ONIwTeY8egUKvNFGTx4fXKKyidndFcu0bKrt3mDgddbCxhw0fISZBajf/0byg1fpxIgkookyZC5cuXx8/Pj127/l1em5WVxYEDB2jevPkj92vWrFmufQB27txp3MfW1pbg4OA82+zateuRx504cSKTJk2ibNmy6PV6tFqt8TGdTmf+ZfRVOkP1XnIvoyL+duTUpAkA6adOIZnwdZD0epKz5wdZY6f5p+H2sjyCkHboMNroaDNHYyZdv4GAZqBJhlWDIbOQ59OZisEgX5oGaDAMStfN9XD8b7+hT0zEtnx53Hr1MkOAxY/K3R3P4cMBiJszB8lgMFssmdevEzpgAJkXLqBycyNwyWLcXnzRbPEI5vfUn8CpqamcO3eOc+fOAfIE6XPnzhEeHo5CoWDixIl8/fXXbNiwgYsXL/LKK6/g6OjI4MGDjccYPnw4U6dONf7+9ttvs3PnTqZPn87Vq1eZPn06u3fvZuLEicZtJk+ezKJFi1iyZAlXrlxh0qRJhIeH57oUlmPXrl3cuHGDsWPHAtC4cWOuXr3Ktm3bWLBgASqViqpVqz7tUy827GvUQOnsjCE5mcwrV0123PTTZ9DFxKB0dcXpEXOwigu78uVxaBgMBgNJ+dS8KhFsbOVK0y7+EHcNNoyWkwxLd34F3D8Hdq7Q/uNcD+mTkoj/dSmAPEJgzeU2LIznKyNQuriguXGDlB07zBJD6uEjhA0egu7efWwDAwlavQrHRo3MEotgQaSntG/fPgnI8zNixAhJkiTJYDBIn376qeTn5yfZ2dlJrVu3li5cuJDrGG3atDFun2PNmjVS1apVJbVaLVWrVk1at25dnnP//PPPUmBgoGRrays1aNBAOnDgQJ5t0tPTpSpVqkghISG57l+4cKHk6+srBQQESFu2bCnw801KSpIAKSkpqcD7WIPwN0dLl6tWk+IWLTbZMe999LF0uWo16d5HH5nsmJYsYf0G6XLVatKNjp0kg15v7nDMJ+K0JH1eSpI+dZWkfd+YO5rHy0iSpG8rybEe+SnPw9GzZkmXq1aTbvV6oWT/Py0kMXPmSJerVpNu9ughGXS6Ij13/MpV0uUaNaXLVatJd4YMlbTx8UV6fqHoFfTz+7nqCJUEZqsjVMgeLF1KzDfTcWrdioDsXjrPw5CVxY2WrTAkJxOwdClOTZuYIErLZkhP50ar1hjS0ghYtgynJo2fvFNxFfIHbJRHYBm4Eqp1N288j7LrUzgyS24fM+a4PKqVTZeQwK0OHTGkp1Nm9k+4Zq9AFUxHn5LCzY6dMCQl4T9jRpFcQpf0emJmfE/8r78C4PbiC/h98YVYCVgCFPTz2/xT9wWzyJknlHH6DNJ/5k89q7RDhzAkJ2Pj44Njo4bPfTxroHR0NPZRS1y31szRmFn9odD4Dfn2+jcgNv/FCGb14BYcl5un0uXrXEkQQPzixRjS07GvUaPYrng0N5WLC16vvgJA3M8/m3SOYn4M6elEvv22MQnynjCe0t98I5IgIReRCJVQdlWronJzw5CeTmYBesE9SdKW7NpB3buXqHkV7n1eBiBlx070hV1809J1+RoCW8gd3FcNhswkc0eU286P5ZpdFTvkWe6vi40l/o/lgPxhKVYPFR6PoUNRubmRFRpqXFxRGLQxMYQNG07q7j0o1Gr8Z8yg1Jgx4v+tkIdIhEoohVKJY+PsZfTHTzxh68fTp6aRuncfULx6ixWEfZ062FWuhKTRFOqbulVQqeXJ065l5H5369+wnMnTt/bBta1yO5ouX+cpShq3cCFSZiYOdevi3KaNmYIsGVTOzniOHAlA3M9zkXT5tA16TpnXrnFnwEAyL11C5e5OwLKlxX4lq/DsRCJUgjnmLKM/+XyJUOqe3UgaDbZBQdjXrGGK0KyGQqHArY/ciDWxpDVizY9zKbk+lsoOrm+HA9+YOyLQ6+QmxQCNXwefarke1kZFkbhqNQCl3p4gRgyKgOeQwag8PMgKCyMpuxK9qaQePEjYoMHo7t/Htnx5gv5cjWODBiY9h1C8iESoBMuZ0Jx+5iyGrPzafBSMsdN8r54l8kPE7cUXQa0m89IlMq+arhyB1SrTAHplNzg+MF1uxWFOZ36F2Cvg4Alt38/zcNwvvyBlZeHYsCGOzZqZIcCSR+nkhNeo7FGhefNMMk8RIH7FCiJGv4UhPR3Hxo0JWrUS24AAkxxbKL5EIlSC2VasiMrbG0mjIfP8+Wc6hu7BA9KyG9+69SiZQ882Hh64tG8PQOK69WaOxkLUGwRN3pJvbxgNMWZKENPjYd9X8u12H4BD7kaaWZF3jSN5YjSoaHkMGoTKywtteDhJmzY917EkvZ7oadOI/vwLMBhw692bgEULUbm5mSjavI7ejGPe/lto9RZy+Vd4ZiIRMiONzrzVrRUKBU6N5WJiz9puI3nbdtDrsa9dG9ugIBNGZ11yJk0nb9r0XKNrxUrnLyCoFWSlypOnMxKLPob930BGAvjUgOBX8zwcN28uaLU4NW8mCusVMaWjI16jRgEQN2/+M48KGdLSiBw3nvhlvwFQauJESn/9VaE2Q07T6Bj9xxmmb7/KV1uvFNp5hKIhEiEzkCSJ347docsPB3mQqjFrLI5NmgKQfvz4M+2fnL1arKRPRHRq0QIbPz/0SUmk7jZ/LyWLoFJDv6XgVg7ib8H618FQhMl/zBU4tUi+3XUaqHL3mM4KCyPpr40AlJowoejiEow8Bg5A5e2NNjKSxL/+eur9tdHR3Bk2jNR9+1DY2lJm5vd4j36z0Ef21pyOIDlTnuS99Ogd1p+NLNTzCYVLJEJmkKk18OuRO9x5kM74lSHozDi0mlMEMOP8eQwZGU+1b1ZEBBnnzoFCgUu3boUQnfVQqFS49X4JEJOmc3HylidP29jDjZ2w7+uiOa8kyROkJT1U6wkV2ubZJPbnn0Gvx7lNGxzq1SuauIRclA4OeL/xOpA9V+gpRlMzr1zhTv8BaC5fQeXpScCypbh2L/xCnnqDxJIjdwCoVUYu0jd1/QUu3rWwchFCgYlEyAwcbFX8MiwYR1sVR2894Nsd18wWizowEBs/PyStloyQkKfaN3nr3wA4Nm2C2senMMKzKu4vy5fH0o4dQ3v3rpmjsSD+9eCF2fLtQzPg8sbCP+f17XB7H6hs5Ut0D9HcukVy9mol7wnjCz8e4ZHc+/fHplQpdPfuk7i+YH37Uvbv586Qoeiio7GtWFHuGVa/fiFHKtt1OYrw+HTcHdWsfqMZHar5oNEZePP3M8Snicvi1kgkQmZSxdeF7/rKXa8XHLzN5vP3zBKHQqEwjgo9zTwhSZJI2iKvBnLrKTp0A9iWK4dj06YgSSRu+Mvc4ViWOv2h2Tj59oa3IPpy4Z1Lp4EdH8i3m40Fzwp5NomdMwckCZdOHXGoWbPwYhGeSGlvj9ebbwIQN3/+E+fYxf/2O5FjxiKlp+PYrClBK1dgW65cUYQKwMJDoQAMaRKAk50NMwfUo7y3E3cTMxi/8qxZR/iFZyMSITPqUac0b7aR36T/b+0/XItKMUscjo2zl9GfKHg9Ic21a2TdvIXC1haXzqInU46cSdNJ69cjWUoxQUvR8X9Qvg1o07InTycUznlO/ALxt8HZF1q9k+fhzKtXSdm2HRQKvMeJ0SBL4N6vLza+vuiiokhcm3+7GkmvJ+rLr4j++mt5ZVjfPgQsWICqCHtAng1P4ExYArYqJSOaBQHg5qA2jvAfuWneEX7h2YhEyMze61yVlpW8ydDqefP30yRlmKaextPIqSeUceEC+tS0Au2TM0nauU0bVC4uhRabtXHp1Amliwvae/dIO3bM3OFYFpUN9P0V3AIgIRTWjjT95OnUGDjwrXy7w6dgl/dvM3b2HABcu3XFvmoV055feCZKOzu83pR71T2Y/wsGTe5FJPrUNCLHjCXhjz8AKPXOZEp/8QUKtbpI41ycPRr0Qj1/fFztjfdX8XVhRj/zj/ALz0YkQmZmo1Iye1B9yrg7cOdBOhNXhWAwSEUag7pMGdRly4JeT8bZM0/cXjIYSMqeH1TSWmo8idLeHrde8muStE5Mms7DyQsGLgcbB7i1B/bmnb/zXPZ8Lvc6868PdQfleTjjwkVS9+wBpRLvceNMe27hubj37YtN6dLoYmJI/HON8X5tVBRhQ4eSeuAACjs7ysyahffrrxd5zaeI+HS2XbwPwMiW5fM83r12ad5qWxGQR/ivRpXw3oNWRCRCFsDDyZZfhgVjZ6Nk37VYZu25UeQxOBrnCT358ljG2bPo7t9H6eyMc5vWhR2a1clpuZGyazf6xETzBmOJSteBF+VRGQ7/ABdNVITy3jkIkUcM6DodlHnf3mJn/wSAW69e2FXIO3dIMB+lrS3eOXOFFvyCITOTjEuXuNOvP5qrV1F5eRH42zJcu3Z5wpEKx69H7mCQoFVlb6qXzv9y3Ludq9KqsjzC/8ZvZ0hKL/oRfuHpiUTIQtQq48bXvWsD8NOeG+y6HF2k53dqmlNP6MmJUE6neZdOnVDa2z9h65LHvkYN7KpVQ9JqTd5Hqdio3ReaZ8/P2TgWoi4+3/EkCba/D0hQux8ENMmzSfrZENIOHgKVCu+xY57vfEKhcH+5N2p/f/Sxcdz/4APChg5DFxuLbaWKBK1ejUPdumaJKylDy+pT4QCMavXoBFqlVPDTwPqU9XAgPD6dt1eHoC/iEX7h6YlEyIL0CS7LiGaBAExefY7bsalFdu6cCdOZV66gT3p0PQwpK0ueaArGS0BCbgqFAvecRqzr1iFJ4o0wXx0+gwrtQJsuT55Oj3/2Y13aAOHHQO0oT8rOR+xP8miQ+8u9Rf8pC6WwtcXrrdEAJP+9DSkjA6fmzQlauRLbsmXMFtfqU+GkZemp4utM68rej902Z4TfXq1k/7VYfth1vYiiFJ6VSIQszEc9a9AoyIMUjY43fz9DqkZXJOdV+/pgW748GAyknz79yO1SjxxBn5SEytvb2L1eyMutV08UtrZorl4l81IhLhW3Ziob6LsE3AMhMQzWviZ3in9aWemw6xP5douJ4Jb3AzPtxEm5erpajffo0c8Xt1Co3F96CdtA+Quh+4ABlPtlvlkXZGj1cgFcgFEtKxRoblJNfzem96kDwJx9N9lxKaowQxSek0iELIxapeTnIQ3wdbXjRkwq/7f2fJGNKBRknlByTqf57t1QqFRFEpc1Urm749KxIwCJ6/JfDiwAjp4wcIU8knN7H+zJfzTnsY7OhqQIuZVH87zL4SVJMo4GefTrh7qM+UYWhCdTqNUErlhO4MoV+H32aZGvDHvY3xfucz8pE29nW16o51/g/V6sV8Y4qfqdP89zM8Y85VGEJxOJkAXycbFn7pBg1CoFf1+IYv6B20VyXqcmOfWE8i+saEhLI2XvXgDcxGqxJ3LvK18eS96yFUNmppmjsWB+teDFn+XbR3+CC0+ROCZFyhOuATp9DraOeTZJO3KUjDNnUNjZGQv3CZbNxssLx/r1i3xl2MMkSWLhIfn9d3izIOzVT/flb2q3ajSt4EmqRscbv58hJVNMnrZEIhGyUMGBHnz2glzx9rsdVzl0I7bQz+nYWB4R0ly7hi4+73yNlL37kDIyUAcEYF+7dqHHY+0cmzZF7e+PISWFlF27zB2OZav1snxZC2DjOLj/T8H22/0Z6DIgoDnU7J3n4VyjQQMHovYVrWCEgjsRGs/Fu8nY2SgZ2jTwqfe3USmZM7gB/m723I5NY/Kf54u8PIrwZCIRsmCDGwfQv2FZDBKMXxlCRHx6oZ7PxssLu8qVAUg/eSrP4/92mu9p9m9q1kChVOKW3X9MNGItgA6fQMUOcmKzesiTJ0+Hn4ALawCF3F0+n7/J1H37yfznHxQODnhlN/cUhIJalF1AsU9wWTydbJ/pGN7OdswfFoytjZJdl6OZs++mKUMUTEAkQhZMoVDw+Yu1qFvWjcR0LW/+foaMLBNX4n2IY/Yy+rQTx3Pdr0tIIPXIEQBce/Yo1BiKE/eXe4NCQfqJE9zq3oOwYcO5O3kyUV99Tdz8X0hcu5aU/fvJuHAR7f37T9V9u9hRqqDvYvAoD4nhsOaVR0+eNhhg+xT5doNhcmPXh0gGA7Gz5WavnkOHYOPlVThxC8XS7dhU9lyVy5jkV0DxadQp686XL9UC4Ifd19l7tWjLowiPZ2PuAITHs1ermDc0mF6zD3P5fjIfbrjA9/3rFtqIjFOTxiT8/nueeUIp27eDTifXyBGF6ApM7e+PS5cupGzfTtbt22TdfvJ8L6WbGzZeXth4eaHy9sLGu5T8u7cXKq/s37NvK22f7VuqxXLwkCdPL+oIoQdg96fQ5au8251fCfdCwNYF2n+c76FSdu1Gc+UKSicnPF97rZADF4qbxYdDkSToUM2HiqWcn/t4/RuW40JkEr8fD+PtVefYNK4l5b2dTBCp8LxEImQF/N0dmDO4AUMXn2B9yF3qlHXjlRbP9w3lURwbNQKFgqzbt9HGxKD2kedUJOWsFhOTpJ9amZnfkzV+HLrYOHRxcegfxKGLe4DuwQN0cbHoc24/eAA6HYakJLKSkgqWNLm6YuPt/W/S5OUt/25MmuTfrSpp8q0BvefBn8Ph2BwoXVfuXp9Dk/Lv6rI2/wfOeef9SHo9cXOyR4NGjMDGw6MoIrcK8WlZRCdnPrI6siC/RuvORgKPL6D4tD7uWYMr95M5HZbAm7+fZsOYFjjZiY9hcxP/B6xEs4peTO1WjS+3XuHLrVeo4e9G4/KeJj+Pys0N++rVybx8mfQTJ3Hr1RPt3btknDkDCgWu3buZ/JzFnUKpxK5iRewqVnzsdpIkYUhKQheXkyjFoX/wQE6gHmT/HvdAfjw+HrRaDMnJZCUnFzxpyk6O/k2avIyJkm1gEHYVCifBfmo1XpQ7xx/6HjaNB+8q/17+OvQ9pEaDZ0Vokn9NoORt29HcuInS1RXPV0YUXdwWTm+QGLzwONeiU1j2amNaVyll7pAs0vLjYWRqDdQq40rTCqZ7n7W1UTJ3SAN6zj7M9ehU/m/tP8wZbP7VcSWdSISsyMiW5TkfmcTm8/cYs/wMW8a3ws/N9C0uHJs0kROhkydw69WTpL/lBquOjRqh9vMz+fkEmUKhQOXujsrdHbtKlR67rTFpMiZK2UlTTqKUkzTljDT9N2kKDX3kcZ3btqXU2xOwr17d1E/v6bX7EKIuwI2dsHoovLEfNMlwLHupfZevwCbvKJek0xGXPTfI67VXUbmKkY8cf4Xc5WqUXM/miy2X2fZ2K2xUYqrof2l0epYdCwMKXkDxafi42jNvaDADFxxj64X71D7oxug2j/+SJBQukQhZEYVCwfQ+tbkRncLVqBRG/3GG1W82xc7GtIUNnZo2If7XX0nL7jtmLKIoWmpYjFxJU0FHmoyJUuy/SdN/Rpkyr14ldf9+Uvfvx6VbV0qNn2DeESKlCl5eCAvbQ/wtefK0nQvos6Bie6jSNd/dkjZtJissDJW7Ox5DhxVtzBYsS2dg1p5/2z3ciEll5clwhjULMl9QFmjjuXvEpWoo7WZPjzqlC+UcwYEefNqrJh/9dZFvt1+lpr8rrSqL0TlzEYmQlXG0teGXYfLk6XMRifxv82Vjs1ZTcQhuCCoV2ogIUg8cQHPtGqjVuHbubNLzCEWjoEmTJjSUuDk/k/z336Rs207Kjp24vfgi3mPHmq/Pk4N79uTpDnDnkHyfQgVd8l8uL2m1xM2dC4DX66NQOYvJqDn+PB1BRHwG3s52vN6qPNO2XWXmruu8UK8Mbg7mrd5sKSRJYnH2kvlXmgehLsTRsiFNArgQmcTq0xGMXxnC5nEtKeeZtyCoUPjEmKgVCvRy4qdB9VEoYMWJcGNXZFNROTthX0su5hj1pbxix7l1a1RubiY9j2BZ7MqXp8z3Myj/1184d+gABgNJGzZwq1s3oj7/HG1MjHkC86kGvef/+3ujUfJ9+UhcvwFtZCQqb288Bg8uogAtX6ZWz+y9NwAY164iI1uWp5KPMwnpWuZk3y/AoRtxXItOwclWxcDGhduYV6FQ8L8XaxZpeRQhfyIRslJtq/rwTqcqAHz81yXORSSa9PhOTeR6QtqICADcRO2gEsO+ahXK/TyHoNWrcGreDLRaElas5FbnLkR/9x26hISiD6p6L+gxU64e3e6DfDcxaDTEzZsHgPcbb6B0cCjKCC3aH8fDiE7WUMbdgUFNArBRKfmwhzwPbOnRO9yJSzNzhJYhp51G/0blimSULKc8irezLZfvJzN1/T9F1ltS+JdIhKzYmLaV6FzDlyy9gbf+OENcqsZkx85pwAqgdHTEuW1bkx1bsA4OdesSsGQJAcuW4VC/PlJmJvGLl3CrYydiZ89Bn5patAE1Ggn9lsqXy/KR+OcadFFR2Pj54T6gf77blESpGh1z998CYEKHSsY5he2q+tC6Sim0eomv/75izhAtwtWoZA7diEOpgNcKqTxJfnLKo6iUCv46d8/Y6V4oOiIRsmJKpYLv+9elQikn7idlMnb5WbR6g0mO7digAWR3fXbp1FF8uy7BnJo0JnDFcsr9Mh+7GtUxpKUR9/PP3OrQkQeLFmHIyDB3iBgyMohb8AsA3qPfRGlnZ+aILMevh0OJT8uivLcTfRqUzfXYRz2qo1Iq2Hk5mqO34swUoWXImRvUtZZfkc/VaVrBi4+yR+i++vsKx28/KNLzl3QiEbJyLvZqFgwLxslWxYnQeKb9fdUkx1U6OODcsiUoFLj37WuSYwrWS6FQ4NymDeXXrqXMrFnYVqiAPimJmBnfc7NzZ+L/WI7BjO1BElauQh8bh7pMGdyz+7sJkJSuZUH25Z6JHSvnWSpfxdeFwdlzYb7ccgV9CW0IGpOSycZz9wDTFlB8Gq80D6J3/TLoDRJjl5/lXqL5v2CUFCIRKgYq+bjwff96ACw5EsrGc3dNclz/b6dTYfMmudq0ICAXh3Tt2oUKmzdR+ptpqMuWRR8bR/SXX3Kra1cS161D0j2iP1ghMaSl8WDhQgC8x4xBYS0VtIvALwdvkZKpo5qfC73q+Oe7zaROVXCxt+Hy/WTWnYks4ggtw+/HwsjSG2gQ4E6DAPNUIVcoFHzduzY1SrvyIC2Lt/44Q6ZWTJ4uCiZPhHQ6HR999BHly5fHwcGBChUq8Pnnn2MwPP6SzYEDBwgODsbe3p4KFSowf/78PNusW7eOGjVqYGdnR40aNdiwYUOux5cvX065cuXw9PTkvffey/XYnTt3qFKlCsnJyc//JC1Q11p+jG0nL42esu4fLt97/uepcnF5YmE/oWRSqFS4v/QSFf/eit9nn2Lj44Pu3n3uf/gRt3v2ImnrVqQn/Js3lfg/lqNPSMA2MBC3F18oknNag7hUjXG+yeROVVAq8y8M6Olky4T2lQH4buc1UjVFm8iaW0aWnj+OywUUXzfTaFAOB1sVvwwLxt1RzfnIJD7ZeFFMni4CJk+Epk+fzvz585kzZw5Xrlzh22+/5bvvvmN2dqXX/ISGhtK9e3datWpFSEgIH3zwARMmTGDdunXGbY4dO8aAAQMYNmwY58+fZ9iwYfTv358TJ+Sif3FxcYwaNYoZM2awY8cOli1bxtatW437v/XWW3zzzTe4FuMqs5M7VaV1lVJkag28+cdpEtNLcCdzoUgobG3xGDiQijt34PN//4fK3Z2sO3e49867hPZ+mZS9+wr1jVyfksKDJUsA8B43FoWNKI2WY+6+W2Ro9dQt506nGr6P3XZ480ACvRyJTdEwb//NIorQMqw9G0lCupZyng50rmn+yvnlPB2ZM6gBSgX8eTqS5SdMWx5FyMvkidCxY8d48cUX6dGjB0FBQfTt25fOnTtz+vTpR+4zf/58AgICmDVrFtWrV2fUqFG89tprzJgxw7jNrFmz6NSpE1OnTqVatWpMnTqVDh06MGvWLABu376Nm5sbAwYMoFGjRrRr147Lly8DsGLFCmxtbXm5mM8dUCkV/DSwHuU8HYiIz2DCqnMl9pq/ULSU9vZ4vfYqFXfvxnvCeJTOzmiuXSNyzBjuDBxI2rFjhXLe+KXLMCQlYVupIq7duxfKOazR/aQM/jghj3K827nKE9tE2Nmo+KC7PFl34aFQIhPSCz1GS2AwSCw5LE+Sfq1FeVSPGDUrai0rezOlq1wr63+bL3EmLN7MERVvJk+EWrZsyZ49e7h+XS7lfv78eQ4fPkz3x7xJHTt2jM4PVS3u0qULp0+fRqvVPnabo0ePAlC5cmXS09MJCQkhPj6eU6dOUadOHeLj4/nkk0+YM2eOKZ+mxXJ3tOWXoQ2xVys5eD2WmbuumTskoQRROTtRaswYKu3ehdfrr6NwcCDz/D+Ev/oaYSNeIT0kxGTn0icmEr9sGQClxo1DoTJtqxlr9tOem2TpDDQp70nLSt4F2qdzDV+aVvAkS2dg+vaS8b6x52oMoXFpuNjb0L9hOXOHk8sbrSvQo05ptHqJ0X+cJSY509whFVsmT4SmTJnCoEGDqFatGmq1mvr16zNx4kQGDRr0yH2ioqLw9c09dOvr64tOpyMuLu6x20RFRQHg4eHBsmXLGD58OI0bN2b48OF06dKFd999l/HjxxMaGkr9+vWpVasWa9eufWQsGo2G5OTkXD/Wpoa/K9P71AHg53232H4xyswRCSWNyt0dn3cmU2nnDjyGDkWhVpN+4gRhgwYT8eZoMq88f92aB0t+xZCail21ariI9i9GYQ/SWHNaLoT6XpeqBW4aqlAo+LhnDRQK2Hz+HmfCzFA4s4gtyl5RN7hJAE52lnVZVaFQ8G2fOlT1dSE2RcNby8+SpSuaeXcljckTodWrV/PHH3+wYsUKzp49y7Jly5gxYwbLsr+5PcrD/1hz5hX89/78tvnvfb179+bChQvcvHmTzz77jP3793PhwgVef/11Bg4cyKxZs1i3bh0jR44k5hHtAqZNm4abm5vxp1w5y/qWUFAv1itjLAr2zp/nuBlTxMXvBAGwKVUKv48+pOKO7bj17QMqFakHDhDa+2UiJ05Cc/v2Mx1X9+AB8X/8AUCpCeNRKMUC2Byzdt9AZ5BoW7UUDYM8n2rfmv5u9AuWaw19seUyhmJ8af1CZBInQuOxUSp4pXmQucPJl5Od3FvS1d6GM2EJfLHlsrlDKpZM/u7x3nvv8f777zNw4EBq167NsGHDmDRpEtOmTXvkPn5+fsaRnRwxMTHY2Njg5eX12G0eHiXKodFoGDNmDL/88gs3b95Ep9PRpk0bqlatSpUqVYyTrB82depUkpKSjD8R2S0mrNHU7tVoUt6TtCw9b/x+mpRMrblDMhmDQWLftRiGLzlJ++/3c/FukrlDEh5D7e+P/5dfUnHrFlx79ACFgpTt27ndsxf33p9KVuTTLdt+sHARUno69rVr49yuXSFFbX2uR6fwV3b5jHc6VX2mY7zbuSqOtirORSSy6fw9U4ZnUXLaafSq609pN8stGBvk7cSPA+Xekr8fD+PP09b7mWSpTJ4Ipaeno3zo25lKpXrs8vlmzZqxa9euXPft3LmThg0bos6ubvyobZo3b57vMb/44gu6detGgwYN0Ov16P5T20Sr1aLX51+fwc7ODldX11w/1kqtUvLzkAb4udpzOzaNd/48b/Xf8NKzdPx+PIyOPxzg1V9PcfB6LLdj0xix5CS3Y8Wol6WzDQrK29j1r7+41a273Ng1+smNXbXRMSSsXAlkjwYV8NJPSTBz53UkCbrW9KN22Wdrkuzjas/YdnLZjOnbrxbLRqD3EjPYeuE+ACNbFl07jWfVrpoPkzvKvSU/+usi503cW7KkM3ki1KtXL7766iu2bt3KnTt32LBhAzNnzqR3797GbaZOncrw4cONv48ePZqwsDAmT57MlStXWLJkCYsXL+bdd981bvP222+zc+dOpk+fztWrV5k+fTq7d+9m4sSJeWK4dOkSq1ev5vPPPwegWrVqKJVKFi9ezNatW7l69SqNSkiRQG9nO+YPC8ZWpWTn5WjmWunS2HuJGUzbdoWmX+/h478ucjs2DWc7G15rUZ6a/nIBsmGLT3I/SVRjtQbGxq5/rsapefP/NHbtTPS3j2/s+mDBAiSNBof69XFq2bIIo7ZsFyKT2H4pCoUCJneu8lzHGtmyPGXcHbiflGkcOSlOlh69g94g0ayCF7XKPFvCWNTGtqtEpxq+ZOkMjDZxb8kSTzKx5ORk6e2335YCAgIke3t7qUKFCtKHH34oaTQa4zYjRoyQ2rRpk2u//fv3S/Xr15dsbW2loKAgad68eXmOvWbNGqlq1aqSWq2WqlWrJq1bty7PNgaDQWrevLm0efPmXPdv3rxZCggIkHx9faWFCxcW+PkkJSVJgJSUlFTgfSzRyhNhUuCULVLQ+1ukvVejzR1OgZ0Ji5fGLj8jVZi6VQqcskUKnLJFav3tXmnJ4dtSckaWJEmSFJuSKbX7bp8UOGWL1OH7/dKDVM0TjipYmtTjJ6TQgYOky1WrSZerVpOuNgiWYn78SdIlJ+faLuvuXelKrdrS5arVpNRjx8wUrWUavviEFDhlizRxVYhJjrfx3F0pcMoWqdpH26SopAyTHNMSpGRqpVqfbJcCp2yRdl+OMnc4TyU5I0tqN0N+rxvwy1FJq9ObOySLVtDPb4UkibKVj5OcnIybmxtJSUlWfZkMYOr6C6w8GY6rvQ2bx7ck0MvJ3CHlS6s3sO1iFEsOh3LuP0PAzSp48VrL8rSv5pOn3kdkQjr95h/jflImdcq6seL1pjhb2CoQ4fEkSSLt4EFifvwRzWV5VZnKzQ3PUSPxHDIEpaMj9z/5lMQ//8SxSRMCly01b8AW5NSdePrNP4aNUsGed9qY5N+2JEn0mXeUs+GJ9A0uy4x+dU0QqfktPhzKF1suU6GUE7sntXlkxW1LdTMmlZd+PkKqRsfIluX5uGcNc4dksQr6+S2WWpQgn71Qg/oB7iRn6njz9zOkZ1lWKf3E9Czm7r9Jq+n7mLAyhHMRidiqlPQLLsvfE1qx8o2mdKrhm2/Rs7Iejvw+sjEejmr+iUzi9WWnRZ8eK5OnsWvFiuiTkoj9fiY3O3chdu5cEtevB6DU2xPMHK3lkCSJ73bIdX/6NSxnsi84OcvpAdaeieRCpPUvSNDpDcYCiqNaVrC6JAigko8z3/eXk9LFh03XW7IkE4lQCWJno2LekGC8nW25GpXC++suWEQfm5sxKXy44QJNp+3h2+3XiErOxNvZlokdK3Pk/fZ8168uNfyfPBpXyceFZa81xslWxbHbD5iwMgSdXtTdsDbGxq6bNv7b2DUujrifZoNOh1PLljg2aGDuMC3GoRtxnAyNx9ZGyYQOpu0NWD/Ag5fqyc1av9hy2SLeL57HjkvR3E3MwNPJlpcblDF3OM+sS00/xreX/19PWfcPl+5Zf5JqTiIRKmH83Oz5eXADbJQKNp2/x+Lsb0dFTZIkDlyPZcSSk3SceZDlJ8LJ1BqoXtqVGf3qcuT99kzsWIVSLnZPddw6Zd1ZOKIhtjby5PD311+w+pVyJVV+jV0V9vaUymeBREklSRLf75RHg4Y2CSyUZeD/17Ua9molJ+/EW3VxVkmSjBO/hzYNxF5t3ZXIJ3asQtuq2b0lfz9DQproLfmsRCJUAjWp4MWHPeS+QtO2XeXorbgiO3dGlp4VJ8Lp9MNBRiw5yYHrsSgUcnn/VW805e8JLekbXBY7m2d/k2pe0Zs5g+qjUipYeyaSr/6+YvXfZEuynMaulfbspvKB/TjUqmnukCzGrsvRnI9MwtFWxZh2FQvlHP7uDryR3ZV92raraHTWecn5bHiCfLndRsmwpoHmDue5qZQKfhxQn0AvRyITMpiwKkT0lnxGIhEqoV5pHkTv+mXQGyTGrwjhXmLhLjuPSsrk2+1XafbNHj7YcIGbMak42ap4tUUQ+99ty4LhDWlawctkNWE61/QzthlZfDiUn/dZZ9kA4V8KtRqVm3UsdS4KBoPEzF1yT8dXWwTh7fx0o6dP4802FfFxsSM8Pp2lR+4U2nkK08KD8uh373plnnqk2VK5OapZMKwhDmoVh27EMWNnyegRZ2oiESqhFAoFX/euTY3Scg2et/44UyiTi89FJDJhZQgtp+9l7v5bJKZrKefpwMc9a3Dsgw582qtmoa1e6xtclk+yJ3vO2Hmd34/dKZTzCII5bP7nHlejUnCxt+GNVoUzGpTDyc6G/8vuhj57702rq2ET9iCNHZfly3ojW1l+AcWnUdXPhe/6yV/65u2/xd/ZhSKFghOJUAnmYKvil2HBuDuqOR+ZxKcbL5nkEpJOb2DrP/fpM+8oL/18hE3n76EzSDQp78kvw4LZ/247RrYsj6u92gTP4vFea1meCdmTCj/ZdEmssBCKBZ3ewKzdNwB4s3UF3BwL/9/Sy/XLULuMG6kanXEkylr8euQOkgRtqpSiiq+LucMxuZ51/HmztXz58t0157kenWLmiKyLSIRKuHKejsweVB+lAlafjmDFyfBnPlZSupZfDtyi9bf7GLviLGfCElCrFLzcoAxbxrdk9ZvN6FLTL9/l74VpUqcqDG8WiCTBO3+eZ9/VJ7dxEARLtu5sJKFxaXg52fJqi6IZ4VAq/11Ov+pkOFejkovkvM8rKV1r7M/1evZcp+LovS5VaVHJi/QsPW/8dpqkjOLTW7KwiURIoFXlUrzbRW7Q+NmmS5wJe3R7g/zcik3l478u0nTaHqZtu8q9pEy8nGyZ0EFe/j6zfz2zlrFXKBR81qsmL9bzR2eQGP3HGU6GxpstHkF4Hhqdnp/2yHPe3mpbEaciLBzauLwn3Wr5YZDgq63WsQhhxclw0rP0VPNzoUUlL3OHU2hsVEpmD2pAGXcH7jxIZ/Lqc2LFbAGJREgA4K02FelWyw+tXmLM8jPEpGQ+dntJkjh8I47Xlp6iw/cH+P14GBla+c3m2751OPJ+eyZ3qoKPi30RPYPHUyoVzOhXl/bVfNDoDIxcekrU3hCs0soT4dxNzMDX1Y6hZlj9NLVbdWxVSg7diGPfNcseXc3SGVh6NLuAYqsKxb5Br6eTLb8MC8bORsmeqzH8uOeGuUOyCiIREgB51OS7fnWp7ONMdLKGscvPkqXLW4wwU6tn1clwusw6yNDFJ9h7NQaFAjpW92HFqCZse7sV/RuWs8gaHWqVkp8HN6BxkCcpGh0jlpwkNC7N3GEJQoGlZ+mYs+8WAOPbVzbLv7MAL0debRkEwJdbr6C14KKlW/65R3SyBh8XO16o62/ucIpErTJuTHu5NgA/7rnBrsvRZo7I8olESDBytrPhl2HBuNjZcOpOAl//fcX4WHRyJjN2XKPZtD28v/4C16NTcbRV8UrzIPa+05ZFIxrRvJK3xX/jcrBVseiVhtQo7UpcahZDF50QHesFq7HsaBhxqRrKeTrQv2E5s8Uxrl0lvJxsuR2bxh/Hw8wWx+NIksSiQ/Jo0IjmQdjalJyPu5cblOWV5kEAjFtxlrErzvJXyF0S00XRxfyIpqtPUJyarhbUrsvRvP7baQDe7VyFW7FpbPnnHlq9/KdSxt2BV1sE0a9hOdwcCn+1SmGIS9XQb/4xQuPSqOTjzJ9vNsPTydbcYQnCIyVnamk1fR9JGVq+71eXPsFlzRrP8hNhfLjhIm4Oag681xZ3R8v693P0ZhyDF53AQa3i2NT2FhdfYdPqDYxcdpqD12ON96mUChoGetCphi8dq/sS5G2ZjbdNpaCf3yIReoKSmAgBzNx5jZ/25i5C2CjIg5Ety9Oxui82Kuv/dhWZkE7feceISs6kblk3louO9YIFm7nrOj/tuUElH2d2TGxd5KsvH6bTG+jx02GuRafwaosgPu1lWRW/X/31JPuuxTK8WSCfv1jL3OGYhcEgcT4ykT1XYth9JZqrUbmX1VfycaZjdV86VvehfoCH2f+mTE0kQiZSUhMhg0Fi3Mqz7LwUTa+6/rzaIog6Zd3NHZbJ3YxJod/8YySka2le0YslrzSyyPlNQskWn5ZFq+l7ScvSM3dIA7rXLm3ukAA4dCOWYYtPYqNUsGNSayqWcjZ3SID877rjzIMoFLDvnbbFfuSjoCLi09l9JZrdV6I5cTse3X9WlXk62dK+mg8dq/vSqrJ3ka5GLCwiETKRkpoIgXyNXWeQUBeD0Z/H+ScykUELjpOWpadzDV/mDmlQLEa8hOJj2t9X+OXgbWr6u7J5XEuUFvTNfeTSU+y5GkOHaj4sfqWRucMBYOr6f1h5MoLONXxZMLyhucOxSMmZWg5ci2X3lWj2XY0hOVNnfMzWRknzil50rO5Lh+o+hdLMtyiIRMhESnIiVJIcvRXHK7+eIktnoG9wWb7tU8eiPmyEkismOZPW3+0jU2vg11ca0a6aj7lDyuVWbCpdfjiIziDxx8gmtKzsbdZ4HqRqaPbNXrJ0BtaMbkajIE+zxmMNtHoDp+8kGEeLwh6k53q8VhnX7EtovtT0d7X4RTE5RCJkIiIRKjl2XorireVn0RskRrUsz4c9qlvNP3ih+Ppk40V+OxZGcKAHa0c3s8i/yc82XWLp0TtU83Nh64RWZp1rMmv3dWbtvkHdsm78NbaFRb5elkySJG7GpLI7e17R2fAE/psllHazp0N1+RJas4pe2NlY7lQCkQiZiEiESpa1ZyJ5d815QC5ZP7ZdJTNHJJRkEfHptP9+P1q9xMrXm9KsomVWRk5Mz6LNd/tJytDyde/aDG4SYJY4MrV6WnyzlwdpWcweVJ9eJaR2UGGKS9Ww92oMuy9Hc+hGHBn/ac7tZKuidZVSdKzuS7tqPha38ragn9/WPxtKEEyob3BZkjK0fLHlMt/tuIarg5phZqjeKwgAP+25gVYv0bKSt8UmQQDujra83aEyn2+5zPc7r9Gzbukiaar8sL9C7vIgLYsy7g50q+VX5Ocvjryd7ejfsBz9G5YjU6vn2K0H7LoSzZ4r0UQna9h2MYptF6NQKiA40EO+hFbD12ImzheEGBF6AjEiVDLllA9QKGDWgHq8WK+MuUMqNKfuxPPLgdvcT8pg7pAGBHqJFTaW4FZsKp1mHsAgwV9jW1CvnLu5Q3osrd5Al1kHuR2bxpttKjC1W/UiPb/BINF51kFuxqTyUY/qjCrGDVYtgSRJXLybzK4r0ey+HM3l+7mb8Jb3dqJj9iW04EAPsyxAEZfGTEQkQiWTJEl8uukSvx0Lw0apYOHwhhY3SfV5GAwSe6/GMP/ALU7/p8lujdKurB/TXJQQsADjVpxlyz/36Vjdl0UjrGPl054r0YxcdhpblZLdk9sQ4OVYZOfedy2GV389hbOdDcemtsfFDCNSJdndxAz2Xolm15UYjt2KMxbgBXB3VNOuqpwUta7iXWT/b0QiZCIiESq5DAaJSX+eY+O5e9irlfw+sonVr0DR6g1sOnePXw7e4np0KgC2KiUv1fdnz5UYHqRlMahxOaa9XMfMkZZsl+8l0/2nQwD8PaEVNfyt471HkiSGLT7J4ZtxdK/tx9whwUV27iGLjnPk5gNGtSzPRz1rFNl5hbxSNToOXY9l15Vo9l6NITFda3xMrVLQtMK/S/PLehResiwSIRMRiVDJptUbeOO30+y7FouLvQ2r32hmNR9K/5WepWPVyQgWHw7lbqLcW83ZzoYhTQMY2aI8Pq72HL4Rx7AlJ5AkmNm/Li83MG8Lh5Js1LJT7L4SQ886pZkzuIG5w3kqV6OS6f7jIQwS/PlmMxqXL/wvD5fuJdHjp8OolAoOvNe2UD9chaej0xs4G55oXJp/OzZ3o+vqpV3pWN2H/g3LUc7TtP/fRCJkIiIREjKy9AxfcoJTdxLwdrZjzehmlLeSSrXxaVksO3qHZcfuGL+VeTvb8VrLIIY0CczTKy5n6bGDWsXGcS2o4utijrBLtLPhCbw89yhKBeya3MaqJp3m+GDDBVacCKd2GTc2jm1R6DW5Jv95jvVn71pl4ljS3IpNZc+VaHZfjuF0WDw5xa1XjGpC80qmrUElVo0Jgok42KpYNKIRgxYc5/L9ZIYuOsG6t5rj52Zv7tAeKTIhnUWHQll9KsK43DXQy5E3WlegT4Oyj5wDNL59Zc6EJXDoRhxv/XGGTeNaFotS+9bk+53XAOjToKxVJkEAkztVYfO5e1y4m8T6kLv0LcQGsdHJmWw+fw+A18UEaYtXsZQzFUs580briiSkZbHvWgwHr8fSqAhGDh9F9BEQhAJwc1Cz7LXGBHk5cjcxg2GLT5CQlmXusPK4FpXC5NXnaPvdfpYevUOGVk+tMq7MGVyfve+0ZUiTwMdOhFYpFcwaUA8/V3tuxaYxdf0FxKBx0Tl6K44jNx+gVimY0KGyucN5Zt7OdoxtL9fg+m7HVdKzdE/Y49ktO3oHrV6icZAndS18ZZ2Qm4eTLS83KMusgfXN2spJJEKCUEClXOz4fWQT/FztuRGTyitLT5GqKbw3+Kdx6k48I5eeosusg6wPuYvOINGikhe/j2zM5nEt6VnHv8DVfr2c7ZgzuD4qpYJN5+/xx4nwQo5eAHmi8Ywd8mjQoMYBJp8vUdRebRFEOU8HopM1zD9wu1DOkZ6lY3n23+fIVuUL5RxC8ScSIUF4CuU8Hfl9ZGM8HNWcj0jkjd9Ok/mfSqtFyWCQ2H05mr7zjtJv/jH2XI1BoYDutf3YOLYFy0c1pVXlUs/UYqBhkCfvd60GwBebL/NPZKKJoxcetv9aLGfDE7FXKxlXDCqa29mojLWEFhy8xf2kDJOfY83pSJIytAR5OdKxuq/Jjy+UDCIREoSnVNnXhaWvNsbJVsXRWw94e1UIOr2hyM6v1RtYdyaSrj8eZNRvpzkdloCtSsmgxuXYM7kNc4cEm+QSwahW5elcw5csvYExy8+S9J8lsIJpGQwSM7LnBo1oFoSPq+XOP3sa3Wr50TjIk0ytgW+3XzPpsfUGiSVHQgEY2bK8WfubCdZNJEKC8AzqlnNn4fCG2KqU7LgUXSRzadKzdCw5HErb7/bzzprzXI9OxdnOhjfbVODQlHZMe7kOFUw4uVahUPBdv7oEeDoSmZDBO2vOi/lChWT7pSgu3UvG2c6G0W0qmjsck1EoFHzcswYKBWwIucu5iESTHXvXZblLupuDmj6FOBlbKP5EIiQIz6h5JW9mD66PUgFrzkTy1dYrhZIoxKdl8cOu6zT/Zi+fb7nM3cQMvJ3t+L+uVTnyfnumdquObyGNILg5qJk7pAG2Nkp2X4lmwcHCmetRkukNEjN3XQfkkQ0PC2tc+bxql3Xj5fpyovLFlssm+zey+LD8tzi0aQCOtmJlo/DsRCIkCM+hS00/pveRqzAvOhzK3P23THbsyIR0Ptt0iRbf7OXHPTdITNcS6OXIV71rcXhKO8a0rZSnDlBhqFXGjU97yZV6v91xjZOh8YV+zpLkr5C73IxJxd1RzahiOuH3/7pWxUGt4kxYAlv+uf/cxzsXkcipOwmoVQqGNwt6/gCFEk0kQoLwnPo1LMdHPeRJod/tuMYfx8Oe63j5LYGv6V/wJfCFYXDjAF6q54/eIDF+5VniUjVFev7iKktnYNYeeTRodJuKxbY/lq+rvfGS3zfbrj73AoOFh+TRoBfqlim00VCh5BCJkCCYwKhWFRifXTfl440X2ZRd4O1p5LcEvnlFeQn8lvFPtwTe1BQKBV/1rk0lH2eikzW8vSoEvUHMF3pef56OICI+g1Iudowo5iMbb7SuQGk3e+4mZrD4cOgzHyciPp1tF+RRpeI6giYULZEICYKJTO5UhWFNA5EkmLz6HPuvxTxxn0ctge9WS14Cv+L1Z18Cb2pOdjbMG9IAB7WKIzcf8OOeG+YOyaplavXM3iu/huPaVcLBtmhH+Yqag62KKdklGebuu0lMSuYzHWfp0TsYJGhZyZvqpUXbI+H5FUoidPfuXYYOHYqXlxeOjo7Uq1ePM2fOPHafAwcOEBwcjL29PRUqVGD+/Pl5tlm3bh01atTAzs6OGjVqsGHDhlyPL1++nHLlyuHp6cl7772X67E7d+5QpUoVkpOTn/8JCkI+FAoF/3uhJi/U9UdnkBj9xxlO38l/Ps2jlsAPbCQvgZ831DRL4E2tsq8L016uDcDsvTc4eD3WzBFZrz+OhxGdrKGMuwMDG5czdzhF4oW6/tQt505alp7vd1x/6v2TM7WsPhUBiNEgwXRMngglJCTQokUL1Go127Zt4/Lly3z//fe4u7s/cp/Q0FC6d+9Oq1atCAkJ4YMPPmDChAmsW7fOuM2xY8cYMGAAw4YN4/z58wwbNoz+/ftz4sQJAOLi4hg1ahQzZsxgx44dLFu2jK1btxr3f+utt/jmm29E41ShUCmVCr7vX5d2VUuRqTXw6tJTXL73b/L9yCXwreUl8N/0Me0S+MLwUv0yDG4SgCTBxNXnCqVQXnGXqtEZJ9ZP6FAJO5viPRqUQ6lU8ElPeT7dn2ciuHQv6an2X30yglSNjso+zrSpUqowQhRKIJN3n3///fc5cuQIhw4dKvA+U6ZMYdOmTVy5csV43+jRozl//jzHjh0DYMCAASQnJ7Nt2zbjNl27dsXDw4OVK1dy8uRJXnjhBaKioozbN2zYkPfee48VK1awevVqNm7c+NTPR3SfF57Fwx3rFw4PZv+12Dxd4F9tEcTQpnm7wFu6TK2evvOPcvFuMsGBHqx6o6lZewVZm9l7bvD9ruuU93Zi16TW2JSw127cirNs+ec+TSt4svL1pgW69KvVG2jz7T7uJWUyvU9tBjQKKIJIBWtW0M9vk//r27RpEw0bNqRfv374+PhQv359Fi5c+Nh9jh07RufOnXPd16VLF06fPo1Wq33sNkePHgWgcuXKpKenExISQnx8PKdOnaJOnTrEx8fzySefMGfOnALFr9FoSE5OzvUjCE8rp2N99dKuxKVq6D33qHEJfIDnv0vgx7YrmiXwpmavVjF3cDAu9jacCUtg+rar5g7JaiSla1mQveppYsfKJS4JAni/WzVsbZQcvx3PzsvRBdpn28Uo7iVl4u1sy4v1yhRyhEJJYvJ/gbdv32bevHlUrlyZHTt2MHr0aCZMmMBvv/32yH2ioqLw9c3dJ8bX1xedTkdcXNxjt8kZAfLw8GDZsmUMHz6cxo0bM3z4cLp06cK7777L+PHjCQ0NpX79+tSqVYu1a9c+MpZp06bh5uZm/ClXrmRcuxdMz81BzW+vNaa8txMANf1dmT2oPnvfaWOWJfCmFuDlyIx+dQG5htL2i1Fmjsg6/HLwFimZOqr5udCrjr+5wzGLsh6OvJ49x2fa31fI0j2+RY0kSSzKTh6HNQ2y+n87gmUxeTlOg8FAw4YN+frrrwGoX78+ly5dYt68eQwfPvyR+z08NJpzxe6/9+e3zX/v6927N7179zb+vn//fi5cuMCcOXOoVKkSK1euxM/Pj8aNG9O6dWt8fHzyxDF16lQmT55s/D05OVkkQ8IzK+Vix6ZxLQh7kE5Nf1eLWP1lSl1q+vF6q/IsPBTKe2vOU720C4FeTuYOy2LFpWr49cgdQF5lqCzB/bHealuJP09HcudBOr8du8OoVhUeue3J0Hj+iUzCzkbJ0KbikphgWiYfESpdujQ1atTIdV/16tUJDw9/5D5+fn7GkZ0cMTEx2NjY4OXl9dhtHh4lyqHRaBgzZgy//PILN2/eRKfT0aZNG6pWrUqVKlWMk6wfZmdnh6ura64fQXgeLvZqapVxK3ZJUI7/61qNhoEepGh0jFl+9rmL5RVnc/fdIkOrp245dzrVKNnd0p3tbHi3cxUAftxzg/i0rEduuyi77lCf4LJ4OdsVSXxCyWHyRKhFixZcu5a7y/D169cJDAx85D7NmjVj165due7buXMnDRs2RK1WP3ab5s2b53vML774gm7dutGgQQP0ej06nc74mFarRa8Xb9aCYApqlZLZg+vj6WTLpXvJ/G/zZXOHZJHuJ2Xwxwm56vi7nasU28T4afQNLkeN0q6kZOqYtTv/5fShcWnsviLPI3qthVgyL5ieyROhSZMmcfz4cb7++mtu3rzJihUrWLBgAWPHjjVuM3Xq1FyXyUaPHk1YWBiTJ0/mypUrLFmyhMWLF/Puu+8at3n77bfZuXMn06dP5+rVq0yfPp3du3czceLEPDFcunSJ1atX8/nnnwNQrVo1lEolixcvZuvWrVy9epVGjRqZ+qkLQolV2s2BHwfWQ6GAlSfD2RASae6QLM5Pe26SpTPQpLwnLSt5mzsci6BSyt3pAZafCOdGdEqebZYcDkWSoEM1Hyr5WHZpCcFKSYVg8+bNUq1atSQ7OzupWrVq0oIFC3I9PmLECKlNmza57tu/f79Uv359ydbWVgoKCpLmzZuX57hr1qyRqlatKqnVaqlatWrSunXr8mxjMBik5s2bS5s3b84TU0BAgOTr6ystXLiwwM8lKSlJAqSkpKQC7yMIJdXMndekwClbpGofbZOuRSWbOxyLcScuVao4dasUOGWLdCr0gbnDsTivLzslBU7ZIg1ffCLX/fGpGqnqR39LgVO2SEduxpopOsFaFfTz2+R1hIobUUdIEApOb5AYseQkh2/GUcnHmY1jW+BkZ/I1GVZn0upzbAi5S9uqpVj6amNzh2Nx7sSl0emHA2j1EktfbUTbqvJCljl7bzBj53Vq+ruyZXxLcTlReCpmqyMkCELJpVIqmDWwHr6udtyMSeWDDRco6d+1rken8Ne5uwC827mqmaOxTEHeTsams19tvYJOb0Cj07PsmDyn6vVWFUQSJBQakQgJgmBS3s52zBncAJVSwcZz91h+4tErRkuCmTuvI0lyI91aZdzMHY7FGt+hMh6Oam7EpLLyZDibzt0jNkWDn6s9PeqUNnd4QjEmEiFBEEyuUZAnU7rKox+fb77Mhcin6ylVXFyITGL7pSgUCpjUqYq5w7Fobg5qJme/RjN3XeeXg3IBxVdaBIn2LUKhEn9dgiAUitdbVaBTDV+y9AbGrDhDUnaPtZJkxk65lMhL9cpQxdfFzNFYvkGNA6js40xCupabMak42qoYJHqKCYVMJEKCIBQKhULBjH51KefpQER8Bu+sOV+i5guduhPPgeux2CgVTOxY2dzhWAUblZIPe1Q3/t6/YTncHK2vF59gXUQiJAhCoXFzUDN3cDC2KiW7r0SzIPtyR3EnSRLf7ZBHg/o1LCfajjyFtlV9eLl+Gfxc7Xm99aPbbgiCqYhESBCEQlW794ogBwAAGIJJREFUrBuf9JKL5n274xonQ+PNHFHhO3QjjpOh8djaKJnQoZK5w7E6MwfU4/gHHSjj7mDuUIQSQCRCgiAUuiFNAnixnj96g8T4lWeJS9WYO6RCI0kS32fPDRraJJDSbuLDXBAsmUiEBEEodAqFgq9716aSjzPRyRreXhWC3lA85wvtuhzN+cgkHG1VjGlX0dzhCILwBCIREgShSDjZ2TBvSAMc1CqO3HzAj3tumDskkzMYJGbukpuHvtoiCG/RKV0QLJ5IhARBKDKVfV34+uVaAMzee4OD12PNHJFprQ+5y9WoFFzsbXijlRgNEgRrIBIhQRCKVO/6ZRnUOABJgomrz3E/KcPcIT2XjCw9a89E0nfeUd5dcx6AN1tXEMu+BcFKiG6IgiAUuU971eCfyEQu3Utm3IoQVr3R1OqqB1++l8yqU+FsCLlLSqYOkHut9axTmpEtxbJvQbAWIhESBKHI2atVzB3SgJ6zD3MmLIHp267yUc8a5g7ridI0Orb8c48VJyM4H5FovL+cpwMDGwXQL7gsPq725gtQEISnJhIhQRDMItDLie/61mX0H2dYdDiUhkGedK3lZ+6w8nUhMokVJ8PZdO4uaVl6ANQqBZ1r+DGwcTlaVPRGqRTd0QXBGolESBAEs+lay49RLcuz6HAo7605T/XSLhZThTklU8vGc/dYeTKcS/eSjfeX93ZiYKNy9AkuK1aFCUIxIBIhQRDMakq3aoREJHImLIExy8+y7q3m2KtVZolFkiRCIhJZdTKczefvk6GVR39sVUq61fZjYKMAmlbwRKEQoz+CUFyIREgQBLNSq5TMGVyfHj8d5tK9ZP63+TLTXq5dpDEkpWvZEBLJqlMRXI1KMd5fyceZQY0DeLl+GTycbIs0JkEQioZIhARBMLvSbg7MGlCPEb+eZOXJcBqX96B3/bKFek5JkjgdlsDKE+FsvXAfjc4AgJ2Nkh51SjO4cQDBgR5i9EcQijmRCAmCYBFaVynF+PaV+WnPDT5Yf5Ga/m5U8XUx+XkS0rJYd1Ye/bkZk2q8v5qfC4MaB/BSvTKiBpAglCAiERIEwWK83aEyZ8MSOHwzjjHLz7JxbAuc7J7/bUqSJI7dfsCqkxFsvxhFll4e/XG0VdGrjj+DmgRQt6ybGP0RhBJIJEKCIFgMlVLBrIH16PHTIW7GpPLBhgvMGlDvmROUuFQNa89EsupkOHcepBvvr13GjYGNy/FCXX9c7MXojyCUZCIREgTBong72zFncAMGLjjOxnP3aBTkydCmgQXe32CQOHwzjlWnwtl5KRpddpd7ZzsbXqznz6DGAdQq41ZY4QuCYGVEIiQIgsVpFOTJ/3WpyrRtV/l882XqlnWndtnHJy/RyZmsOR3B6tMRRMT/27+sXjl3BjcOoEed0ia5zCYIQvEi3hUEQbBIb7SuwKk7Cey+Es2YFWfYMq5VnknMeoPEweuxrDgZzt6rMeizR39c7G14uX4ZBjYOoHppV3OELwiClRCJkCAIFkmhUPB9v7r0mH2IiPgM3l17ngXDglEoFNxLzGD1qQjWnI7gXlKmcZ9GQR4MbBRA99qlcbA1T1FGQRCsi0KSJMncQViy5ORk3NzcSEpKwtVVfLMUhKJ2ITKJPvOOkqU3MKRJAPeTMtl/LYbswR/cHdX0aVCWgY3KUbkQltsLgmCdCvr5LUaEBEGwaLXLuvFxrxp8/NdFlp8IN97frIIXAxuXo0tNP7O15BAEwfqJREgQBIs3tEkAN6NT2HM1hh51SjOwUQDlvS2jOasgCNZNXBp7AnFpTBAEQRCsT0E/v5VFGJMgCIIgCIJFEYmQIAiCIAgllkiEBEEQBEEosUQiJAiCIAhCiSUSIUEQBEEQSqxCT4SmTZuGQqFg4sSJj93uwIEDBAcHY29vT4UKFZg/f36ebdatW0eNGjWws7OjRo0abNiwIdfjy5cvp1y5cnh6evLee+/leuzOnTtUqVKF5OTk535OgiAIgiAUD4WaCJ06dYoFCxZQp06dx24XGhpK9+7dadWqFSEhIXzwwQdMmDCBdevWGbc5duwYAwYMYNiwYZw/f55hw4bRv39/Tpw4AUBcXByjRo1ixowZ7Nixg2XLlrF161bj/m+99RbffPONWAIvCIIgCIJRoSVCqampDBkyhIULF+Lh4fHYbefPn09AQACzZs2ievXqjBo1itdee40ZM2YYt5k1axadOnVi6tSpVKtWjalTp9KhQwdmzZoFwO3bt3Fzc2PAgAE0atSIdu3acfnyZQBWrFiBra0tL7/8cmE9XUEQBEEQrFChJUJjx46lR48edOzY8YnbHjt2jM6dO+e6r0uXLpw+fRqtVvvYbY4ePQpA5cqVSU9PJyQkhPj4eE6dOkWdOnWIj4/nk08+Yc6cOQWKW6PRkJycnOtHEARBEITiqVASoVWrVnH27FmmTZtWoO2joqLw9fXNdZ+vry86nY64uLjHbhMVFQWAh4cHy5YtY/jw4TRu3Jjhw4fTpUsX3n33XcaPH09oaCj169enVq1arF279pGxTJs2DTc3N+NPuXLlnuapC4IgCIJgRUzeaywiIoK3336bnTt3Ym9vX+D9FApFrt9zOn/89/78tvnvfb1796Z3797G3/fv38+FCxeYM2cOlSpVYuXKlfj5+dG4cWNat26Nj49PnjimTp3K5MmTjb8nJyeLZEgQBEEQiimTJ0JnzpwhJiaG4OBg4316vZ6DBw8yZ84cNBoNKlXuTtF+fn7GkZ0cMTEx2NjY4OXl9dhtHh4lyqHRaBgzZgx//PEHN2/eRKfT0aZNGwCqVKnCiRMn6NWrV5797OzssLOze/onLgiCIAiC1TH5pbEOHTpw4cIFzp07Z/xp2LAhQ4YM4dy5c3mSIIBmzZqxa9euXPft3LmThg0bolarH7tN8+bN843jiy++oFu3bjRo0AC9Xo9OpzM+ptVq0ev1z/tUBUEQBEGwciYfEXJxcaFWrVq57nNycsLLy8t4/9SpU7l79y6//fYbAKNHj2bOnDlMnjyZ119/nWPHjrF48WJWrlxpPMbbb79N69atmT59Oi+++CIbN25k9+7dHD58OE8Mly5dYvXq1Zw7dw6AatWqoVQqWbx4MX5+fly9epVGjRqZ+qkLgiAIgmBlTJ4IFcT9+/cJDw83/l6+fHn+/vtvJk2axM8//4y/vz8//fQTffr0MW7TvHlzVq1axUcffcTHH39MxYoVWb16NU2aNMl1bEmSeOONN/jhhx9wcnICwMHBgaVLlzJ27Fg0Gg1z5syhTJkyBYo1Z66SWD0mCIIgCNYj53M753P8URTSk7Yo4SIjI8VkaUEQBEGwUhEREZQtW/aRj4tE6AkMBgP37t3DxcUlz6q155GzGi0iIkJUu34O4nU0DfE6moZ4HU1DvI6mUdJfR0mSSElJwd/fH6Xy0VOizXJpzJoolcrHZpLPy9XVtUT+gZqaeB1NQ7yOpiFeR9MQr6NplOTX0c3N7YnbiO7zgiAIgiCUWCIREgRBEAShxBKJkJnY2dnx6aefiuKNz0m8jqYhXkfTEK+jaYjX0TTE61gwYrK0IAiCIAgllhgREgRBEAShxBKJkCAIgiAIJZZIhARBEARBKLFEIiQIgiAIQoklEqHncPDgQXr16oW/vz8KhYK//vor1+PR0dG88sor+Pv74+joSNeuXblx40aubaKiohg2bBh+fn44OTnRoEED1q5dm2ubs2fP0qlTJ9zd3fHy8uKNN94gNTW1sJ9ekTHF63jr1i169+5NqVKlcHV1pX///kRHR+faJigoCIVCkevn/fffL+ynVySmTZtGo0aNcHFxwcfHh5deeolr167l2kaSJD777DP8/f1xcHCgbdu2XLp0Kdc2Go2G8ePH4+3tjZOTEy+88AKRkZG5tvnqq69o3rw5jo6OuLu7F/ZTK1JF9Tru378/z99izs+pU6eK5LkWJlO9jgsWLKBt27a4urqiUChITEzMc64XXniBgIAA7O3tKV26NMOGDePevXuF+fSKTFG+jsX9c+ZxRCL0HNLS0qhbty5z5szJ85gkSbz00kvcvn2bjRs3EhISQmBgIB07diQtLc243bBhw7h27RqbNm3iwoULvPzyywwYMICQkBAA7t27R8eOHalUqRInTpxg+/btXLp0iVdeeaWonmahe97XMS0tjc6dO6NQKNi7dy9HjhwhKyuLXr16YTAYch3v888/5/79+8afjz76qEieY2E7cOAAY8eO5fjx4+zatQudTkfnzp1z/a19++23zJw5kzlz5nDq1Cn8/Pzo1KkTKSkpxm0mTpzIhg0bWLVqFYcPHyY1NZWePXui1+uN22RlZdGvXz/eeuutIn2ORaGoXsfmzZvn+ju8f/8+o0aNIigoiIYNGxb58zY1U72O6enpdO3alQ8++OCR52rXrh1//vkn165dY926ddy6dYu+ffsW6vMrKkX1OpaEz5nHkgSTAKQNGzYYf7927ZoESBcvXjTep9PpJE9PT2nhwoXG+5ycnKTffvst17E8PT2lRYsWSZIkSb/88ovk4+Mj6fV64+MhISESIN24caOQno35PMvruGPHDkmpVEpJSUnGbeLj4yVA2rVrl/G+wMBA6Ycffij052AJYmJiJEA6cOCAJEmSZDAYJD8/P+mbb74xbpOZmSm5ublJ8+fPlyRJkhITEyW1Wi2tWrXKuM3du3clpVIpbd++Pc85fv31V8nNza1wn4iZFcXrKEmSlJWVJfn4+Eiff/55IT4b83mW1/G/9u3bJwFSQkLCE8+1ceNGSaFQSFlZWSaL31IU1utY0j5nHiZGhAqJRqMBwN7e3nifSqXC1taWw4cPG+9r2bIlq1evJj4+HoPBwKpVq9BoNLRt29Z4HFtb21wN4xwcHAByHae4KsjrqNFoUCgUuYqG2dvbo1Qq87xG06dPx8vLi3r16vHVV1+RlZVVBM+i6CUlJQHg6ekJQGhoKFFRUXTu3Nm4jZ2dHW3atOHo0aMAnDlzBq1Wm2sbf39/atWqZdympCmq13HTpk3ExcUV22/gz/I6Pov4+HiWL19O8+bNUavVzxe0BSqs17Gkf86IRKiQVKtWjcDAQKZOnUpCQgJZWVl88803REVFcf/+feN2q1evRqfT4eXlhZ2dHW+++SYbNmygYsWKALRv356oqCi+++47srKySEhIMA5v/vc4xVVBXsemTZvi5OTElClTSE9PJy0tjffeew+DwZDrNXr77bdZtWoV+/btY9y4ccyaNYsxY8aY66kVGkmSmDx5Mi1btqRWrVqAPBcNwNfXN9e2vr6+xseioqKwtbXFw8PjkduUJEX5Oi5evJguXbpQrlw5Uz8Ns3vW1/FpTJkyBScnJ7y8vAgPD2fjxo3PH7iFKczXsaR/zohEqJCo1WrWrVvH9evX8fT0xNHRkf3799OtWzdUKpVxu48++oiEhAR2797N6dOnmTx5Mv369ePChQsA1KxZk2XLlvH999/j6OiIn58fFSpUwNfXN9dxiquCvI6lSpVizZo1bN68GWdnZ9zc3EhKSqJBgwa5XqNJkybRpk0b6tSpw6hRo5g/fz6LFy/mwYMH5np6hWLcuHH8888/rFy5Ms9jCoUi1++SJOW572EF2aY4KqrXMTIykh07djBy5MjnC9hCmfp1zM97771HSEgIO3fuRKVSMXz4cKRi1jShMF/Hkv45Y2PuAIqz4OBgzp07R1JSEllZWZQqVYomTZoYJ0PeunWLOXPmcPHiRWrWrAlA3bp1OXToED///DPz588HYPDgwQwePJjo6GicnJxQKBTMnDmT8uXLm+25FaUnvY4AnTt3/v/27i4kir4NA/ilZX6SspqtpVlpiVFpimTSB0UihVkoHoilSEEq1kGEEVQifZiQG2oHhZFmdaCBhFpQipb0JWgbri1YGm5UgliBX5mt3s+BPPPmk28875s75u71gz2Z+e/M3Lewc834HwZdXV3o6+vD3Llz4eHhAa1W+8seRUZGAgA6Ozvh6elp8TrUcOjQIVRXV6OpqQm+vr7Kcq1WC2DiCtLHx0dZ3tvbq1xNarVa5Wrwx7sZvb29iIqKUqmCP4OafSwtLYWnpyfi4uIsVc6M+Z0+/i+8vLzg5eWFlStXIjg4GH5+fnj+/Dk2bNjw+0X8AdTooy2fZ3hHSAXu7u5YsGAB3rx5g5aWFuzevRvAxEx+AJP+LwtMzIH559NOwMTtTjc3N1RUVMDJyQnR0dGWP/g/yH/r44+8vLzg4eGBhoYG9Pb2/vLk8veTeT/+gMxWIoKsrCxUVVWhoaHhpx+vZcuWQavVoq6uTlk2OjqKR48eKSfn8PBwODg4TBrT09OD9vZ2mwlCavdRRFBaWoqUlBSrmtMyHX38nX0D/5lfOJvNRB9t8jyj+vRsKzIwMCB6vV6ZXa/T6USv14vJZBIRkcrKSmlsbJSuri65c+eO+Pv7S3x8vPL90dFRCQwMlE2bNklzc7N0dnbKhQsXxM7OTu7evauMKy4ultbWVuno6JBLly6Js7OzFBYWql6vpfxuH0VErl27Js+ePZPOzk65ceOGaDQaOXLkiLL+6dOnynbfvn0rFRUVsmjRIomLi1O1VkvJyMgQd3d3efjwofT09Cif4eFhZcz58+fF3d1dqqqqxGAwSFJSkvj4+Eh/f78yJj09XXx9faW+vl5evHgh27Ztk5CQEDGbzcoYk8kker1ecnNzxc3NTfnbDQwMqFqzJajZRxGR+vp6ASBGo1G1GtUwXX3s6ekRvV4vJSUlAkCamppEr9fLp0+fRESkublZiouLRa/XS3d3tzQ0NMjGjRslICBARkZGVK97uqnVRxHrP8/8CoPQb/j7UcR/flJTU0VEpLCwUHx9fcXBwUGWLFkiJ06ckG/fvk3axuvXryU+Pl68vb3FxcVF1q5d+9Pj9Pv27RONRiPz5s2bcv1sNx19PHbsmCxcuFAcHBxkxYoVUlBQIOPj48r61tZWWb9+vbi7u4uTk5MEBQVJTk6ODA0NqVmqxUzVPwBSWlqqjBkfH5ecnBzRarXi6OgomzdvFoPBMGk7X79+laysLNFoNOLs7CyxsbHy7t27SWNSU1On3FdjY6MKlVqWmn0UEUlKSpKoqChLl6W66epjTk7OL7fT1tYmW7duFY1GI46OjrJ06VJJT0+X9+/fq1it5ajVRxHrP8/8ip2Ilc0oIyIiIvqXOEeIiIiIbBaDEBEREdksBiEiIiKyWQxCREREZLMYhIiIiMhmMQgRERGRzWIQIiIiIpvFIEREREQ2i0GIiIiIbBaDEBHR/2FsbGzKlyMT0ezCIEREs155eTk8PT1/euN4QkICUlJSAAA1NTUIDw+Hk5MTli9fjtzcXJjNZmWsTqfDmjVr4OrqCj8/P2RmZmJwcFBZX1ZWBg8PD9TW1mLVqlVwdHSEyWRSp0AishgGISKa9RITEzE2Nobq6mplWV9fH2pra5GWlob79+9j7969OHz4MIxGI65cuYKysjKcPXtWGW9vb4+ioiK0t7fj+vXraGhoQHZ29qT9DA8PIy8vD1evXsWrV6/g7e2tWo1EZBl86SoRWYXMzEx0d3fj3r17AIDCwkIUFRWhs7MTW7ZswY4dO3D8+HFl/M2bN5GdnY2PHz9Oub3bt28jIyMDfX19ACbuCKWlpeHly5cICQmxfEFEpAoGISKyCnq9HhERETCZTFi8eDFCQ0ORkJCAkydPwtXVFePj45gzZ44yfmxsDCMjIxgaGoKLiwsaGxtx7tw5GI1G9Pf3w2w2Y2RkBIODg3B1dUVZWRkOHjyIkZER2NnZzWClRDSd5s70ARARTYd169YhJCQE5eXliImJgcFgQE1NDQBgfHwcubm5iI+P/+l7Tk5OMJlM2LlzJ9LT03H69GloNBo8fvwY+/fvx/fv35Wxzs7ODEFEVoZBiIisxoEDB3Dx4kV8+PAB27dvh5+fHwAgLCwMHR0dCAwMnPJ7LS0tMJvNKCgogL39xNTJyspK1Y6biGYOgxARWY3k5GQcPXoUJSUlKC8vV5afOnUKsbGx8PPzQ2JiIuzt7dHW1gaDwYAzZ84gICAAZrMZxcXF2LVrF548eYLLly/PYCVEpBY+NUZEVmP+/PlISEiAm5sb9uzZoyyPiYlBbW0t6urqEBERgcjISOh0Ovj7+wMAQkNDodPpkJ+fj9WrV+PWrVvIy8uboSqISE2cLE1EViU6OhrBwcEoKiqa6UMholmAQYiIrMLnz5/x4MEDJCcnw2g0IigoaKYPiYhmAc4RIiKrEBYWhi9fviA/P58hiIj+Nd4RIiIiIpvFydJERERksxiEiIiIyGYxCBEREZHNYhAiIiIim8UgRERERDaLQYiIiIhsFoMQERER2SwGISIiIrJZDEJERERks/4CFuq+QNU3SHwAAAAASUVORK5CYII=", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "scf_data.groupby([\"year\", \"edcl_lbl\"]).apply(\n", + " weighted_mean,\n", + " \"savres5\",\n", + " \"wgt\",\n", + ").unstack().plot()\n", + "plt.title(\"Reason for saving: purchases\")\n", + "plt.gca().yaxis.set_major_formatter(formatter)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_356464/2315203495.py:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " scf_data.groupby([\"year\", \"edcl_lbl\"]).apply(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "scf_data.groupby([\"year\", \"edcl_lbl\"]).apply(\n", + " weighted_mean,\n", + " \"savres6\",\n", + " \"wgt\",\n", + ").unstack().plot()\n", + "plt.title(\"Reason for saving: retirement\")\n", + "plt.gca().yaxis.set_major_formatter(formatter)" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_356464/992497806.py:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " scf_data.groupby([\"year\", \"edcl_lbl\"]).apply(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "scf_data.groupby([\"year\", \"edcl_lbl\"]).apply(\n", + " weighted_mean,\n", + " \"savres7\",\n", + " \"wgt\",\n", + ").unstack().plot()\n", + "plt.title(\"Reason for saving: liquidity/the future\")\n", + "plt.gca().yaxis.set_major_formatter(formatter)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_356464/2742888852.py:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " scf_data.groupby([\"year\", \"edcl_lbl\"]).apply(\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "scf_data.groupby([\"year\", \"edcl_lbl\"]).apply(\n", + " weighted_mean,\n", + " \"savres8\",\n", + " \"wgt\",\n", + ").unstack().plot()\n", + "plt.title(\"Reason for saving: investment\")\n", + "plt.gca().yaxis.set_major_formatter(formatter)" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_356464/3958459272.py:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " scf_data.groupby([\"year\", \"edcl_lbl\"]).apply(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "scf_data.groupby([\"year\", \"edcl_lbl\"]).apply(\n", + " weighted_mean,\n", + " \"savres9\",\n", + " \"wgt\",\n", + ").unstack().plot()\n", + "plt.title(\"Reason for saving: no particular reason\")\n", + "plt.gca().yaxis.set_major_formatter(formatter)" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_356464/902805575.py:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " scf_data.groupby([\"edcl_lbl\", \"retired\"]).apply(\n" + ] + }, + { + "data": { + "text/plain": [ + "edcl_lbl retired\n", + "Bachelors degree or higher False 0.033786\n", + " True 0.058411\n", + "high school diploma or GED False 0.053497\n", + " True 0.079938\n", + "no high school diploma/GED False 0.072402\n", + " True 0.053860\n", + "some college or Assoc. degree False 0.045830\n", + " True 0.066180\n", + "dtype: float64" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scf_data.groupby([\"edcl_lbl\", \"retired\"]).apply(\n", + " weighted_mean,\n", + " \"savres3\",\n", + " \"wgt\",\n", + ") # family" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_356464/3313085655.py:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " scf_data.groupby([\"edcl_lbl\"]).apply(weighted_mean, \"savres6\", \"wgt\") # retirement\n" + ] + }, + { + "data": { + "text/plain": [ + "edcl_lbl\n", + "Bachelors degree or higher 0.375299\n", + "high school diploma or GED 0.256231\n", + "no high school diploma/GED 0.179858\n", + "some college or Assoc. degree 0.277846\n", + "dtype: float64" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scf_data.groupby([\"edcl_lbl\"]).apply(weighted_mean, \"savres6\", \"wgt\") # retirement" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_356464/921483200.py:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " scf_data.groupby([\"edcl_lbl\"]).apply(weighted_mean, \"savres7\", \"wgt\") # the future\n" + ] + }, + { + "data": { + "text/plain": [ + "edcl_lbl\n", + "Bachelors degree or higher 0.338060\n", + "high school diploma or GED 0.350402\n", + "no high school diploma/GED 0.336672\n", + "some college or Assoc. degree 0.359677\n", + "dtype: float64" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scf_data.groupby([\"edcl_lbl\"]).apply(weighted_mean, \"savres7\", \"wgt\") # the future" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_356464/976294478.py:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " scf_data.groupby([\"edcl_lbl\"]).apply(weighted_mean, \"savres5\", \"wgt\") # purchases\n" + ] + }, + { + "data": { + "text/plain": [ + "edcl_lbl\n", + "Bachelors degree or higher 0.069803\n", + "high school diploma or GED 0.118682\n", + "no high school diploma/GED 0.150370\n", + "some college or Assoc. degree 0.106968\n", + "dtype: float64" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scf_data.groupby([\"edcl_lbl\"]).apply(weighted_mean, \"savres5\", \"wgt\") # purchases" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_356464/2717839900.py:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " scf_data.groupby([\"edcl_lbl\"]).apply(weighted_mean, \"savres1\", \"wgt\") # can't save\n" + ] + }, + { + "data": { + "text/plain": [ + "edcl_lbl\n", + "Bachelors degree or higher 0.020601\n", + "high school diploma or GED 0.052192\n", + "no high school diploma/GED 0.105367\n", + "some college or Assoc. degree 0.036308\n", + "dtype: float64" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scf_data.groupby([\"edcl_lbl\"]).apply(weighted_mean, \"savres1\", \"wgt\") # can't save" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_356464/4287443728.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " .apply(weighted_mean, f\"savres{i}\", \"wgt\")\n", + "/tmp/ipykernel_356464/4287443728.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " .apply(weighted_mean, f\"savres{i}\", \"wgt\")\n", + "/tmp/ipykernel_356464/4287443728.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " .apply(weighted_mean, f\"savres{i}\", \"wgt\")\n", + "/tmp/ipykernel_356464/4287443728.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " .apply(weighted_mean, f\"savres{i}\", \"wgt\")\n", + "/tmp/ipykernel_356464/4287443728.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " .apply(weighted_mean, f\"savres{i}\", \"wgt\")\n", + "/tmp/ipykernel_356464/4287443728.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " .apply(weighted_mean, f\"savres{i}\", \"wgt\")\n", + "/tmp/ipykernel_356464/4287443728.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " .apply(weighted_mean, f\"savres{i}\", \"wgt\")\n", + "/tmp/ipykernel_356464/4287443728.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " .apply(weighted_mean, f\"savres{i}\", \"wgt\")\n", + "/tmp/ipykernel_356464/4287443728.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " .apply(weighted_mean, f\"savres{i}\", \"wgt\")\n" + ] + } + ], + "source": [ + "all_reasons = []\n", + "\n", + "reasons = [\n", + " \"Can't save\",\n", + " \"Education\",\n", + " \"Family\",\n", + " \"Home\",\n", + " \"Purchases\",\n", + " \"Retirement\",\n", + " \"Liquidity/the future\",\n", + " \"Investment\",\n", + " \"No particular reason\",\n", + "]\n", + "\n", + "for i in range(1, 10):\n", + " temp = (\n", + " scf_data.groupby(\n", + " [\n", + " \"edcl_lbl\",\n", + " ],\n", + " )\n", + " .apply(weighted_mean, f\"savres{i}\", \"wgt\")\n", + " .reset_index()\n", + " )\n", + " temp[\"Reasons for Saving\"] = reasons[i - 1]\n", + " all_reasons.append(temp)\n", + "\n", + "all_reasons = pd.concat(all_reasons)\n", + "pivot = all_reasons.pivot_table(\n", + " index=[\n", + " \"edcl_lbl\",\n", + " ],\n", + " columns=\"Reasons for Saving\",\n", + " values=0,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_356464/3822881605.py:2: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", + " pivot = pivot.applymap(lambda x: f\"{x:.0%}\")\n" + ] + }, + { + "data": { + "text/html": [ + "
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Reasons for SavingCan't saveEducationFamilyHomeInvestmentLiquidity/the futureNo particular reasonPurchasesRetirement
edcl_lbl
Bachelors degree or higher2%9%4%4%2%34%1%7%38%
high school diploma or GED5%8%6%5%3%35%1%12%26%
no high school diploma/GED11%7%7%4%3%34%1%15%18%
some college or Assoc. degree4%9%5%5%3%36%1%11%28%
\n", + "
" + ], + "text/plain": [ + "Reasons for Saving Can't save Education Family Home Investment \\\n", + "edcl_lbl \n", + "Bachelors degree or higher 2% 9% 4% 4% 2% \n", + "high school diploma or GED 5% 8% 6% 5% 3% \n", + "no high school diploma/GED 11% 7% 7% 4% 3% \n", + "some college or Assoc. degree 4% 9% 5% 5% 3% \n", + "\n", + "Reasons for Saving Liquidity/the future No particular reason \\\n", + "edcl_lbl \n", + "Bachelors degree or higher 34% 1% \n", + "high school diploma or GED 35% 1% \n", + "no high school diploma/GED 34% 1% \n", + "some college or Assoc. degree 36% 1% \n", + "\n", + "Reasons for Saving Purchases Retirement \n", + "edcl_lbl \n", + "Bachelors degree or higher 7% 38% \n", + "high school diploma or GED 12% 26% \n", + "no high school diploma/GED 15% 18% \n", + "some college or Assoc. degree 11% 28% " + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# New code to format as percentages\n", + "pivot = pivot.applymap(lambda x: f\"{x:.0%}\")\n", + "\n", + "pivot" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [], + "source": [ + "pivot.to_html(\"../../content/slides/tables/table1.tex\")" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [], + "source": [ + "scf_data = scf_data[scf_data[\"retired\"] == True]" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_356464/4287443728.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " .apply(weighted_mean, f\"savres{i}\", \"wgt\")\n", + "/tmp/ipykernel_356464/4287443728.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " .apply(weighted_mean, f\"savres{i}\", \"wgt\")\n", + "/tmp/ipykernel_356464/4287443728.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " .apply(weighted_mean, f\"savres{i}\", \"wgt\")\n", + "/tmp/ipykernel_356464/4287443728.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " .apply(weighted_mean, f\"savres{i}\", \"wgt\")\n", + "/tmp/ipykernel_356464/4287443728.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " .apply(weighted_mean, f\"savres{i}\", \"wgt\")\n", + "/tmp/ipykernel_356464/4287443728.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " .apply(weighted_mean, f\"savres{i}\", \"wgt\")\n", + "/tmp/ipykernel_356464/4287443728.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " .apply(weighted_mean, f\"savres{i}\", \"wgt\")\n", + "/tmp/ipykernel_356464/4287443728.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " .apply(weighted_mean, f\"savres{i}\", \"wgt\")\n", + "/tmp/ipykernel_356464/4287443728.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " .apply(weighted_mean, f\"savres{i}\", \"wgt\")\n" + ] + } + ], + "source": [ + "all_reasons = []\n", + "\n", + "reasons = [\n", + " \"Can't save\",\n", + " \"Education\",\n", + " \"Family\",\n", + " \"Home\",\n", + " \"Purchases\",\n", + " \"Retirement\",\n", + " \"Liquidity/the future\",\n", + " \"Investment\",\n", + " \"No particular reason\",\n", + "]\n", + "\n", + "for i in range(1, 10):\n", + " temp = (\n", + " scf_data.groupby(\n", + " [\n", + " \"edcl_lbl\",\n", + " ],\n", + " )\n", + " .apply(weighted_mean, f\"savres{i}\", \"wgt\")\n", + " .reset_index()\n", + " )\n", + " temp[\"Reasons for Saving\"] = reasons[i - 1]\n", + " all_reasons.append(temp)\n", + "\n", + "all_reasons = pd.concat(all_reasons)\n", + "pivot = all_reasons.pivot_table(\n", + " index=[\n", + " \"edcl_lbl\",\n", + " ],\n", + " columns=\"Reasons for Saving\",\n", + " values=0,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_356464/3822881605.py:2: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", + " pivot = pivot.applymap(lambda x: f\"{x:.0%}\")\n" + ] + }, + { + "data": { + "text/html": [ + "
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Reasons for SavingCan't saveEducationFamilyHomeInvestmentLiquidity/the futureNo particular reasonPurchasesRetirement
edcl_lbl
Bachelors degree or higher5%2%6%1%2%42%3%13%27%
high school diploma or GED7%1%8%0%2%42%3%14%22%
no high school diploma/GED14%1%5%1%2%40%2%17%19%
some college or Assoc. degree7%1%7%0%2%43%3%15%21%
\n", + "
" + ], + "text/plain": [ + "Reasons for Saving Can't save Education Family Home Investment \\\n", + "edcl_lbl \n", + "Bachelors degree or higher 5% 2% 6% 1% 2% \n", + "high school diploma or GED 7% 1% 8% 0% 2% \n", + "no high school diploma/GED 14% 1% 5% 1% 2% \n", + "some college or Assoc. degree 7% 1% 7% 0% 2% \n", + "\n", + "Reasons for Saving Liquidity/the future No particular reason \\\n", + "edcl_lbl \n", + "Bachelors degree or higher 42% 3% \n", + "high school diploma or GED 42% 3% \n", + "no high school diploma/GED 40% 2% \n", + "some college or Assoc. degree 43% 3% \n", + "\n", + "Reasons for Saving Purchases Retirement \n", + "edcl_lbl \n", + "Bachelors degree or higher 13% 27% \n", + "high school diploma or GED 14% 22% \n", + "no high school diploma/GED 17% 19% \n", + "some college or Assoc. degree 15% 21% " + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# New code to format as percentages\n", + "pivot = pivot.applymap(lambda x: f\"{x:.0%}\")\n", + "\n", + "pivot" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [], + "source": [ + "pivot.to_html(\"../../content/slides/tables/table2.tex\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "scf-tools", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/msm_notebooks/termbeq_results.pkl b/src/msm_notebooks/termbeq_results.pkl new file mode 100644 index 0000000..6867b5d Binary files /dev/null and b/src/msm_notebooks/termbeq_results.pkl differ diff --git a/src/msm_notebooks/trp_results.pkl b/src/msm_notebooks/trp_results.pkl new file mode 100644 index 0000000..d214257 Binary files /dev/null and b/src/msm_notebooks/trp_results.pkl differ diff --git a/src/msm_notebooks/wgbeq_results.pkl b/src/msm_notebooks/wgbeq_results.pkl new file mode 100644 index 0000000..0907deb Binary files /dev/null and b/src/msm_notebooks/wgbeq_results.pkl differ diff --git a/src/notebooks/IndShock.ipynb b/src/notebooks/IndShock.ipynb new file mode 100644 index 0000000..be7bbf5 --- /dev/null +++ b/src/notebooks/IndShock.ipynb @@ -0,0 +1,195 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import annotations\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "from HARK.utilities import plot_funcs\n", + "\n", + "from estimark.agents import IndShkLifeCycleConsumerType\n", + "from estimark.parameters import init_calibration" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "csv_file_path = \"../../content/tables/TRP/IndShock_estimate_results.csv\"\n", + "res = pd.read_csv(csv_file_path, header=None)\n", + "res = res.set_index(res.columns[0])[res.columns[1]].to_dict()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(4.475555494107589, 1.0)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "indshk_agent = IndShkLifeCycleConsumerType(**init_calibration)\n", + "indshk_agent.CRRA = float(res[\"CRRA\"])\n", + "indshk_agent.CRRA, indshk_agent.DiscFac" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "indshk_agent.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_funcs([sol.cFunc for sol in indshk_agent.solution[:-1:5]], 0, 20)\n", + "# plt.savefig(\"../content/figures/IndShock_cFunc.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the variables we want to keep track of.\n", + "indshk_agent.track_vars = [\"aNrm\", \"cNrm\", \"pLvl\", \"t_age\", \"mNrm\"]\n", + "\n", + "indshk_agent.T_sim = indshk_agent.T_cycle + 1\n", + "# Run the simulations\n", + "indshk_agent.initialize_sim()\n", + "history = indshk_agent.simulate()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "raw_data = {\n", + " \"Age\": indshk_agent.history[\"t_age\"].flatten() + 25,\n", + " \"pIncome\": indshk_agent.history[\"pLvl\"].flatten(),\n", + " \"nrmM\": indshk_agent.history[\"mNrm\"].flatten(),\n", + " \"nrmC\": indshk_agent.history[\"cNrm\"].flatten(),\n", + "}\n", + "\n", + "Data = pd.DataFrame(raw_data)\n", + "Data[\"Cons\"] = Data.nrmC * Data.pIncome\n", + "Data[\"M\"] = Data.nrmM * Data.pIncome\n", + "\n", + "# Find the mean of each variable at every age\n", + "AgeMeans = Data.groupby([\"Age\"]).median().reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(AgeMeans.Age, AgeMeans.nrmM, label=\"Market resources\")\n", + "plt.plot(AgeMeans.Age, AgeMeans.nrmC, label=\"Consumption\")\n", + "plt.legend()\n", + "plt.xlabel(\"Age\")\n", + "plt.ylabel(\"Thousands of USD\")\n", + "plt.title(\"Variable Medians Conditional on Survival\")\n", + "plt.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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WKFEi01PfvXr14vr16wQFBQFw6dIlgoOD0xUsmalVqxaVK1fmjz/+MK/7448/KFq0KC1btjSvS0xMZOLEieZ+ZGmfc1FRUdk+DiLnSJ8dkSPq1q1L5cqV+f3333n//ff5/fffURQlXcvCjBkzmDBhAkOGDGHatGl4eHhgZWXF6NGjM+1Im5VWHaPRSJs2bYiMjOR///sflStXxsnJiZs3bzJo0KBM95tdaft45ZVXMpyvT1OzZs0s769hw4aUK1eO0aNHc+XKFfr27fvY5x03btwjWxbKly+f5ed9lMWLFzNo0CC6du3Ku+++i7e3N3q9npkzZ3Lp0qUM26cVr//18H/Ko0ePpnPnzqxatYqNGzcyYcIEZs6cybZt26hTp84jX4u1tTUnT5585teUmazktlRZyd6zZ0/27dvHu+++S+3atXF2dsZkMtG+ffsc+T3IiYxP4urqSps2bWjTpg02NjYsXLiQAwcO0KxZs0f2OXrUuEuP+vzo3Lkzjo6OLFu2jEaNGrFs2TKsrKzMHcwfp1evXnz00UdERETg4uLC6tWr6dOnD9bWD/6Ujho1ivnz5zN69GgCAwNxc3NDp9PRu3fvPDkOInNS7Igc069fPyZMmMCJEydYsmQJFSpUSNcJ8M8//6RFixb8/PPP6R4XFRVF0aJFn+o5T548yfnz51m4cGG6S983b96c6fYmk4nLly+bW3MAzp8/D/DIjrNeXl64uLhgNBofOShgdvXp04fp06dTpUqVDB2A06S1QNnY2Dzxef39/blw4UKG9efOnXtilj///JOyZcuycuXKdH9Q0lqPnla5cuUYO3YsY8eO5cKFC9SuXZvPP/+cxYsXZ7q9o6MjLVu2ZNu2bVy/fh0/P7/H7t/f358tW7YQGxubrnXn33//Nd+fHV5eXjg4ODz1+5idDsD+/v6cOHECk8mUrnXnabPfu3ePrVu3MmXKFCZOnGhen9lryWo+yPx1//vvvxQtWhQnJ6en2ndW1atXj4ULF3L79m3gQUtRVFRUuu3+2wr2JE5OTrzwwgssX76cL774gj/++IMmTZpkuEAiM7169WLKlCmsWLECHx8fYmJi6N27d7pt/vzzTwYOHMjnn39uXpeUlJQht8hbchpL5Ji0VpyJEydy7NixDP1F9Hp9hv/yli9fbu538jTS/pt8eL+KovD1118/8jGzZ89Ot+3s2bOxsbEx9y3K7Dl69OjBihUrMh3j5c6dO9nO/eqrrzJp0qR0H4j/5e3tTfPmzfnhhx/MH/iPet6OHTuyf/9+Dh48mO7+33777YlZMnsPDxw4YG7mz66EhIQMpx7KlSuHi4sLycnJj33spEmTUBSF/v37ExcXl+H+4OBg8+W/HTt2xGg0pjueAF9++SU6nY4OHTpkK7der6ddu3asWrWKkJAQ8/qzZ8+ycePGJz4+7Y9/Vv6odezYkdDQ0HSnRFJTU/n2229xdnY2n4bNTnbI2Iry1VdfZWs/aYoVK0bt2rVZuHBhutdz6tQpNm3aRMeOHZ9qv/+VkJDwyJ+ztD5XaafS0vpb7dq1y7yN0Wjkxx9/zPbz9urVi1u3bvHTTz9x/PjxJ57CSlOlShVq1KjBH3/8wR9//EGxYsVo2rRpum0y+5z79ttvLXbk78JCWnZEjilTpgyNGjXi77//BshQ7LzwwgtMnTqVwYMH06hRI06ePMlvv/32yHEvsqJy5cqUK1eOcePGcfPmTVxdXVmxYsUj+wnY29uzYcMGBg4cSMOGDVm/fj1r167l/fffx8vL65HP8/HHH7N9+3YaNmzIsGHDqFq1KpGRkRw5coQtW7YQGRmZrdz+/v5Zmntrzpw5PP/889SoUYNhw4ZRtmxZwsLCCAoK4saNG+Yxit577z1+/fVX2rdvz9tvv22+9DytBeFxXnjhBVauXEm3bt3o1KkTV65c4fvvv6dq1aqZFhxPcv78eVq1akXPnj2pWrUq1tbW/PXXX4SFhWX4L/i/GjVqxJw5c3jzzTepXLlyuhGUd+zYwerVq5k+fTqgno5o0aIFH3zwAVevXqVWrVps2rSJv//+m9GjR6frjJxVU6ZMYcOGDTRp0oQ333zTXIBUq1btie9j7dq10ev1fPLJJ0RHR2NnZ0fLli3TjRWT5rXXXuOHH35g0KBBBAcHU7p0af7880/27t3LV199leVO2mlcXV3NfaMMBgMlSpRg06ZNXLlyJVv7edinn35Khw4dCAwMZOjQoeZLz93c3HJs3riEhAQaNWpEQEAA7du3x8/Pj6ioKFatWsXu3bvp2rWr+bRntWrVCAgIYPz48URGRuLh4cHSpUtJTU3N9vN27NgRFxcXxo0bZ/5nJqt69erFxIkTsbe3Z+jQoRn6Xb3wwgv8+uuvuLm5UbVqVYKCgtiyZUuOjCAvnkEeX/0lCrg5c+YogNKgQYMM9yUlJSljx45VihUrpjg4OCiNGzdWgoKCMlyWm3ZJ5vLlyzPsI7PLNc+cOaO0bt1acXZ2VooWLaoMGzbMfFnx/PnzzdsNHDhQcXJyUi5duqS0bdtWcXR0VHx8fJRJkyZluCSe/1x6riiKEhYWpowYMULx8/NTbGxsFF9fX6VVq1bKjz/++MT3Je3S88fJ7NJzRVGUS5cuKQMGDFB8fX0VGxsbpUSJEsoLL7yg/Pnnn+m2O3HihNKsWTPF3t5eKVGihDJt2jTl559/fuKl5yaTSZkxY4bi7++v2NnZKXXq1FHWrFmjDBw4UPH39zdvl3YZdGaXlD/8fkVERCgjRoxQKleurDg5OSlubm5Kw4YNlWXLlj3xfUoTHBys9O3bVylevLhiY2OjFClSRGnVqpWycOHCdMcqNjZWeeedd8zbVahQQfn0008Vk8mUId+IESMyPM9/Ly9WFEXZuXOnUrduXcXW1lYpW7as8v3335svr37SY+fNm6eULVvWfKl62s/pf99zRVF/ngYPHqwULVpUsbW1VWrUqJHu51VRsv6eK4qi3LhxQ+nWrZvi7u6uuLm5KS+//LJy69atDNtl9dJzRVGULVu2KI0bN1YcHBwUV1dXpXPnzsqZM2fSbZP23jw8dENWn8dgMCjz5s1Tunbtav75c3R0VOrUqaN8+umnGYZEuHTpktK6dWvFzs5O8fHxUd5//31l8+bNmV56Xq1atce+tn79+imA0rp160zvz+z4KoqiXLhwQQEUQNmzZ0+G++/du2c+rs7Ozkq7du2Uf//9N8P+5NLzvKVTlHzQO08IIYQQ4ilJnx0hhBBCFGhS7AghhBCiQJNiRwghhBAFmhQ7QgghhCjQpNgRQgghRIEmxY4QQgghCjQZVBB1CoFbt27h4uKSrSHfhRBCCKEdRVGIjY2lePHiGQZ4fJgUO8CtW7eeOA+PEEIIISzT9evXKVmy5CPvl2IHzEOzX79+HVdXV43TFC4Gg4FNmzbRtm1bbGxstI4jHiLHxrLJ8bFscnzyRkxMDH5+fk+cYkWKHR7MVuzq6irFTh4zGAw4Ojri6uoqHwgWRo6NZZPjY9nk+OStJ3VBkQ7KQgghhCjQpNgRQgghRIEmxY4QQgghCjTpsyOEEAWU0WjEYDBoHaNQMhgMWFtbk5SUhNFo1DpOvmVjY4Ner3/m/UixI4QQBYyiKISGhhIVFaV1lEJLURR8fX25fv26jN/2jNzd3fH19X2m91GKHSGEKGDSCh1vb28cHR3lj60GTCYTcXFxODs7P3awO/FoiqKQkJBAeHg4AMWKFXvqfUmxI4QQBYjRaDQXOp6enlrHKbRMJhMpKSnY29tLsfMMHBwcAAgPD8fb2/upT2nJERBCiAIkrY+Oo6OjxkmEyBlpP8vP0v9Mih0hhCiA5NSVKChy4mdZih0hhBBCFGhS7AghhBCiQJNiRwghhOYGDRqETqdDp9Nha2tL+fLlmTp1KqmpqVpH01Tz5s0ZPXp0jm1XWMnVWEKIZ6MokHgP7FxALxMeiqfXvn175s+fT3JyMuvWrWPEiBHY2Ngwfvz4bO/LaDSi0+nkSigBSMuOEOJJFJNazERchJADcPYf2PMlrHoTfmoNn/jDrDLwaTlY8SqcWgFJ0VqnFvmQnZ0dvr6++Pv7M3z4cFq3bs3q1asBSE5OZty4cZQoUQInJycaNmzIjh07zI9dsGAB7u7urF69mqpVq2JnZ0dISAilS5dm+vTpDBgwAGdnZ/z9/Vm9ejV37tyhS5cuODs7U7NmTQ4fPmze1927d+nTpw8lSpTA0dGRGjVq8Pvvv6fL2rx5c9566y3ee+89PDw88PX1ZfLkyem2iY6OZtiwYXh5eeHq6krLli05fvy4+f7JkydTu3Ztfv31V0qXLo2bmxu9e/cmNjYWUFu7du7cyddff21u9bp69WqW3svSpUszY8YMhgwZgouLC6VKleLHH39Mt82NGzfo06cPHh4eODk5Ua9ePQ4cOGC+f+7cuZQrVw5bW1sqVarEr7/+mu7xOp2OH374gRdeeAFHR0eqVKlCUFAQFy9epHnz5jg5OdGoUSMuXbqU7nF///03zz33HPb29pQtW5YpU6bkegueFDtCCFVqCoSehGNLYMN49Iu70O7kKKxnFoNPSsPsuvBLW/jjFdgyGY79BjcOPShskqLh5HL4cwjMKgeLukLQHLh1FIyF+1SE1hRFISElNc+/FEV5ptwODg6kpKQAMHLkSIKCgli6dCknTpzg5Zdfpn379ly4cMG8fUJCAp988gk//fQTp0+fxtvbG4Avv/ySxo0bc/ToUTp16kT//v0ZMGAAr7zyCkeOHKFcuXIMGDDAnDcpKYm6deuydu1aTp06xWuvvUb//v05ePBgunwLFy7EycmJAwcOMGvWLKZOncrmzZvN9w8aNIjw8HDWr19PcHAwzz33HK1atSIyMtK8zaVLl1i1ahVr1qxhzZo17Ny5k48//hiAr7/+msDAQIYNG8bt27e5ffs2fn5+WX7/Pv/8c+rVq8fRo0d58803GT58OOfOnQMgLi6OZs2acfPmTVavXs3x48d57733MJlMAPz111+8/fbbjB07llOnTvH6668zePBgtm/fnu45pk2bxoABAzh27BiVK1emb9++vP7664wfP57Dhw+jKAojR440b797924GDBjA22+/zZkzZ/jhhx9YsGABH330UZZf19OQ01hCFGYmI1zYBId+hss7wPRgHAsrwP7hbW1dwMkTHD3BoywUrQhFK6jfi5RWC6Vz6+Dceog4D5e3q19pj/VrAP6BUK4llKibd69RkGgwUnXixjx/3jNT2+Fom/0/M4qisHXrVjZu3MioUaMICQlh/vz5hISEULx4cQDGjRvHhg0bmD9/PjNmzADUcVi+++47atWqlW5/HTt25PXXXwdg4sSJzJ07l/r16/Pyyy8D8L///Y/AwEDCwsLw9fWlRIkSjBs3zvz4UaNGsXHjRpYtW0aDBg3M62vWrMmkSZMAqFChArNnz2br1q20adOGPXv2EBwcTFhYmHlgvM8++4xVq1bx559/8tprrwHq4IMLFizAxcUFgP79+7N161Y++ugj3NzcsLW1xdHREV9f32y/jx07duTNN980v8Yvv/yS7du3U6lSJZYsWcKdO3c4dOgQHh4eAJQvX9782M8++4xBgwaZHz9mzBj279/PZ599RosWLczbDR48mJ49e6Z7HydMmEC7du0AePvttxk8eLB5+ylTpvB///d/DBw4EICyZcsybdo03nvvPfN7mRuk2BGiMIq7A0cXweEFEB3yYL2dG/jWAN8apHpVZc+FSBq3646Nqw9Y2z1+n6UC1K82U9VTXufXw5XdELIfkqPh0lb1a9t0KNMUmr+vFj9C3LdmzRqcnZ0xGAyYTCb69u3L5MmT2bFjB0ajkYoVK6bbPjk5Od0o0ba2ttSsWTPDfh9e5+PjA0CNGjUyrAsPD8fX1xej0ciMGTNYtmwZN2/eJCUlheTk5AwDNf73uYoVK2ae2uDEiRPEx8fj5eWVbpvExMR0p3VKly5tLnT+u49n9XA+nU6Hr6+ved/Hjh2jTp065kLnv86ePWsuyNI0btyYr7/++pHP8aj3NikpiZiYGFxdXTl+/Dh79+5N15JjNBpJSkoiISEh1wbDlGJHiMLEkAQbx8ORXx+04jgUgdr94LkBaivN/QG8FIOB6JvrwKUYWGez43HR8lB0FDQapbYehZ2GkCC4ukdt+bmyS/0q2wJavK+2+ohc42Cj58zUdpo8b3a0aNGCuXPnYmtrS/HixbG2Vv9ExcXFodfrCQ4OzjBdgLOz84Pnc3DIdAA6G5sHP79p92e2Lu0UzqeffsrXX3/NV199RY0aNXBycmL06NHmU2qZ7TdtP2n7iIuLw9fXl+3bt2foJO3u7p6lfTyrx+07rbUpJ58jK+9tXFwcU6ZMoXv37hn2ZW9vn2FdTpFiR4jCIj4ClvaD6/vV2yXqQf2hUK0b2OTMB1+mrPRQrKb61fB1iAqBXZ+pfX7STnWVbwPdflBPk4kcp9Ppnup0Ul5zcnJKdyolTZ06dTAajYSHh9OkSZNcz7F37166dOnCK6+8Aqh/qM+fP0/VqlWzvI86deoQFhaGtbU1ZcuWfeostra2GI3Gp378o9SsWZOffvqJyMjITFt3qlSpwt69e82nm0B9X7LzHmTmueee49y5c5ke59wkHZSFKAzunIefWqmFjp0bvLIChm2F2n1zt9DJjHspePEbGBUMdfqDTg8XN8Nfr0MO/UcrCpaKFSvSr18/BgwYwMqVK7ly5QoHDx5k5syZrF27Nsefr0KFCmzevJl9+/Zx9uxZXn/9dcLCwrK1j9atW1O/fn26d+/Opk2buHr1Kvv27eODDz5Id+XXk5QuXZoDBw5w9epVIiIicqzVp0+fPvj6+tK1a1f27t3L5cuXWbFiBUFBQQC8++67LFiwgLlz53LhwgW++OILVq5cma4v09OYOHEiixYtYsqUKZw+fZqzZ8+ydOlSPvzww5x4WY8kxY4QBd3lnfBza7h3Fdz94dXNUL611qnUTs1dZsNr28HaXi14DszVOpWwUPPnz2fAgAGMHTuWSpUq0bVrVw4dOkSpUqVy/Lk+/PBDnnvuOdq1a0fz5s3NRUF26HQ6li1bRpMmTRg8eDAVK1akd+/eXLt2zdy3JSvGjRuHXq+natWqeHl5ERIS8uQHZYGtrS2bNm3C29ubjh07UqNGDT7++GPzacKuXbvy9ddf89lnn1GtWjV++OEH5s+fT/PmzZ/pedu1a8eaNWvYtGkT9evXJyAggC+//BJ/f/8ceFWPplOe9drAAiAmJgY3Nzeio6NxdXXVOk6hYjAYWLduHR07dsxwflnkgCO/wprRYEoFv4bQewk4Fc3SQ/P02Bz6CdaOBSsbeHULFK+du89XADzq+CQlJXHlyhXKlCmTq30gxOOZTCZzp1wZ2PDZPO5nOqt/v+UICFFQ7f8eVo9UC53qPWDA6iwXOnmu3lCo/ILaafrPIZAcp3UiIUQBIsWOEAXRwXmw4X/qcuO3ocfPYGPB/+XrdPDit+BaAiIvwfr3tE4khChApNgRoqA59BOsu9+JsPFoaD3FfDm5RXP0gO7zQGelXql1YrnWiYQQBYQUO0IUJIfnq31fABq9Ba0n549CJ03pxtD0fqvOmncg8rK2eYQQBYIUO0IUFMEL1c7IAIEj1ZGM81Ohk6bpu1AqEFJiYUkvSIh88mOEEOIxpNgRoiA48iv887a63HA4tJ2ePwsdAL01vPQLuBRX59ha2lcd+VkIIZ6SFDtC5HdHF8PqUYACDV6H9jPzb6GTxrU4vPIn2Lmq00zIgINCiGegabEzefJkdDpduq/KlSub709KSmLEiBF4enri7OxMjx49MoxiGRISQqdOnXB0dMTb25t3332X1NTUvH4pQmjj6G/w90jUQuc16PBJ/i900vhUg16L1bF3zqyCzRO0TiSEyKc0b9mpVq0at2/fNn/t2bPHfN8777zDP//8w/Lly9m5cye3bt1KN3mY0WikU6dOpKSksG/fPhYuXMiCBQuYOHGiFi9FiLx1bAn8PQJQoP4w6DCr4BQ6aco2g673R1UOmg1B32mbRwiRL2le7FhbW+Pr62v+KlpUHfQsOjqan3/+mS+++IKWLVtSt25d5s+fz759+9i/X53IcNOmTZw5c4bFixdTu3ZtOnTowLRp05gzZ06G2WmFKFCOLYFVb6IWOq9Cx08LXqGTpubL6lVlABvfhzOrNY0j8rcFCxakm3VcFA6aT4N74cIFihcvjr29PYGBgcycOZNSpUoRHByMwWCgdesHc/hUrlyZUqVKERQUREBAAEFBQdSoUSPdPCPt2rVj+PDhnD59mjp16mT6nMnJySQnJ5tvx8TEAOrw6waDIZdeqchM2vst73vW6c7+jX7Vm+hQMD43GFObmZALp24t6tg0GIHVvRD0wb+g/P0mqV5V1Xm+CrFHHR+DwYCiKJhMphybNDIvDB48mEWLFvHaa68xd276OdJGjhzJ3LlzGTBgAPPnz3+m50l7T572vdmxYwetWrXi7t27jy2a0mZiSjsW4umZTCYURcFgMJjn7kqT1c8nTYudhg0bsmDBAipVqsTt27eZMmUKTZo04dSpU4SGhmJra5vhh8nHx4fQ0FAAQkNDM0yolnY7bZvMzJw5kylTpmRYv2nTJhwdHZ/xVYmnsXnzZq0j5AtuCVd5/vx0dChc9WzBcVMzWL8+V5/TUo6NTmlCY6fdeMZfIHZ+T/ZU/ABFp/n/a5r77/FJay2Pi4vLVy3cBoOBEiVKsHTpUiZPnoyDgwOg9t1csmQJJUuWxGAwmP85fdrnSEpKQlGUp95PQkICALGxsVma8yo2NhZQu13odDqLnCfLYDBY9NyEKSkpJCYmsmvXrgx9ctOOx5No+knRoUMH83LNmjVp2LAh/v7+LFu2zPyDnhvGjx/PmDFjzLdjYmLw8/Ojbdu2MhFoHjMYDGzevJk2bdpY9C+bRYiPwPqX99EpKZjKtqJEryWUsNI/+XFPySKPTXRtlHnN8Ei4RCen05iaj9c6kWYedXySkpK4fv06zs7O+WoiUBsbG+rWrcvly5fZsmUL/fr1A2DNmjX4+/tTunRpbGxszJ/RGzZsYMaMGZw6dQq9Xk9AQABfffUV5cqVA+Dq1auUK1eOJUuW8P3333PgwAG+++477O3t0el05v3cuXOHTp06UbJkSX7//XdsbGyYNWsW8+bNIzQ0lIoVK/LBBx/w0ksvcfXqVTp37gxA6dKlAR7Z2rRgwQLeeecdFi5cyPvvv8/58+c5f/48xYoV48MPP2Tp0qVERUVRvXp1Zs6caZ5N/Nq1a4waNYq9e/eSkpJC6dKl+eSTT+jYsSMAO3fu5H//+x/Hjx/Hw8ODAQMGMG3aNKyt1T/nZcuW5e233+btt982Z3nuuefo0qULkyZNAkCv1zN79mw2bNjAtm3bGDduHJMmTeKff/5h+vTpnDx5EmdnZ55//nlWrlwJqGdEniX3s0hKSsLBwYGmTZtmOhFoVljUv0Xu7u5UrFiRixcv0qZNG1JSUoiKikrXuhMWFoavry8Avr6+HDx4MN0+0q7WStsmM3Z2dtjZ2WVYb2NjYzkf6oWMvPdPYDTAX0Mh5gZ4lMPq5Z+xssubP2QWdWyKloXOX8Ofg9Hv/QJ9hZZQ+nmtU2nqv8fn4RYEcyuCooAha/8B52w4xyz3JUu7InfIkCEsXLiQ/v37A2rRMHjwYHbs2JGuZSQxMZExY8ZQs2ZN4uLimDhxIj169ODYsWPpXvv777/P559/Tp06dbC3t2fjxo0AWFlZcf36ddq0aUNAQAA///wzer2ejz76iMWLF/P9999ToUIFdu3axYABA/Dx8eH5559nxYoV9OjRg3PnzuHq6oqDg8MjW2sSExP59NNP+emnn/D09MTX15e33nqLM2fOsHTpUooXL85ff/1Fx44dOXnyJBUqVGDUqFGkpKSwa9cunJycOHPmjHnm9Js3b/LCCy8waNAgFi1axL///suwYcNwcHBg8uTJ6d7L/2b677qpU6fy8ccf8/XXX2Ntbc369evp0aMHH3zwAYsWLSIlJYV169aZH/MsuZ+VlZUVOp0u08+irH42WVSxExcXx6VLl+jfvz9169bFxsaGrVu30qNHDwDOnTtHSEgIgYGBAAQGBvLRRx8RHh6Ot7c3oDbpurq6UrVqVc1ehxA5bsN4uLYXbF2gz+/gUETrRNqp3h0ubVXHF1r5GryxR51XSzyaIQFmFM/7533/Ftg6Zeshr7zyCuPHj+fatWsA7N27l6VLl7Jjx45026X9XUjzyy+/4OXlxZkzZ6hevbp5/ejRo9NdxZvm3LlztGnThm7duvHVV1+h0+lITk5mxowZbNmyxfx3pmzZsuzZs4cffviBZs2a4eGh/qx5e3s/saOzwWBg9uzZ5v6jISEhzJ8/n5CQEIoXV4/HuHHj2LBhA/Pnz2fGjBmEhITQo0cPatSoYX7+NN999x1+fn7Mnj3bPFTLrVu3+N///sfEiROzVVj07duXwYMHm2/37t2b3r17p+viUatWrRzJbQk0LXbGjRtH586d8ff359atW0yaNAm9Xk+fPn1wc3Nj6NChjBkzBg8PD1xdXRk1ahSBgYEEBAQA0LZtW6pWrUr//v2ZNWsWoaGhfPjhh4wYMSLTlhsh8qXghXBonrrc/UfwqqRtHkvQ/hMI2Q93L8I/b0HPXwvu1WiFjJeXF506dWLBggUoikKnTp3MV+k+7MKFC0ycOJEDBw4QERFh7gQcEhKSrtipV69ehscmJibSpEkT+vbty1dffWVef/HiRRISEmjTpk267VNSUh55wcvj2NraUrNmTfPtkydPYjQaqVixYrrtkpOT8fT0BNQWlOHDh7Np0yZat25Njx49zPs4e/YsgYGB6B76WW/cuDFxcXHcuHGDUqVKZTnbf9+XY8eOMWzYsEy3fdbclkDTYufGjRv06dOHu3fv4uXlxfPPP8/+/fvx8vIC4Msvv8TKyooePXqQnJxMu3bt+O67B+Ns6PV61qxZw/DhwwkMDMTJyYmBAwcydepUrV6SEDkr5MCDiT1bfAiVn/38d4Fg5ww9foKf2sDZfyB4AdQb/MSHFVo2jmorixbP+xSGDBnCyJEjAZgzZ06m26T9ozxv3jyKFy+OyWSievXqGTplOzllbFmys7OjdevWrFmzhnfffZcSJUoA6tkFgLVr15rXPfyY7ErrH5QmLi4OvV5PcHBwhquKnJ2dAXj11Vdp164da9euZdOmTcycOZPPP/+cUaNGZek5rayszFeCpcnsiqX/vi+P6yebF7lzm6bFztKlSx97v729PXPmzHnkDzuAv78/69aty+loQmgvORZWvAomA1R5EZqO0zqRZSleB1pNVEdW3vg+lG4CRctrncoy6XTZPp2kpfbt25OSkoJOp6Ndu3YZ7r979y7nzp1j3rx5NGnSBCDdgLRPYmVlxa+//krfvn1p0aIFO3bsoHjx4lStWhU7OztCQkJo1qxZpo+1tbUF1L5R2VWnTh2MRiPh4eHm3Jnx8/PjjTfe4I033mD8+PHMmzePUaNGUaVKFVasWIGiKOYiau/evbi4uFCyZElAbRm7ffu2eV8xMTFcuXLlidlq1qzJ1q1b053ayqnclsDyroETQqg2T4ToEHAvBV2/k9M0mQkcCWWaqX1S/npN7cgt8j29Xs/Zs2c5c+ZMhpYEgCJFiuDp6cmPP/7IxYsX2bZtW7orbLP6HL/99hu1atWiZcuWhIaG4uLiwrhx48xXUV26dIkjR47w7bffsnDhQkD9B1un07FmzRru3Lljbg3KiooVK9KvXz8GDBjAypUruXLlCgcPHmTmzJmsXbsWUPsYbdy4kStXrnDkyBG2b99OlSpVAHjzzTe5fv06o0aN4t9//+Xvv/9m0qRJjBkzxtxfp2XLlvz666/s3r2bkydPMnDgwEzfw/+aNGkSv//+O5MmTeLs2bOcPHmSTz75JEdyWwIpdoSwRJd3wOFf1OUuc8DORdM4FsvKSp1Owt4NbgbD7s+1TiRyiKur6yOHArGysmLp0qUEBwdTvXp13nnnHT799NNsP4e1tTW///471apVo2XLloSHhzNt2jQmTJjAzJkzqVKlCu3bt2ft2rWUKVMGgBIlSjBlyhT+7//+Dx8fH/PptqyaP38+AwYMYOzYsVSqVImuXbty6NAhc38bo9HIiBEjzM9dsWJFc/eNEiVKsG7dOg4ePEitWrV44403GDp0KB9++KF5/+PHj6dZs2a88MILdOrUia5du5ovx3+c5s2bs3z5clavXk3t2rVp2bJluqudnyW3JdAp/z25VwjFxMTg5uZGdHS0jLOTxwwGA+vWraNjx46Wc3mz1pJj4btGaqtO/VehkzZ/wPPVsTn5J6wYCjo9DN0MJetqnSjXPer4JCUlceXKFcqUKZOvxtkpaEwmEzExMTl2+XVh9rif6az+/ZYjIISl2TTh/ukrf2idcaRvkYkaL0H1l0AxwsphkBKvdSIhhAWRYkcIS3JpOwTfH421y2z1qiORNZ0+A5fiEHlJLRiFEOI+KXaEsBRJMbD6/pUL9V+FMk21zZPfOBSBbvcnkDz8M1ywjDm9hBDak2JHCEux6QOIvi6nr55F2eYQ8Ka6vOpNiNFgbBkhhMWRYkcIS3B4PhxZpC7L6atn02oieFeF+HD4vQ+kaDAnlBDCokixI4TWru6BdfcHDGzxoZy+elY2DtB7CTh6wu1jsOoNuD+VgBCicJJiRwgt3bsKf/QHUypU7yGjJOcUjzLQazFY2cCZv2HHDK0TCSE0JMWOEFpJjlVPsyRGQrHa8OJsGSU5J/k3ghe/UZd3fQonlmmbRwihGSl2hNCCyQQrX4PwM+DsA31+B9unmzRRPEbtvtB4tLr890i4fvCxmwshCiYpdoTQwrZpcG4d6O3U/iWuxbVOVHC1mgSVOoExGZb2hdgwrRMJkS06nY5Vq1ZpHSNfk2JHiLx2dQ/s+UJdfvFbKFlP2zwFnZUVdP8RvKtB/B3YMVPrROIxQkNDGTVqFGXLlsXOzg4/Pz86d+7M1q1btY6W6yZPnkzt2rUzrL99+zYdOnTI+0AFiBQ7QuQlQxL887a6XHcQ1OqlaZxCw875wRxjRxZBxAVt84hMXb16lbp167Jt2zY+/fRTTp48yYYNG2jRogUjRozQOp5mfH19sbOz0zpGvibFjhB5adencPciOPtCm6lapylc/AOhUkd1/qytMmijJXrzzTfR6XQcPHiQHj16ULFiRapVq8aYMWPYv38/ACEhIXTp0gVnZ2dcXV3p2bMnYWEPTk2mtY78+uuvlC5dGjc3N3r37k1sbKx5mz///JMaNWrg4OCAp6cnrVu3Jj5enU+tefPmjB49Ol2url27MmjQIPPt0qVLM336dAYMGICzszP+/v6sXr2aO3fumLPVrl2bo0ePmh+zYMEC3N3dWbVqFRUqVMDe3p527dpx/fp18/1Tpkzh+PHj6HQ6dDodCxYsADKexjp58iQtW7Y053/ttdeIi4sz3z9o0CC6du3KZ599RrFixfD09GTEiBEYDIZnOj75mRQ7QuSVsDOw9yt1ueOnYO+maZxCqdVE0FnB2X/g+iGt0+QZRVFIMCTk+ZeiKFnOGBkZyYYNGxgxYgROTk4Z7nd3d8dkMtGlSxciIyPZuXMnmzdv5vLly/Tqlb6F9NKlS6xatYo1a9awZs0adu7cyccffwyop4T69OnDkCFDOHv2LDt27KB79+7Zygrw5Zdf0rhxY44ePUqnTp3o378/AwYM4JVXXuHIkSOULVuW4cOHp9tvQkICH330EYsWLWLv3r1ERUXRu3dvAHr16sXYsWOpVq0at2/f5vbt2xleF0B8fDzt2rWjSJEiHDp0iOXLl7NlyxZGjhyZbrvt27dz6dIltm/fzsKFC1mwYIG5eCqMrLUOIEShYDLCP2+p4+lUfgGqvqh1osLJu4p6hdbRxbB5IgxeVygu909MTaThkoZ5/rwH+h7A0SZrVxlevHgRRVGoXLnyI7fZunUrJ0+e5MqVK/j5+QGwaNEiqlWrxqFDh6hfvz4AJpOJBQsW4OLiAkD//v3ZunUrH330Ebdv3yY1NZXu3bvj7+8PQI0aNbL92jp27Mjrr78OwMSJE5k7dy7169fn5ZdfBuC9996jcePGhIWFUby4egGCwWBg9uzZNGyoHouFCxdSpUoVDh48SIMGDXB2dsba2hpfX99HPu+SJUtISkpi0aJF5qJw9uzZdO7cmU8++QQfHx8AihQpwuzZs9Hr9VSuXJlOnTqxdetWhg0blu3XWhBIy44QeeHQz3DjENi6qK06QjvN3wdrewjZB+c3ap1G3JeVlpWzZ8/i5+dnLnQAqlatiru7O2fPnjWvK126tLnQAShWrBjh4eEA1KpVi1atWlGjRg1efvll5s2bx71797Kdt2bNmubltALj4aIpbV3a8wJYW1ubCzKAypUrZ8j+JGfPnqVWrVrpWr8aN26MyWTi3Llz5nXVqlVDr9ebbz/8HhRG0rIjRG6LvvGgj0jrSXKZudbcSkDDN9RTilsmQ4U2YKV/0qPyNQdrBw70PaDJ82ZVhQoV0Ol0/Pvvv8/8vDY2Nulu63Q6TPenDNHr9WzevJl9+/axadMmvv32Wz744AMOHDhAmTJlsLKyylB4ZdbX5eHn0N1vHcxsnUmjqUoe9x4URtKyI0RuUhRYOw5S4sCvIdQbqnUiAfD8O2DvDnfOwvHftU6T63Q6HY42jnn+pcvGKUIPDw/atWvHnDlzzJ2FHxYVFUWVKlW4fv26uVMvwJkzZ4iKiqJq1arZej8aN27MlClTOHr0KLa2tvz1118AeHl5cfv2bfO2RqORU6dOZXnfj5Oamsrhw4fNt8+dO2d+XQC2trYYjcbH7qNKlSocP3483Xu0d+9erKysqFSpUo7kLIik2BEiN/27Fs6vV+do6vyNOuaL0J6D+4N5yLbPAEOipnGEas6cORiNRho0aMCKFSu4cOECZ8+e5ZtvviEwMJDWrVtTo0YN+vXrx5EjRzh48CADBgygWbNm1KuXtfGqDhw4wIwZMzh8+DAhISGsXLmSO3fumAuOli1bsnbtWtauXcu///7L8OHDiYqKypHXZ2Njw6hRozhw4ADBwcEMGjSIgIAAGjRoAKin365cucKxY8eIiIggOTk5wz769euHvb09AwcO5NSpU2zfvp1Ro0bRv39/86kzkZF88gqRWwyJsGG8utz4bfB+dMdLoYH6w8DND2Juwt6vtU4jgLJly3LkyBFatGjB2LFjqV69Om3atGHr1q3MnTsXnU7H33//TZEiRWjatCmtW7embNmy/PHHH1l+DldXV3bt2kXHjh2pWLEiH374IZ9//rl50L4hQ4YwcOBAcxFVtmxZWrRokSOvz9HRkf/973/07duXxo0b4+zsnC57jx49aN++PS1atMDLy4vff8/Y6ujo6MjGjRuJjIykfv36vPTSS7Rq1YrZs2fnSMaCSqdk93q7AigmJgY3Nzeio6NxdXXVOk6hYjAYWLduHR07dsxwjjnf2z4Tdn6s/kEdcTDfzX1VoI9NmlMr4M8hYGUNw7ZDsZpPfoyFeNTxSUpK4sqVK5QpUwZ7e3sNExZuJpOJmJgYXF1dsbKyYsGCBYwePTrHWokKk8f9TGf177e07AiRGyKvwJ4v1eW20/NdoVNoVOsOVTqrQwKsehNSU7ROJITIBVLsCJEbNn6gTjxZphlU7aJ1GvEoOh10+gIcPCDsJOz+XOtEQohcIMWOEDntwmY4t1Y9NdJhVqEYtC5fc/aGTp+py7s/g9vHtc0jCqRBgwbJKSwNSbEjRE5KTYb1/1OXG74hnZLzi2rd1RY4OZ0lRIEkxY4QOSloDkReAmcfaPY/rdOIrNLpoOPn4OgJYafUCVvzObn2RBQUOfGzLMWOEDkl+uaDP5JtpoG9XNmXrzh7Qaf7fXZ2fw63jj5+ewuVdmVWQkKCxkmEyBlpP8vPclWoTBchRE7ZOhUMCeAXADV7ap1GPI1q3eDM33D6L/jrDXhtJ9jkr8u39Xo97u7u5nmQHB2zN5KxyBkmk4mUlBSSkpKwksFEn4qiKCQkJBAeHo67u3u6ub6yS4odIXLC7eNw4v7gYO1nSqfk/Kzj53B1L9z5F7ZNg3YfaZ0o29JmzS7MEz9qTVEUEhMTcXBwkGLzGbm7uz92JviskGJHiGelKLB5IqBA9ZegxHNaJxLPwskTXvwWfu+l9sGq2B7KNNE6VbbodDqKFSuGt7d3ppNYitxnMBjYtWsXTZs2LbiDcuYBGxubZ2rRSSPFjhDP6tJWuLxDnf+q1QSt04icUKk9PDcAjixSr84avjdf9sHS6/U58odCZJ9eryc1NRV7e3spdiyAnEgU4lmYjLB5krrc4DUoUlrTOCIHtZsB7v4QHQIb/k/rNEKIZyDFjhDP4vhS9VJle7cHs2iLgsHOBbp9D+jg2G9wdo3WiYQQT0mKHSGeliERtk1Xl5uMBUcPbfOInOffCBq/pS7/8zbE3dE2jxDiqUixI8TT2v8dxN5SZzVv8LrWaURuafEBeFeDhAhYPRJMJq0TCSGySYodIZ5GfATsvj+recsJ+W4sFpEN1nbQ/QfQ28H5DbDvG60TCSGySYodIZ7GzlmQEgu+NaDGy1qnEbnNtwZ0+ERd3joFru7RNo8QIluk2BEiuyIuwuGf1eU200BGRy0c6g6Cmr1BMcHywRAbqnUiIUQWyae0ENm1eaI6O3aFdlCuhdZpRF7R6eCFL8C7KsSHw59DwJiqdSohRBZIsSNEdlzZDefWgk4PbadpnUbkNVsn6LkIbF3g2l51OgkhhMWTYkeIrDKZYNMH6nLdQeBVSdM4QiNFK0CXb9XlvV/Bv+s0jSOEeDIpdoTIqhN/qBN+2rpA8/FapxFaqtYNGg5Xl1e9AbFh2uYRQjyWFDtCZEVKAmydqi43GQPOXtrmEdprMxWK1YKkaNj4vtZphBCPIcWOEFkRNOf+AIKlIOBNrdMIS2BtC52/Bp0VnPoTLm7VOpEQ4hGk2BHiSWLDYM/9AQRbT5IBBMUDxes8GD177Rh1ChEhhMWRYkeIJ9n+ERjioURdqN5D6zTC0rT8AFyKw72rsOtTrdMIITIhxY4QjxNxAY7+qi63/Ugda0WIh9m5QMf7Rc7ebyD8rLZ5hBAZSLEjxONsm66OmFuxA/gHap1GWKoqL0CljmAywJp3ZLJQISyMFDtCPMqto3BmFaCDVhO0TiMsXYdZYOMEIUFwbLHWaYQQD5FiR4hHSbvUvMbL4FNN2yzC8rn7qf13ADZNkLF3hLAgFlPsfPzxx+h0OkaPHm1el5SUxIgRI/D09MTZ2ZkePXoQFpb+AyQkJIROnTrh6OiIt7c37777LqmpMl+NeEZXdsOlbWBlDS1kAEGRRQ1evz/2ThSsHgWKonUiIQQWUuwcOnSIH374gZo1a6Zb/8477/DPP/+wfPlydu7cya1bt+jevbv5fqPRSKdOnUhJSWHfvn0sXLiQBQsWMHHixLx+CaIgURTYOkVdrjsIPMpqGkfkI3pr6Po96O3gwkYIXqB1IiEEFlDsxMXF0a9fP+bNm0eRIkXM66Ojo/n555/54osvaNmyJXXr1mX+/Pns27eP/fv3A7Bp0ybOnDnD4sWLqV27Nh06dGDatGnMmTOHlJQUrV6SyO/OrYcbh8DaAZq+q3Uakd/4VIVW9//h2vgB3L2kbR4hBNZaBxgxYgSdOnWidevWTJ8+3bw+ODgYg8FA69atzesqV65MqVKlCAoKIiAggKCgIGrUqIGPj495m3bt2jF8+HBOnz5NnTp1Mn3O5ORkkpOTzbdjYmIAMBgMGAyGnH6J4jHS3m+Led9NRqy3TkUHGBu8hsneEywlWx6zuGOTn9Qbhv7ceqyu7cG08nWMA/5RT4nmIDk+lk2OT97I6vurabGzdOlSjhw5wqFDhzLcFxoaiq2tLe7u7unW+/j4EBoaat7m4UIn7f60+x5l5syZTJkyJcP6TZs24ejomN2XIXLA5s2btY4AQMnIvdS9c5YUvSNbYitjWCczWlvKsclvHJy708IqGJubhzi3YCTnfV/MleeR42PZ5PjkroSEhCxtp1mxc/36dd5++202b96MvX3eDr8/fvx4xowZY74dExODn58fbdu2xdXVNU+zFHYGg4HNmzfTpk0bbGxstA1jNGD9vXqJub7pWNo0elnbPBqzqGOTT+nK2sE/I6gctoryHYarnZdziBwfyybHJ2+knZl5Es2KneDgYMLDw3nuuefM64xGI7t27WL27Nls3LiRlJQUoqKi0rXuhIWF4evrC4Cvry8HDx5Mt9+0q7XStsmMnZ0ddnZ2Gdbb2NjID6VGLOK9P7UMoq6Bkxf6wOHotc5jISzi2ORXz/WDixvRnV2Nzeo34fWdYOOQo08hx8eyyfHJXVl9bzXroNyqVStOnjzJsWPHzF/16tWjX79+5mUbGxu2bn0wk/C5c+cICQkhMFAdyTYwMJCTJ08SHh5u3mbz5s24urpStWrVPH9NIh8zGWHPF+pyo1Fg66RtHlEw6HTwwlfg5A0R52Dv11onEqJQ0qxlx8XFherVq6db5+TkhKenp3n90KFDGTNmDB4eHri6ujJq1CgCAwMJCAgAoG3btlStWpX+/fsza9YsQkND+fDDDxkxYkSmLTdCPNKZVXD3Iti7Q70hWqcRBYmTJ3T4BP4crBY7zw0A1+JapxKiUNH80vPH+fLLL3nhhRfo0aMHTZs2xdfXl5UrV5rv1+v1rFmzBr1eT2BgIK+88goDBgxg6tSpGqYW+Y7JBLs+U5cD3lQndhQiJ1XrBn4BYEh4MDK3ECLPaH7p+cN27NiR7ra9vT1z5sxhzpw5j3yMv78/6+SKGfEszq+H8DNg6wINX9M6jSiIdDpoPwPmtYTjv0OD16DEc09+nBAiR1h0y44QuU5RYNen6nKDYeBQ5PHbC/G0StSFmr3V5Y3vy1QSQuQhKXZE4XZpqzq7ubUDBI7QOo0o6FpNVH/WQoLgzN9apxGi0JBiRxReigI777fq1BsCTkW1zSMKPrcS0PhtdXnzRDAkaZtHiEJCih1ReF3bC9f3g95WvdxciLzQ+C1wKaaO6XTge63TCFEoSLEjCq+0vjp1+oNrMW2ziMLD1glaTVKXd30GceGP314I8cyk2BGF081guLxDnZwx7bSCEHmlZi8oXgdSYmHbNK3TCFHgSbEjCqd936rfq78ERfy1zSIKHysraP+xunzkV7h5RNs8QhRwUuyIwufetQdXwjQaqW0WUXiVCoAaPQEF1r+nDm4phMgVUuyIwmf/XFBMULYF+NbQOo0ozNpMBVtnuHEITizVOo0QBZYUO6JwSbwHRxapy3IFltCaazFo+q66vHkSJEVrm0eIAkqKHVG4HJ4PhnjwrgblWmqdRgh1PjbP8hAfDjtnaZ1GiAJJih1ReKSmwIEf1OVGI9X5ioTQmrXtg87KB76HO+e0zSNEASTFjig8Tv0JcaHqgG7VX9I6jRAPVGgDFTuAKRXW/0/mzRIih0mxIwoHRYF9s9Xlhq+r/00LYUnazwC9HVzeDv+u0TqNEAWKFDuicLi0DcJPg40T1B2kdRohMvIo+6DT/Ib3ISVe2zxCFCBS7IjCIW0QwecGgEMRbbMI8ShNxoCbH0SHqFNJCCFyhBQ7ouALPameGtBZQcBwrdMI8Wi2Tg86K+/7Fu6c1zaPEAVEtoqd+Ph4Jk6cSPXq1XF2dsbFxYWaNWsydepUEhISciujEM9m9xfq96pdZGoIYfkqd4IK7cBkgHVjpbOyEDnAOqsbpqSk0KxZM06dOkWHDh3o3LkziqJw9uxZPvroI9avX8+uXbuwsbHJzbxCZE/ERTj9l7rcZKy2WYTICp0OOnwCV3bClV1wagXUkKsHhXgWWS525s6dy40bNzh+/DiVKlVKd9+///5L8+bN+f777xk1SkalFRZk75eAAhXby9QQIv/wKKMW59s/go0fQIW2YO+qdSoh8q0sn8ZauXIlEyZMyFDoAFSuXJkPPviAP//8M0fDCfFMoq7D8fvzDTUZp20WIbKr0VvqFVpxobBjptZphMjXslzsnDlzhubNmz/y/hYtWnDmzJmcyCREztj3jTpIW5mm4Fdf6zRCZI+NPXS8f0XWge/VjvZCiKeS5WInKioKT0/PR97v6elJdLRMYicsRFz4gwk/pVVH5FflW6kd6xUTrHtXOisL8ZSyXOyYTCb0ev2jd2RlhdFozJFQQjyzoNmQmgQl66stO0LkV+1mgrU9hASpHZaFENmW5Q7KiqLQqlUrrK0zf0hqamqOhRLimSREwqGf1eUm42TCT5G/uZVQB8M8+CPs/hzKNtM6kRD5TpaLnUmTJj1xmx49ejxTGCFyxMEfISUOfGpAxXZapxHi2TUaBYd/US9Hv3EYStbTOpEQ+UqOFjtCaC45FvbPVZebjJFWHVEwuJeCmr3g2G/qIJl9lmidSIh85Zmni9i5cyfr1q3j3r17OZFHiGcTvACSosCzgtqxU4iCovFoQAfn1kKYXPkqRHZkudj55JNPmDBhgvm2oii0b9+eFi1a8MILL1ClShVOnz6dKyGFyBJjKhz4QV1u/BZYPbpDvRD5jldFqPqiurz3K02jCJHfZLnY+eOPP6hevbr59p9//smuXbvYvXs3ERER1KtXjylTpuRKSCGy5Nw6iL4Ojp5Qo6fWaYTIec+PUb+f/BPuXdU0ihD5SZaLnStXrlCzZk3z7XXr1vHSSy/RuHFjPDw8+PDDDwkKCsqVkEJkyYHv1e91B6kDsglR0BSvDeVbg2LEav9srdMIkW9kudhJTU3Fzs7OfDsoKIhGjRqZbxcvXpyIiIicTSdEVt0+Adf2gk4P9V/VOo0Qued+647V8SXYGaK0zSJEPpHlYqdcuXLs2qUOaBUSEsL58+dp2vTBYG03btx47AjLQuSqtL46VbuAa3FtswiRm/wbgV8AOmMK5cLXa51GiHwhy8XOiBEjGDlyJEOHDqVDhw4EBgZStWpV8/3btm2jTp06uRJSiMeKj4CTy9XlgOHaZhEit+l06ozoQJmIberPvxDisbJc7AwbNoxvvvmGyMhImjZtyooVK9Ldf+vWLYYMGZLjAYV4ouD5YEyG4nXU6SGEKOgqtMHkWwtrUzJWe7/QOo0QFi/LgwoCDBky5JEFzXfffZcjgYTIFqPhwdQQDYfLIIKicNDpMLWchNWS7lgFz4fAN8GjjNaphLBYWS52Tpw4kel6Nzc3SpUqhU7+yAgtnPkbYm+DkzdU66p1GiHyjFKmKWEuNfCJPQnbpsFLv2gdSQiLleVip3bt2uh0OhRFSbdep9Nhb2/P6NGjmTp16mNnRhcix6V1TK43BKztHr+tEAXMmeK98D53Ct2pFRA4Eko8p3UkISxSloudK1euZLo+KiqK4OBgJkyYQJEiRRg3blyOhRPisW4Gw42DYGWjFjtCFDIxjqVQaryM7uQy2DIJBqyWU7lCZCLLxY6/v/8j19eqVQtXV1emTJkixY7IO2mtOtW7g4uPtlmE0Iix2XiszqyCK7vg4lao0FrrSEJYnGeeCDRN3bp1H9n6I0SOiwuHUyvV5Yava5tFCC25+UGD19TlLZPAZNQ2jxAWKMeKndDQULy8vHJqd0I8XvACMBmgRD0oUVfrNEJoq8lYsHeDsFNwYpnWaYSwODlS7Ny5c4cJEybQokWLnNidEI9nNMDh+1eeSKuOEODo8WCS0G3TwZCkbR4hLEyW++zUqVMn08vLo6OjuXHjBpUqVWLx4sU5Gk6ITJ395/7l5l7q9BBCCLXwP/gjxNyAoNnQVPpPCpEmy8VO165dM13v6upKpUqVaNeunVx2LvLGwR/V73UHy+XmQqSxcYDWk2HlMNj9OdTqA24ltE4lhEXIcrEzadKk3MwhRNbcPgEhQWBlLZebC/FfNV6GQz/B9QNqZ+UeP2mdSAiLkGMdlIXIE2mtOlU6g2sxbbMIYWl0OugwC9Cpk+OG7Nc6kRAWQYodkX8kRD6Y3byBdEwWIlPFa8NzA9Tlde/KpehCIMWOyE+O/gqpSeBbA0oFaJ1GCMvVaiLYuUHoCTiySOs0QmguS8VOTExMbucQ4vFMRrUvAqitOjIkvhCP5lQUWoxXl7dNg8R72uYRQmNZKnaKFClCeHg4AC1btiQqKio3MwmR0fmNEBUCDkWgxktapxHC8tV/FbwqQ8Jd2PGx1mmE0FSWih1nZ2fu3r0LwI4dOzAYDLkaSogMDt6fB+u5AeoltkKIx9PbQPuZ6vLBeRB+Vts8QmgoS5eet27dmhYtWlClShUAunXrhq2tbabbbtu2LefSCQFwIxgu7wCdHuoN1TqNEPlHuZZQ+QX4dw1sGA/9/5JTwKJQylLLzuLFi5k8eTL16tUDoFq1atSqVSvTr+yYO3cuNWvWxNXVFVdXVwIDA1m/fr35/qSkJEaMGIGnpyfOzs706NGDsLCwdPsICQmhU6dOODo64u3tzbvvvktqamq2cggLt2uW+r1mLyjir20WIfKbttNAbwuXt8OFTVqnEUITWWrZcXBw4I033gDg8OHDfPLJJ7i7uz/zk5csWZKPP/6YChUqoCgKCxcupEuXLhw9epRq1arxzjvvsHbtWpYvX46bmxsjR46ke/fu7N27FwCj0UinTp3w9fVl37593L59mwEDBmBjY8OMGTOeOZ+wALeOwvkNoLOS4e+FeBoeZaHhG7DvG9j4gdrao7fROpUQeSrbl55v377dXOgoioKiKE/95J07d6Zjx45UqFCBihUr8tFHH+Hs7Mz+/fuJjo7m559/5osvvqBly5bUrVuX+fPns2/fPvbvVwfK2rRpE2fOnGHx4sXUrl2bDh06MG3aNObMmUNKSspT5xIWZOen6vcaL4NnOW2zCJFfNR0HjkXh7gU49LPWaYTIc1meLuJhixYt4tNPP+XChQsAVKxYkXfffZf+/fs/dRCj0cjy5cuJj48nMDCQ4OBgDAYDrVu3Nm9TuXJlSpUqRVBQEAEBAQQFBVGjRg18fHzM27Rr147hw4dz+vRp6tSpk+lzJScnk5ycbL6ddmm9wWCQztd5LO39zvR9Dz2Jzbm1KOhIDRwNcmzy1GOPjdBcto6P3hGrZv+Hfv04lB0zSa3aXb2yUeQa+f3JG1l9f7Nd7HzxxRdMmDCBkSNH0rhxYwD27NnDG2+8QUREBO+880629nfy5EkCAwNJSkrC2dmZv/76i6pVq3Ls2DFsbW0znC7z8fEhNDQUgNDQ0HSFTtr9afc9ysyZM5kyZUqG9Zs2bcLR0TFb+UXO2Lx5c4Z19S9/Q3HgZpGGBB+8AFzI81wi82MjLEdWj49O8aS5fUlck24Q8usITpV8JZeTCZDfn9yWkJCQpe2yXex8++23zJ07lwEDBpjXvfjii1SrVo3Jkydnu9ipVKkSx44dIzo6mj///JOBAweyc+fO7MbKlvHjxzNmzBjz7ZiYGPz8/Gjbti2urq65+twiPYPBwObNm2nTpg02Ng/1Iwg/g83Rwyjo8Hn5Mzp6VdYuZCH1yGMjLMLTHB9dFRf4/SXK3t1Gqe5ToGiFXE5ZeMnvT97I6qDH2S52bt++TaNGjTKsb9SoEbdv387u7rC1taV8+fIA1K1bl0OHDvH111/Tq1cvUlJSiIqKSte6ExYWhq+vLwC+vr4cPHgw3f7SrtZK2yYzdnZ22NnZZVhvY2MjP5QayfDe7/sSAF3VLtgUr6FRKgHye2HpsnV8KrWBiu3Rnd+AzbbJ0G9ZrmYT8vuT27L63ma7g3L58uVZtizjL8gff/xBhQrP/l+CyWQiOTmZunXrYmNjw9atW833nTt3jpCQEAIDAwEIDAzk5MmT5tGdQW0ydHV1pWrVqs+cRWgk/F84vUpdbvaeplGEKHDaTgcra7iwES5uffL2QhQA2W7ZmTJlCr169WLXrl3mPjt79+5l69atmRZBjzN+/Hg6dOhAqVKliI2NZcmSJezYsYONGzfi5ubG0KFDGTNmDB4eHri6ujJq1CgCAwMJCFAngWzbti1Vq1alf//+zJo1i9DQUD788ENGjBiRacuNyCd2fQooUKUz+FTTOo0QBUvRClB/GByYC5snQdkWYCVzQouCLdvFTo8ePThw4ABffvklq1atAqBKlSocPHjwkVc/PUp4eDgDBgzg9u3buLm5UbNmTTZu3EibNm0A+PLLL7GysqJHjx4kJyfTrl07vvvuO/Pj9Xo9a9asYfjw4QQGBuLk5MTAgQOZOnVqdl+WsBSRV+DUCnW5qbTqCJErmr0Hx36DsJPq71vNl7VOJESueqpLz+vWrcvixYuf+cl//vnx4z3Y29szZ84c5syZ88ht/P39Wbdu3TNnERbi0E+AAuVaQbGaWqcRomBy9IDGb8G26bB9OlTtAtaZTwEkREEgbZfCcqTEw9Ff1eWGr2ubRYiCLuBNcPKGe1fhyEKt0wiRq6TYEZbj5HJIioYipaF8G63TCFGw2To9uABg5yz1nw0hCigpdoRlUBQ48KO6XH+YdJgUIi88N1D95yI+HPbP1TqNELlG/qIIi6C7HgThp8HGEer00zqOEIWDtS20+FBd3vs1JERqm0eIXPLMxU5MTAyrVq3i7NmzOZFHFFJWh35SF2r2lDl7hMhL1XuATw1IjoE9X2idRohcke1ip2fPnsyePRuAxMRE6tWrR8+ePalZsyYrVqzI8YCi4LNPiUR3bq16o8Fr2oYRorCxsoLWk9TlAz9C9E1t8wiRC7Jd7OzatYsmTZoA8Ndff6EoClFRUXzzzTdMnz49xwOKgq90xDZ0ihH8n5dBBIXQQvnW4N8YjMmwY6bWaYTIcdkudqKjo/Hw8ABgw4YN9OjRA0dHRzp16sSFCzIrtcim1CRK392uLjeUVh0hNKHTQevJ6vLRxXD7uKZxhMhp2S52/Pz8CAoKIj4+ng0bNtC2bVsA7t27h729fY4HFAWb7uxq7FJjUVyKQ6VOWscRovDya6D230GB9f+nXiEpRAGR7WJn9OjR9OvXj5IlS1K8eHGaN28OqKe3atSQ2alF9lgdmgeAqe5g0D/VgN5CiJzSZipYO0DIPji9Uus0QuSYbBc7b775JkFBQfzyyy/s2bMHq/vjoZQtW1b67IjsuXEYq9tHMeqsMdV+Res0Qgi3ktBkjLq8aSKkJGibR4gc8lSXnterV49u3brh7OxsXtepUyfzLOhCZMm+bwG4WSQAnLw0DiOEAKDRKHArBTE3YO9XWqcRIkdk6bzBmDFjsrzDL76QcRpEFkRegbOrAbjo3ZFiGscRQtxn4wBtp8HygepAg3VeAfdSWqcS4plkqdg5evRouttHjhwhNTWVSpUqAXD+/Hn0ej1169bN+YSiYNr/HSgmTGVbEetQUus0QoiHVe0CpZvA1d2w6UPouUjrREI8kywVO9u3bzcvf/HFF7i4uLBw4UKKFFFHur137x6DBw82j78jxGMlRKqXtwKmgBFwNk7jQEKIdHQ6aP8x/NAEzvwNV3ZDGfl8F/lXtvvsfP7558ycOdNc6AAUKVKE6dOn8/nnn+doOFFAHf4ZDAngWwOltHyACmGRfKtDvSHq8ob/A5NR2zxCPINsFzsxMTHcuXMnw/o7d+4QGxubI6FEAWZIejC7eaO31P8ghRCWqcUHYO8OYafgyEKt0wjx1LJd7HTr1o3BgwezcuVKbty4wY0bN1ixYgVDhw6le/fuuZFRFCQnl0F8OLiWhGrdtE4jhHgcRw9oPl5d3jYdkqK1zSPEU8p2sfP999/ToUMH+vbti7+/P/7+/vTt25f27dvz3Xff5UZGUVCYTObLzQkYDnobbfMIIZ6s/lAoWhES7sKuT7VOI8RTyXax4+joyHfffcfdu3c5evQoR48eJTIyku+++w4nJ6fcyCgKigubIOI82LnCcwO0TiOEyAq9DbSboS7v/x7uXtI2jxBP4akGFQRwcnKiZs2a1KxZU4ockTVprTp1B4G9q6ZRhBDZUKGNOjO6yQCbJmidRohsy/ZkRPHx8Xz88cds3bqV8PBwTCZTuvsvX76cY+FEAXIzGK7tAStraPiG1mmEENnVbgZc2g7n1sLlHVC2udaJhMiybBc7r776Kjt37qR///4UK1YMnVxNI7IiaI76vXoPcCuhbRYhRPZ5VYL6r8LBH2DD+/DGbrDSa51KiCzJdrGzfv161q5dK/NgiayLvgGnV6nLgSM1jSKEeAbN/w9O/AHhp9VL0dPG4RHCwmW7z06RIkXw8PDIjSyioDr4IyhGdfj5YjW1TiOEeFoZLkWP0TaPEFmU7WJn2rRpTJw4kYSEhNzIIwqa5DgIXqAuB7ypaRQhRA6oPxQ8y6uXoh//Xes0QmRJtk9jff7551y6dAkfHx9Kly6NjU36sVKOHDmSY+FEAXD8d3UgMo+yULG91mmEEM9Kb6NeZLBuHBz6CRq8JiOhC4uX7WKna9euuRBDFEgmkzq7OaitOlZPPdKBEMKS1OwFWyar42Zd2SlXZgmLl+1iZ9KkSbmRQxRE5zdA5GWwd4NafbROI4TIKfauUKu32rJzcJ4UO8Liyb/aIvekterUHQR2zppGEULksPqvqt/PrVOvuBTCgmW72DEajXz22Wc0aNAAX19fPDw80n0JAcDtE3B1N+j06jl9IUTB4l1FvcJSMcHh+VqnEeKxsl3sTJkyhS+++IJevXoRHR3NmDFj6N69O1ZWVkyePDkXIop8Ka1Vp1o3cCupbRYhRO5Ia905shBSk7XNIsRjZLvY+e2335g3bx5jx47F2tqaPn368NNPPzFx4kT279+fGxlFfhMbCif/VJcD5XJzIQqsyp3ApRjE34Ezq7VOI8QjZbvYCQ0NpUaNGgA4OzsTHR0NwAsvvMDatWtzNp3Inw7OUycM9AuAEnW1TiOEyC16G7VPHqidlYWwUNkudkqWLMnt27cBKFeuHJs2bQLg0KFD2NnZ5Ww6kf+kJMDhX9RladURouCrO0id4Pf6fgg9qXUaITKV7WKnW7dubN26FYBRo0YxYcIEKlSowIABAxgyROZJKfSO/w6JkeDuD5Vf0DqNECK3ufhClc7q8sF52mYR4hGyPc7Oxx9/bF7u1asXpUqVIigoiAoVKtC5c+ccDSfymQyDCMqMyEIUCvWHwem/4ORyaDMVHNy1TiREOtkudv4rMDCQwMDAnMgi8rsLm+DuRbBzgzr9tE4jhMgr/o3Au5o6G/rx3yFguNaJhEgn26exFi5cmK4j8nvvvYe7uzuNGjXi2rVrORpO5DNBs9Xv9QaBnYumUYQQeUing3qD1eXghaAo2uYR4j+yXezMmDEDBwcHAIKCgpg9ezazZs2iaNGivPPOOzkeUOQTt46pgwhaWUOD17VOI4TIazV7grUD3DkL1w9qnUaIdLJd7Fy/fp3y5csDsGrVKl566SVee+01Zs6cye7du3M8oMgn0g0iWELbLEKIvGfvBtW7q8vBCzSNIsR/ZbvYcXZ25u7duwBs2rSJNm3aAGBvb09iYmLOphP5Q/RNOLVCXQ4coW0WIYR20sbcOf0XJEZpmUSIdLJd7LRp04ZXX32VV199lfPnz9OxY0cATp8+TenSpXM6n8gPDv4IplTwfx6K19E6jRBCKyXrg3dVSE1Ur8wSwkJku9iZM2cOgYGB3LlzhxUrVuDp6QlAcHAwffr0yfGAwsIlx0Hw/UkApVVHiMJNp3vQunN4vnRUFhYj25eeu7u7M3v27Azrp0yZkiOBRD5zbAkkRYNHWajYXus0Qgit1ewJmyeql6HfDIaS9bROJET2i51du3Y99v6mTZs+dRiRzxhTH1xuHvAmWGW7oVAIUdA4FFEvVDj+u9rqK8WOsADZLnaaN2+eYZ1OpzMvG43GZwok8pHTf0HUNXD0hNoyiKAQ4r7nBqrFzqmV0G6GeqWWEBrK9r/i9+7dS/cVHh7Ohg0bqF+/vnlSUFEImEyw5wt1OWA42Dpqm0cIYTlKBUDRSmBIkI7KwiJku2XHzS1jhd6mTRtsbW0ZM2YMwcHBORJMWLgLGyH8DNi6qPPiCCFEmrSOyhvHw+EFUG+ouk4IjeRYJwsfHx/OnTuXU7sTlkxRYPf9Vp36Q2TSPyFERrV6g94Owk7CrSNapxGFXLZbdk6cOJHutqIo3L59m48//pjatWvnVC5hya7thRsH1Q+yALncXAiRCUcPqNoFTi6Dg/OgW12tE4lCLNvFTu3atdHpdCj/GT8hICCAX375JceCCQu2+3P1e51XwMVH2yxCCMvV8A212Dm5HFq8D+6ltE4kCqlsn8a6cuUKly9f5sqVK1y5coVr166RkJDAvn37qFy5crb2NXPmTOrXr4+Liwve3t507do1w6mwpKQkRowYgaenJ87OzvTo0YOwsLB024SEhNCpUyccHR3x9vbm3XffJTU1NbsvTWTFraNwaRvo9ND4La3TCCEsWcm6UKapOsL6vm+1TiMKsWwXO/7+/um+/Pz8sLe3f6on37lzJyNGjGD//v1s3rwZg8FA27ZtiY+PN2/zzjvv8M8//7B8+XJ27tzJrVu36N69u/l+o9FIp06dSElJYd++fSxcuJAFCxYwceLEp8oknmDPl+r36j2gSGlNowgh8oHnx6jfjyyCuDvaZhGF1lN1UN65cyedO3emfPnylC9fnhdffPGpZjzfsGEDgwYNolq1atSqVYsFCxYQEhJivqIrOjqan3/+mS+++IKWLVtSt25d5s+fz759+9i/fz+gTkZ65swZFi9eTO3atenQoQPTpk1jzpw5pKSkPM3LE48ScQHOrFaXn39H2yxCiPyhbHMo/hykJsGBuVqnEYVUtoudxYsX07p1axwdHXnrrbd46623cHBwoFWrVixZsuSZwkRHRwPg4eEBqPNtGQwGWrdubd6mcuXKlCpViqCgIACCgoKoUaMGPj4P+o60a9eOmJgYTp8+/Ux5xH/s+QpQoFJH8KmqdRohRH6g00GT+607B3+CpBht84hCKdsdlD/66CNmzZrFO+88+M/+rbfe4osvvmDatGn07dv3qYKYTCZGjx5N48aNqV69OgChoaHY2tri7u6eblsfHx9CQ0PN2zxc6KTdn3ZfZpKTk0lOTjbfjolRf/kMBgMGg+Gp8hd4sbexPvEHOiA18C2UHHqf0t5ved8tjxwby5avjk+5tlgXrYgu4jzGA/MwNSr4/f3y1fHJx7L6/ma72Ll8+TKdO3fOsP7FF1/k/fffz+7uzEaMGMGpU6fYs2fPU+8jq2bOnJnpxKWbNm3C0VFGAs5MlVvLqWgycNepInuOh8PxdTm6/82bN+fo/kTOkWNj2fLL8fFzbMZznMew+2s2R5bCZGWrdaQ8kV+OT36VkJCQpe2yXez4+fmxdetWypcvn279li1b8PPzy+7uABg5ciRr1qxh165dlCxZ0rze19eXlJQUoqKi0rXuhIWF4evra97m4MGD6faXdrVW2jb/NX78eMaMGWO+HRMTg5+fH23btsXV1fWpXkOBZkjA+tvRALi1f5+OlTvm3K4NBjZv3kybNm2wsbHJsf2KZyfHxrLlu+NjbIPy3XrsY27QsVgkprpDtE6Uq/Ld8cmn0s7MPEm2i52xY8fy1ltvcezYMRo1agTA3r17WbBgAV9//XW29qUoCqNGjeKvv/5ix44dlClTJt39devWxcbGhq1bt9KjRw8Azp07R0hICIGBgQAEBgby0UcfER4ejre3N6BW0q6urlStmnm/Ejs7O+zs7DKst7GxkR/KzBxfCYmR4F4K62ovgpU+x59C3nvLJcfGsuWb42NjA43fhvXvot8/B339oaDP9p+gfCffHJ98KqvvbbZ/0oYPH46vry+ff/45y5YtA6BKlSr88ccfdOnSJVv7GjFiBEuWLOHvv//GxcXF3MfGzc0NBwcH3NzcGDp0KGPGjMHDwwNXV1dGjRpFYGAgAQEBALRt25aqVavSv39/Zs2aRWhoKB9++CEjRozItKAR2aQosP/+FRQN38iVQkcIUUjUeQV2fgJR1+D0SqjZU+tEopB4qrK6W7dudOvW7ZmffO5c9Y9o8+bN062fP38+gwYNAuDLL7/EysqKHj16kJycTLt27fjuu+/M2+r1etasWcPw4cMJDAzEycmJgQMHMnXq1GfOJ4BLWyHinDrhZ53+WqcRQuRnto4QMBy2TVNHYq/+Eljl2BSNQjzSU7chpqSkEB4ejslkSre+VKmsDwf+3yknMmNvb8+cOXOYM2fOI7fx9/dn3bqc7TAr7ktr1anzCthLfyYhxDNqMAz2fgN3/oV/10DVF7VOJAqBbJfUFy5coEmTJjg4OODv70+ZMmUoU6YMpUuXztDnRuRzd87BxS2ADhq+rnUaIURBYO/24PNk16fqqXIhclm2W3YGDRqEtbU1a9asoVixYuh0utzIJSzB/vunCyt3Ag8pZIUQOSRguPr5EnoCLmyCiu20TiQKuGwXO8eOHSM4ODjbk36KfCYhEo4vVZcD3tQ2ixCiYHH0gPpDYe/XsHMWVGirjrQsRC7J9mmsqlWrEhERkRtZhCU5/Is6l02xWuDfSOs0QoiCJnAkWDvAzcNwebvWaUQBl6ViJyYmxvz1ySef8N5777Fjxw7u3r2b7r6sDu4jLFxqChz6SV0OeFP+4xJC5Dxnb6g7SF3e9ZmmUUTBl6XTWO7u7un65iiKQqtWrdJtoygKOp0Oo9GYswlF3jvxB8TeBmdfqNZd6zRCiIKq8Vtw+Ge4theu7oXSjbVOJAqoLBU727dLE2OhYUyFPV+oy41GgnXhmL9GCKEB1+LqsBaHf1GvzJJiR+SSLBU7zZo1Y+rUqYwbN04myizoTq+EyMvg4AF1B2udRghR0DUeDUcWqf12bhyGkvW0TiQKoCx3UJ4yZQpxcXG5mUVozWR6cO48cATYOWubRwhR8BXxh5q91eWds7TNIgqsLBc7WRntWORzZ1erU0PYu6mjnAohRF5oMgZ0VnBhI4Ts1zqNKICydem5DCBYgCnKg1adhm+oBY8QQuQFz3Jq3x2ALZNlVGWR47I1qGDFihWfWPBERkY+UyChkfMbIOwk2DqrxY4QQuSl5uPhxDIICVI/jyp10DqRKECyVexMmTIFNzf5j7/AURT1SgiA+q+qo5sKIUReci2u/qO19yvYMkUdVdlKr3UqUUBkq9jp3bs33t7euZVFaOXSNrgZrI5mGjhS6zRCiMLq+dEQvADunFWnq6nTT+tEooDIcp8d6a9TgKX11ak3GJy9tM0ihCi8HIqonZUBts8AQ5K2eUSBIVdjFXZX90LIPtDbQqNRWqcRQhR2DV4D1xIQcwMOzdM6jSggslzsmEwmOYVVEKX11anzinrOXAghtGTjoHZWBtj9OSRGaRpHFAzZnvVcFCA3g9VRS3V6aPy21mmEEEJVqw8UrQSJ92Dv11qnEQWAFDuF2a7P1e81e0KR0ppGEUIIM701tJ6kLu+fCzG3tM0j8j0pdgqrsDNwbi2gg+fHaJ1GCCHSq9QR/BpCaqLaWVmIZyDFTmGVNrN51RfBq6K2WYQQ4r90OmgzTV0+9pv6D5oQT0mKncLo7iU4tUJdbjJW2yxCCPEopRpClc6gmNRpJIR4SlLsFEZ7v1I/PCq0hWK1tE4jhBCP1mqyehHFhY1wZZfWaUQ+JcVOYRN9A479ri43GadtFiGEeJKi5dUBTwE2TwSTSds8Il+SYqew2fctmAxQuonaRCyEEJau2f+pkxTfOgqnV2qdRuRDUuwUJnF3IHihuix9dYQQ+YWzFzQerS5vnQqpyZrGEfmPFDuFyf7v1Ms4iz8HZZtrnUYIIbIu8E1w9oWoa3DoJ63TiHxGip3CIjHqwQdE03HqZZ1CCJFf2DpBi/fV5Z2z1NGVhcgiKXYKi0PzIDkGvKtCxQ5apxFCiOyr3Q+8KkNSFOz5Uus0Ih+RYqcwSIlXh1wHdbRkKznsQoh8SG8NrSery/u/V68uFSIL5K9eYXBkESTcVee/qtZN6zRCCPH0KraHUo3AmAzbZ2qdRuQTUuwUdKkpsPcbdbnxaPU/IyGEyK90OmgzVV0+vkSmkRBZIsVOQXf8d4i9BS7FoHZfrdMIIcSz86sPVV6UaSRElkmxU5AZUx904ms0CqzttM0jhBA5pdWkB9NIXN2jdRph4aTYKcjOrIJ7V8DBA54bqHUaIYTIOUXLQ91B6vLmiaAomsYRlk2KnYJKUWD3F+pywHCwc9Y2jxBC5LTm/wc2TnAzWP3nTohHkGKnoDq/EcJPq/PJNBimdRohhMh5zt7qKXpQp5EwGrTNIyyWFDsFkaLArk/V5fpDwaGItnmEECK3NBoJTl4QeRmCF2idRlgoKXYKootb4OZhsHaAwJFapxFCiNxj5wLN/qcu75wFyXHa5hEWSYqdgkZRYPsMdbn+ULWZVwghCrLnBqqDpsaHw4G5WqcRFkiKnYLmwma4dURt1Wk8Wus0QgiR+6xtoeUEdXnvN5AQqW0eYXGk2ClIFAV23G/VafAqOHtpm0cIIfJKte7gW0Od8Hj351qnERZGip2C5PxGuHUUbByh0dtapxFCiLxjZQWtJqvLB+dB1HVN4wjLIsVOQaEosOP+pHgNhkmrjhCi8CnfCvyfVycJ3fmx1mmEBZFip6A4vwFuH1MH2Gr0ltZphBAi7+l00HqyunxsCdw5p2kcYTmk2CkI/tuq41RU2zxCCKEVv/pQ+QV1ktCtU7VOIyyEFDsFwbn1cPu4OlqytOoIIQq7lhNAZwX/roEbh7VOIyyAFDv5XbpWndfAyVPbPEIIoTXvylCrr7q8ZbKmUYRlkGInv/t3DYSeUFt1ZLRkIYRQNf8/0NvC1d1weafWaYTGpNjJz0wm2H6/VSdguLTqCCFEGnc/qDtYXd42TW0FF4WWFDv52dm/1ZnN7VwhcITWaYQQwrI0GauOJn/jEFzYpHUaoSEpdvIrk/FBq07gCJnZXAgh/svFBxq+pi5vm6a2hotCSYqd/OrUSog4B/bu6iksIYQQGTUeDbYuEHpSbQ0XhZKmxc6uXbvo3LkzxYsXR6fTsWrVqnT3K4rCxIkTKVasGA4ODrRu3ZoLFy6k2yYyMpJ+/frh6uqKu7s7Q4cOJS4uLg9fhQaMqQ9GB200CuzdtM0jhBCWytHjwWn+7TPUVnFR6Gha7MTHx1OrVi3mzJmT6f2zZs3im2++4fvvv+fAgQM4OTnRrl07kpKSzNv069eP06dPs3nzZtasWcOuXbt47bXX8uolaOPkcrh7ERw8oOHrWqcRQgjLFvimeqo/4jycWKZ1GqEBTYudDh06MH36dLp165bhPkVR+Oqrr/jwww/p0qULNWvWZNGiRdy6dcvcAnT27Fk2bNjATz/9RMOGDXn++ef59ttvWbp0Kbdu3crjV5NHjIYHrTrPjwY7F03jCCGExbN3g8b3J0feMVP9HBWFisX22bly5QqhoaG0bt3avM7NzY2GDRsSFBQEQFBQEO7u7tSrV8+8TevWrbGysuLAgQN5njlPHP8d7l0FJy+o/6rWaYQQIn9o8Bo4eUPUNTj6q9ZpRB6z1jrAo4SGhgLg4+OTbr2Pj4/5vtDQULy9vdPdb21tjYeHh3mbzCQnJ5OcnGy+HRMTA4DBYMBgsOCK35iC9c5Z6ABj4FuYdLZgyXmzIO39tuj3vZCSY2PZ5Phkk84Wq8aj0W96H2X7TFIrdQF711x7Ojk+eSOr76/FFju5aebMmUyZMiXD+k2bNuHo6KhBoqwpFbGDOtHXSbJ2Y/OdYpjWrdM6Uo7ZvHmz1hHEI8ixsWxyfLLOyuRDCzsfnOPDCFn0BqdKvpLrzynHJ3clJCRkaTuLLXZ8fX0BCAsLo1ixYub1YWFh1K5d27xNeHh4uselpqYSGRlpfnxmxo8fz5gxY8y3Y2Ji8PPzo23btri65l6l/0yMBqznfgiATYt3ad+gq7Z5cojBYGDz5s20adMGGxsbreOIh8ixsWxyfJ6Orqor/P4yZSO2UOrF8eBbI1eeR45P3kg7M/MkFlvslClTBl9fX7Zu3WoubmJiYjhw4ADDh6vjygQGBhIVFUVwcDB169YFYNu2bZhMJho2bPjIfdvZ2WFnZ5dhvY2NjeX+UJ76A6JDwMkbff2h6C0151Oy6Pe+kJNjY9nk+GRTpbZQrRu6039hs/E9GLIJrHKv+6ocn9yV1fdW0w7KcXFxHDt2jGPHjgFqp+Rjx44REhKCTqdj9OjRTJ8+ndWrV3Py5EkGDBhA8eLF6dq1KwBVqlShffv2DBs2jIMHD7J3715GjhxJ7969KV68uHYvLKcZU2HXZ+py47fA1nJPtQkhhMVrN0OdPPnGIemsXEhoWuwcPnyYOnXqUKdOHQDGjBlDnTp1mDhxIgDvvfceo0aN4rXXXqN+/frExcWxYcMG7O3tzfv47bffqFy5Mq1ataJjx448//zz/Pjjj5q8nlxzcjncuwKORaHeEK3TCCFE/uZaHFq8ry5vmQTxd7XNI3KdpqexmjdvjvKYmWh1Oh1Tp05l6tSpj9zGw8ODJUuW5EY8y2BMhV2fqsuNRoGtk7Z5hBCiIGjwOhxbAmGn1IKny2ytE4lcZLHj7Ij7Tq+EyEvqaMkyro4QQuQMvTV0+lxdPvorhBTQsdkEIMWOZTMZH2rVGQl2ztrmEUKIgqRUANS5f/n5P29DQqS2eUSukWLHkp3+S53Lxd4d6g/TOo0QQhQ8raeq/SHvnIX5HSGmgE41VMhJsWOpTKYHrTqBI3J1pE8hhCi0nDxh4D/gUkwteH5uBxEXtU4lcpgUO5bqzF9w51+wc5OZzYUQIjf5VIUhG8GjnDqe2S/t4NZRrVOJHCTFjiUyGmDbR+pyo5HqjL1CCCFyTxF/teApVgsSImBBZ7iyS+tUIodIsWOJjv2mXoHlWBQChmudRgghCgdnLxi4Bko3gZRYWNwDTv6pdSqRA6TYsTSGRNjxibrcdBzYuWibRwghChN7V+j3J1R5EYwpsGIo7P4CHjMmnLB8UuxYmkM/Q+wtcC0JdQdrnUYIIQofG3t4eQEEjFBvb50C/7yldjEQ+ZIUO5YkKQZ23x/kqvn/qb9wQggh8p6VHtrPgA6fgs4KjiyCJT3Vz2mR70ixY0mC5kBiJHhWgFp9tE4jhBCi4WvQewnYOMKlbfBLexmLJx+SYsdSxEdA0P25WVp+oA5lLoQQQnuVOsDgdeDsA+Gn1YLn3lWtU4lskGLHUuz+AlLi1Mseq3TROo0QQoiHFa8DQzdDkTIQdQ1+6QB3zmudSmSRFDuWIPoGHPpJXW41EazksAghhMUp4g+D14NXZfVCkvkd4PYJrVOJLJC/qpZg5ydgTAb/56FcK63TCCGEeBTXYjBo3YPBBxe+ANcPaZ1KPIEUO1q7cx6OLlaXW08CnU7bPEIIIR4vbT4tvwBIioZFXeD8Jq1TiceQYkdr26eDYoJKncCvgdZphBBCZIW9G/RfCWWbgyEelrwMmyfKWDwWSoodLd0MhjN/AzpoNUHrNEIIIbLD1gn6LoMGr6m3934N8ztC1HVtc4kMpNjR0pYp6vdafcC7irZZhBBCZJ+1HXT8FHouAjs3uHEQvn8e3fn1WicTD5FiRyuXtsOVnaC3VUdLFkIIkX9V7QJv7ILiz0FSFNbL+1Pvymx0l7aByah1ukJPih0tKApsmawu1xuqXs4ohBAifytSGoZsNM+pVSLqINZLe8KX1dWW/IgL2uYrxKTY0cKZv+H2MbB1hiZjtU4jhBAip1jbQvsZGIZu43LR1igORdQxefZ8AbPrwcIXZWweDUixk9eMqbBtmrocOBKcvbTNI4QQIuf51uSk3wBS3zql9uep0A50erX7wg9N4e+REBumdcpCQ4qdvHbsN7h7ERw9IXCE1mmEEELkJms7tT9Pv2Xw1lGo3gNQ4Oiv8O1zsPtzMCRpnbLAk2InL6XEw46Z6nKTcWDvqm0eIYQQeaeIP7z0izrHVol66nyIW6fCdwEQdkbrdLlnx8fqZfkxtzWLIMVOXtr/HcTeBvdSUH+o1mmEEEJowa+BWvB0nweuJeDeFfi5LVzY/OjHRF6Bg/MgITLvcuYEkxH2zVYHXEyI0CyGFDt5Je4O7PlaXW41SW3aFEIIUThZWUHNnvDGHijdBFJiYUlP2D9XvWI3TUIkbHgfZteHdePUoujeNe1yZ1fYafW12bmCd1XNYlhr9syFzc5P1ANevA5U6651GiEsQqoplYjECKKTo7HV22KntzN/t7GywagYMRgNpJhSMJgMGO4Pxa/T6bDCCnRgpbPCCit1nc7K/AVgNBkxKkZMigmjoo51Yqe3w05vh4O1AzZWNujuz0dnUkykGO8/j8mAoigoPPijoygKJsWESTGRqqSm26cVVumeW0Eh1ZRq/jKYDBgVI9Y6a/RWevQ6PXorPdY6a3Sknw9P4f7zYMJkuv9dMaFDh42VDTZWNlhbWaOYFOJN8dxLuoet0db8OnQ6nTmreV+KKV2WtGWdToe1lTXWVtbY6NT9Pvw60r7S9vkwGysbHG0cc+GnopBx9IBXVsK6sXBkEWz4P4g4D22nw+H5sGuWOv8WgI0T3L0AP7eBfn9CsZraZs+K6wfU7yXrgZVesxhS7OSFiIsQPF9dbjNVreiFKIAMJgNxKXHEpcQRY4ghOimae8n3uJd0j8ikSKKSo4hIjCA8IZzwhHDuJt3FpJg0y6tDh63ellRTqrlwyW9mrpyp2XO72Lrg4+ijfjn54OvoSwmXEvi5+FHSuSRFHYqaizDxGNa20PkbKFoJNn0Ih3+BY79DaqJ6v3c1aDtVbRlZ/BKEn1anpei9WJ2by5KF7Fe/+wVoGkOKnbywdQqYUtVLD8s01TqNENliMBmIS4ojNiWW2JRYopOjuR1/m5txN7kVf4vbcbe5FX+L6ORoEtM+nLPBWmeNq50rBpOBFGMKycbkDNvodXpzi4YOHSZM5pYXk6Iup61La9EAzC0Tep3amqKgkGxMNhdYabefRIfO3Jqkt9Kb95nW6pGWwagYzS0gaS0maV96nR6jYjS3Nj2qwFIUJV1unU6HXqc3tygZjPdbZu63LmU1e9r7l/Zdr9OjKAqpSmq6Vihzq1IW9p32M3Ex6mKm99vr7SnhXAI/Vz9KuZSilEsp/Fz88HP1w8fRB1u97ROfo9DQ6aDRSPAsB38OVScXdfaFlh9C7b4PWkWGrIel/eDqbrXw6ToXar6sbfbHSWvZKdVQ0xhS7OS26wfh7GrQWUHryVqnEQJQ/6DGpMQQmRRJZFIkdxPvmltcwhLCCE8IJzQ+lNDYUD5c+mG29+9o7YizrTOutq542HtQxL4IReyK4GHvgYe9B96O3ng7eePj6IOHvYf5tFNatlRTKimmFLVQuH/qJ7uvD8i0VSFt/0nGJJKNyRiMBnMRYKN/cJro4UyWyGAwsHbtWjp07IC1tTUKirkANJ9+ul/oPI2HT9uZ6e4XT+hITE1Uf04SQgmLDyMsIUwtgmNvcj32OqEJoSQZk7gUfYlL0ZcyfQ43Oze8HLwo6lAULwcvijkXw9/Vn1IupfB39cfdzr3wtQxV6gCv74Lr+6FaN3Wy0YfZu8ErK+Cv1+H0X7DyVTAmQ51XtMn7ONE3Ifq6Or5QiXqaRpFiJzcpCmy6P5t57X7go13nLFE4JBuTiUy8X8Ak3U1XxNxJvMOdhDuEJYRxN+kuqabUbO3b0doRF1sXXGxd8HXypYRzCYo7F1e/nIrjYe+Bi60LTjZOWFs9/UeLTqdTiw69zTPtIyv7d8HlqZ/DEqT1U8puMZjVfet1evRkvm9nW2ecbZ0p61420/sNRgO3429zPfY612OvExIbwvWY6+bbKaYUopOjiU6OfmTLkIutCyWdS+Lh4IGnvaf65eCJl4MXfi5+lHIthZudW469ZotRtLz69SjWdtDjF3D2gQPfw8YPoPIL4OCeZxGz5Pr9U1i+1cHOWdMoUuzkpn/Xqgfb2gFavK91GpFPJaYmEpEQwZ3EO4QnhhOREEFEYgSRSZFqX5jkSCITI7mXfI94Q3y29u1i44KHg9ra4mnvibejNz5OPng7elPUtiinD57mxbYvUsSxyDMVMKLwsdHbUMq1FKVcS2W4T1EUopOj1QI88Q53E+8SnhDOzbibhMSEcC32GqHxocSmxHI28uxjn8fdzp1SrqUo7Vqaap7VqOVVi4oeFbGxevpiOV+wsoJ2M+DyTrhzFvZ9C60maJ0qvZD7p7A07q8DUuzkHqMBtkxSlwNHgGtxbfPkEZNJITIhhXvxKcSnGElMMZJoSCUhxUhCspHoRAP3ElK4l2AgOlHd7na4nnnX9pNqUjAYTRiM6ikIG70OG73V/S+1Od5gNJGSajJvZzQp5u2s9VbY6nVY663Q63RYWYHeSoeVToe1lQ5HW2uc7PQ429ngbKfH2d4aO2s9eiud+cvaSoedtR4nOz0u9tY42VnjbGeNq4MNLnbWOdakbjAa0rW8PPyVtv5uknpfdgsYaytrc/HiYe9BUYei6mkjR2+8HL3wdvCmqENRPBw8sNM/eggEg8FAmD4Mdzt3KXREjtLpdLjbu+Nu706FIhUy3SYxNVE9HRYfav59SDvlGpYQRkhMCHcS7xCVHEXUnShO3DnB6kurAfWKu7TCp0GxBtTzqYe9tX1evsS8YaVX+/T80U8dx63Ba+Dio3WqB9JadjTurwNS7OQeowEqdYRTK6Hx21qneSaKohAZn8Lt6CTuxCYTEZfM3fgU7sYlczcuhTtxyUTEpajr45IxKU/eZ3o6iIvJjeg5ytbaCi9nO7xc1K+iznYUcdTj5JiCvV0ytjbJWNskYWWdBLokElLjiU2JJc4QZ/6QTitkYlKy93rt9fZ4OXo96N/g6GXuC+Nhd79PjH0RPB08cbFxKXz9HESB42DtQMUiFalYpOIjt0kwJBASG8K1mGtcjrrM8YjjnLhzgtiUWI6EH+FI+BHmn56Pnd6Oej71aFyiMY1LNKaMa5mC8ztSuZPaH+bmYdj9GXT8VOtEquRYCD0JwAF7e4rHXsfPxU+zOFLs5BZbR2g7Ta26LXgAQUVRuBOXTGh0ErejkwiNTiI0Rv1+KyqR0Bh1fUpq9i4PdnOwwclWj4OtHkdb6/vf9bg52FDE0RZ3R/W7s60VZ04eI6BBPRxsbbHW67DRqx9CBqPa0pN6/7tJAVvrh1t7rLDSgdGkkPLQdgajgklRW33SvqcaFRINRuKSU9WvJPV7itGE0aiQalIwmkykmAwkGKKJNUSTYIwmwRhDsikWoy4WnXUcd/Vx3LOK40JyHLrUOHSxT76S51H0Oj2eDp4UdShq/krrk5C2XNShKJ4OnjjbOBecD2chcoijjSOVPSpT2aOyeZ1JMXE15ion7pzgaPhR9t7cS1hCGHtv7WXvrb1wCIo5FSOgWAABxQJoWKwhng6eGfZtUkwkpSaZx1NKu5JOp9PhautqOVeS6XTQehIs7KyOyxM4AoqU1joV3DgMignFzY+3939GvDGCTxrPpmP5ZprEkWInt2lY6BhNCvcSUrh7v9UlIi6ZO7HJXI9M4Pq9REIiE7hxL4EkQxYuX9VBUWc7fFzt8HSyw9PZlqLOdng62eLpbEfR+7e9XOzwcLLFRp+1K1kMBgPWN4/SvKIXNja5c47dYDIQlRTFveR7RCdHcy/pntr0nRxl7rB7LyGMsPgwIpMiUawVcHjweGue/ItipdijMzlgMjqQmmqHyWgPRnsU0/2vVCcUozNKqjNKqou6bHQg1kpPvLMdSa52GFztMbjYkehgQ4y9DXcdbAhzsKGIYzJ+HnqKudljncX3VYjCykpnRVm3spR1K0vX8l1RFIVLUZfYe2sve27uITgsmNvxt/nr4l/8dfEvACoVqURx5+Lq58P9z4no5OjHjr3kYO2Au5077nbueNh7UMatDJU8KlHZozLl3Mrl1ctVlWkKZVvA5e3qPFTdvs/b58/M/UvOj/hWJT7lLIrRlqLWlZ/woNwjxU4+pygKd2KTuRwRz6U7cVy+8+D7jXsJWTqlpNOBt4sdvm4OFHO1x9dN/SrmZk8xNweKudnj42qPrbX2f2hNiono5GjzJdP3ku4RnaJ+MMUkxxCdEm0ubO4l3eNu0l1iU2Kz9Rx6nR53O3eK2BdJ993T4cHVIGn9Ydzs3HCxdUnXp8VoUohONHD3odN7YTFJ3IpKIjQmkVtRSdyOTuRObDJGk6K2pMUkAdGPz2Wlo4S7A6U8HPHzcKC4mwPF3B0obj5eDjjYajdCqRCWSKfTUb5IecoXKc/AagNJMCRwNPwo+2/vJ+hWEOfunTN/PXFf90e7VlBITE0kMTWR2/Hq5JZ7b+01b2ets6aMWxls4205d/Qcfq5+6tWLTsXxc/V7bF+5p9ZqolrsHF+qdp3wrpLzz5Ed9wcTXHr/b5CdoQb1/b01iyPFTj4Rm2Tg8p14LobHcSUinit347kaoX7Fpzx+5NcijjYUdVb7mBR1sUv3B9OviCPF3R00K2SMJiPxpniuxVwjzhhHTEqMuZh5uPNuWl+XqOSopxrpVodO/S/M3t3835i7nTtFHYqaR39N++5u5/5MY6zorXR4ONni4WRLhcf0FUw1mrgbn0JYTBJhMWpBFBGXTExiKtGJBqITDcQkGoiIT+bGvURSUk2ERCYQEpnwyH0621mbn1ttdbOliJMtHo7q9yKOtng42eDtYk8JdwesrOTUmChcHG0czX13AO4m3uVQ6CFiUmIefDbc/5xwtnF+ML3H/QEeTYrJPLhmWgtxRGIEF+5dUIumyHPEpMRwIeoCAKfPnk73/HqdnnLu5ajiUYUqnv/f3p3HN1mmCx//PVmatEnTllK60BYKFHADWRUZjxsKylERZkQPOD0CMzqjLyi+Kowjju+I66jzugwwc1DGERdwA1TUChwcPIgFWS3TFqhSWtrSjSZpm2a5zx9JQyMgdZw0bXp9P+ST9NlyP7lKe/V+7ue6z+Ls5LMZkjTkx0+90XcknHWdv67bxkfgppU/7ng/hs8LR7bjAza3HgENfpI+IaKX4iXZ6WJqHS5Kqh2UVNkpqXZwoNrBwWMOqhpPPzZEp0HfpFgGplgZmGJlQIqFgSlW+idb6G2NidilD6fbGSxQ13a5qG2agCqn/3VNS42/aNn7P+zYbcXqepl7YTPZSIhJIMEUeMQkBAfsBreJsYWlFsmPYdDrSLX5e83OxOdTVNtdwWTncF0TlcebORoYW3X0eAtNrSfGJH1fQtQmxqAjJ9nCgBQLOb0t9E+2hAy+tpkkERLRLzk2mUk5kzq8vU7TBX/WZHPq2+ornZUU1hSS/2U+idmJVDZXUu4op9xejt1tp7i+mOL6YtYcXAP4e4LOSj6LUamjGNlnJCNTR5JgSqCupY6S+hKK64spqS+htqWWq3Ou5ur+V5/659nlv4V/vO9/lBVA1ph/+nP5UQKTf+60JtKs2VFeE7ePuToybQmQZCdC6p2tFFfZKQ4kNsVVdkqqHNQ6W0+7T594EwNTrOSkWMhJDvyC6m0hq1csJkPn/iJv8bT4K+w2Vfqf2z2OOo9S6aykyXPmX7htrEZrSLKSaE485cDdZHMyiebE6K+h8R06nRa8vDg2p9dJ65VSNLZ4qHW4qHO2Uutspa7do97ZSn1TK3VNbuqcLqqOu2j1+CiqslNUdfrLfBaDnucOfB7sGUy2xpBs8Y/dSrWZ6WMzkWYzkxQXI71EQuC/bJZuTae3qTcOk4NrRl0THI+olKKqqYrC2kL21+2nsLaQwtpCappr2Fuzl701e1nx9QrAXz+owdVw0vE/O/IZy/cuZ+6IuVyadWlob0nKEP/UEjtfhfX3Qt46MEWgcGZgvM67CemAE4v3fM5KP3kQeGeSZCfMlFKUNzSzr/w4+8ob2Vt+nMKjjRz7nrt4snrFMrhPPINSreT2iWdgioUBKVYSYjvnF7zT7aTMXkaVsypYdbd99d1KZyX1rvoOHctqtAZrvLQ9UuNSg89JMUl8sfELrp18bdgGKPcEmqaREGskIdbIgJQzb+/1KcrrmzlY46D0mJPSGieH65qCg9hrna14fQqnR+PgMScHj31/rR+jXiMlcJk0JZAYtfUS9Qk+m0mJN8m4ItFjaZpGmiWNNEsal2dfHlxe4ahgR9WO4OObxm9ocDWgoZEZn8ngpMHkJuXi9SneKHqNAw0HmLtpLsNShjFvxDzGpo898SaXLoTCdVCx0z931ozVYLZ17oke/gIvsMHgBuCyzCs79/1PQZKdMFFKMfuv29l5uJ76Jvcpt+mbGMvgVCuD0+IZ3CeewanxDOxjIS4mvGFxeV0cdRwN9sBUOCuCJdyP2I9Q11LXoePEGmL9/3Hj0ki3ppMWlxb8j5xmSSM1LvWM16Hdbjd6TX75dTa9TiM7OY7s5DguG3Lyep9PUd3YxLsffso5oy6gocUXrKtU43BRbfeXK6i2t1DjaMXtVVQcb6HieMsZ39sSoycxLoYki5HEWH8ZgsQ4I/FmI/FmA/EmA/FmI7ZYA9m9LPRLjuvw3X1CdEdt065cO/BagGDhxP62/jQ26Vi3u4K1f6/g64rj3HzhsyTn/g9vFL3OnmN7mP3JbCb2n8hD4x4iPiYeEjLh5+/B36b4i/q9Os0/l1ZnJjxl2/jKbMKha0V5Y7ltbMcvE4aLJDthomkaVY0t1De5Meo1BqfGc25GAuf2tXFO3wQGp8ZjNYXn43d5XRxuPEyZvYyjzqNUOCpCnjuSzCSZkkizpAWr7qbEppASl+IfxBuXSpolDVuMTWq/RCmdTiPZEkOGBcYNSP7eXrdWjy+Y9LQVnTxmdwVfVwdeV9tbaHH7cLZ6cbY2U97QsRnS9TqN7F5xDAyMLYo3GzHo/dWu9boT1bVPua+modeBTmtfJVsX3N+g12HQtd1j0wFa+5f+LzTtlKtRgE8p8P8LbtvWFp12Yg4v/7ylKvha4U842x/ju2/i83opOa7x+cFaNJ0er8+H1+ff3j+Jp/9YPqWCD0+wBhV4A9sF37Pd5Km6QPv0gde+QC0rl9sXePbiVSrw+fo/z7ZK5YZAFXNj4PNti48ucO5t5932WbW91jT/951BpwWO6z+WT4HL7aPF7aXF48Xl9uHx+TDqdcQYdMFnk16HyajDZNBjDj7rMej856APHFengxi97ow/u5pbvdQ6XdhbPDQ2u2ls8WBvcdPs9vqrqgeSc1usEYvJgNercHm8uDw+XB4vjuZWvjymcWjTQY42uihvaKa8vhmPTwUTe5s5kNibDSTEGrEFHgmxRmodVv7f7t1sK60LfH/4rdxawzkZo1kydSofl69kddFqPv7mYwprC/nDJX/g7OSz/YOVf74GXpkCR76EV6cGEp5OmEcsMPnn+mT/5fZENZKc5MjPXybJThgt+veziYsxMDjN+i8fU+NTPqqbqjl0/BDfHP+Gbxu/5ZtG/3OFowLF999zHmuIJcOSQZo1jXRLOpnWTLLis8iKzyIzPtP/F4IQHRBj0JGZFEdm0vf34imlcLg81Dr844camtpNHdLUij1Q7NHe4sHuclPvdPNNrZOmVq//DsSaHzZtRs+gh8IdkW5EtxOj15Fk8Rc2Tbb671L0Bcp4tCXtDtcPmyj31PRw4FQzvncs0W8zpn8S153fl6Q4I4vWfM3XFY3c8uf9PHzdLayYNJn7PruPMnsZMz+cyb1j7uWmITehZYyAvLXwyvVwpAD+dgPMfCf8k4WWfYEH+MTin/jzyn5Xhff9OkiSnTC6YMCPH5Dl8rr8pdCPH6L0eCmlDaWUNpbybeO3NHtO/x8m3hhPti07OCN1ujWddEs6GdYM0i3p0isjOp2maYG/aI30x9KhfZRSVDW6ArWjHJTWNNHs9uL1+Stme3wKj88X8pfviX39vRc+n8IbqKTt9bVVy1Z42s2v1qG2tPsDou392u+p2jVCQaC34kSvRfs2tfW4eH0KTQvtJQruF+gJ0YLLtWAb2npiHA4HCbZ49Dqdv7eofY9M+2NoJ+aJa+vh0rVbB4E2aP7j+tr1/Hh9Cn1gzrgYgw6Twd+TotO04Dm0/1zbqp57fP7Pty0+vmBvk387xYleLNSJXqy23qe242mA2ajHZNRjMuiCvTXt58lrDbx2eQI9QG5/78rpCqa2en2Bcg/fXwE9Rq/z97aYDcQHns1GPU6Xh8YWd7DXx+nyYtBrmAz+HiWT0d+zpWu1M2xgFtnJFjKTYumbGIvRoPMn9IH97S3ukHITbQ+9TuPKs1O5dngGfRNPVDkd078Xd72xi62Harn3rT1cf34GT/3by/zX/sfZdGQTj257lILKAh6+6GHi04fDz9fCK9dB+Q5/4jPzbbD0/t7z/lEOb6PAbOK4HpQnjtvGTgzfe/0Akux0EV6fl8P2w5TUl1DSUOJ/ri/hiOOI/9bsUzBoBjLjM+mf0J8cWw79E/rTz9aP/rb+9DL3kmRGdHuaduIutPGDwvgDuhtyu918+OGHXHPNRTK4/zTaLueFTB3j8/cw1jlag5MW1zpb0YA+NlPI/HfWHzH574n4nPMvjU+qzcyrcy5g6eaDPJNfzJpdFazZVYHVdA1pWX2oNr5F/rf57KrezW8vfMA/EDpvnT/ROboLll/lT3h65Zyi0c3w6e+g6EOY9AQMveaHN7DsCz62+P+Y6a0bQ5qtY3/YhJskO51MKUV1UzUHGg6EJDaHjh/C5T31XxnxxnhyEnMYkDCAnIScYGKTGZ/Z427BFkKIjtI0DX2gV6u9hFhjSG9Jd6PXadxx2SAuHJDM4+v3s+fIcRwuLwcOjEBn7k1s3zc4RjXzNs1jfPql/G78A6TN+hj+NhXqDgYSnrcgffiJg1bug7fnwLH9/q9X3QLTlsM5UzresKpCWiv38WlWGgCTB0R+YHIbSXbCyOl2BhOa4rriYGJzuhmvzXozgxIHkZuUG3wMShxEsjlZemmEEEKEGNUvidW3X4TH6+PgMSf7yo/zdUUO274ZTEnju8Qkf8bnR/+bq9/6gtnn/orbb12P4fXpULUPXp4MN70KOZfAtmWQvwi8LrCm+pOgkk/grVng88B5Pz1zYw5vg9dupMBs5Lhej/JYmTPmivB/CB0kyU6YKKWY9PakUxaF0mt6+tn6nUhsEnMZlDSITGtml6vyK4QQomsz6HUMSYtnSFo800aBUmex9eAwnti4mWLvyxD3Lcv2Pcuq/Wv4rylLGPzxb+Cbv/vr8PQd5b9FHWDwJLj+RYhNgrX/B3athHd+Ad5Wf7HC0yn+BFb9HDzNvJ02EHCTYRxLUlzX6T2TZCdMNE1jYOJAyhrLyE3KDRaFyk3KJSchJzwTwQkhhOjxNE3jokG9WTNoGp8f+Dce/u+XqdCtpp5D/PTjWcwffi//GZcMhe/5Ex2DGa56hKqzrmPhxj/wdf0Obhj4M+4ZocO482/w3q/B64ZReSe/2e43/OuVl69zLuZTXxXgZkruPzHeJ4wk2QmjZVcuk6RGCCFExIwflMIng+7jnT2T+d3W36DMh3h6zyNs7nUlL/7kbuIq9+G4bAGLizfxwdvXoDT/lEUrDzzPu2Tw2NnXcXnhWlg3F778CyRmQUKW/7m5Hv7+NK3As/3H8KoqA50P3Cnkjbwssif+HZLshJEkOkIIIbqCqcPO4YLs15jx1iPUGD5ge10+E3T+qs2rPp2LB7u/9EBLNlmmMZR5P6LJUMG85grOzz6fRyv2kVW1F6r2hhx3jymG/5uWw1GtCgBjy3Cevvx3WEwxETjL04uaZOfFF1/kqaeeorKykuHDh/P8888zduzYM+8ohBBC9AB9Ey18euti7n1/NJ8cewa7sYLXSpYB4HOlMCZhJk/e8B/0sZn54pvZLNjwNDX6TezS13FNZiaJykaSZiAZSFFelLeFjw3NKK0Zn8fKmPjZvHjTf2IJ0+wAP0ZUTDjz5ptvMn/+fB566CG++uorhg8fzsSJE6muro5004QQQoguw6DX8ez103h07MvgPA9fazKZ3ltY9e9vsWL6LPrYzABc2D+LTbOe5f7z/oKxdShoPhp0DZRqNWzXalivq+cjYzNKA51zNE+MfYUVN87pkokOREnPzjPPPMMvfvELbr31VgCWLl3KBx98wEsvvcSCBQsi3DohhBCia7l+2GDGD3iZioZmzuubcMryJpqmccuosdw47A3e3beLoppyjjQeo8pRR01zHU63g2HJY/n/edPpZelal62+q9snO62trezYsYOFCxcGl+l0OiZMmMDWrVsj2DIhhBCi6+ptNdHbeuaxpSajnptGjAJGhb9RYdLtk52amhq8Xi+pqakhy1NTU/nHP/5xyn1cLhcu14lqxY2N/iJ/brcbt9sdvsaKk7R93vK5dz0Sm65N4tO1SXw6R0c/326f7PwzHnvsMR5++OGTln/yySfExX3/zM0iPPLz8yPdBHEaEpuuTeLTtUl8wqupqalD23X7ZKd3797o9XqqqqpClldVVZGWlnbKfRYuXMj8+fODXzc2NpKVlcVVV12FzWYLa3tFKLfbTX5+PldeeaVMZtjFSGy6NolP1ybx6RxtV2bOpNsnOzExMYwaNYoNGzYwZcoUAHw+Hxs2bODOO+885T4mkwmT6eTrlEajUb4pI0Q++65LYtO1SXy6NolPeHX0s+32yQ7A/PnzycvLY/To0YwdO5Y//vGPOJ3O4N1ZQgghhOi5oiLZmT59OseOHWPRokVUVlZy/vnn89FHH500aFkIIYQQPU9UJDsAd95552kvWwkhhBCi54qKCspCCCGEEKcjyY4QQgghopokO0IIIYSIapLsCCGEECKqSbIjhBBCiKgmyY4QQgghopokO0IIIYSIalFTZ+fHUEoBHZ9jQ/zruN1umpqaaGxslJLqXYzEpmuT+HRtEp/O0fZ7u+33+OlIsgPY7XYAsrKyItwSIYQQQvxQdrudhISE067X1JnSoR7A5/NRUVFBfHw8mqZFujk9StuM82VlZTLjfBcjsenaJD5dm8SncyilsNvtZGRkoNOdfmSO9OwAOp2OzMzMSDejR7PZbPIDoYuS2HRtEp+uTeITft/Xo9NGBigLIYQQIqpJsiOEEEKIqCbJjogok8nEQw89hMlkinRTxHdIbLo2iU/XJvHpWmSAshBCCCGimvTsCCGEECKqSbIjhBBCiKgmyY4QQgghopokO0IIIYSIapLsiE73+OOPo2kad911V3BZS0sLd9xxB8nJyVitVqZNm0ZVVVXkGtnDlJeXM3PmTJKTk4mNjeW8885j+/btwfVKKRYtWkR6ejqxsbFMmDCBkpKSCLa4Z/B6vTz44IPk5OQQGxvLwIED+f3vfx8yD5DEpvN89tlnXHvttWRkZKBpGu+9917I+o7Eoq6ujhkzZmCz2UhMTGT27Nk4HI5OPIueSZId0akKCgpYtmwZw4YNC1l+9913s27dOlavXs3mzZupqKhg6tSpEWplz1JfX8/48eMxGo2sX7+ewsJCnn76aZKSkoLbPPnkkzz33HMsXbqUbdu2YbFYmDhxIi0tLRFsefR74oknWLJkCS+88AL79+/niSee4Mknn+T5558PbiOx6TxOp5Phw4fz4osvnnJ9R2IxY8YMvv76a/Lz83n//ff57LPP+OUvf9lZp9BzKSE6id1uV7m5uSo/P19dcsklat68eUoppRoaGpTRaFSrV68Obrt//34FqK1bt0aotT3H/fffr37yk5+cdr3P51NpaWnqqaeeCi5raGhQJpNJvf76653RxB5r8uTJatasWSHLpk6dqmbMmKGUkthEEqDefffd4NcdiUVhYaECVEFBQXCb9evXK03TVHl5eae1vSeSnh3Rae644w4mT57MhAkTQpbv2LEDt9sdsnzo0KFkZ2ezdevWzm5mj7N27VpGjx7Nz372M/r06cOIESP4y1/+ElxfWlpKZWVlSHwSEhK44IILJD5hdtFFF7FhwwaKi4sB2L17N1u2bOHqq68GJDZdSUdisXXrVhITExk9enRwmwkTJqDT6di2bVunt7knkYlARad44403+OqrrygoKDhpXWVlJTExMSQmJoYsT01NpbKyspNa2HMdOnSIJUuWMH/+fH7zm99QUFDA3LlziYmJIS8vLxiD1NTUkP0kPuG3YMECGhsbGTp0KHq9Hq/Xy+LFi5kxYwaAxKYL6UgsKisr6dOnT8h6g8FAr169JF5hJsmOCLuysjLmzZtHfn4+ZrM50s0R3+Hz+Rg9ejSPPvooACNGjGDfvn0sXbqUvLy8CLeuZ1u1ahUrV67ktdde45xzzmHXrl3cddddZGRkSGyE+AHkMpYIux07dlBdXc3IkSMxGAwYDAY2b97Mc889h8FgIDU1ldbWVhoaGkL2q6qqIi0tLTKN7kHS09M5++yzQ5adddZZHD58GCAYg+/eHSfxCb97772XBQsWcNNNN3Heeedxyy23cPfdd/PYY48BEpuupCOxSEtLo7q6OmS9x+Ohrq5O4hVmkuyIsLviiivYu3cvu3btCj5Gjx7NjBkzgq+NRiMbNmwI7lNUVMThw4cZN25cBFveM4wfP56ioqKQZcXFxfTr1w+AnJwc0tLSQuLT2NjItm3bJD5h1tTUhE4X+mNar9fj8/kAiU1X0pFYjBs3joaGBnbs2BHcZuPGjfh8Pi644IJOb3OPEukR0qJnan83llJK3X777So7O1tt3LhRbd++XY0bN06NGzcucg3sQb788ktlMBjU4sWLVUlJiVq5cqWKi4tTr776anCbxx9/XCUmJqo1a9aoPXv2qOuvv17l5OSo5ubmCLY8+uXl5am+ffuq999/X5WWlqp33nlH9e7dW913333BbSQ2ncdut6udO3eqnTt3KkA988wzaufOnerbb79VSnUsFpMmTVIjRoxQ27ZtU1u2bFG5ubnq5ptvjtQp9RiS7IiI+G6y09zcrH7961+rpKQkFRcXp2644QZ19OjRyDWwh1m3bp0699xzlclkUkOHDlV//vOfQ9b7fD714IMPqtTUVGUymdQVV1yhioqKItTanqOxsVHNmzdPZWdnK7PZrAYMGKAeeOAB5XK5gttIbDrPpk2bFHDSIy8vTynVsVjU1taqm2++WVmtVmWz2dStt96q7HZ7BM6mZ9GUaleKUwghhBAiysiYHSGEEEJENUl2hBBCCBHVJNkRQgghRFSTZEcIIYQQUU2SHSGEEEJENUl2hBBCCBHVJNkRQgghRFSTZEcIIYQQUU2SHSFEt7R161b0ej2TJ0+OdFOEEF2cVFAWQnRLc+bMwWq1snz5coqKisjIyIh0k4QQXZT07Aghuh2Hw8Gbb77Jr371KyZPnsyKFStC1q9du5bc3FzMZjOXXXYZf/3rX9E0jYaGhuA2W7Zs4eKLLyY2NpasrCzmzp2L0+ns3BMRQnQKSXaEEN3OqlWrGDp0KEOGDGHmzJm89NJLtHVSl5aW8tOf/pQpU6awe/dubrvtNh544IGQ/Q8ePMikSZOYNm0ae/bs4c0332TLli3ceeedkTgdIUSYyWUsIUS3M378eG688UbmzZuHx+MhPT2d1atXc+mll7JgwQI++OAD9u7dG9z+t7/9LYsXL6a+vp7ExETmzJmDXq9n2bJlwW22bNnCJZdcgtPpxGw2R+K0hBBhIj07QohupaioiC+//JKbb74ZAIPBwPTp01m+fHlw/ZgxY0L2GTt2bMjXu3fvZsWKFVit1uBj4sSJ+Hw+SktLO+dEhBCdxhDpBgghxA+xfPlyPB5PyIBkpRQmk4kXXnihQ8dwOBzcdtttzJ0796R12dnZ/7K2CiG6Bkl2hBDdhsfj4ZVXXuHpp5/mqquuClk3ZcoUXn/9dYYMGcKHH34Ysq6goCDk65EjR1JYWMigQYPC3mYhROTJmB0hRLfx3nvvMX36dKqrq0lISAhZd//997Nx40ZWrVrFkCFDuPvuu5k9eza7du3innvu4ciRIzQ0NJCQkMCePXu48MILmTVrFnPmzMFisVBYWEh+fn6He4eEEN2HjNkRQnQby5cvZ8KECSclOgDTpk1j+/bt2O123nrrLd555x2GDRvGkiVLgndjmUwmAIYNG8bmzZspLi7m4osvZsSIESxatEhq9QgRpaRnRwgR9RYvXszSpUspKyuLdFOEEBEgY3aEEFHnT3/6E2PGjCE5OZnPP/+cp556SmroCNGDSbIjhIg6JSUlPPLII9TV1ZGdnc0999zDwoULI90sIUSEyGUsIYQQQkQ1GaAshBBCiKgmyY4QQgghopokO0IIIYSIapLsCCGEECKqSbIjhBBCiKgmyY4QQgghopokO0IIIYSIapLsCCGEECKqSbIjhBBCiKj2v28UCdUi8oUYAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(AgeMeans.Age, AgeMeans.pIncome, label=\"Permanent Income\")\n", + "plt.plot(AgeMeans.Age, AgeMeans.M, label=\"Market resources\")\n", + "plt.plot(AgeMeans.Age, AgeMeans.Cons, label=\"Consumption\")\n", + "plt.legend()\n", + "plt.xlabel(\"Age\")\n", + "plt.ylabel(\"Thousands of USD\")\n", + "plt.title(\"Variable Medians Conditional on Survival\")\n", + "plt.grid()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/src/notebooks/Model_Comparisons.ipynb b/src/notebooks/Model_Comparisons.ipynb new file mode 100644 index 0000000..4a51861 --- /dev/null +++ b/src/notebooks/Model_Comparisons.ipynb @@ -0,0 +1,338 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import annotations\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from estimark.agents import (\n", + " BequestWarmGlowLifeCyclePortfolioType,\n", + " PortfolioLifeCycleConsumerType,\n", + " WealthPortfolioLifeCycleConsumerType,\n", + ")\n", + "from estimark.estimation import get_weighted_moments\n", + "from estimark.parameters import age_mapping, init_calibration\n", + "from estimark.scf import scf_data\n", + "from estimark.snp import snp_data_full\n", + "\n", + "results_dir = \"../../content/tables/TRP/\" # This is AEL's\n", + "# results_dir = \"../estimark/content/tables/min/\" # This is MNW's" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "csv_file_path = results_dir + \"Portfolio_estimate_results.csv\"\n", + "res = pd.read_csv(csv_file_path, header=None)\n", + "res = res.set_index(res.columns[0])[res.columns[1]].to_dict()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(9.252286005027539, 1.0)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "portfolio_agent = PortfolioLifeCycleConsumerType(**init_calibration)\n", + "portfolio_agent.CRRA = float(res[\"CRRA\"])\n", + "portfolio_agent.CRRA, portfolio_agent.DiscFac" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "csv_file_path = results_dir + \"WarmGlowPortfolio_estimate_results.csv\"\n", + "res = pd.read_csv(csv_file_path, header=None)\n", + "res = res.set_index(res.columns[0])[res.columns[1]].to_dict()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(9.206775856414323, 1.0, 23.05054873023735, 45.64298427855443)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "warmglow_agent = BequestWarmGlowLifeCyclePortfolioType(**init_calibration)\n", + "warmglow_agent.CRRA = float(res[\"CRRA\"])\n", + "warmglow_agent.BeqCRRA = float(res[\"CRRA\"])\n", + "warmglow_agent.BeqFac = float(res[\"BeqFac\"])\n", + "warmglow_agent.BeqShift = float(res[\"BeqShift\"])\n", + "(\n", + " warmglow_agent.CRRA,\n", + " warmglow_agent.DiscFac,\n", + " warmglow_agent.BeqFac,\n", + " warmglow_agent.BeqShift,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "csv_file_path = results_dir + \"WealthPortfolio_estimate_results.csv\"\n", + "res = pd.read_csv(csv_file_path, header=None)\n", + "res = res.set_index(res.columns[0])[res.columns[1]].to_dict()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(5.335577372664163, 1.0, 0.1706005756625005, 0.0)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trp_agent = WealthPortfolioLifeCycleConsumerType(**init_calibration)\n", + "trp_agent.CRRA = float(res[\"CRRA\"])\n", + "trp_agent.WealthShare = float(res[\"WealthShare\"])\n", + "trp_agent.CRRA, trp_agent.DiscFac, trp_agent.WealthShare, trp_agent.WealthShift" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "agents = [portfolio_agent, warmglow_agent, trp_agent]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "for agent in agents:\n", + " agent.update()\n", + " agent.solve()\n", + "\n", + " agent.track_vars = [\"aNrm\", \"cNrm\", \"t_age\", \"bNrm\", \"Share\"]\n", + " # agent.LivPrb = [1.0] * portfolio_agent.T_cycle\n", + " agent.T_sim = portfolio_agent.T_cycle\n", + " agent.initialize_sim()\n", + " history = agent.simulate()\n", + "\n", + " raw_data = {\n", + " \"Age\": agent.history[\"t_age\"].flatten() + 25,\n", + " \"nrmB\": agent.history[\"bNrm\"].flatten(),\n", + " \"nrmC\": agent.history[\"cNrm\"].flatten(),\n", + " \"Share\": agent.history[\"Share\"].flatten(),\n", + " }\n", + "\n", + " Data = pd.DataFrame(raw_data)\n", + "\n", + " # Find the mean of each variable at every age\n", + " agent.AgeMeans = Data.groupby([\"Age\"]).median().reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "moments = get_weighted_moments(\n", + " data=scf_data,\n", + " variable=\"wealth_income_ratio\",\n", + " weights=\"weight\",\n", + " groups=\"age_group\",\n", + " mapping=age_mapping,\n", + ")\n", + "moments\n", + "\n", + "moments_values = []\n", + "for key in moments:\n", + " moments_values.append([np.mean(age_mapping[key]), moments[key][0]])\n", + "\n", + "moments_values = np.asarray(moments_values).T" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(\n", + " portfolio_agent.AgeMeans.Age,\n", + " portfolio_agent.AgeMeans.nrmB,\n", + " label=\"Life-Cycle Portfolio\",\n", + " # alpha=0.5, # make line more faded\n", + " # linewidth=1, # thinner line\n", + ")\n", + "plt.plot(\n", + " warmglow_agent.AgeMeans.Age,\n", + " warmglow_agent.AgeMeans.nrmB,\n", + " label=\"Warm-Glow Portfolio\",\n", + " # alpha=0.5, # make line more faded\n", + " # linewidth=1, # thinner line\n", + ")\n", + "plt.plot(\n", + " trp_agent.AgeMeans.Age,\n", + " trp_agent.AgeMeans.nrmB,\n", + " label=\"TRP Portfolio\",\n", + " # alpha=1, # full color\n", + " # linewidth=2, # thicker line\n", + ")\n", + "plt.plot(\n", + " moments_values[0],\n", + " moments_values[1],\n", + " label=\"SCF\",\n", + " linestyle=\"--\",\n", + ")\n", + "plt.legend()\n", + "plt.xlabel(\"Age\")\n", + "plt.ylabel(\"Wealth to Income Ratio\")\n", + "plt.title(\"Wealth Medians for Portfolio Models\")\n", + "plt.xlim(25, 95)\n", + "plt.ylim(0.0, 12.0)\n", + "plt.grid()\n", + "plt.savefig(\"median_wealth.pdf\")\n", + "plt.savefig(\"median_wealth.svg\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(\n", + " portfolio_agent.AgeMeans.Age,\n", + " portfolio_agent.AgeMeans.Share,\n", + " label=\"Life-Cycle Portfolio\",\n", + " # alpha=0.5, # make line more faded\n", + " # linewidth=1, # thinner line\n", + ")\n", + "plt.plot(\n", + " warmglow_agent.AgeMeans.Age,\n", + " warmglow_agent.AgeMeans.Share,\n", + " label=\"Warm-Glow Portfolio\",\n", + " # alpha=0.5, # make line more faded\n", + " # linewidth=1, # thinner line\n", + ")\n", + "plt.plot(\n", + " trp_agent.AgeMeans.Age,\n", + " trp_agent.AgeMeans.Share,\n", + " label=\"TRP Portfolio\",\n", + " # alpha=1, # full color\n", + " # linewidth=2, # thicker line\n", + ")\n", + "plt.plot(\n", + " snp_data_full[\"age\"],\n", + " snp_data_full[\"share\"],\n", + " label=\"TDF Glide Path\",\n", + " linestyle=\"--\",\n", + ")\n", + "plt.legend()\n", + "plt.xlabel(\"Age\")\n", + "plt.ylabel(\"Risky Portfolio Share\")\n", + "plt.title(\"Portfolio Share Medians for Portfolio Models\")\n", + "plt.xlim(70, 95)\n", + "plt.ylim(0.0, 1.0)\n", + "plt.grid()\n", + "plt.savefig(\"median_share.pdf\")\n", + "plt.savefig(\"median_share.svg\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/src/notebooks/Portfolio.ipynb b/src/notebooks/Portfolio.ipynb new file mode 100644 index 0000000..234bbe8 --- /dev/null +++ b/src/notebooks/Portfolio.ipynb @@ -0,0 +1,281 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import annotations\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from HARK.utilities import plot_funcs\n", + "\n", + "from estimark.agents import PortfolioLifeCycleConsumerType\n", + "from estimark.parameters import init_calibration\n", + "from estimark.snp import snp_data, snp_data_full" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "csv_file_path = \"../../content/tables/TRP/Portfolio_estimate_results.csv\"\n", + "res = pd.read_csv(csv_file_path, header=None)\n", + "res = res.set_index(res.columns[0])[res.columns[1]].to_dict()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(9.252342476844415, 1.0)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "portfolio_agent = PortfolioLifeCycleConsumerType(**init_calibration)\n", + "portfolio_agent.CRRA = float(res[\"CRRA\"])\n", + "\n", + "portfolio_agent.CRRA, portfolio_agent.DiscFac" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "portfolio_agent.update()\n", + "portfolio_agent.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_funcs(\n", + " [\n", + " *[sol.ShareFuncAdj for sol in portfolio_agent.solution[:-1:5]],\n", + " lambda m: portfolio_agent.ShareLimit * np.ones_like(m),\n", + " ],\n", + " 0,\n", + " 100,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the variables we want to keep track of.\n", + "portfolio_agent.track_vars = [\"aNrm\", \"cNrm\", \"pLvl\", \"t_age\", \"mNrm\", \"Share\"]\n", + "\n", + "portfolio_agent.LivPrb = [1.0] * portfolio_agent.T_cycle\n", + "\n", + "portfolio_agent.T_sim = portfolio_agent.T_cycle\n", + "# Run the simulations\n", + "portfolio_agent.initialize_sim()\n", + "history = portfolio_agent.simulate()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "95" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "portfolio_agent.T_sim" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "raw_data = {\n", + " \"Age\": portfolio_agent.history[\"t_age\"].flatten() + 25,\n", + " \"pIncome\": portfolio_agent.history[\"pLvl\"].flatten(),\n", + " \"nrmM\": portfolio_agent.history[\"mNrm\"].flatten(),\n", + " \"nrmC\": portfolio_agent.history[\"cNrm\"].flatten(),\n", + " \"Share\": portfolio_agent.history[\"Share\"].flatten(),\n", + "}\n", + "\n", + "Data = pd.DataFrame(raw_data)\n", + "Data[\"Cons\"] = Data.nrmC * Data.pIncome\n", + "Data[\"M\"] = Data.nrmM * Data.pIncome\n", + "\n", + "# Find the mean of each variable at every age\n", + "AgeMeans = Data.groupby([\"Age\"]).median().reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(AgeMeans.Age, AgeMeans.nrmM, label=\"Market resources\")\n", + "plt.plot(AgeMeans.Age, AgeMeans.nrmC, label=\"Consumption\")\n", + "plt.legend()\n", + "plt.xlabel(\"Age\")\n", + "plt.ylabel(\"Normalized Wealth\")\n", + "plt.title(\"Variable Medians Conditional on Survival\")\n", + "plt.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(AgeMeans.Age, AgeMeans.Share, label=\"Portfolio Share\")\n", + "plt.plot(\n", + " snp_data_full[\"age\"],\n", + " snp_data_full[\"share\"],\n", + " label=\"Fin. Advice\",\n", + " linestyle=\"--\",\n", + ")\n", + "plt.plot(snp_data[\"age\"], snp_data[\"share\"], label=\"Fin. Advice (retirement)\")\n", + "plt.legend()\n", + "plt.xlabel(\"Age\")\n", + "plt.ylabel(\"Fraction of Savings\")\n", + "plt.title(\"Portfolio Share Median Conditional on Survival\")\n", + "plt.ylim(0, 1)\n", + "plt.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/src/notebooks/SCF_notebook.ipynb b/src/notebooks/SCF_notebook.ipynb new file mode 100644 index 0000000..e6f59eb --- /dev/null +++ b/src/notebooks/SCF_notebook.ipynb @@ -0,0 +1,662 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import annotations\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from statsmodels.stats.weightstats import DescrStatsW\n", + "\n", + "from estimark.estimation import get_weighted_moments\n", + "from estimark.parameters import age_mapping\n", + "from estimark.scf import scf_data_full" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ageage_groupwealth_income_ratioweightwave
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23251043(40,45]9.6024616283.1873152019
23251143(40,45]11.4446356639.6580202019
23251243(40,45]11.5470226580.3437222019
23251343(40,45]10.4131756515.0819452019
23251443(40,45]10.3170246663.8767222019
\n", + "

94529 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " age age_group wealth_income_ratio weight wave\n", + "30 31 (30,35] 6.697993 3676.299028 1995\n", + "31 31 (30,35] 6.697993 3822.532451 1995\n", + "32 31 (30,35] 6.697993 3779.582462 1995\n", + "33 31 (30,35] 6.697993 3570.089875 1995\n", + "34 31 (30,35] 6.697993 3803.353076 1995\n", + "... ... ... ... ... ...\n", + "232510 43 (40,45] 9.602461 6283.187315 2019\n", + "232511 43 (40,45] 11.444635 6639.658020 2019\n", + "232512 43 (40,45] 11.547022 6580.343722 2019\n", + "232513 43 (40,45] 10.413175 6515.081945 2019\n", + "232514 43 (40,45] 10.317024 6663.876722 2019\n", + "\n", + "[94529 rows x 5 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scf_data_full" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "moments = get_weighted_moments(\n", + " data=scf_data_full,\n", + " variable=\"wealth_income_ratio\",\n", + " weights=\"weight\",\n", + " groups=\"age_group\",\n", + " mapping=age_mapping,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'(25,30]': array([0.65763249]),\n", + " '(30,35]': array([0.97360885]),\n", + " '(35,40]': array([1.78172387]),\n", + " '(40,45]': array([2.38910063]),\n", + " '(45,50]': array([3.23681528]),\n", + " '(50,55]': array([4.24488131]),\n", + " '(55,60]': array([5.32876747]),\n", + " '(60,65]': array([6.45894082]),\n", + " '(65,70]': array([7.92872889]),\n", + " '(70,75]': array([8.80298421]),\n", + " '(75,80]': array([9.85313601]),\n", + " '(80,85]': array([8.75530344]),\n", + " '(85,90]': array([11.36179422]),\n", + " '(90,95]': array([9.9756071])}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "moments" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(moments.values())\n", + "plt.scatter(range(len(moments)), moments.values())\n", + "plt.xticks(range(len(moments)), moments.keys(), rotation=45)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from estimark.snp import snp_data" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ageshareage_group
51710.393936(70,75]
52720.382074(70,75]
53730.370212(70,75]
54740.363326(70,75]
55750.356440(70,75]
56760.349554(75,80]
57770.342668(75,80]
58780.335781(75,80]
59790.332880(75,80]
60800.329979(75,80]
61810.327077(80,85]
62820.324176(80,85]
63830.321274(80,85]
64840.321274(80,85]
65850.321274(80,85]
66860.321274(85,90]
67870.321274(85,90]
68880.321274(85,90]
69890.321274(85,90]
70900.321274(85,90]
71910.321274(90,95]
72920.321274(90,95]
73930.321274(90,95]
74940.321274(90,95]
75950.321274(90,95]
\n", + "
" + ], + "text/plain": [ + " age share age_group\n", + "51 71 0.393936 (70,75]\n", + "52 72 0.382074 (70,75]\n", + "53 73 0.370212 (70,75]\n", + "54 74 0.363326 (70,75]\n", + "55 75 0.356440 (70,75]\n", + "56 76 0.349554 (75,80]\n", + "57 77 0.342668 (75,80]\n", + "58 78 0.335781 (75,80]\n", + "59 79 0.332880 (75,80]\n", + "60 80 0.329979 (75,80]\n", + "61 81 0.327077 (80,85]\n", + "62 82 0.324176 (80,85]\n", + "63 83 0.321274 (80,85]\n", + "64 84 0.321274 (80,85]\n", + "65 85 0.321274 (80,85]\n", + "66 86 0.321274 (85,90]\n", + "67 87 0.321274 (85,90]\n", + "68 88 0.321274 (85,90]\n", + "69 89 0.321274 (85,90]\n", + "70 90 0.321274 (85,90]\n", + "71 91 0.321274 (90,95]\n", + "72 92 0.321274 (90,95]\n", + "73 93 0.321274 (90,95]\n", + "74 94 0.321274 (90,95]\n", + "75 95 0.321274 (90,95]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "snp_data" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "share_moments = get_weighted_moments(\n", + " data=snp_data,\n", + " variable=\"share\",\n", + " groups=\"age_group\",\n", + " mapping=age_mapping,\n", + " weights=None,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'(70,75]': 0.37021211462000003,\n", + " '(75,80]': 0.33578140508,\n", + " '(80,85]': 0.32127446132,\n", + " '(85,90]': 0.32127446132,\n", + " '(90,95]': 0.32127446132}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "share_moments" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Gzz//fAu8RdsxL9IfX/x8CTt/zcWi/DL06txB7khERERtxuL7tFgrW71Py/We2ngY35+6gsnB3vjnI4FyxyEiIrotrXafFpJf7N29AQA7jl3EpaJKmdMQERG1HZYWhRnm0wmhvdxRbRRYl5IhdxwiIqI2w9KiQLGja1dbkg5loaBMf4u9iYiIbANLiwKF93bHEC9XVFWb8O6PmXLHISIiahMsLQokSRLm/We1ZVPaBZRUVcuciIiIqPWxtCjUff090adLB5Tqa7B5/wW54xAREbU6lhaFUqkkzBvtDwDYkJqJSgO/EZuIiGwbS4uCjQvsDq9OjigsN2Db4axbv4CIiEjBWFoUzE6tQkxE7WrL2n0ZMNSYbvEKIiIi5WJpUbhHgrzQ2VmDS8VV+PR4jtxxiIiIWg1Li8Jp7dV4epQfAGD1nnQYTTbxrQxEREQNsLTYgKkhvnB1tEdGQTm+/i1P7jhEREStgqXFBnTQ2GFmWE8AQPzuc7CR78AkIiKqh6XFRswK7wknBzVO5JZgz5l8ueMQERG1OJYWG9HRyQHTRvoCABJ2n5M5DRERUctjabEhs+/0g4NahcPnr+FgRqHccYiIiFoUS4sN6eKixaRgLwBA/J50mdMQERG1LJYWGxN9lz/UKgn7zuTj14vFcschIiJqMSwtNsbH3QkPDukOAEjYw3NbiIjIdrC02KC5kbW39v/69zycu1IqcxoiIqKWwdJig/p6OiNqgCeEABL3ZMgdh4iIqEWwtNioeaN7AwA+PZ6D7KsVMqchIiK6fSwtNmqod0fc2dsDRpPAuhSuthARkfKxtNiweaNrz23ZejgbV0qrZE5DRER0e1habFhoL3fc4dMRhhoTNqSelzsOERHRbWFpsWGSJCE2svbcli0HLqC4olrmRERERM3H0mLj7g7ogoCuzijT12DT/vNyxyEiImo2lhYbp1JJ5iuJNvyYiQpDjcyJiIiImoelpR0YO7gbero74VpFNT44mCV3HCIiomZhaWkH1CoJMRG1VxKtS8mAvsYocyIiIiLLsbS0ExOG9UBXFy0ul+ix46ccueMQERFZjKWlndDYqfH0Xb0AAKv3pqPGaJI5ERERkWVYWtqRKSO80cnJHhcKK7Dz11y54xAREVmEpaUdcXKww5PhfgCAhN3pMJmEzImIiIiajqWlnXkitCc6aOxw+nIpfjh1Re44RERETcbS0s64Otlj2khfAMDbu89BCK62EBGRMrC0tENP3ekHjZ0Kx7OLsD+jUO44RERETcLS0g51dtZg8nBvALXnthARESkBS0s7NeeuXrBTSUg9V4Dj2UVyxyEiIrollpZ2yquTEx4a2gMAkLD7nMxpiIiIbo2lpR2bG+kPSQK+PXEZZy6Xyh2HiIjoplha2rHeXTpgzKCuALjaQkRE1o+lpZ2bF9kbAPD5z5eQVVghcxoiIqLGsbS0c4N6uCKib2eYBLB6H68kIiIi68XSQogdXbva8vGRi7hcUiVzGiIiohtjaSGM8HPD8J6dYDCa8E5KhtxxiIiIboilhQAA8/6z2vL+wSxcKzfInIaIiKghlhYCAET27YwB3VxQYTBiY9p5ueMQERE1wNJCAABJkszntmxMO48yfY3MiYiIiOpjaSGzPw3qil4eOhRXVuODgxfkjkNERFQPSwuZqVUSYiL9AQDrUjJRVW2UOREREdF/sbRQPeOH9kB3Vy3yS/X4+OhFueMQERGZsbRQPQ52Ksy5qxcAYPXedNQYTTInIiIiqsXSQg08NsIHHh0ccPFaJb745ZLccYiIiACwtNANaO3VePJOPwBAwu50mExC5kREREQsLdSIaSN94ay1w9krZfj2xGW54xAREbG00I25aO0xI7QnACBhzzkIwdUWIiKSF0sLNWpWeE9o7VX45WIxUs8VyB2HiIjauWaVloSEBPj5+UGr1SIoKAgpKSmN7puamorw8HC4u7vD0dERAQEBWLlyZb19Nm7cCEmSGjyqqviNw3Jy76DBlBE+AID43edkTkNERO2dnaUv2LZtGxYuXIiEhASEh4djzZo1GDNmDE6cOAEfH58G++t0OsyfPx+BgYHQ6XRITU1FdHQ0dDod5syZY97PxcUFp0+frvdarVbbjLdELenpUb2w5cAFHMi4iqMXriLI103uSERE1E5JwsKTFUJCQjBs2DAkJiaat/Xv3x/jx49HXFxck44xceJE6HQ6bN68GUDtSsvChQtRVFRkSZR6SkpK4OrqiuLiYri4uDT7ONTQ8x//gm1HsnFPQBesnzlc7jhERGRDLPn9bdHHQwaDAUePHkVUVFS97VFRUUhLS2vSMY4dO4a0tDRERETU215WVgZfX194eXnhgQcewLFjx256HL1ej5KSknoPah0xkf5QScD3p67gxCX+dyYiInlYVFoKCgpgNBrh6elZb7unpyfy8vJu+lovLy9oNBoEBwcjNjYWs2fPNj8XEBCAjRs34vPPP0dSUhK0Wi3Cw8Nx9uzZRo8XFxcHV1dX88Pb29uSt0IW8PPQ4c+DuwEAEvemy5yGiIjaq2adiCtJUr0/CyEabLteSkoKjhw5gtWrV2PVqlVISkoyPzdy5EhMmzYNQ4YMwahRo/Dhhx+ib9++eOuttxo93uLFi1FcXGx+ZGdnN+etUBPNi+wNANj5yyVkFpTLnIaIiNoji07E9fDwgFqtbrCqcuXKlQarL9fz86u9w+rgwYNx+fJlvPzyy5gyZcoN91WpVBg+fPhNV1o0Gg00Go0l8ek2DOjugrsDuuCHU1ewZm86lj8cKHckIiJqZyxaaXFwcEBQUBCSk5PrbU9OTkZYWFiTjyOEgF6vv+nzx48fR7du3SyJR60sdrQ/AGD7TxeRW1wpcxoiImpvLL7kedGiRZg+fTqCg4MRGhqKtWvXIisrCzExMQBqP7bJycnBpk2bAADx8fHw8fFBQEAAgNr7tqxYsQILFiwwH/OVV17ByJEj0adPH5SUlODf//43jh8/jvj4+JZ4j9RCgnzdMLKXGw5kXMXafRl4adxAuSMREVE7YnFpmTx5MgoLC7F06VLk5uZi0KBB2LVrF3x9fQEAubm5yMrKMu9vMpmwePFiZGZmws7ODv7+/li+fDmio6PN+xQVFWHOnDnIy8uDq6sr7rjjDuzbtw8jRoxogbdILSl2dG8cyDiEpENZmD+6N9w78CM6IiJqGxbfp8Va8T4tbUMIgYfif8QvF4sxf3Rv/PX+fnJHIiIiBWu1+7QQSZJkvpLovf3nUVJVLXMiIiJqL1hayGJRAzzRu0sHlFbVYMuBC3LHISKidoKlhSymUkmYF1l7JdH6lExUGowyJyIiovaApYWaZdyQ7vDq5IjCcgM+PMIb+xERUetjaaFmsVerEB1Ru9qyZm86DDUmmRMREZGtY2mhZpsU5AWPDhpcKq7CZ8dz5I5DREQ2jqWFmk1rr8bTo2q/niFxbzqMJpu4ep6IiKwUSwvdlsdH+sJFa4eM/HJ88/vNv+mbiIjodrC00G3poLHDzPDa1Zb43edgI/cqJCIiK8TSQrdtVlhPODmo8fulEuw9ky93HCIislEsLXTbOukc8HiID4Da1RYiIqLWwNJCLWL2qF5wUKtw+Pw1HMq8KnccIiKyQSwt1CI8XbR4JNgLAFdbiIiodbC0UIuJucsfKgnYeyYfv+UUyx2HiIhsDEsLtRgfdyc8OKQ7ACBhD1dbiIioZbG0UIuaG9kbAPDVb3k4d6VM5jRERGRLWFqoRfXr6oz7BnhCCGD13nS54xARkQ1haaEWNy+y9osUPz2Wg4vXKmROQ0REtoKlhVrcHT6dEN7bHTUmgXX7MuSOQ0RENoKlhVpF7H/Obdl6OBv5pXqZ0xARkS1gaaFWEervjqHeHaGvMWHDj5lyxyEiIhvA0kKtQpIkxI6uXW3ZvP8CiiurZU5ERERKx9JCreaegC7o5+mMMn0NNu8/L3ccIiJSOJYWajUqlYR5o2uvJFqfmokKQ43MiYiISMlYWqhVjR3cDb7uTrhWUY2kQ9lyxyEiIgVjaaFWZadWISaidrVl3b4M6GuMMiciIiKlYmmhVjdxWA94umiQV1KFT37KkTsOEREpFEsLtTqNnRpPj+oFAEjcm44ao0nmREREpEQsLdQmpozwQScne1worMCu3/LkjkNERArE0kJtQqexw6xwPwBAwu5zEELInIiIiJSGpYXazIzQntA5qHEqrxQ/nLoidxwiIlIYlhZqM65O9pgW6gsAeJurLUREZCGWFmpTT93pBwc7FY5lFeFAxlW54xARkYKwtFCb6uKsxeRgbwBAwp5zMqchIiIlYWmhNjfnrl5QqySknC3Az9lFcschIiKFYGmhNuft5oSHhnYHwNUWIiJqOpYWksW8SH9IEvDN75dx5nKp3HGIiEgBWFpIFr27OONPA7sCABL3pMuchoiIlIClhWQzL7I3AODzny8hq7BC5jRERGTtWFpINoO9XHFX384wmgTW7ONqCxER3RxLC8kqNtIfAPDRkYu4UlIlcxoiIrJmLC0kqxF+bgj27QSD0YR3UjPljkNERFaMpYVkJUkSYkfXntuy5cAFFFUYZE5ERETWiqWFZBfZrzP6d3NBhcGIjWnn5Y5DRERWiqWFZFe72lJ7bsu7P55Hmb5G5kRERGSNWFrIKowZ1A1+HjoUV1Yj6WCW3HGIiMgKsbSQVVCrJMyNqF1tWZeSgapqo8yJiIjI2rC0kNUYf0cPdHPV4kqpHtt/uih3HCIisjIsLWQ1HOxUmHNXLwDA6r3pqDGaZE5ERETWhKWFrMpjw33grnNA9tVKfPHLJbnjEBGRFWFpIavi6KDGk3f6AQASdqfDZBIyJyIiImvB0kJWZ3qoL5w1djh7pQzJJy/LHYeIiKwESwtZHRetPZ4I8wUAJOw+ByG42kJERCwtZKVmhftBa6/CzxeL8eO5QrnjEBGRFWBpIavk0UGDx4b7AADid5+TOQ0REVkDlhayWnPu6gU7lYT9GYU4euGa3HGIiEhmLC1ktbp3dMTEYT0AAIl7uNpCRNTesbSQVYuJ8IckAd+dvIKTuSVyxyEiIhk1q7QkJCTAz88PWq0WQUFBSElJaXTf1NRUhIeHw93dHY6OjggICMDKlSsb3X/r1q2QJAnjx49vTjSyMb06d8CfB3cDACTuSZc5DRERycni0rJt2zYsXLgQS5YswbFjxzBq1CiMGTMGWVk3/mZenU6H+fPnY9++fTh58iReeOEFvPDCC1i7dm2DfS9cuIC//vWvGDVqlOXvhGzWvMjaL1L88pdLOF9QLnMaIiKSiyQsvAlGSEgIhg0bhsTERPO2/v37Y/z48YiLi2vSMSZOnAidTofNmzebtxmNRkRERGDWrFlISUlBUVERPv300ybnKikpgaurK4qLi+Hi4tLk15EyzHr3EHafzseUEd6ImxgodxwiImohlvz+tmilxWAw4OjRo4iKiqq3PSoqCmlpaU06xrFjx5CWloaIiIh625cuXYrOnTvjqaeeatJx9Ho9SkpK6j3IdsWO7g0A+PjoReQVV8mchoiI5GBRaSkoKIDRaISnp2e97Z6ensjLy7vpa728vKDRaBAcHIzY2FjMnj3b/NyPP/6I9evXY926dU3OEhcXB1dXV/PD29vbkrdCChPc0w0j/NxQbRRYuy9D7jhERCSDZp2IK0lSvT8LIRpsu15KSgqOHDmC1atXY9WqVUhKSgIAlJaWYtq0aVi3bh08PDyanGHx4sUoLi42P7Kzsy1/I6Qo8/+z2pJ0KAuFZXqZ0xARUVuzs2RnDw8PqNXqBqsqV65cabD6cj0/v9pv7h08eDAuX76Ml19+GVOmTEF6ejrOnz+PcePGmfc1mUy14ezscPr0afj7+zc4nkajgUajsSQ+KdyoPh4Y3MMVv+YUY2Paefy/qH5yRyIiojZk0UqLg4MDgoKCkJycXG97cnIywsLCmnwcIQT0+tp/KQcEBODXX3/F8ePHzY8HH3wQo0ePxvHjx/mxD5lJkoTY0bUFdmPaeZRWVcuciIiI2pJFKy0AsGjRIkyfPh3BwcEIDQ3F2rVrkZWVhZiYGAC1H9vk5ORg06ZNAID4+Hj4+PggICAAQO19W1asWIEFCxYAALRaLQYNGlTv7+jYsSMANNhOFDWgK/w765CeX44tB7IwN7LhKhwREdkmi0vL5MmTUVhYiKVLlyI3NxeDBg3Crl274OvrCwDIzc2td88Wk8mExYsXIzMzE3Z2dvD398fy5csRHR3dcu+C2g2VSsK8yN74fx/9jPWpGZgV3hNae7XcsYiIqA1YfJ8Wa8X7tLQf1UYTIt/Yg5yiSix9aCCeCO0pdyQiImqmVrtPC5E1sFerEBPRCwCwZm8Gqo0mmRMREVFbYGkhRZoU7A2PDhrkFFXis+OX5I5DRERtgKWFFElrr8bsUbWX0SfsOQejySY+5SQioptgaSHFejzEBy5aO2Tkl+Pb329+R2YiIlI+lhZSLGetPWaG9QQAxO85Bxs5p5yIiBrB0kKKNjPcD472avyWU4J9ZwvkjkNERK2IpYUUzU3ngKkhPgCA+B/OyZyGiIhaE0sLKd7To3rBQa3CofNXcSjzqtxxiIiolbC0kOJ1ddXi4SAvALVXEhERkW1iaSGbEBPRCyoJ2HM6H7/lFMsdh4iIWgFLC9kEX3cdxg3pDgBI3JMucxoiImoNLC1kM+q+8XnXb7lIzy+TOQ0REbU0lhayGQFdXXBvf08IAazmagsRkc1haSGbMm907WrLJ8dykFNUKXMaIiJqSSwtZFOG+XRCmL87akwC6/ZlyB2HiIhaEEsL2ZzY0b0BAEmHslBQppc5DRERtRSWFrI5Yf7uGOLdEfoaEzakZsodh4iIWghLC9kcSZIQ+58riTbvv4DiymqZExERUUtgaSGbdG9/T/T17IBSfQ22HLggdxwiImoBLC1kk1QqCfMia89tWZ+aiQpDjcyJiIjodrG0kM16ILAbfNyccLXcgK2HsuWOQ0REt4mlhWyWnVqFmIjac1sS96Yjs6Bc5kRERHQ7WFrIpj0c1AO9OuuQX6rHw4lp+CnrmtyRiIiomVhayKZp7NTYNicUg3u44mq5AVPXHcC3v+fJHYuIiJqBpYVsXmdnDbbOGYnR/TqjqtqEmC1HsZlXFBERKQ5LC7ULOo0d1j0RjMeGe8MkgBc//Q3//PoUTCYhdzQiImoilhZqN+zUKsRNHIxF9/UFACTuSceiD4/DUGOSORkRETUFSwu1K5Ik4S/39MHrjwRCrZLw6fFLmPnuIZRU8a65RETWjqWF2qVHg72xYeZw6BzUSEsvxKOr9yO3uFLuWEREdBMsLdRuRfTtjG3RoejsrMGpvFJMTEjD6bxSuWMREVEjWFqoXRvUwxU75obBv7MOucVVeGR1GtLSC+SORUREN8DSQu2et5sTts8Nw/CenVBaVYMZGw7hs+M5csciIqLrsLQQAejo5IDNT4VgzKCuqDYKPLP1ONbsTYcQvCSaiMhasLQQ/YfWXo34qcPwZLgfACDuq1N4+fPfYeS9XIiIrAJLC9EfqFQS/nfcALwwtj8kCXhv/wXM3XIUVdVGuaMREbV7LC1ENzB7VC+8PWUYHNQqfHviMqauO4Cr5Qa5YxERtWssLUSNGBvYDZufGgEXrR1+yirCI4lpyCqskDsWEVG7xdJCdBMhvdyxfW4YenR0REZBOSYm/ohfLhbJHYuIqF1iaSG6hT6eztgxLwz9u7mgoMyAyWsOYPepK3LHIiJqd1haiJrA00WLD6NHYlQfD1RWGzF70xFsPZQldywionaFpYWoiZy19tgwczgeHuYFo0ngbzt+xb+Sz/BeLkREbYSlhcgC9moVVkwKxF/u7g0A+Pf3Z/E/H/+CaqNJ5mRERLaPpYXIQpIkYVFUPyybMBgqCfj46EU8ufEwyvQ1ckcjIrJpLC1EzTQ1xAfrngiGo70aKWcLMHnNflwpqZI7FhGRzWJpIboN9/T3xNY5I+Guc8Dvl0owISEN566Uyh2LiMgmsbQQ3aYh3h2xY14Y/Dx0yCmqxMOJ+3Eo86rcsYiIbA5LC1EL8HXXYfvcMNzh0xHFldWYtv4gdv2aK3csIiKbwtJC1ELcdA74YPZI3DfAE4YaE2I/+AnrUzPljkVEZDNYWohakKODGqunBWH6SF8IAbz65Qm8+uUJmEy8lwsR0e1iaSFqYWqVhKUPDcTzfwoAAKxPzcSCpGOoqjbKnIyISNlYWohagSRJmBvpj1WTh8JeLWHnr7l4Yv0hFFUY5I5GRKRYLC1ErWj8HT3w3qwRcNbY4dD5q3hk9X5cvFYhdywiIkViaSFqZWG9PfDR3FB0ddHi3JUyTEhIw285xXLHIiJSHJYWojYQ0NUFn8SGoZ+nM/JL9Zi8Zj/2ncmXOxYRkaKwtBC1kW6ujvgwJhShvdxRbjDiyY2H8fHRi3LHIiJSDJYWojbk6miPjU8Ox0NDu6PGJPDXj37GW9+fhRC8JJqI6FZYWojamMZOjZWPDkVMhD8A4M3kM/j7J7+ixmiSORkRkXVrVmlJSEiAn58ftFotgoKCkJKS0ui+qampCA8Ph7u7OxwdHREQEICVK1fW22fHjh0IDg5Gx44dodPpMHToUGzevLk50YgUQaWS8LcxAVj60EBIEpB0KBtzNh9FhaFG7mhERFbLztIXbNu2DQsXLkRCQgLCw8OxZs0ajBkzBidOnICPj0+D/XU6HebPn4/AwEDodDqkpqYiOjoaOp0Oc+bMAQC4ublhyZIlCAgIgIODA7788kvMmjULXbp0wf3333/775LISj0R2hOeLlr8JekYfjh1BY+tPYD1M4ajs7NG7mhERFZHEhZ+mB4SEoJhw4YhMTHRvK1///4YP3484uLimnSMiRMnQqfT3XQ1ZdiwYRg7dixeffXVJh2zpKQErq6uKC4uhouLS5NeQ2Qtjl64htnvHca1imr4uDlh46zh6NW5g9yxiIhanSW/vy36eMhgMODo0aOIioqqtz0qKgppaWlNOsaxY8eQlpaGiIiIGz4vhMD333+P06dP46677rIkHpFiBfl2wva5YfB2c0TW1Qo8nJiGoxeuyR2LiMiqWFRaCgoKYDQa4enpWW+7p6cn8vLybvpaLy8vaDQaBAcHIzY2FrNnz673fHFxMTp06AAHBweMHTsWb731Fu67775Gj6fX61FSUlLvQaRkvTp3wI654Qj0csW1impMXXcA3/x+83lFRNSeNOtEXEmS6v1ZCNFg2/VSUlJw5MgRrF69GqtWrUJSUlK9552dnXH8+HEcPnwY//jHP7Bo0SLs2bOn0ePFxcXB1dXV/PD29m7OWyGyKp2dNdg6ZyTuDugCfY0Jc7ccxab95+WORURkFSw6p8VgMMDJyQkfffQRJkyYYN7+zDPP4Pjx49i7d2+TjvPaa69h8+bNOH36dKP7zJ49G9nZ2fjmm29u+Lxer4derzf/uaSkBN7e3jynhWxCjdGEFz/7DUmHsgEAMRH+eO7+flCpbv6PAyIipWm1c1ocHBwQFBSE5OTketuTk5MRFhbW5OMIIeoVjubso9Fo4OLiUu9BZCvs1CosmzAY/+++vgCA1XvT8eyHx6GvMcqcjIhIPhZf8rxo0SJMnz4dwcHBCA0Nxdq1a5GVlYWYmBgAwOLFi5GTk4NNmzYBAOLj4+Hj44OAgAAAtfdtWbFiBRYsWGA+ZlxcHIKDg+Hv7w+DwYBdu3Zh06ZN9a5QImpvJEnCgnv6oKurFot3/IrPjl/ClRI9Vk8PgqujvdzxiIjanMWlZfLkySgsLMTSpUuRm5uLQYMGYdeuXfD19QUA5ObmIisry7y/yWTC4sWLkZmZCTs7O/j7+2P58uWIjo4271NeXo558+bh4sWL5hvQbdmyBZMnT26Bt0ikbJOCveHposXcLUexP6MQj67ej3dnDUf3jo5yRyMialMW36fFWvE+LWTrfr9UjJnvHkZ+qR5dXbTY+ORwBHTl/+tEpGytdk4LEclnYHdXfDIvDL27dEBeSRUmJe5H2rkCuWMREbUZlhYiBfHq5ISPY0IxoqcbSvU1mPHuIXx2PEfuWEREbYKlhUhhOjo5YNNTIzB2cDdUGwWe2XociXvSYSOf9BIRNYqlhUiBtPZqvDXlDjx1px8A4J9fn8L/fvY7jCYWFyKyXSwtRAqlUkl48YEBeGFsf0gSsPnABcRsOYpKA+/lQkS2iaWFSOFmj+qFt6cMg4OdCsknLmPqOwdwtdwgdywiohbH0kJkA8YGdsOWp0Lg6miPY1lFeDgxDRcKy+WORUTUolhaiGzECD83bJ8bih4dHZFZUI6JCWn4ObtI7lhERC2GpYXIhvTu4oxP5oVhYHcXFJYb8NjaA/jh1GW5YxERtQiWFiIb08VFi23Robirb2dUVhsx+70j+OBg1q1fSERk5VhaiGxQB40d1s8IxiNBXjAJ4O+f/Io3vz3Ne7kQkaKxtBDZKHu1Cm88Eoi/3NMHAPDWD+fw149+QbXRJHMyIqLmYWkhsmGSJGHRfX2xfOJgqFUStv90EU9uPIzSqmq5oxERWYylhagdeGyED955IhiO9mqknC3A5DUHcLmkSu5YREQWYWkhaidGB3TBtuiR8OjggBO5JZiYkIazl0vljkVE1GQsLUTtSKBXR+yYGw4/Dx1yiirxcGIaDmYUyh2LiKhJWFqI2hkfdydsnxuGYT4dUVJVg+nrD2HnL7lyxyIiuiWWFqJ2yE3ngA+eHomoAZ4wGE2I/eAnvJOSIXcsIqKbYmkhaqe09mokTgvCE6G+AIDXdp7E0i9OwGTivVyIyDqxtBC1Y2qVhFceHIjFYwIAABt+zMT8pJ9QVW2UORkRUUMsLUTtnCRJiI7wx/89NhT2agm7fs3D9PUHUVRhkDsaEVE9LC1EBAB4aGgPvPfkCDhr7XD4/DU8nJiG7KsVcsciIjJjaSEiszB/D3wUE4purlqk55djYmIafsspljsWEREAlhYiuk5AVxfsmBeGgK7OyC/VY/Ka/dh7Jl/uWERELC1E1FA3V0d8GBOKMH93lBuMeHLjYXx4JFvuWETUzrG0ENENuWjtsXHWCIwf2h1Gk8BzH/+C//vuLITgJdFEJA+WFiJqlIOdCv96dCjmRvoDAFZ+dwaLd/yKGqNJ5mRE1B6xtBDRTalUEp7/UwBefWggVBKw9XA2nt50BOX6GrmjEVE7w9JCRE0yPbQnVk8LgtZehd2n8/HY2gPIL9XLHYuI2hGWFiJqsqiBXfHB0yPhpnPArznFmJj4I9Lzy+SORUTtBEsLEVlkmE8nbJ8bBl93J2RfrcQjiWk4euGq3LGIqB1gaSEii/l56LB9bhiGeLniWkU1pq47iK9/y5M7FhHZOJYWImoWjw4aJM0ZiXsCukBfY8Lc94/ivbTzcsciIhvG0kJEzebkYIc104MwZYQPhABe+vx3xO06CZOJ93IhopbH0kJEt8VOrcKyCYPwP/f3AwCs2ZeBhduOQ19jlDkZEdkalhYium2SJCF2dG+8OWkI7FQSPv/5EmZsOITiymq5oxGRDWFpIaIW83CQF96dNRwdNHY4kHEVk1an4VJRpdyxiMhGsLQQUYsa1acztkWPRBdnDc5cLsOEhB9xMrdE7lhEZANYWoioxQ3s7opPYsPRp0sHXC7R49HV+/HjuQK5YxGRwrG0EFGr6NHRER/HhGGEnxtK9TWY+e4hfHLsotyxiEjBWFqIqNW4Otlj05MjMDawG6qNAs9u+xnxu89BCF4STUSWY2kholaltVfjrcfuwNOj/AAAb3xzGi9+9huMvJcLEVmIpYWIWp1KJWHJ2AH43wcGQJKALQeyEL35KCoNvJcLETUdSwsRtZkn7/RDwtRhcLBT4buTlzFl3QEUlunljkVECsHSQkRtaszgbnh/dghcHe1xPLsIDyem4UJhudyxiEgBWFqIqM0N7+mG7XPD0KOjI84XVmBiQhqOZxfJHYuIrBxLCxHJoneXDvgkNgwDu7ugsNyAx9bux3cnLssdi4isGEsLEcmmi7MW26JDcVffzqiqNmHO5iN4/+AFuWMRkZViaSEiWXXQ2GH9jGBMCvKCSQBLPvkNb3xzivdyIaIGWFqISHb2ahVefyQQz9zTBwAQvzsd/++jn2GoMcmcjIisCUsLEVkFSZLw7H198c+HB0OtkrDjpxw8ufEwSquq5Y5GRFaCpYWIrMrk4T54Z0YwnBzUSD1XgEmr9yOvuEruWERkBSRhIx8cl5SUwNXVFcXFxXBxcZE7DhHdpl8vFmPWxsMoKNOju6sWT97pB0mS5I5F1K6NC+yGLi7aFj2mJb+/7Vr0byYiaiGDvVzxybwwzNhwCBkF5Xht50m5IxG1e3f4dGzx0mIJlhYislrebk7YPjcMCXvO4Uopb/dPJLdOTg6y/v0sLURk1TrpHLBk7AC5YxCRFeCJuERERKQILC1ERESkCM0qLQkJCfDz84NWq0VQUBBSUlIa3Tc1NRXh4eFwd3eHo6MjAgICsHLlynr7rFu3DqNGjUKnTp3QqVMn3HvvvTh06FBzohEREZGNsri0bNu2DQsXLsSSJUtw7NgxjBo1CmPGjEFWVtYN99fpdJg/fz727duHkydP4oUXXsALL7yAtWvXmvfZs2cPpkyZgt27d2P//v3w8fFBVFQUcnJymv/OiIiIyKZYfJ+WkJAQDBs2DImJieZt/fv3x/jx4xEXF9ekY0ycOBE6nQ6bN2++4fNGoxGdOnXC22+/jSeeeKJJx+R9WoiIiJTHkt/fFq20GAwGHD16FFFRUfW2R0VFIS0trUnHOHbsGNLS0hAREdHoPhUVFaiuroabm1uj++j1epSUlNR7EBERke2yqLQUFBTAaDTC09Oz3nZPT0/k5eXd9LVeXl7QaDQIDg5GbGwsZs+e3ei+f/vb39CjRw/ce++9je4TFxcHV1dX88Pb29uSt0JEREQK06wTca+/lbYQ4pa3105JScGRI0ewevVqrFq1CklJSTfc7/XXX0dSUhJ27NgBrbbxu+4tXrwYxcXF5kd2drblb4SIiIgUw6Kby3l4eECtVjdYVbly5UqD1Zfr+fn5AQAGDx6My5cv4+WXX8aUKVPq7bNixQosW7YM3333HQIDA296PI1GA41GY0l8IiIiUjCLVlocHBwQFBSE5OTketuTk5MRFhbW5OMIIaDX178l9xtvvIFXX30VX3/9NYKDgy2JRURERO2AxbfxX7RoEaZPn47g4GCEhoZi7dq1yMrKQkxMDIDaj21ycnKwadMmAEB8fDx8fHwQEBAAoPa+LStWrMCCBQvMx3z99dfx4osv4oMPPkDPnj3NKzkdOnRAhw4dbvtNEhERkfJZXFomT56MwsJCLF26FLm5uRg0aBB27doFX19fAEBubm69e7aYTCYsXrwYmZmZsLOzg7+/P5YvX47o6GjzPgkJCTAYDHjkkUfq/V0vvfQSXn755Wa+NSIiIrIlFt+nxVrxPi1ERETKY8nvb5v5lue67sX7tRARESlH3e/tpqyh2ExpKS0tBQDer4WIiEiBSktL4erqetN9bObjIZPJhEuXLsHZ2fmW94yxRElJCby9vZGdnc2PnRSKY6h8HENl4/gpX2uOoRACpaWl6N69O1Sqm1/UbDMrLSqVCl5eXq12fBcXF042heMYKh/HUNk4fsrXWmN4qxWWOs26Iy4RERFRW2NpISIiIkVgabkFjUaDl156iV8ZoGAcQ+XjGCobx0/5rGUMbeZEXCIiIrJtXGkhIiIiRWBpISIiIkVgaSEiIiJFYGkhIiIiRWBpISIiIkVgaSEiIiJFYGlpYXv27MHu3bvljkG3gWOobBw/5eMYKl9rjSFLSwuKj4/HvffeiyVLluCHH36QOw41A8dQ2Th+yscxVL7WHEPeXK6FXLx4EVFRURg3bhwyMjJQVFSEv/3tb7jnnnvkjkZNxDFUNo6f8nEMla+1x5ClpYVUV1ejqKgInTt3RnJyMv7973+jqqqKE05BOIbKxvFTPo6h8rX2GLK03AYhBCRJMv/ZZDJBpar9xO3777/HqlWrOOEU4I/jyDFUFs5B28A5qGxtOQ95TkszGY1GSJKEiooK7Nu3D0IIqFQq1HXAe+65BwsXLoRWq8Xy5cvx/fffy5yYrmcymQCg3mRTqVSorq4GwDG0dpyDysc5qHxtPQ9ZWprBaDRCrVajpKQEPj4+2Lt3r3nSSZLEH5oKUPcvgbKyMrz44otYuHAhlixZgsuXL8Pe3t68H8fQOnEOKh/noPLJMQ/58ZCF/jhIgYGBGDp0KD799NMG+xkMBjg4OADgEqe1KisrQ2BgIPz8/KBSqVBQUIDz588jPj4e48aNg7Ozs3lfjqH14By0HZyDyiXbPBRksZKSEuHv7y/Gjx9v3vbDDz+ILVu2iA8++EBUVVUJIYSoqakxP//tt9+KBx54QNx///3im2++afPM1NBzzz0nRo0aJYQQorq6WtTU1Ii5c+cKnU4nVq9eLcrLy+vtzzG0HpyDtoFzUNnkmIcsLc3wz3/+U0iSJL766ishhBAzZswQw4YNE126dBG+vr7C19dXnD17VgghhMFgML/uxIkTonfv3uLPf/6zuHr1qizZ6b/mzJkjpk6dKoSoP6kWLVokdDqdeXw5htbn9ddf5xy0AZyDyibHPGRpaYI/TqY6M2bMEG5ubiIyMlKMGDFC7N+/X2RnZ4tTp06Je++9V/Tu3VtUVFQIIYQwmUxCCCE+++wzIUmS2LlzZ5vmpxv7y1/+Ivz8/Mx/rvtXgRBCTJs2Tfj4+Jj/pccxtD5PPPEE56DCPfPMM5yDCtfWvwtZWm6hrrCUlZWJrVu31ntuxowZwtXVVRw8eLDe9v379wsPDw/x7bff1jvOd999J77++uvWD031GI3GG24/efKkGDBggIiJiTFPprofmmfPnhXe3t71xotjKI/Gxk+I2uLCOWj9GhvDEydOcA4qhLXMQ7vbPBfHptWdaFRaWorAwEBcuHABgwcPxoABAwAAGzduxM6dO+Hv7w/gv9eqq9VqODo6wt3d3XwstVqNu+++u96lfdT66sawvLwcb731Fi5cuICRI0fi0UcfhZ+fH2bMmIEPP/wQf/vb3xAXFweNRgMAcHR0hL29vfleAwDHUA6Njd9DDz2Ejh074r333uMctHJ1Y1hZWYl9+/bBYDDAx8cHQ4YMQZ8+ffDkk0/i/fff5xy0YjcaQ19fXwQGBgIA3nvvPXzxxRdtMg9ZWhrxxzOjBw4ciJ49e6Jbt27Ytm0bXnnlFfMZ0WPHjjW/pm4QDh06hM6dO8PDw6PeMTnR2pYQwlw6Q0JC4Obmhg4dOmDNmjUoLy/HvHnzMGfOHBQVFeGbb77BhQsX8M4776C8vBzff/89ysvL0bVr13rH5Bi2nZuNX0JCAmJiYgCAc9CK/XEMw8LC4ODggOrqapw+fRoLFy7E/PnzERsbi+LiYnz55Zecg1boZmO4aNEiTJ06FYMHD8a4cePMr2nVedis9RkbV7dMWVRUJPz8/MSjjz4qhBBiwYIFwtfX17x8WbdfnQsXLoj4+Hjh5OQktm/f3rah6YYMBoP405/+JB5//HHziWDPPfecmDFjRr2P/jZs2CAGDhwonJycxKBBg4S7u7tISkqSMzqJm4/fjXAOWh+9Xi8iIiLE1KlTRVFRkSgpKRHr168XkiSJyZMni5MnT4rKykrxzjvviEGDBnEOWqGbjeHUqVMbfCzUmvOQpaURVVVVokuXLvUu5Tp//rzw8fERy5Yta7D/2bNnxZIlS4SXl5f4+OOPhRANSw21vStXrojhw4eLzz77zLzt73//uxg/fryYNGmSePXVV8WxY8eEELUTc+vWrSI5Odm8jWMor1uN37Jly0RaWpoQQoj09HTOQSt04cIFERISIo4cOSKEqD2nITc3V/j7+4vOnTuLGTNmiMrKSlFdXS0qKirEtm3bOAetzM3GsEuXLmLWrFmisLBQCCHEmTNnWnUesrQ0Ij8/X2zevLnetrKyMvHoo4+K++67r8FJSVVVVWL//v3i559/FkLUDhInm/zS09OFk5OT+Ne//iUuX74sPvnkE2FnZydmzJgh5s+fL/r16ycmTpwoLl26JHdUuoFbjV9AQIB45JFHRH5+vqisrOQctEI//fSTkCRJ/Pjjj+ZtZWVl4qGHHjJfMvvBBx/ImJBupSljWHehSmvPQ5aWJvjjf/T9+/cLSZLEhx9+KHMqaqq6++rce++9wsnJSaxcudL83MGDB4WdnR0vn7RiTRm/L774Qr6AdFMmk0k8/PDDYujQoeKrr74SP/30kxgwYIAYM2aMEEKIefPmiYkTJwqDwcCSaaWaOoZ/vGS9tfC7h5pAkiTz9ygMHToUDz30ED766COUl5ebv1uBrE/d2Dz33HM4fvw4EhMTMXDgQNx9992oqamByWRCr169MHDgQJ7cZ4UsGT+1Wi1zWmqMJEmIjY1Fnz59MG7cOEycOBEDBgzAzp07AdR+B1FBQQHs7e05D61UU8ew7sqv1sSrhywgSRK0Wi3+9Kc/4X/+53+QlZWF/v37yx2LGlFXNCVJQmBgILKysnDx4kUYjUbY2dX+r//ll1/i6tWr8PX1lTktXY/jZztGjx6Nu+66CydOnIBKpcLAgQPNz2m1WgwdOhQmk8n8D0SyPtYyhu36CxPrfiD+Ud03j95q/8DAQAwcOBBJSUmtnpMaV3dp+o3G8kYef/xxfP7553jyySdRVVWFDz/8EGvXrsWkSZPaIC1dz9I5yPGzPpbOwTqFhYX4/PPPMX/+fHzyySeIiopqxZR0M5bOwzpyjGG7/XjIaDRCkiTU1NSgsLAQxcXFAACVStXoRz51g2oymTBhwgRER0e3WV5qqO6HZXFxMV544QXk5+ff8jXLli3D/PnzkZaWhsrKSmzbtg2TJk3ix3wyaM4c5PhZl+bMQQAoKCjAa6+9hhdeeAEbNmxAVFQUx1AmzZmHgHxj2C5XWuoaZElJCR577DFkZ2fDwcEB/fr1Q0JCAjp27HjLY9Q1U0v/dUEt4483/+vXrx+Cg4PxxRdfNLr/9eNUUVEBBwcH2NnZmScax7HtWDoHOX7Wx9I5eL0jR45ACIHhw4dzDGVyu78L5RjDdllaAKCyshIjRoxAv379MHnyZGRnZ+O9995DWVkZkpKSMGLECPO+dQOr1+vb5EQjurm6X2AlJSUIDAxEcHAwPv744xvuW/eDtW4Mq6qqoNVq2zgx3Uhz5iDHzzpwDtoOxc3DVr02yYqlpKSI/v37i/Pnz5u3FRYWivvuu094eXmZrzGvu2vqoUOHxMMPP8xL8qxEVVWV6N69u7j77rvN2/71r3+JWbNmiQkTJoiXXnrJvJ1jaJ04B5WNc9A2KG0etttzWvLz85GVlQVPT08AQE1NDdzc3PDll1+iT58+mDJlivk7F+qe37FjBzZu3ChjaqpTUVEBDw8PFBcXIz8/H0888QTWr18PtVoNtVqN999/HxEREQDAMbRSnIPKxjloG5Q2D9tFaTEajQBql7bqREZGwt3dHa+99hoAwM7ODkajEQ4ODnjnnXdQU1ODN954A0DtUmhoaCheffVV5Ofnm49Hbef6MezUqRO+//57qFQqeHp6IiMjA9u3b8e6devw0UcfISEhAVlZWVi5ciUAjqHcxA0+hY6MjISbmxvnoEJcP4Z1c1CSJM5BBakbx7qfpUqbhzZfWuo+Ty0tLUVkZCR++uknAIBGo8Hjjz+OvXv34r333gMA82V7vr6+8PHxQXp6OoD/nlh0//33Y/r06byRVRtrbAw9PDywc+dOTJs2DTNnzkS/fv3MEzIyMhKurq4cQyvwx6sTSktLzVcnaLVaTJs2jXNQAa4fw6KiIgC1c3DXrl2cgwpQV1KuP1HWwcEB06ZNw+7duxUxD2365nJ/PLs9NDQUJ0+exNq1a7Fq1So4OTkhOjoav//+O959911UV1dj9uzZkCQJarUaPj4+0Gg0EELAZDJBrVYjODhY7rfU7jQ2hv/3f/8HBwcHdO7cGW+//Xa9M9frbnDk6+uLvn37AvjvCWQcw7ZVN3dKS0sxbdo05Ofn4+zZs1i/fj0efPBBzJ49m3PQyjU2hhs2bMC4ceM4BxWg7r99WVkZXn31VRQXF0OtVuOpp57CsGHDEBsbixMnTmD9+vVWPw9tdqXlj7/sBg4ciEGDBuHFF1/Ed999h6qqKgCAr68v3nzzTXh6eiIhIQEzZ87E559/jn/84x/YsmULxo0bZx44ans3G8PKykrzJecuLi5wdXU1v06lUmHLli04ePAgQkJCzNuo7dX9oBw+fDgcHBwQExODRx55BDNnzkRBQQF69eqF119/HZ6enoiPj+cctEKNjeGMGTNw9epVAICzszPnoBWrG8Nhw4bh1KlTqKmpQXZ2NkaMGIF//etf8PT0xL///W9069bN+n8XtuVZv22tqKhIdO/eXUyaNEkIUfvtk126dBHPPfdcvf1ycnJEQkKCGDJkiOjTp48IDg4W27dvF0Lwa9Hl1tQxrPPzzz+Ll156Seh0On6ppZV4/vnn611hcvbsWXH//feLq1evisuXLwshar9VPTExUQQGBnIOWqHGxvDatWuioKCg3r7Hjx/nHLQyJpNJxMbGigceeMC8Ta/Xi8jISKHT6cTLL78shKj9XRgfH2/VvwttsrRUVFQIIYSYOXOmePDBB83b9Xq9WLBggRg1apS4cuWKMJlM5su46ly9elUUFRUJIfjV9nJq6hhe/5o1a9aIBx54QHz22WdCCOuZaO1N3fgZjUbx+OOPi9mzZ5ufe//994Wzs7MICgoSHTt2FM8//7y4du2a+XnOQetgyRj+/e9/Fzk5OaKyspJz0IqUl5cLIYQwGAxi7Nix4rXXXhNC1P7jTwghnn32WREWFiZUKpX44IMP6r3WWuehzZWW1NRUMXLkSFFTUyMyMjLM2+v+gx84cEDY29uLLVu21Hud0Whs05zUuOaOoRBClJaWiry8PPP+1jLR2pO68av7b//8888LZ2dnsW7dOhEXFyfUarVYtmyZOHjwoNiwYYNwcnISGzdulDk1/dHtjGFJSQnnoBVITU0VISEh5v/+kydPFvfcc0+933X9+vUTX331lViwYIHw8fERRUVFVj9eNldaysvLxejRo8WRI0caPFc3GLNnzxZ33nmneWmarAvHUNnKy8vFPffcIw4cOCCEqP0X29NPPy3+/Oc/i5CQEDFnzpx6+z/wwAPi/vvvb7DqSfJp7hgaDAY54tINXD+GSUlJIiwsTISGhop//vOfomfPnmL06NFCCCEOHz4s/Pz8RGZmpoyJm8bmzoyquzRv+/btDZ6ru1zrrrvuwqlTp5CdnQ2g/v1bSH4cQ2UzGo0QQuDTTz8FUHs/j7Vr1+LLL7+En58f+vfvDwDmE+JdXFwwcOBA6zjJjwA0fwzt7e3likzXqRvDTz75BAAwadIkPPvss/D29sbevXsxYcIE/PDDDwAAvV6P6upqZdw7R+bS1CqSk5NFx44dxVdffdXoPqNGjRL3339/G6YiS3AMla1u/Hbt2lVv+4IFC8Qdd9whysrKRElJifjggw+Em5ub+O6772RKSo3hGCpf3Rh+8cUX9bbXna9U54033hDDhw9vcJ6gNbLJ0lJZWSmeeuop8cADD4ijR4/We67u87w33nhD9OvXT2RnZ8sRkW6BY6hsjY1fcnKyCA0NFRqNRoSFhYmuXbuarzCx9s/S2xuOofL9cQz/+HF73c/QQ4cOiRUrVghHR0fzVULWzma/5fns2bN46qmn4O/vj2eeeQZDhw4F8N+b7OTl5SEzMxOhoaHyBqVGcQyV7Y/j95e//AV33HEHhBA4d+4cvvrqK3Tq1AkDBgxAUFBQm32tPVmGY6h8jf0cBYCvvvoKzz33HJYuXYoJEyaYv73bmtlsaQGAX375BfPmzYObmxvmzp2LMWPGyB2JLMQxVLY/jl90dDTGjh1rfk4JPyCJY2gLbvZzNCcnBz169FBM6bS5E3H/KDAwEBs2bEDnzp0xdepUvPLKKzhy5IjcscgCHENl++P4TZs2Da+88goOHz4MwPp/OFItjqHy3ejn6MGDBwEAPXr0AFA7lkoYT5teaalTVVWFb7/9Fm+++SZMJhP69euH1157DV27dpU7GjURx1DZOH7KxzFUPlsYw3ZRWuqUl5ejrKwMv/32G0JCQtChQwe5I5GFOIbKxvFTPo6h8il5DNtVaSEiIiLlsulzWoiIiMh2sLQQERGRIrC0EBERkSKwtBAREZEisLQQERGRIrC0EBERkSKwtBAREZEisLQQERGRIrC0EBERkSKwtBAREZEi/H95E//WRMC3kQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(share_moments.values())\n", + "plt.xticks(range(len(share_moments)), share_moments.keys(), rotation=45)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1341642/4146709120.py:8: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " .apply(weighted_median, var=\"wealth_income_ratio\", weights=\"weight\")\n" + ] + }, + { + "data": { + "text/plain": [ + "age_group\n", + "(25,30] 0.731227\n", + "(30,35] 0.943020\n", + "(35,40] 1.795652\n", + "(40,45] 2.158729\n", + "(45,50] 3.092055\n", + "(50,55] 4.271946\n", + "(55,60] 5.254823\n", + "(60,65] 6.300603\n", + "(65,70] 7.420071\n", + "(70,75] 9.049109\n", + "(75,80] 10.122164\n", + "(80,85] 9.787650\n", + "(85,90] 11.361794\n", + "(90,95] 9.056685\n", + "Name: 0, dtype: float64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def weighted_median(data, var, weights):\n", + " dsw = DescrStatsW(data[var], weights=data[weights])\n", + " return dsw.quantile(0.5, return_pandas=False)[0]\n", + "\n", + "\n", + "temp = (\n", + " scf_data_full.groupby([\"age_group\", \"wave\"])\n", + " .apply(weighted_median, var=\"wealth_income_ratio\", weights=\"weight\")\n", + " .reset_index()\n", + ")\n", + "\n", + "temp.groupby(\"age_group\")[0].median()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1341642/3494005669.py:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " scf_data_full.groupby([\"age_group\", \"wave\"]).apply(\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "scf_data_full.groupby([\"age_group\", \"wave\"]).apply(\n", + " weighted_median,\n", + " var=\"wealth_income_ratio\",\n", + " weights=\"weight\",\n", + ").unstack().plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "estimatingmicrodsops", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/notebooks/WarmGlow.ipynb b/src/notebooks/WarmGlow.ipynb new file mode 100644 index 0000000..a2c9e42 --- /dev/null +++ b/src/notebooks/WarmGlow.ipynb @@ -0,0 +1,178 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "***NOTE: using a 'quick fix' for an attribute error. See 'Error Notes' in EstimationParameter.py for further discussion.***\n" + ] + } + ], + "source": [ + "from __future__ import annotations\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from HARK.utilities import plot_funcs\n", + "\n", + "from estimark.agents import BequestWarmGlowLifeCycleConsumerType\n", + "from estimark.calibration import parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "DiscFacAdj, CRRA = np.genfromtxt(\n", + " \"tables/WarmGlow_estimate_results.csv\",\n", + " skip_header=1,\n", + " delimiter=\",\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "indshk_agent = BequestWarmGlowLifeCycleConsumerType(**parameters.init_consumer_objects)\n", + "\n", + "indshk_agent.CRRA = CRRA\n", + "indshk_agent.DiscFac = [b * DiscFacAdj for b in parameters.timevary_DiscFac]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "indshk_agent.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_funcs([sol.cFunc for sol in indshk_agent.solution[:-1:5]], 0, 20)\n", + "plt.savefig(\"../content/figures/WarmGlowIndShock_cFunc.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the variables we want to keep track of.\n", + "indshk_agent.track_vars = [\"aNrm\", \"cNrm\", \"pLvl\", \"t_age\", \"mNrm\"]\n", + "\n", + "indshk_agent.T_sim = 200\n", + "# Run the simulations\n", + "indshk_agent.initialize_sim()\n", + "history = indshk_agent.simulate()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "raw_data = {\n", + " \"Age\": indshk_agent.history[\"t_age\"].flatten() + 25 - 1,\n", + " \"pIncome\": indshk_agent.history[\"pLvl\"].flatten(),\n", + " \"nrmM\": indshk_agent.history[\"mNrm\"].flatten(),\n", + " \"nrmC\": indshk_agent.history[\"cNrm\"].flatten(),\n", + "}\n", + "\n", + "Data = pd.DataFrame(raw_data)\n", + "Data[\"Cons\"] = Data.nrmC * Data.pIncome\n", + "Data[\"M\"] = Data.nrmM * Data.pIncome" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Find the mean of each variable at every age\n", + "AgeMeans = Data.groupby([\"Age\"]).median().reset_index()\n", + "\n", + "plt.figure()\n", + "plt.plot(AgeMeans.Age, AgeMeans.pIncome, label=\"Permanent Income\")\n", + "plt.plot(AgeMeans.Age, AgeMeans.M, label=\"Market resources\")\n", + "plt.plot(AgeMeans.Age, AgeMeans.Cons, label=\"Consumption\")\n", + "plt.legend()\n", + "plt.xlabel(\"Age\")\n", + "plt.ylabel(\"Thousands of USD\")\n", + "plt.title(\"Variable Medians Conditional on Survival\")\n", + "plt.grid()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/src/notebooks/WarmGlowPortfolio.ipynb b/src/notebooks/WarmGlowPortfolio.ipynb new file mode 100644 index 0000000..90ad3da --- /dev/null +++ b/src/notebooks/WarmGlowPortfolio.ipynb @@ -0,0 +1,240 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import annotations\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "from HARK.utilities import plot_funcs\n", + "\n", + "from estimark.agents import BequestWarmGlowLifeCyclePortfolioType\n", + "from estimark.parameters import init_calibration\n", + "from estimark.snp import snp_data, snp_data_full" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "csv_file_path = \"../../content/tables/TRP/WarmGlowPortfolio_estimate_results.csv\"\n", + "res = pd.read_csv(csv_file_path, header=None)\n", + "res = res.set_index(res.columns[0])[res.columns[1]].to_dict()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(9.206775856414323, 23.05054873023735, 45.64298427855443)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "portfolio_agent = BequestWarmGlowLifeCyclePortfolioType(**init_calibration)\n", + "\n", + "portfolio_agent.CRRA = float(res[\"CRRA\"])\n", + "portfolio_agent.BeqCRRA = float(res[\"CRRA\"])\n", + "portfolio_agent.BeqFac = float(res[\"BeqFac\"])\n", + "portfolio_agent.BeqShift = float(res[\"BeqShift\"])\n", + "(\n", + " portfolio_agent.CRRA,\n", + " portfolio_agent.BeqFac,\n", + " portfolio_agent.BeqShift,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "portfolio_agent.update()\n", + "portfolio_agent.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_funcs([sol.cFunc for sol in portfolio_agent.solution[:-1:5]], 0, 20)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_funcs([sol.ShareFuncAdj for sol in portfolio_agent.solution[:-1:5]], 0, 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the variables we want to keep track of.\n", + "portfolio_agent.track_vars = [\"aNrm\", \"cNrm\", \"pLvl\", \"t_age\", \"mNrm\", \"Share\"]\n", + "\n", + "portfolio_agent.LivPrb = [1.0] * portfolio_agent.T_cycle\n", + "\n", + "portfolio_agent.T_sim = portfolio_agent.T_cycle\n", + "# Run the simulations\n", + "portfolio_agent.initialize_sim()\n", + "history = portfolio_agent.simulate()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "raw_data = {\n", + " \"Age\": portfolio_agent.history[\"t_age\"].flatten() + 25,\n", + " \"pIncome\": portfolio_agent.history[\"pLvl\"].flatten(),\n", + " \"nrmM\": portfolio_agent.history[\"mNrm\"].flatten(),\n", + " \"nrmC\": portfolio_agent.history[\"cNrm\"].flatten(),\n", + " \"Share\": portfolio_agent.history[\"Share\"].flatten(),\n", + "}\n", + "\n", + "Data = pd.DataFrame(raw_data)\n", + "Data[\"Cons\"] = Data.nrmC * Data.pIncome\n", + "Data[\"M\"] = Data.nrmM * Data.pIncome\n", + "\n", + "# Find the mean of each variable at every age\n", + "AgeMeans = Data.groupby([\"Age\"]).median().reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(AgeMeans.Age, AgeMeans.nrmM, label=\"Market resources\")\n", + "plt.plot(AgeMeans.Age, AgeMeans.nrmC, label=\"Consumption\")\n", + "plt.legend()\n", + "plt.xlabel(\"Age\")\n", + "plt.ylabel(\"Thousands of USD\")\n", + "plt.title(\"Variable Medians Conditional on Survival\")\n", + "plt.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(AgeMeans.Age, AgeMeans.Share, label=\"Portfolio Share\")\n", + "plt.plot(\n", + " snp_data_full[\"age\"],\n", + " snp_data_full[\"share\"],\n", + " label=\"S&P 500\",\n", + " linestyle=\"--\",\n", + ")\n", + "plt.plot(snp_data[\"age\"], snp_data[\"share\"], label=\"S&P 500 (retirement)\")\n", + "plt.legend()\n", + "plt.xlabel(\"Age\")\n", + "plt.ylabel(\"Fraction of Savings\")\n", + "plt.title(\"Warm Glow Portfolio Share Median Conditional on Survival\")\n", + "plt.ylim(0, 1)\n", + "plt.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/src/notebooks/WealthPortfolio.ipynb b/src/notebooks/WealthPortfolio.ipynb new file mode 100644 index 0000000..116d180 --- /dev/null +++ b/src/notebooks/WealthPortfolio.ipynb @@ -0,0 +1,299 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import annotations\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from HARK.utilities import plot_funcs\n", + "\n", + "from estimark.agents import WealthPortfolioLifeCycleConsumerType\n", + "from estimark.parameters import init_calibration\n", + "from estimark.snp import snp_data, snp_data_full" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "csv_file_path = \"../../content/tables/TRP/WealthPortfolio_estimate_results.csv\"\n", + "res = pd.read_csv(csv_file_path, header=None)\n", + "res = res.set_index(res.columns[0])[res.columns[1]].to_dict()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(5.335577372664163, 1.0, 0.1706005756625005, 0.0)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "portfolio_agent = WealthPortfolioLifeCycleConsumerType(**init_calibration)\n", + "portfolio_agent.CRRA = float(res[\"CRRA\"])\n", + "portfolio_agent.WealthShare = float(res[\"WealthShare\"])\n", + "# portfolio_agent.WealthShift = float(res[\"WealthShift\"])\n", + "(\n", + " portfolio_agent.CRRA,\n", + " portfolio_agent.DiscFac,\n", + " portfolio_agent.WealthShare,\n", + " portfolio_agent.WealthShift,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "portfolio_agent.update()\n", + "portfolio_agent.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_funcs([sol.cFuncAdj for sol in portfolio_agent.solution[:-1:5]], 0, 20)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Qdqj49DdQCCGEEN+4kAswX6yBCTS3tv+ctx3DUwaYw0iVR0pOa9uEEEIIcXqEfIDxNXmCPze88CzefbmAGWBqiktPa9uEEEIIcXqEXoD5wjTqQIsZYAK6H1X3EzYoG4BIWwwN5WWnu3lCCCGEOA1CL8B8cQipxWv+aQSwucOJ+78rAXBZ3TTXVp729gkhhBDimxdyAUbHwDDaQ8yxAKMZAVwZXbBGOwEIs0Tg8zR0OFYIIYQQ3w8hF2AMQNO04O+B5uN6YKJcqBE2DDRURcWp6rQ2+r+llgohhBDimxKCAcbA5/MGf/e1ePHgY7UznzKXA0VVUBzmZGuXaqWuvOnbaqoQQgghviEhF2B0DHzeluDvgRY/BZZKSm0NrLJb8Hq9WGPMBe3CrVGUH5ap1EIIIcT3TcgFGAOD1ubG4O9aqx8fAQA8QG5uLvaUaADCrW4qDhz+FlophBBCiG9SyAUYFPA0NAd/1T0B/Ep7TcyaNWvQos2VeV3WKGoKi057E4UQQgjxzQq9AAN4juuB0X0aftoDjMfj4bm8VwGzB6axtvq0t08IIYQQ36wQDTDt2wcYPg1/2xBSvN3cjdrTYODBT7g1Co+n8YTfIYQQQojQFZIBxtfSXsRr+PXgEFKWO5x6ez0Ww8JuaxFh1ig0rRlfa+DbaqoQQgghvgGhGWBa26dREzCCPTBFrUUcdu+ja8Z2amN2YlEsOJUA9ZWtJ/kmIYQQQoSikAwwAW/7Bo5oRrAHZl/TAXKSisnI2EH64PcJ2OtwqSo1h8u/pZYKIYQQ4psQmgHG4wv+rGpKsIi3kUYGu9t7W5rc+bisEZTtyj/tbRRCCCHENyc0A4y/fXsAxVDai3gTmomwtM9IagwvItzqprpQFrMTQgghvk9CM8D42otyVV0JDiF1T+4446g1ophwq5v62prT2j4hhBBCfLNCM8D423tZVFQCaNjtLXSNNot7PZ5zzeNcZbisUbQct/WAEEIIIUJfSAYYPdDeA6MYKn5FIynpIKoCbvdwIsLHmp+FV+GyRuHXPR1CjxBCCCFCW2gFGKPtj8BxYURRAEhILAAgLfUK4iIzMAxQbR4cLgVDb6DuiKzIK4QQQnxfnFKAefLJJ8nMzMTpdDJy5Ehyc3P/4/F1dXXceuutpKSk4HA46NmzJ4sXL+70dZW2P3VND76ntb1pt5vDRFGRA4lb+wAeT4R5bGQldnxU7yro9PWEEEII8d3U6QAzd+5cZs6cyb333suWLVsYNGgQkydPpqKi4oTH+3w+zjnnHAoKCnj33XfZt28fc+bMIS0t7RQaa6YVQzOC7xltPTAWizmsZKkvo665mF2+wQC0hB8114I5KGvBCCGEEN8X1s6e8PDDD3PTTTcxffp0AJ555hkWLVrEiy++yF133fWl41988UVqampYu3YtNpu5S3RmZuYpNVZpCzDHemB0TcdQFRRFQ1XN99SDa5gy9FlaLU7+ahzFEV6EyxpDdbEMIQkhhBDfF53qgfH5fGzevJlJkya1f4GqMmnSJNatW3fCcxYuXMioUaO49dZbSUpKon///jzwwANo2smLar1eLw0NDR1eZmPbemAMswdG9/rwKVqw9wWg9PBWWi1OADYxAm94KeE2N3X1DZ25VSGEEEJ8h3UqwFRVVaFpGklJSR3eT0pKoqys7ITnHDp0iHfffRdN01i8eDF//OMf+ec//8n9999/0uvMnj0bt9sdfKWnpwPmonXQPoTka2rFTwCLxVzYTsfC1ub2Re4O0h09vJxwaxRNfh9CCCGE+H74xmch6bpOYmIizz33HEOHDuXKK6/kD3/4A88888xJz5k1axb19fXBV1FREdBexGu01fD6mz340doDjKGyJbJ38HuO0hU1rJ4whwuvouGvb/pG7lEIIYQQp1enamDi4+OxWCyUl3csiC0vLyc5OfmE56SkpGCz2bBYLMH3+vTpQ1lZGT6fD7vd/qVzHA4HDofjS+8rKBjH/R5o8eJXAsEhJEMz2BLZN/h5jRJPsxFOrNuLUd1Izc58ksbmdOKOhRBCCPFd1KkeGLvdztChQ1m+fHnwPV3XWb58OaNGjTrhOWPGjCE/Px9db5/6vH//flJSUk4YXv5zY9uGkHQzxgRavB16YAJ+2BnZo8M5R0nHEtWIodVTlXe0U9cTQgghxHdTp4eQZs6cyZw5c3jllVfYs2cPM2bMoLm5OTgr6brrrmPWrFnB42fMmEFNTQ2//OUv2b9/P4sWLeKBBx7g1ltv7XRjlWML2bX9bvbAtBfxFhnpeFUHMVYLE9xh5ntk4I0sw0oTNUdkTyQhhBDi+6DT06ivvPJKKisrueeeeygrKyMnJ4clS5YEC3sLCwtR1fZclJ6ezscff8wdd9zBwIEDSUtL45e//CW/+93vOt3YY9OoaSvmDbT6OvTAHFZ6AjA4ykWfiDBW1LdSRDqt4ZtwqdFUVjR3+ppCCCGE+O7pdIABuO2227jttttO+NnKlSu/9N6oUaNYv379qVyqg2NFvMe6YDSPHz8BVNWckn3QYgYYe1OAfSXV4DQLef3hHxJu7Updq//LXyqEEEKIkHNKAebbohgdf9dafQQUDaVtEbuDVjPALF9bhOLTYXQiRXRFd1XhskVQpSsEamuxxsSc7qYLIYQQ4msUUps5BoeQODaE5MeHhqLoNBJBmSUVALXeh9LsRzEMmpVI6i2RhEepaDRRs3Xft9R6IYQQQnxdQizAmI51xOieAH4lgKroHMTsfYnRQQkYKDrYWszF64rIwBntR9drqNhVePobLoQQQoivVUgFmGByaSvi1bwafswhpHzM6dNq/XF1Lo3mz0WkY4tuxdBqKD984k0nhRBCCBE6QirAtA8htfFp+AmgKAb5bT0wDWXmTCO7RUWvORZgMghEVaNoDdRUyZYCQgghRKgLrQDzhXVgdL9OQNFQFY0S0sxj6syA4tN0LOUewFzMzhdeQqQtnOaAHeO4RfWEEEIIEXpCKsAEKWZPjOI3zCEkxcBP26q+vvZdrhW/GVSKSccXXkGkLY5muw1fodTBCCGEEKEspALMF4t4Dc3Ar2goqkbg2Ixw7bgTDFB9AXyKg0p7FFER4QTUFuq27z+NrRZCCCHE1y20AkxbcjlWC6O1DQWpihEMMKrWcbEYe3ULYBbyhscFMLQaCrbLVGohhBAilIVWgGn781hE0do2dVQU3QwwhsGxPa8dVvPWlJr2qdSOuCYMrZaqQtkTSQghhAhlIRVggsmlLcnoxrEuGQNdsaC0ami6gd2icukQs6jX0mz20hylK1p0JYZeQ0tDSC1ALIQQQogvCKkA094D0zaE1JZojGNFvW0zkPqmRjEgLRqACN28xSLS8boLcakKXiUavVk2dhRCCCFCVUgGmGAPTFtRjN4WYNR6M8DkpEfTKzkSAK1tVlIZqXhcNUQ5o2kMc9K8f+9pa7cQQgghvl4hFWAIrgPzxR4Y8zbUBnPhukHpbnomRQDQ2BrA0uJDVyyUqim441X8qo9Duf/77thCCCGE+HaEVID5wjq8aMf3wOgGSoPZAzOwSzSRThtp0WEAhNd6AbOQ15XgxdBqKc0rOF3NFkIIIcTXLKQCDB1nSBPALNDVFQWlyY+iQ6TTSre4cAB6tw0jxfrM6HOUrlhjG9D1GlrLAqev3UIIIYT4WoVUgFG+ULQbUNt6YFQ1uInjwC5uVNX8/FgdTIxhTq4+QiZadDmGVoPuicQwvpCIhBBCCBESQirAHM/QDALKcT0w9e3DR8ccCzB6i9nbcpgsApGl2GnF40ikrjD/9DZaCCGEEF+LkAowwc0cFdD9Afxt+wZoitpewHtcgOmdHAVAcVULiq7TqLipskQTG2+lKcxN/qZPTmv7hRBCCPH1CKkAc3wRjObxE8DsWdFUFcVrhpn02DBoLIMFt5HlycNmUWjyBkhsNQNOAVlEJeh4rRrl23ec/lsQQgghxP8spALM8evABDxe/Ep7Dwxt2wo4rCq8MhW2vobto1+TFW9Op87QzFs9TDZh8V4MrQZvfv3pvgUhhBBCfA1CKsC0rwMDAY8PX9sQUkBVg585dC9Ute02Xb4zWAcTr9sAOEQ2Smwthl6DoyoMvW1DSCGEEEKEjpAKMG2TjzAU0Dw+/MqxISRLsAfGVb2zwzk5sebQUaCpvZBXd7fNRLIlU7hbFrQTQgghQk1IBZjjd3PUvH4Cx3pgFDVY4Oss29ThjBz7UQDKqluwGDpNShRV9ggcYVU0RGZwYNUHp6vxQgghhPiahFiAMbtgDMVA8/qCs5B8hi14hL1sc4czsvQCAA5XNtHFMI8/TDYxiT4aIpJp3rTlNLRbCCGEEF+nkAowwYXsAN0bINBWxOtTjgUYA2tJWw9M9kQA3A37iHBY8WsGmW2FvIfIJjIhgEY9jkIV3ZA6GCGEECKUhFSAOcaAtiEkM3j4DTsA6UoFSnMlqDYYfDUASvnu4MaOSW2FvIfJwhHfgh4ow0Im+/I3nP6bEEIIIcQpC6kAc6yIFwU0n4YWDDBmMBmqHjA/TxkEaUPNnyv30SfJBYC9bU+kw2RDTA16oJR6dxYHPlt42u5BCCGEEP+7kAowxxaCMQDd528LMHqwBmaopS3ApI+A6AywR4LmY2RkFQAVVa1YDJ1mJZIqVxiKtYj6qG40bcw9/fcihBBCiFMWUgHmWAeMgYHf48NQDFRVx982NDREOS7AKAok9QPaZyLtOFpHWsDcM+kwWbhiq/HYHUQc9ODX/Kf1XoQQQghx6kIrwCjHZiGB1+Nre88MMGF46KUUmgd2GWH+mdwfgDTvIexWlboWPxn6sWGk7sQkWtADZUT6MtlZKL0wQgghRKgIrQDT1loD8AcDjIHfsDJIPYQVHaLSwJ1mHtjWA2OpzGNAmhuAOJ8VMHtgIhM0dK2Uhqhu7FsldTBCCCFEqDilAPPkk0+SmZmJ0+lk5MiR5OaevPfi5ZdfRlGUDi+n03lqrQ1W8ULAZ66sa1F0ArqdHoo5TERKTvvxSWYPDGW7GJwebf7cav5xmCyscY0YgVIaorJoyl17am0SQgghxGnX6QAzd+5cZs6cyb333suWLVsYNGgQkydPpqKi4qTnREVFUVpaGnwdOXLk1Bqrms01FAO/36xZsWDg160kKbVtF0ttPyGxr/lnUxkjkswZS0crWrDoGs1KJJWRNgxKaAhPw32wgRZ/yym1SwghhBCnV6cDzMMPP8xNN93E9OnT6du3L8888wwul4sXX3zxpOcoikJycnLwlZSUdEqNVSxtNTCA5jcXsbMoZoBJpM48KDK5/QRHBMR0A2CIowSAvaWNpHi9ABxSsnEnedCNWpIa09lcLOvBCCGEEKGgUwHG5/OxefNmJk2a1P4FqsqkSZNYt27dSc9ramoiIyOD9PR0LrroInbv3v0fr+P1emloaOjwAlDammtgENCOBRidgGElUakzTz4+wECwkDeuaT+JkQ4CukGqx+yNOUwW8V1BD5TQ6spi7+r5X/VRCCGEEOJb1KkAU1VVhaZpX+pBSUpKoqys7ITn9OrVixdffJEFCxbw73//G13XGT16NEePHj3pdWbPno3b7Q6+0tPTgfYeGB3QjgUYDAL6cQEm4gsBpq0ORinfTU5bHYw7WMibjSulEV0rod7djaaNsjO1EEIIEQq+8VlIo0aN4rrrriMnJ4ezzjqLefPmkZCQwLPPPnvSc2bNmkV9fX3wVVRUBIDFYjEPUAw03exFURUzwMQojeZn4XEAVLVWoelaeyFv+S4Gd40BINB6bCp1FkpcJQpHqY/KIvpwIzWemq/7EQghhBDia9apABMfH4/FYqG8vLzD++Xl5SQnJ5/krI5sNhuDBw8mPz//pMc4HA6ioqI6vABUq9lcHY4LMDoB3YIDX9sFXKwtXsuEtyfw+NbHg1OpqdzL4C7mnkgFZU1YdZ0WJYISNYXwxAp8VoVulfHkHl3zVR+HEEIIIb4lnQowdrudoUOHsnz58uB7uq6zfPlyRo0a9ZW+Q9M0du7cSUpKSudaClis5tCPgYHeFmAsaGi6BQdtK+la7Dy06SEAXtz1IlpUl+CWAgOdlagKlNZ76N5WyLuTHOLSDfRAKbq9G3lr3+50u4QQQghxenV6CGnmzJnMmTOHV155hT179jBjxgyam5uZPn06ANdddx2zZs0KHv/nP/+ZpUuXcujQIbZs2cI111zDkSNHuPHGGzvdWOtxAUYzzACjoKMZFuyY68IYFge1ntrgOXvr9kOSOZ3aVbuXXslmb062Zm4/sJNBRKQ2owdKaIjKomH7rk63SwghhBCnl7WzJ1x55ZVUVlZyzz33UFZWRk5ODkuWLAkW9hYWFgbXawGora3lpptuoqysjJiYGIYOHcratWvp27dvpxtrsdug2dxKQDcM8z10DE3BpphFvQWeCqo91cFztldup19SPyjaAGU7Gdy1F3tKG3AHbIDBHvqhxzWAUkS9ezxJhV5KGotJjUzrdPuEEEIIcXqcUhHvbbfdxpEjR/B6vWzYsIGRI0cGP1u5ciUvv/xy8PdHHnkkeGxZWRmLFi1i8ODBp9RYi83sNTEwggFGUTQsbTOSADZUbe9wzo6qHZA80PyleHNwJtLRihYiAj68ipP9Si/CEw/T6EqkR7GDDfkfnFL7hBBCCHF6hNReSDa7GWB0DHTaamAUHUtbPQzA+vItAAxPHg7A9ort0LWtPufoJoakhQOw82g9gzCDz05yiOnix9CrCDMy2bbtw9NyP0IIIYQ4NaEVYNr2UDIw0JW2Hhg0LJoZYPyo5JZvBODGATeioHC06SjV4XEQFgOBVrL8B4l0Wmn1a/S3hQFmHUxUWit6oJT6qCwa9x3BaOvhEUIIIcR3T0gFGHuYHTC3EjjW56IoGjbDLODd6XTR6Gsk0hbJyOSRZLmzzPdrdgd7YdSidcFhpARcABQoWTTGGChqAfXubmQc0dheuOq03ZcQQgghOie0AozTHP4xh5Dae2BsbTUw68PMHpXhycOxqBYGJpi1Lzsqd7QPIxWuC+5MfbiilZTWZgB2KwNxJR6gLjKT4ftg0aYnT9dtCSGEEKKTQirAOMLMHhNzCMl8T1ECWHUzwOQ6zR6aM1LPAOgYYDJGmycUriMn3ZxKva2oliFmWQ07GYQ7tYGA6semJHN4x278Ad/puC0hhBBCdFJIBZiwCLOHBYVgDQyKhl3341EUdjrM2zkjpWOA2Vm1Ey2pP1jDoLWWIa4qAA5WNjM2Jto8hhwi0zwYWgn1UVkM2mfw+bY5p+/mhBBCCPGVhVSAsTvDgj9rSttCdkoAmx5gq8OOT1FIdCWSGZUJQLY7G5fVRUughYNNhdBlGADRlRvJjDN7c1KcbqxagDolhtKYWBTrYWpiejNyr8GivLmn9waFEEII8ZWEVICxRDiDPweOlfGqAex6gIK2NWIGxA9AUczxJYtqYUD8AOALw0hH2gt595a10K3Z3AhyJ4NwJeylOq4/YX4HNfnVNNQdOQ13JoQQQojOCKkA42hbBwbA37byLkoAmxGgsW3132hHdIdzTlbIOzQzFoC1B6sZ0Tb0tIMcIlMq0FSoju3H8D0Gy9b/4xu8IyGEEEKcipAKMDaHI/izn2MBxo/D8AcDTKQ9ssM5HQJMl+GgWKC+iIkp5maOm4/UcmZSPAD76IMjTUcPlFGROIQR+w0+KvwMZE0YIYQQ4jslpAKMxda+ddOxHhhd1bAbARotJw4wx4aQDtYfpEHRIWUQAKn12+ieGIGmG+haBBHeZvyKnUPuDCy2A1TF9ccRcBAo1Cg58NHpuD0hhBBCfEUhFWBUVaVt+RcCbT0wmhUcuo+Gk/TAxIXF0SWiCwC7qnZ1mE49oVcCAGsO19Gj0ayD2UEOEck7MVQbVXEDGL3HYNGWp77pWxNCCCFEJ4RUgAFQMAt0jw0haRawGz4aVfP9YwHGCAQw/H7gi3Uw5hRrjqxjQq9EAFbuq+QMh9m7s5NBhCUUo2v1VCQOZdgBgyW1BzFaak/PDQohhBDivwq5AKO2BRijbR0YXQWH4QvWwETZo9A9Ho5cfQ0Hxk8gUFsbDDDbK4/b2LFyD8MSIdxuoarJS6/oeBRD56jSFa1LGLp/P9WxfbFpTtxFKns2Si+MEEII8V0RcgFG+cLvhkXBcdwspEh7JHVvv0Pr9u1o1dU0r1rFoASz7mVH5Q4MVxzE9wTAXpLLmO5mAW9Jq53ERrOXZU9kb5xR2zFUK5XxAxm9x2DhvnelmFcIIYT4jgjBANMxwmgWswemWTFvxWV10fBRe9Ft05o19IrphV210+Br4FD9oeOmU69lQm9zGGn1wVp6Npn7Iu0kh8iMfHStjorEIQzNN1iheQgU5Z6GOxRCCCHEfxNyAUb9QoAxrAp2w4+3rQbGXtVA69atwc+b167DqlgYnDQYgPWl648r5F3P+LZC3q1FdQx1mtO0dzKQ6J6N6IG91MT2xWKEkVGgsnr9o9/w3QkhhBDiqwi5AKN8YRRHU1UchrkXEoDy2QYAnAMHorhcaFVVePftY3SqGVrWlqxtL+Qt2UpKmEHv5EgMA2KjkrAH/DQpURSHpxOetBFDsVAZP4gxeww+qtwEnobTdq9CCCGEOLHQCzBf6IHRVRUrPgJtASbwySoA3BdeSPiIEQA0r1kTDDAbyzbii0yByFTQA1C8KTiMtLvaIKPBrIPZzmDc3QrQtVoqEoYw+KDBBtVG47Z/n5b7FEIIIcTJhXSAUQwFv2rFqvgAiG0w8G/fCUDk5HMJHzMGMOtgesb0JNYZS2ugle1VOyCjrQ7myNrgdOpV+ys5q22/pdWchbtbExi7qI3tDUo4/fMVlmx58XTdqhBCCCFOIgQDTDsLCj7FidIWYM7Ya44vhQ0dii0pifCxZoBp3bQZWj2MSjVDy9qStZA5zvyS/R8zpGs0kU4rtS1+xianYwv4KVNS2WftQ1TGBnMYKWEQY/IMlit1ULIVIYQQQnx7Qi/AGO0RRjUUfDhQVHPButH7zAATNXkyAPbMTGypqRh+Py2bNnWsg+k1BVCgZAvWxmLO7GkW8+aV+xhUXwPACiYR3a0IXauhPGEIAwoMdhkO9q/81+m6XSGEEEKcQOgFmON+Vg0FP3YUxU9sg0HPo2aAiZx8rnmsogSHkZrXrGFUitkDs6d6DzU2W/t06j0fBIeRVuyr5KqkGAA2cgakqljsm6iN6YVmjWDEPlhZ/ik0VZ6GuxVCCCHEiYRggGmPMBbMHhgUPwMK2oaPBg3ClpQUPCZ87FgAmlavIcGVQI+YHhgYbCjdAH2nmQftWchZbT0wO4vrGdu7J/GNdfgVO6s5i+isXFBUKuNzGJOn87o7jOLF95ymOxZCCCHEF4VggGln9sA4QNVwec33bGmpHY4PP2MkqCq+gwfxl5YyOuW4YaQ+U82DCteTQB0Du7gB2HCkkfGt5qJ2n3IO7qwSdK2S8sQh9C4CmlUWFS/CqNj7Td6qEEIIIU4i9AKM0fFnv2HDUPw4/W3vhYV1ON7idhM2YAAAzWvXdqiDMaLSIG0oYMDeDxjf1guzcl8l1/fphlULUKykU+jOwuFeTV10D/z2KMbtNnjFHcHBeb/5xu9XCCGEEF8WegGGjkW8fsOGoWo4/GayUcNcXzqnfRhpNUOShuCwOKhoqTC3FejTNoyUt5DxbevBrDpQycDePehZVQaYxbwx2VswUKiMz2FaLng1laWe7bTs+/SbvF0hhBBCnEAIBph2qgF+w46uaDjaemDUth6YvDUlfPbGPprrvcFC3pa163AoNoYmDQXahpGO1cEUrGZQrE6My0ajJ8D24kamtm1PsJ4x2DKbQCmlMn0UkS065202eNUdSeHC34Kun5Z7F0IIIYQp9ALMF4aQNKzo6nEBxhVGc72Xla/vY9eqYla+vo+wgQNQIyPR6uvx5OV1nE4dmwXJA8DQsOxfHCzm/Xh3GVeMHEhMcwM+xcEGxxjCkz6lNiwdr93NxRtA9ysstldS8vnLp/kpCCGEED9sIRdgjqca4NdtZg+MuZYdSlgYe9aWYuhm0incXY3XaxB+hrn/UfPq1cEF7TaVbcKn+aDPRebJexYydZBZBPzu5qPEJqcwtPK4YaSsHRgGVPU+l/BWnfM3GbwRFUHj6gcwfM2n8c6FEEKIH7aQCzAdthLQDfyGBU3R24t4HWHkrS4JHqNrBoe2VQbrYBpXrKRHdA/iw+LxaB62VmxtH0Y6uILxGQ7SY8Oob/WzcHsJP4qNxaJrFChZVKbFoNoOcyTlLDTVxkUbFSw+hXeiAuyZ9+BpewZCCCHED13oBZgOQ0gGAcOKpujBIaTylggaqz04XFaGTckEIH9TOZFnTwBFwbNjB4GKio7DSAm9IL4X6H4s+Uu57gzzvJfXFjBu3FC6VZqBaIU6iZis5Xi8CmUDLyasVeOCjTrvRUZgy3+exqqjp+sxCCGEED9opxRgnnzySTIzM3E6nYwcOZLc3NyvdN5bb72FoihcfPHFp3LZL1EMHU1TCKhGcBZSfrG5GWOvkcn0HpUMwNG9tfgcbsJycgBoXL68475I0N4Lk7eAHw1Lx2lT2VvWyIF6g7PqG81jGYcj8wCGoVOQeBYBi4Npm1QcHng52sm+t/7wtdyXEEIIIf6zTgeYuXPnMnPmTO699162bNnCoEGDmDx5MhUVFf/xvIKCAu68807GjRt3yo2FjrOQFDTQFDyKgsMPXnsURaXmEX3HpeJOcJGYEYlhwMEtFUROmgRA0yefcEaKWROzt2Yv5c3l7dOp8z/BbfFyyeA0AF5ZW8DFPbJwtzbiUVxsjR5MVPJmvD6FskGX4fBoTM3V+TAiHHfdIg7lbfyf7k8IIYQQ/12nA8zDDz/MTTfdxPTp0+nbty/PPPMMLpeLF1988aTnaJrG1VdfzZ/+9CeysrL+pwZ3GEIigKKBV1Fx+KE0eRSGASnZbuJSIwDoMdzcVuDApnIiJ00EoDl3IzE+G4MTBwOw6PAicyZSTCYEPJC/jOtHZwKwZHcZCf1607e0CDCLeSO6LMUwDAriRxOwOJm6WSW8FZ6OdVM3fxaGcVwjhRBCCPG161SA8fl8bN68mUltPRkAqqoyadIk1q1bd9Lz/vznP5OYmMhPf/rTU29pmw49MGoAVTOCPTBlScMBs/flmO5DzcXpSg/W44tKwtGjBwQCNK1cyUXZ5uyjhfkLMaDDona9k6MY2S0WTTd4b3s5U3ygGhr5Si9q0iA86gg+n0Lp4B9h82pM22CwLNyFk22sWfrO/3yfQgghhDi5TgWYqqoqNE0j6bjNEgGSkpIoKys74TmrV6/mhRdeYM6cOV/5Ol6vl4aGhg6voOM6N1TFj8Uw8KoKDr9Ca5gZVrr0igkeExHjJKW7GwzI31xB5Dlm+Gr85BPOzTwXh8XBwfqD7K7eDX3bplMfWAp+T7AX5s3cQs4YMoCMtpV5P3eeTUTXt81emJiR+K1hTNmi4G42eCImmsR199PQ4vnK9yuEEEKIzvlGZyE1NjZy7bXXMmfOHOLj47/yebNnz8btdgdf6enpwc+O74ExCGDVdLwoqEYkhmpBUcAVZccwDI7s3IavtYUew44NIx1XB/P5asI1K2d3PRuABfkLIHUIRKWBrwkOfsq5fZNIcTupavKx3xpNTnkpAKs5k7DuRUTE7sPvh9KhP8Hm07h4Pax2hdHkKGPF3Mf/x6cnhBBCiJPpVICJj4/HYrFQXl7e4f3y8nKSk5O/dPzBgwcpKChg6tSpWK1WrFYrr776KgsXLsRqtXLw4METXmfWrFnU19cHX0VFRSc8TlH8qLqBX1fwO6IBcEXZUC0qWxYv4N377+bjZ58ge0giigIVBQ144jOxpaZieDw0r1kTHEb6qOAjfEagfYfqPQuxWlSuOSMDgFfXHWFqcjIRnhaalUg2Os4gMvMdDEPnSNRQ/NZwztsKMY0Gj8W6GVnwJPuKTtwrJYQQQoj/TacCjN1uZ+jQoSxfvjz4nq7rLF++nFGjRn3p+N69e7Nz5062bdsWfE2bNo0JEyawbdu2Dj0rx3M4HERFRXV4HdOhiFfxYdU0dE3B2xZgImKc+D0eNsw361AOrF9DwFdPWtuw0sEtxw0jLTNnIyWGJVLvrWfV0VXtdTD7FoPfw4+Hp2O3qGw/Wk/MwCEMKD0CwHtciTOrjKjE7fj9UDL8aix+jcvWK2xxOjno8rD1rT/jC8g+SUIIIcTXrdNDSDNnzmTOnDm88sor7NmzhxkzZtDc3Mz06dMBuO6665g1axYATqeT/v37d3hFR0cTGRlJ//79sdvt/2Pr/Vh0HT2g4LObIcfldrBt2WJaG+oBMAydHZ8saZ+NtLGCiInmbKTGlStRNZ0Lsy8EYMHBBdD1DHCng6cetr1OXISDCwelAPD2tgp+npBAuKeFKiWRjy0XEJn1rtkLEz4Iny2Cidt04hoMHo9xc3HT2zzz5jsyK0kIIYT4mnU6wFx55ZX84x//4J577iEnJ4dt27axZMmSYGFvYWEhpaWlX3tDj1GOCwOK6sdmaBgBhYDFXMDOYtXZuPA9ALKHjQRg98plZA6IRbUoVBc34UntjSUmBr2+npZNm4LDSKuPrqbGVw+jf2FeYM1joAW4oa2Y98MdJQwcM5KzjuQDsIDL0DJ8RKetIxCAkhHXYQnoXLFeJc/hYFmEjcvyZ/HKJ5u+sechhBBC/BCdUhHvbbfdxpEjR/B6vWzYsIGRI0cGP1u5ciUvv/zySc99+eWXmT9//qlc9ksMix+rrkFAQbM4AKgrWU9rQz3RSSlccPtvCIuMoqm2htIDO+jaNxaA/K1VRJw9AYDGT5aTFZ1F/7j+BIwAiw8thsHXgise6o7A7nkM7BLN4K7R+DWD97aWcXOfniQ11OBVwniHq4jKno9hBDji7IfXHsX4bRoJdQZ/iY/HZ6+n9+e/4OMdJ67jEUIIIUTnhd5eSMf/bPFj083VeDWLA8PwU3ZwBQAjL70Sm8NJ37PM4aKdny6le9tspPxNxw0jLV+OYRhM627Wviw4uADsLjhjhnmR1Y+Argd7Yf694QgDRw3l/KLDAKxiAhUpscRmfoqmQcnI61E1nZ9vjaVVhZmJiQyy7KX03d+w82j9N/x0hBBCiB+GkAswHdg0rHoAxQ+axYHm3UbA20x0Ugp9x5k9LAMmnAvAoS25JGaoWGwqdeUteDIGobpcBMrK8OzaxfmZ52NVreyt2cu+mn0w/EawR0JFHuxfwvn9U4iPcFDe4OWTvZXcMG4UPcqLMBSV15hOVI/FGIaXI7beeBzRDNxYQ+9mN/l2Kw/ExXCD+hFvv/RPSutbv80nJoQQQnwvhF6AOb4e1qZh1wMobTUwmncnYPa+qBYLAHFd0knt2QdD19m//jMyB8QBcHBHLeFnnglA48cfE+2MZnyX8QAsPLgQwqJheNvKwasfxm5RuGpkV8DcH6nXwD5cVFmJVQuwT+nLjtgBJPRahK5D8Rk3gKZx7yfR2HSF+ZERzIsI5/eBp/nr83Np9ga+6ackhBBCfK+FXIDpMITkVLAZGpYAaFYHht4MQFrvvh3OGXC22Quza8XS4NYCBzZWEDllCgB1781D93q5qLtZzLvo0CICegDOuAUsDji6EQpWc/XIrlhVhY0FteSVNnDlhZMYVGQW9L7JdYR1XwVKM0WWHnjjumLJO8hDuwYA8Nf4eA47DH5Xfz+/f30lmi4zk4QQQohTFXIB5nhKhB2r4cPmVwiodsAPgMMV3uG4nqPGYg8Lo66sFKu1DJvDQmONh+aMIVhTU9Bqa2lYtJgxaWOIdcZS7almbclaiEyCIdeaX/L5P0mKcnL+AHNK9d+X7KNLZjpXeAOEe1uoVJJYHnEuSf3eQ9ehfNpvAUhdtIX/q+qLTzG4IzmFKGs1lx++lwcX7Txtz0kIIYT4vgm5AKMY7X0wtvAwrIYXhx8CbUNGAA6XC4DWvTU05ZZiczjpPfosAPI+/4SswQkAHNhcRcxPfgJAzb9fw6pYmdLN7JVZkL/A/LLRt4NigUMroGQrd0zqgd2i8tn+Sj7cUcq0y85n1KE95jlchpaVh2Kp5eARFdu1ZiHw+W8eJMebRIkF7kpMYIxlF3Eb/s4bGwq/wSclhBBCfH+FXIA5VgOjGOB2RqPgw+EHv8UMNharDYvVRsv2Cqpf3k3dvHy8+XXBYaQD69fQbUCk+fPmciIvvhTF4cCbt4fWrVuDw0grilZQ762HmAwYcLl50c8fJishglsndAfgTx/kYQmP4oqIGBIbavAoYcxzXknKoLcwdIOt6ijsQ4djNDXz+w/sROh2Voc5mBMdxc3WD1j3wRxWH6g6jQ9PCCGE+H4I2QBjQSXCGYmKD6ffQGu7E5vThdboo25B+z5LjZ8Xk5Tdg/iumQT8Puort+OKsuNtDlBSouOeZu5/VPPaa/SO7U3PmJ74dT+LDi0yv2DsHeafez6Ayv3cPD6L7IRwqpq8PLhkL5MuncxZB/MAc1p1VWYdTncFFUcaKZw4E0tsLBw4zMPb+gHwZEw0a51O/mZ5lodff5/8isZv/rkJIYQQ3yMhF2DUtiarhkKTMxFV8WH3g6aYPTAOl4va+fnoLQEUpzms5N1fS6Cipb2Y99OP6T7cLObdt6GcmGuuAaBx6TL8ZWVc1uMyAF7Y9QKegAcS+0CvCwAD1jyKw2rhgUvM4tw3cwvZU+Xlsm496d42rfp16w2kDXsLgLzcGry3PAiKQvTHG5lZORQD+F1KMvXWAA/rD/GLl1ZQ3eQ9Lc9PCCGE+D4IvQCjmKHEgkp0lKttCElBU82umXRXLzy7q0FVSPjZQML6mdOmGz8vps+4CVhsNiqPHCYhzVyPpWBHFUrXbFzDh4OmUfvmW1zW8zJSwlOoaKngjb1vmBceN9P8c8dcqCtiZFYcVw4zN6OcNW8nwyafydkFh7DqAfYq/dgSE07vMeZu1GtzNWw/NXtxRr2+nTN93ahDZ2ZqGqlqOb9t+gczXs3FG9BOyzMUQgghQl3IBRjFae55pBoKKW4riuLHEbCB4cOhhtFTGQxA5IR07KkRRJzZBYCWbRXYDQc9RowG4GjeamJSwtECOoe2VhBzrdkLU/f229gCcNvg2wB4fufzZi1Ml2GQOQ70AKx9AoBZU3oTF27nQEUTL68vYtqIkQwqbJtWrV5HveVfpHR3EvDpbGjsj2P0mRitHm5/z0MCkeyywt/j45lg2c64kue5672dsvGjEEII8RWEXICxh5l7HllQ8Ok6Cn7sfieG4SU9vA827FiTXERNMHtH7F0jsadHQsCgaX1pcBhpz+rP6D40BjCHkSLPPrvDlOoLul1Az5ieNPoaeX7n8+bFj/XCbHkVmquIdtn544XmmjOPf5pP7IB+nF1eTrjPnFa9KnkU9rjHcLnt1JW3sm/oz7AkJqIXFPHP9d3BMJgb6eLDcBe/sM6nZft8Hvp4n4QYIYQQ4r8IuQATGd+267Si4jc0UPzYNAcYPtz2eADC+sahWM1bUxSFiHFpADSvL6FLj764E5PwtbagKocAKN5fS321r8OUalVR+dWQXwHwxp43KG0qhawJkDoYAq2w/mkALspJZVyPeHwBnT8u2M2USRMZedCcVj2fy2lNz6fLgGWoqsKhnfU0/HQ2WCw4V2zkzxVjAfhzYhIHbDb+aXuadZ8t4RdvbqXVJ8NJQgghxMmEXICJyjaLb5Uw8OoaKAHsugPD8BJlM+tdbImuDueE9YvHEuNAbw7Qsq2K/m37I+XnriBjQBwYsOXjI0RffnmHKdVj08YyPHk4Pt3Hv7b9CxQFxrb1wmx4FuqPoigK91/cH4dVZU1+NXlKNBNa/cFp1f9Sf4035h16jioBYOMGL+pNswDo89paLvb3pxWNmWnpoHp5034/2q75XP7MWorrZN8kIYQQ4kRCLsBk9euLoih0HzQAv6ZjKAEsugMMLxE2c0jImhDW4RzFohAxxuyFaVpdTN+zzkZRVIr37qbnMDsA+9aX0aI7O0ypVhSFmUPNwPLBwQ/MTR57Xwhpw8DXCPNngK6TERfO7RN7APDXxXuYePnFTN6/C3vAzz6lL6+G3UiL80HS+wbQdYN1ZZnYzp6C4fdz7RvldFMTKVAC/LFbXxyKn6ftjzGu/N9c9MTnbCyoOS3PVQghhAglIRdgUlNTufPOOznvvPNo1TUMRcNqmD0wDtUMLmq47UvnhQ9PQnFYCFS2Yqu00G3wUABKD6yjS+8YdN1g69LCL02p7h/fn3MzzsXA4LEtj4GqwqXPgc0Fh1fBBnMo6aZxWfRMiqCm2cfTueXMmDKFc/M2oRg6q5SzWR53DkT/BXeiheY6L7uyfoy1SzpaSSmzP0vFqlj4xGjk3j6j8QN32d7iTu+TXDdnNW/myoq9QgghxPFCLsAAhIeHo6oqnoCBrmqohhMVDYtqBUANM/+sqKhgyZIllJeXozqshI9MBqBp1VEGnD0ZgLxVnzLkXHOm0p41pWhJmR2mVAPcPuR2rIqVz4s/Z2PZRojLhsl/NRvzyZ+gPA+7VWX2pebaMO9sPkq5M4af9RnImHxzz6O5XENely5Ed3sOi12lOL+R8h//BcVmQ129icdKz0ZVVN73HOXWfmNoVC382LqSOeqDzJ63nnsW7MKv6afnAQshhBDfcSEZYI5pDRjoioaKA5tiztwxAMVu4ejRozz77LOsX7+ed955B13XiRidBip4D9XTJakPLnc0LfV1tDYeICXbjRbQ2fpJYYcp1VpjIxlRGVzW01zc7uFND5uzhIZOhx6TQfPCvJ9BwMvQjFiuGtkVgD+8v5NBZ5/B9fYI+hUfwlBUnuaXlKaXkzn4MwC2b2omcPN9ACS8upRnEm4nzBrGupYiru8zjFJnJGMtu3nXfh+frt/IdS/kUtPsO70PWQghhPgOCvkAo6Gj4MB+bI9HO5SVl/Hvf/8bTTNn8lRVVbFjxw6s0Q7CBpgbObasK6Pf+EkA7FqxlKFTMgHYvaoY6/Cx2DMy0GprqXzkEQBuHnQzYdYwdlXvYtmRZWZB77QnwBUH5TthxQMA/O683iREOjhU1cxTKw4y5dqL+Ul5FV1qyvEqTh5WZ1GbtJjMQWZR7/qD8VimXAGaRuy9z/FS8m+ID4vnQEsp12RmsTc6hZ5qMfPt99J6eAPT/rWavWUNp+kJCyGEEN9NIR5gAF1DszixKeat1NpbePXVV/F4PKSnp3PmmWcCsHLlSgKBAJFtU6pbtlfSb/gEAAq2bSEmUSOhayQBn86Oz0pJ/tN95ve9+RYtW7cSHxbPDf1uAODxrY/j1/0QmQRTHzcbs+YxOLIWd5iNe6eaa8M8vfIg+ZUtXH3zNVyxfw8xzfXUKnE85vwtnvhHievixdsSYGvsFOyDh6E3NGD51V94yXYT2e5sKry1XJ8QxerU3sQr9cx13M+A+pVc+tRaluwqPT0PWQghhPgOCvkAowSMYIDxE2CptpXW1lbS0tK4+uqrGTt2LBEREdTV1bFlyxbsXSKxZ0aBbmA9bNClb38MQ2fniqUMa+uF2bHyKJYBQ3FffDEYBmX33Ivh93N9v+uJdcZypOEI8/bPMxvR50IYfA1gwLyfg6eBCwakMKFXAj5NZ/pLudT6FaZf+xMu2rWZML+HAiWbl+NuxNn1bzhcBlVHWyg87y4iz5mE4ffTOusvPFl2DiOShtMSaOU2p5d3u4/EgY+n7Y9xnfY+N/97M48s24+uy6J3QgghfnhCOsB4NTA0nYDFjt1i5bBaSZPhISoqimuuuQan04ndbmfcuHEArFq1Cp/PR8ToVACac8vImXgBAJsXLSApw0psajh+j8bOlUdJ/N1vscTE4D1wgOoXXyLcFs7Ng24G4OntT9PibzEbct6DEJ0B9YWw5C4UReGfP8ohKyGcknoP17+Yiz02hukTz2Pyro1YdI2Nyhl81GUcSf1ewcAgb105DT/6HTHXXgtAw6P/4s9r0pjW9QI0Q+NPWimP9ZuAjjlD6UHrHJ5cvodbXt9Cszdweh+8EEII8S0L6QDTGlBQAuC3WrGrTloUc0fnrKwswsLa14IZOnQobrebpqYmNm7cSFi/OCxRdvQmP13CepCU1QO/p5X1789l2PmZAGz/tAg9LJKku34HQNVTT+ErLOTyHpeTHplOtaeaRzab9TE4IuGSZ0FRYdvrkLeQ2HA7r/7fCJKjnByoaGL6yxtJH9STa7P6c+b+bQAsUC5jU4aVjMHrAFjxxn5Khl9N4qzfg6LQ8M67/PzfVdza4/8AeL7lIHcNmohPUfmxdSWv2v/O2t0HuezptRTVtJyGJy6EEEJ8N4R0gPEHDAxNwW8xA4xHMWfohIeHdzjOarUyfvx4AFavXo3X7yN8VAoATetKOfPq6QDs+OQjYlMDuBPD8DYH2LWqmKhp03CNOgPD66XsvvuwqlbuGnEXAG/te4v5+fPNi2SMgjG/Mn/+4JfQWEaXGBev/nQE7jAbWwvr+Plrmxl+4VlcoYQz+Mg+AJ5XZ3AofSOZORVgwLp5B9muDCPl8X+hhIXRvGYN5z64kgd6zMSqWPmo4QA3DRhHvT2C0eou3nf+iabyg0z712rWHqz65h62EEII8R0S0gFG8wcwAgoBi9oWYPwAuFyuLx07cOBA4uLiaG1tZf369YQPTwargv9oE0lRmXQbPAxd01g791WGnpcJwLZlhWh+nZT77kNxOGheu46GhQs5s8uZzBg0A4C/rPsLOyvNtV4YPwuSB0JrDSy4DQyDnkmRvHjDcMJsFj4/UMXMt7dxwfTLuLismqyqo2iKjSecv6Y8/gWGXxiDosCetaWs2BFN0nOvYEmIx7t/P71/9xLPdv0tEbYItjQe5poe/Shyp5LNURY57yG7dSfXvpDLy2sOy2aQQgghvvdCOsDoAd0MMKqCTXXg4eQBxmKxMGGCOeto7dq1eNUAroHmlOqmtSWcedUNKIrK/g1riIytJTLWSWujnx0rjmLPyCD+llsAKH/wbwRqa7l50M2MTx+PT/fxq5W/oqq1Cqx2uHQOWByQvww2vQDA0IwYnr12KDaLwoc7Svnz4r1c9bOruWjfXhKbqmlSovhX7O0cqvk9E67pgs1poeRAHR8ubCLmyVdx9OxJoLKSqDse4uWIW0kOT6agpZRrkuPYmdoPt9HAW44HuFRZwX0f5HHJU2vZfES2IBBCCPH9FdIBhoCGElAIqGBXnXj/Qw8MQN++fUlKSsLn87F69epgMW/rzipiYlLpN34iAKvffJmhUzIAyP3gMNXFTcT933QcPXqg1dZS8feHUBWV2WNn083djYqWCn698tf4NT8k9oZz/mRe8OO7oSofgDN7JvDIlTkoCry+oZBnN5Zx/ZVXcsHOLUT6mihR0nmhy5Xs2PFTxl4WRmSck4bKVua/VITlvqcIHzMGo7UV/a7ZPF9/Cb1je1Pjq+f/XD6W9xqPlQAP2Z7jYcdzHC06wmVPr+PW17dQWC21MUIIIb5/QjrAGJoGAdAUsFmceDBrYE4WYFRVZeJEM6Tk5ubidYM9Iwo0g+YNpYy+4mqsdgfFe/OwOwrp2i8OLaCz7MU8dCwk//lPoCjUv/8+zevXE2GP4LEJj5nDOhVb+NvGv5kXGvFzyBoPgVZ49wZorQXgwoGp/PVic7uBf63I58MyP9eMmcjkHRux6T52Kjl82GsK+w/dRN/RpSRnufG1Blj0/H7qr7mH6CuuAF2n5aEneHhrP8aljMGjebnDd5h/D7kEgEuVlax2/ZqbrR+wbGchkx7+jAcW76G+1f/N/UUIIYQQp1loB5iAjhpQ0FX9v9bAHNOjRw/S0tIIBAKsX7+eiNFtxbzrS4mIimHoBRcBsPqNVxh/dQ+cETaqi5tYv/AQrsGDifnJjwEovfdedI+Hbu5uPDjuQRQU5u6by3v73zM3fLzoKXOV3rKd8Mo0aDGHdK4a2ZXfTO4FwP2L9rAnIpHLUntzdt5WMAyWKefzdvpVFHvuxZ30MT2Gx2PoBqvm5pPf/xrif/1rs71vzOWu9xV+3PViDAz+VruZB8dNx5uag1Nv4S7rm3wefhcTjPU8t+og4x9awStrC2Q/JSGEEN8LIR1g1ICGGlDQFB2LasevmFsH/KcAoyhKcHXejRs3QnY4FrcDvclPw8qjDJ92Gc7IKGpKjnJ4yyrOvrY3ANs+KeTo3hoS7rgDa2Ii/iOFVD3zDABnpZ/FrTm3AvDXDX9le+V2cKfB9R9CeAKU7YBXpkKzOUvolvHZ3Di2GwC/e28HvkE5TLNHmRs/GgafKpP5R8zdVCe8T3Pz4ww5NxGAHSuOst4znISHHkGx22lesZKfPJHHbzJvBOD1o8u5KC6MT8b/CiMimSStlGftjzI//EFSWg9w78LdTH50FcvyyqXQVwghREgL6QCDpqH6bRj40S3mrSiKgsPh+I+n9ezZM1gLs3HzJtwXmmGi8bMiLC0qoy4ze1nWvvM6ab0i6DsuFQz45OU9+FUnSXf/AYDq51/As8+cDn3TwJuY1HUSft3PHSvuoLKlEpL6wg2LICIJynfBS1OgoRRFUfjDBX24fGgXNN3gF29tI3XSOdyakMT5u9ZjD/g4oPTmPueDlPc9SmHBbxh1SQxWm0rh7mqWbY0h5okXsMTG4s3LY9R9C/hXxm9IDEukuKmYO47M46f9zmDfGTeC1UmOtpNFjj/wSNgLNFSWcNOrm7hqzgZ2Fdd/U38zQgghxDcqpAOMqmlYAg4Uw0eg7U5cYS4CgRqqqj7l0KHH2L79Jnbn3YnXWxk8T1EUxo4dC8D69eux9IzC0SMaAgZ1Hxxk4KTzcCcl01xXy+YP5zP28h64E8NorvPy2Rv7iJg0iYiJEyEQoOjnN+MvKUFVVO4fez/Z7mwqWyu5Y+Ud+DQfJPSCGxZDVBpU7YOXzoPaIyiKwoOXDuDcvkn4Ajo3vbqZhOFn8Lsx4/jxljXENDdQq8Rxv/oXduakk39wBkPPD+By26kpaWbRh62E/fMl7FlZBEpLSf7147ydOIubBtyEXbWzsWILP6r4hD+Pu56avlNRMLjEWM6a8F9zi+1DNh8qY+q/VnPnO9spq/d8C397QgghxKkL6QCD5seqObEpRrD+JT6hijVrz2L7jps4XPA4VdWfUlb2PrvzZnYYNunXrx+xsbG0trayefNmoi/qDhYFz75a/PsaGPeT6wHYuPA9fK0NnDO9H4qqkL+5ggO55aTc/xfs2dkEysoo/L+fEqiuJtwWzuNnP06kPZLtldt5YIO5QzXx3WH6RxCTCbUFZk9MVT5Wi8rjPxnMqKw4mrwBrn8pF2t6Jr+5/mr+b9dWulWWEFBsPK/cwoLel1BQ9RuyBu0lrks4rY1+Pny9GN9vn8A1YgR6czMVM27nivcrmT/uZc7NOBfd0Hmn4CMuDBzilUm/xp+ag0Nr4beWN1gbOYtzlVze3VzEhH+s5OFl+2VLAiGEECHjlALMk08+SWZmJk6nk5EjR5Kbm3vSY+fNm8ewYcOIjo4mPDycnJwcXnvttVNu8PEULYDtWIBpWwMmKXktuu7BYokgJflSMjJuBhRqa9dSXb0ieK6qqsFemLVr14LbSuSZXQCo+/AQ3YeMIjm7B36vh0+ef4rEzEhGXJgJwGdv7adFc9L1heexpqbgKyig6KafoTU10TWqK38/8+8oKLx34D3e3ve2ecGYDDPExPeEhqPw0vlQnofTZuG564YyIM1NTbOP617YQA1h/OJXP2dGZTkjDueBYbBcmcxjXX7H0bAXcEXPI6N/FHrA4NO3Cii95G7cl18OhkHdO+/ScukNzNrbk5fGP0vv2N40+hv5x8F3uDQhklVn/xojIpl4fwnP2h/lw8i/0S1wkMeXH2DCP1by9qYiNNkgUgghxHdcpwPM3LlzmTlzJvfeey9btmxh0KBBTJ48mYqKihMeHxsbyx/+8AfWrVvHjh07mD59OtOnT+fjjz/+nxpuGBoYfuyaA5ui4FV8gIHdbrZjxPAF9O37EN2zf0NG158BcCB/NrrePp144MCBREVF0dTUxPbt24mckI4l2oFW56VpxVEm/vQWLFYr+RvXkTv/HYZMziA5y43fo/HJy3lYEpPo+oJZi+LJy+PoLbeie72MTRvL7UNuB2B27my2Vmw1LxiVag4nJQ2A5gp4eQqUbCXSaePl6cODmz9O+9dq3tlWztU3X8cvo+M4f9d6HAEvB5Te/MX9V4q676K5+Z/0GxsJwOaPi9jR9Uekvfo6zoED0VtaqHz0Udw33M1zxrXcd8a9xDpjKWg4wq2H32HGwDM5NOrnYHXS37+DRY4/8ET4i+iNFfz23R1c+MRq1uTLtgRCCCG+uzodYB5++GFuuukmpk+fTt++fXnmmWdwuVy8+OKLJzx+/PjxXHLJJfTp04fs7Gx++ctfMnDgQFavXv0/NVzXvaD7sWoO7IqKBz9Wqw9FMYdBnM6U4LGZmTOw2WJpaTlEcclbwfetViujR48GzD2SDAtET80GoPHzo8RFpXH2/5m7T6+e+xqFu7YxaXpfbE4Lpfn1bFlyBEe3bqTPeQ41PJyW3FyK75iJEQjw0/4/5dyMcwnoAW5dfivrSswNG4lIgBs+gLRh5vowr0yDwvXERTh4++ejOKtnAt6Azh/n72LG61sZMnkCvx0zjiu3rCW2pZ46JZYH7PexY1gUJaW/Y9BEC6pF4eCWSpZ8ouH6+3Ok/uMfWFNSCJSWUvbb3zHknnd4r9sD3NDvBqyqlTWl67m0fCkPnnUj9X2noWAwVfuEteF3crtzEQdLq7n6+Q389OWN5Fc0/U9/T0IIIcQ3oVMBxufzsXnzZiZNmtT+BarKpEmTWLdu3X893zAMli9fzr59+4JTmU/E6/XS0NDQ4fVFmuYBAlj1tgCj+LHbzVVnbbYYVLV9JpLVGklWt18BcPjwY/j97d83ZMgQXC4XdXV17Nq1C2ffWJy9Y0EzqFtwkAFnT2bAxMlgGCx67O9g1HPmlT0B2PjhYcoLGgjr148uTz+FYrfT9OmnlN79RzAM/jLmL+Qk5NDoa2TGJzPah5PCYuC6+ZAxBrwN8NolcHAF8REOXrphOHdf0AebRWFpXjnnP/Y59TEp3HndVdywcxvdKovRFBsvqDP4cOhkCit+R98xVTjDbVQWNvLug5vJrcgk8fV5JPzqV6guF57tO6i87kaufP0o84Y/w/j08WiGxuuHFnKhXsBbk+8ikJqDXWtmJq+zPmoW51s2snxvOZMfXcWMf2/m073lBGQNGSGEEN8RnQowVVVVaJpGUlJSh/eTkpIoKys76Xn19fVERERgt9u54IILeOKJJzjnnHNOevzs2bNxu93BV3p6+peO0XUvGAEshgOb2hZgHK0AOOyJXzo+NfVKXK7u+P21FBx5Mvi+3W5n1KhRAHz++efouk701CywKnjz62jdWcXZ028muXtPPM1NLPjHX8kaHE32kER03WDp87torPEQPmIEaY8+AhYL9fPnU/H3hwizhvH85Oe5MOtCNEPjL+v/wgMbHiCgB8ARCVe/C9kTwd8Cb1wJ+z5CVRVuHJfF+7eMISs+nLIGDz+Zs57XdtZxyy9uYkZFBSMO70ZpWy/m6T6/osB4jLSeG+gxPB6A/bnlvPnANvYnTKTL/EVEX3E5KAqNHy3B+6Of8cetmTw7+lG6R3enzlvHX/e/wRVJsayb+FuISCbWV8LTtkdYEv13ehmH+WhXGf/38iZGPfgpsxfvIb+i8T/8KxFCCCG+eadlFlJkZCTbtm1j48aN/PWvf2XmzJmsXLnypMfPmjWL+vr64KuoqOhLx+i6B/CjGg7sqg0Pfux2M8DYHV8OMKpqpUePWQAUFb1Ka2th8LPhw4fjdDqpqqpi9erVWOPCiBpvhqb6Dw+h6grTZv6esCg3lUcO88mcJznrqp5ExTtpqPLw/j+2UF/ZQuTZZ5Ny//0A1Lz8MtXPPofD4uCBsQ/wyyG/BODNvW9y6/JbafA1gN0FP3kTel8ImhfmXgO75gHQP83NB78YyxVDu2AY8MSn+Vz3ylbG/+QKbo+O4/xd63BqHg4ovZmd8mf2p3xGY80znPvTVNJ6xaAFdLYuK+TNx/ZTMf5ndH33PVxnnIHh81E953nir7+H51uu4PfD7sLtcJNfl8/PDr3F7TmTKBx1M1id9PZsZ5HjDyxN+heXu7ZQ19jMs6sOMenhVVz85Bpe33BEtigQQgjxrehUgImPj8disVBeXt7h/fLycpKTk09+EVWle/fu5OTk8Otf/5rLL7+c2bNnn/R4h8NBVFRUh9cXaboXgwBgx6468Co+HG1DSA5H0peOB4iLPYvY2HEYho/8gw8F33c6nUyZMgWAlStXUlxcTORZXbDEOtEafDQsLyQyLp6pd9yFoqrsWb2SPZ8v4eKZQ3AnhtFY42HeP7ZQU9JM9CUXk3jX7wCofPRRat+ai6Io3DjgRh4d/yhh1jDWlqzlmsXXUNhQCFYHXPEyDLgC9AC891PY+joA4Q4rD10xiCd+MphIh5UthXVc8MQaWrMHcueYcVyxeS1xLbXUKbH8LeIeNg3S+Xz+z3A6P2PCNWnEpITjbQ6w+p0DzHuzFt9tD5L21JPYu3VDq6mh4k9/YeRdb/Fe4h+5us/VWBQLK4pXcVHFUh6eMIOmfhejYNCzfi3/0P/B7qhfMifxXfpZCtlWVMcf3t/FiL9+wu1vbuXzA5Uye0kIIcRp06kAY7fbGTp0KMuXLw++p+s6y5cvDw7DfBW6ruP1ejtz6RN8hzmEhGLF1rYP0rEemBMNIYG5gF2P7rMAlYqKxdTVbQp+NmDAAPr164dhGLz//vv4DY3oaWZBb9PnxbTmVZPedwBnXfNTAFa++jz1Zflc8ushxKaG01Lv4/2Ht1BZ1EjcDTcQ9/OfA1D2pz/R8NFHAEzMmMgr571CkiuJw/WHuWrxVWws2wgWG1zyLAy5DgwdFtwCH94BHnOl3KmDUln8y3EM7hpNoyfAL97cysuHdX7xkx9z/c7tZFcVoSk2XrLdzNKJU6mLfZ01828jtdtORl+ahivKTkOVh6XP72bpxijCHn2VpD/8AYvbjfdAPjW3/Iqrnj/EuwMeYUzqGAJ6gJfy3+MCvYC5U++nefRtEJGM3VfLOQ3zWGS7i40Jf+HO6JU4Aw0s3F7CtS/kMu5vn/LPpfsoqGr+n/5uhRBCiP+m00NIM2fOZM6cObzyyivs2bOHGTNm0NzczPTp0wG47rrrmDVrVvD42bNns2zZMg4dOsSePXv45z//yWuvvcY111zzPzVc18whJF2xYlPt5hCSw+yBOdEQ0jEREb1ITb0CgAP5DwQXt1MUhQsuuICIiAiqqqr45JNPCOsdS/iIZDCg5s29eAsbGDJlGr3HnIWh63zw6IPoWiOXzBxCQtdIPE1+FjyylbJD9ST86pdEX3klGAbFv/0dTZ9/DkCfuD68ecGbDIgfQL23np8t/Rnv7n8XVAtMfRxG/8Js6KYX4cmRsOcDANJjXbz981HcNqE7igJvbzrKDW/vZ+KPruJn5ZWMPLwLxTBYqZzD7xMeYeeUQZQZz7Bx8Z30HFbEkMmpWB0Wyg838P4jO9jQPIi41xcQe/31YLPRvOpz/Nf8gnvXpPDU0AfJjMqkxlPD/bueY0LlMn4/4hJyL5iN3mcaqDYSGvdwm+c5trpu48Pk5znfuYuy+hae+DSf8f9YyY+eWcfbm4pkcTwhhBDfCMU4hV39/vWvf/HQQw9RVlZGTk4Ojz/+OCNHjgTMadOZmZm8/PLLANx9993MnTuXo0ePEhYWRu/evfnlL3/JlVde+ZWv19DQgNvtpr6+PjicVF39Gf98+c/0Wz2C7l378FHMIQblfERUVBUDBjxFYsLkk36f11vJuvUT0bRmBvR/isTE9mPz8/P597//DcA111xDdrcsql/Nw7OvFjXcSsKMHIhUePPuO6ksLCCley9+dN+DaAGFRU9upzS/HpvDwgW3DCS1exTFd95J40dLwGIh4fbbibvpRhRVxRPwcM+ae/iowOyduabPNdw57E4sqgUOr4IPfgk1h8xG9ZkK5z8EUebU8HUHq7lj7jbKGjzYLSq/mdyTbjX5vFNwiNXd+1AdHg2A26jlIuM9cg7k0rw3g4Hjr6apsTv71lZgGKCqCv3GpTJokJWGpx6h6ROzZ02NiCDm5zfxyUgHbx58l4KGguDzSYtI46Ku5zDNo5G2e6G523abVmcSy+wTeKRqOId1s60uu4UpA1K4fGgXRnaLRVGUr/z3LoQQIrSd6P/fX5dTCjCn24keQEXlxzzxwv103zSOXl37syRqPyNGzMPhbGbY0Pdwu3P+43cePPQIBQX/Ijy8ByNHLEJRLMHPFi9eTG5uLpGRkcyYMQOnxUHlczvwFzdhiXOSOGMQjc01/HvWL/E2N9Nv/CQm//x2An6DxU/v4OjeWiw2lfNvHkDXHpGU/P4PNHz4IQCuUWeQ+re/YUtMxDAMnt3xLE9uM2dFjU0by0NnPkSEPQL8rfDZ32Ht42ZtjMMN59wHQ24AVaW22cdv39vBsjyzHmlM9zjuPS+bvJWreLehjo3delLvNBe6izcquFSbS8+du/AV92bA+J9QfiSJI7tqAbA5LQw9L4PukeVU//NvePP2mO936UL87b/gyJAU5hctZsnhJTT529eFGZE8govjhzCp7BBhu94z17VpUxKVw7+943ilPodmwgDoGuvi8qFduHRIGl1iTr5juBBCiO8HCTAneABlZQt5+oUH6br9bPpmDuBjVx5jx76BouqMGf05Tmfqf/xOv7+BtevOIhBooF/fR0hOnhb8zOfz8eyzz1JdXU2/fv24/PLL0Zv8VDy9Ha3Gg61LBAk/G8iRvG3Me/A+MAy6Dx/FlF/8GkW18fGc3RTsqEK1KEy+sT/dcuKpnzePsvv/itHaiiU2ltQHZxPRthbOxwUfc/fqu/FoHrLd2Twx8QnSI9umjpftgoW/gJIt5u9dR8PUxyChJ4Zh8O8Nhfx1UR4ev06k08r9F/dnXHoYSxcuZZ7Pw+bMHjTbwwFINYq4zDeXLpsPojQOps+YH3Foh4uqIjOURMQ4GHFhJklluVQ/+giBSnMDTDUqiqgp5+O88DzWxFSy4OBCNpRuwMD8pxNuC2dy10lcbI0n58DnKIc+NWt5AM3qYmvkWTxRM5LPvD0ABUWB0dlxXDE0ncn9kgmzt4dHIYQQ3x8SYE7wAIpL5vLCnH+QvGcyfTMH8VnkJs4Y9S4AE8bvRVVt//V7Cwqe4uChfxIWlsEZI5eiqtbgZ8XFxTz//PMYhsGll17KwIED8Ve2UPn0dvSWAM7escRd25e961fx8VOPoAUCJGX14OLf/pGwqGg+eSmP/E0VKKrCxOv70GtkMt5Dhyi+YybeffsAiL3hBhJn3oFit7O7aje3f3o7Fa0VRDuieWT8IwxLHmY2Rtdgw7Pw6f3gbwaLHc78DYz5FVjtHKxsYubb29leVAfAlAHJ3HluLyL0FhYvXMp8BbZldMdjdQLQzcjn0pa3iV1fSrhlFJk5F7FvIzTVmIXVcV0iGDWlC67186l7+x0Cx63xY8/IwH3xRXgmncGHLRtYkL+Ao01Hg59nRmVyUZcJXNjUTPLO96HmYPCzpvCuLFIn8EjlMMqIAyDSYeXCQalcMawLg9OjZYhJCCG+RyTAnOABFBa9xKvP/Yv4/PPp3W0QG2M+Z8jQRdhscZw57uSbSx4vEGhm7brx+P019Ok9m9TUH3X4fOXKlaxcuRKHw8Ett9yC2+3Ge6SByjk7IaATPiKZ6Eu6U7JvD/P/cT+exgYi4xK45K57ieuSwYrX9rB3XRkoMP6qXvQbl4bu9VLx94eofd2cKu3s35+0f/4De0YG5c3l3L7idvKq87AqVq7uczU/G/Qzouxtf+l1hfDhTMhfZv6e0AemPQ7pIwhoOk+uOMjjnx5A0w0UBab0T2HG+GxijSYWfrCUD8Kc7EjPxq/aAehj7OLiuncIW1NPQtLZxGecw971fnytZuFt176x5EzsQnTdfhoXLqBh6TKM1tbg83GNGEHURdM4ODiR+SUfs/TIUloD5ueqojIq5Qwuju7PhOI8HHkLwWf29BgoHIkeyYvNY5jbOAAvZntS3U5GZcczKjuOUdlxpEWHdfafihBCiO8QCTAneACHC57krWfnEHd0Gl279mJf4kr6D/iUiIi+jBzxwVf+7sLCFzmQ/1ecjlRGjfqkwxYEmqbx4osvUlxcTLdu3bj22mtRVZXW3VVU/3sPGBB1TgZRE7tSV1bKvL/9idqSo9jDwrjwV3eROXAIn8/dz87PigE44+IsBp+bgaoqNC5fTunv/4BWX4/qcpH8p/twT51Ka6CVe9bcw5KCJQBEO6K5JecWruh5BVbVCoYBu96Dj34HLVWAAsNvhIn3gDOKXcX1PPrJfj7Z07655lk9E7hlfDZJRgPzP1rO4mg3u1O7oSlmj9MQYyNTyt/HstpP117n44wcw/5NLeha2xCR206PEcn0GBCFbddaGhYuoGXDBrMtgOJ0EnnOOTgvPI9VSbXMP7yQLRVbgtePtEcypes5XKxE0m/fpyhH1gQ/89vdbAg/m0eqRrDZ39W8nzYZcS5GZZlhZlRWHIlRzq/89yqEEOLbJwHmBA8g/+BDvPvsaySUX0ZcegalXZbRs+d64uLGkzPoha/83ZrmYd26s/H6yunZ8z7Su1zb4fOqqiqeffZZ/H4/48aN4+yzz0ZRFJrWlVC3wBweibm0B+EjkvE0NbHwn3+lKG8niqJy9vSfM+jcKayff5AtH5sr/yZnuZlwbW9iU8Lxl5ZS8pvf0rLJXI/GffHFJP/xbhSXi8+LP+cfm/7B4frDAGS5s7hz2J2MTRtrDrO01MDSu2Gb2ZNDZCpc8E/obS7It7esgadXHuSD7SUcW19uaEYMt4zPpgt1vL9sFUsTYtmblImuWFAMnTNYwzkFizA2qHQffCGqYyRHdjbjbWmfCh2bGk7PEUlkZShony2hfv58fAUFwc+tiYm4p02lZdJIPjC2sfDgQsqa24egukd35+LUcVxQV0P8zvehoX34qTWiK/tdg/mktRdvV3ejXHd3+LvITghnVHYco7PjOSMrjthw+1f+exZCCHH6SYA5wQPYs+8+Pnz2XRJrf4wjLYmWrI/IyNxOasqP6NPn5Kv8nsjRo6+zb/892O0JjB61Aoul49DFli1bWLhwIQBnnnkmEyZMQFEU6pcU0LiyCBSIu6YvYf3i0AJ+lj33JLs/+wSAIVMu4sxrprN3bTlr3svH79FQrQrDL+jG4HO7omJQ9fQzVD31FOg69sxM0h55GGefPvh1P+/uf5entj1FnbcOgNGpo7lz2J30iOlhNu7gCvjwV1BbYP7e92I4/+8Qaa5GfKTaXP7/3U1H8bVtxtg7OZJbxmeTadQw7/O1fJqSxP74TABUQ+MsYzln7VuGvj2C7kMnEp2cQ3VpFEd21qAF2jd0TO0RTc8RSaQ5KvAs+YD6RYvR6+uDnzv79SNy2lTyhyfzftWnLC9cjlcz62wsioVxaWO5OCKbMwu3Y9u72NxO4TjNUdnsceawtKUn71RnUmtEdvi8d3JksHdmZFYc7rD/XvckhBDi9JEAc4IHkLv7bj5/ZgEpLT/Bl+rG2usDUlIP0C3zF2Rl/apT36/rPtatn4THU0z37rPI6Hrjl45Zt24dH3/8MQBjx45l4sSJANS+d4CWTeWgQtS5mUSe2QUUyJ3/DqvfehWA7GEjmfKLO/G2KKx8fR+Fu6sBiE+P4Oxr+5DQNZKWjRspvvM3BMrLUWw2En/zG2KuvQZFUWjwNfDc9ud4fe/rBPQAqqJyWY/LuCXnFuLD4sHXAp89CGv/BYYGTjeMvQOGXA+uWAAqGjy8sPow/15/hGafBphDND8/M4seRhXvbtjEqq6pHI42Zz/ZDB+TtKWM3L4Cz3Y7YRGJdB8+Bld0f8oKHJQcqKdtEhKqVSFzQDw9hsQRV7WLpg8X0vTZZxBo67mxWok480zsF57Lqq7NzC9cxI7KHcFnG+OIYUrXSYyxRDGkppTwI2vb1pdp/6dpoNDo7sVuxyA+aurJ+zUZNNI+FVtRoH+qOxhohneLJcLRXpQthBDi9JMAc4IHsGzbXex+5hPSApdRnRpG7ID3iYs7Sq9ef6FL2lWdvkZJybvs2fs7bLZYRo9agdUa8aVj1q9fz5IlZm3KmDFjmDRpEuhQ+95+WraYNSfOXjHE/KgXlnAbe9euYslTj6D5/SRmZnPx7/5IREwc+3PL+fzt/XibAyiqwpBzuzLsgkxoaqD0D3fT9OmnAESMH0/SXb/DnpkJQFFDEY9seYRlR8wi3nBbODcOuJFr+16Lw+KA0u2w8HYo3WY22OKAAZebNTJpQwCoa/Hx6rojvLTmMLUt5kaMSVEOpo/OpC+VvLdjB2u6pXM0wpyG7jRaOMf4mMFHN2PPa6ChMAJ3YhpZQ8Zgdfbh6D6oKWnfOsDhspI9NJHuvV24dq+iYcF8PLt3Bz+3uN1EXTCFprOHsdC5hw8OfUhVa1X754qFvnF9GR43gOGGncHVRwk/sg4q93T4uzAUlfrofmy3DmRRU3c+qM2glfYaGYuqMLCLm1FZ5pDT0IwYma4thBCnmQSYEzyAN9f/jurnV5OuTqMgWSVj6NtERtYwcOBzJMRP7PQ1dD3AhtzzaGk5TNeuN9Gj+10nPG7Dhg181La30ejRoznnnHMAaNlUTu2CgxDQsbjtxF7VB0dGFCX79zD/oftpbagnIjaOi3/zR5KyutPS4GPVW/s52BZ8YpJdTLi2D8lZUdS+/gYVf/sbht8Pqop76oXE/fxmHFndANhUtomHNj1EXnUeYK6O+6uhv2JyxmQUXYMdb0Huc2agOSZtKIz4mTnEZHPS4gvwZm4Rc1YdoqzBA0C0y8a1I9PpY1Sw8MA+1nfPoMzZvjFmV6OAM7R19D28E/JaaS51kdC1G+n9R2Eo3Snc7ae53hc8PjLWSc8RSWQme1FXL6Z+4QcEKtqLi+3duhE5bSr7R6TwsWczm8o2dZiSDWag6RfXj2GxfRmuWxhSXYSrYF2H6dkAhmqjJnoAWy0DWNDQnaX16cHZTQB2i0pOenRwhtPgrtE4rBJohBDimyQB5gQP4F8rfo/1lXVkOC5gR7KPAWe8gcPRyvBh84mKGnBK16msXMqOnTMAGDjgGRISzjnhcbm5uSxevBiAUaNGce6556IoCr7SZmpe30OgqhVUBfd5mUSMS6Ohspx5D/6JmuIiFFUl59wLGHXFVYRFRHJoayWfvbmPlgYfKDBwfBdGXpSFfuQQlQ8/bA7FACgKUVOmED/jZhzdu6MbOosOLeLRLY9S0WKGgkEJg/jt8N8yMGGgOUPo6CYzyOx+H3SztwVXnDm0NOz/IDodb0Bj/tZinvnsEIfbNmF02S38aGgaA9QKlpUcZUdiDEfcqcFZSwDZxn5G+jbS88BOtLwAnhonKT16k9x9GD5/FoW7W/F7tODxCV0j6TEsgTTlKP6lC2lctgzD4wnem2v4cMLHjKGlXybb45vIrd7KxrKNFDcVd3j2FsVCv/h+DI/uzXBNZXBVIa6CNVBf1OE4w+KgIjqHzWp/5tdl82ljFwK0t99hVRmWGdM2yymegV3c2Cyd3hpMCCHEfyAB5gQP4P6Pfk/S61vIjJzM2sQ6xpz5OopiMHbMOhz/YTPH/2b//r9QdPRlLJYIRgyfj8vV7YTHbdy4kUWLFgFwxhlnMHnyZBRFQfcGqJ2XT+t2cxVbZ59YYq/oic/wsvTZxzmwYa35fkQkY350DQMnnYfPo7Pm3QPmmjFAZJyTCdf0Jr1PLK27dlP19NM0HdsBXFGIPG8y8TfPwNmrJy3+Fl7Je4WXdr0UXINlSrcp/GrIr0iJMPcjoqkCtrwCm16ChrZAoKjQa4o5vJQ1Hs2Aj3aV8tSKg+SVNgBmr8WlQ9K4pHcERfn7+KCymr2J0RRGpGIo5v/sFUOnF3sY0bKZ7D15ePcYBFocdOnbn9guQ2hpTKd4Xwu6fmzTTOjSO4bug2JIKN9Cy6IFtOR2XLdHcTgIy8nBNWwYrQO6sS2+hdy67Wwq3/SlQGNVrGagcfdguB9yqgpwFayFprIOx+k2F2XuweTSn/dqs1jTnIZ+3F6mLruF4ZmxjMqOY2Cam57JkcRHOBBCCHHqJMCc4AHc895ddH1nD93iJ7EhtYiRZ7wHqJw9YW+HfY06S9f9bN16LXX1GwkP78Gwoe9htYaf8NhNmzbxYdseRyNHjuS8885DURQMw6A5t4y6Dw5CwMAS7SD2qt44ukZxZOc2Vr4yh6qiIwDEp2cw/vqbyBiQQ+Huala8vje4Im6fMSmMuaw7DpcNz549VD39DI1LlwavH3nOOcTfMgNnnz5UtFTw+JbHWXhwIQYGDouDadnTmJY9jUEJg8yp11oA9i2GjXPMDSOPie9pBplBP8FwRPLZ/kqeWnGQ3IIaAFQFzumbxHn9kukZ6Wf7zjwW1dayPymGovC04NeohkY/djKiYTtdd+6hdb+CoTnoOmAwkfEDqa9KouKIJ3i81abSLSeBrGwr0QW5eDZvpGXTJrTa9j2VALDZCOvfH9fw4bT2z2RHso/1DTvYWLaR0ubSDodaFSv94/szPCqLYX6dnIpDZqBprelwnGaPosQ9hPVGX96uzmJTazLGFzZnj4+w0zMpkp5JkfRKjmz7OYJIp8x2EkKIr0ICzAkewKzXf0OPhYVkJE9ga/puBg9ZjMOexNixa//n63m9leRunIbPV0Fi4gX07/fYSZe437x5Mx98YC6cN2LECM4///zgsb7iJmre2EOg2mMOKZ3fjYixqRi6zo5PlrDm7X/jaWoEoPvwUZx17U9xueNZP/8QO1eatSCuKDuDJqbTd0wqzggbnn37qXrmaRqXfBxcSC7i7LOJv+UWwvr3I686j4c2PsSm8k3BNmZEZTA1aypTs6eS2lacS8Ve2Pg8bH8zuEIu9ggYeCWMuAkS+7CxoIanVuSzYl9l8LusqsIZWXFM6pNI3yg/u/ftZUlTAwcS4ygJS2k/zvAzkG0Mq9pFyo59tB5SsVjD6Np/KGFR/aksjqWhsr1exmpXSermJiXbTXx4C5Glu/Fv3UjLxo0d6mYAUFWcffuaPTT9u7EjLcCG5t3/OdBEZjLcq5FTcZCwI+vAW9/huIAjhiL3UDZovVjfnMTq+gSqjI7r0ByTFh1Gz6QIeiZH0qst3GQnROC0SU2NEEIcTwLMCR7AXS/eQa/F1aR3PZO8zHX067+SyMgBjBg+/2u5Zl3dJrZsvRrDCNCj+x/o2vX/Tnrs8evEZGdnM3XqVKKjowHQPQFq3ztA605zpo2zbxyxl/dAddlobWpk3TtvsG3pIgxdx2K1MvSCixl5yY+oKvax4rW91JW3AGZvRc+RyQyc0IW4tAi8+flUPfMsDYsXg26uzRJx1lnE33oLzgEDyC3LZeHBhSw7siw4tAQwPHk4U7Omcm7muYTbwsHTADvmQu4cqNrXflOZ48wg0+sC9la28MH2EpbuLudARftu1AADu7iZ2DuRPpE+DhYcZLm3nv1JyVTY24fxHIaHwfpWhpblEbf1AK1HLTjCI+jSZzhWZ2/Kj0TiadI6fK+imHsypWS7SYgO4K7eD7vMQOM/2rHQF8DRsydhw4bhGZDFzjSNDb595JbldlhED8CqWhkQ159h4ekM9/rJKc8nrHCDucfUFwScsdSEZ1No6cpOfxrrGxNZ35RIPV+eoaYqkBkfTq8v9NhkxrmwSm2NEOIHSgLMFx6AYRjc9ewv6LPUS2r3MRzKXkqPHhuIj5/EoIHPfm3XLSp6hf0H/oyiWBic8xoxMSNPeuy2bdv48MMPCQQC2O12zjnnHIYOHYqqquaQ0vpS6j48BJqBJcaB+/xuhPWPR1EVqo8WsuKVORzZsRWA8OgYxl11A73OOIv8LZVs/7QouGM0QFqvGAZO6ELmwHj8BQVUP/ss9R98EAwy4WPHEn/LLbiGDKbF38InhZ+wMH8huWW5wR2knRYnEzMmMi17GiOTR2JRVHNYaeMc2LsouJs0kakwbLpZ+BuZxOGqZpbllbF0dzmbC2s5/l9PRpyLSb0T6RPlo6ikgFVGI/vj06i2xQWPCTOaGaZtZcjRvURuysdbZcHljiGtdw52V1f8/nhqSh3BYbTjRcU7ScmOJjEB3A2HsO3ZSOvmTfgOHfrSsfZu3QgbNgxv/yx2pRus0w6QW5ZLeUt5h+OsqpWBcf0Z5urCcI+XQTUlhFXubVsY8MT/afjCEqlyZVOgprPdl8qa+gS2eZJoOm5dmmA7LCrZiRH0SoqgV3IUvZIj6JkUSVp0mGxcKYT43pMA84UHoGlefv/0bfRZ4SC5x3BKes0nI2MHaWlX0bvXX7626xqGQV7erykrX4DNFseIEQtxOpJPenxVVRULFiygqMicEZOZmcm0adOIjTUXk/MdbaT6jb1oNWYdiC3ZReTEDML6xYECh7bksvLV56krM4dBkrN7MOGGn5HSozelB+vZ8elRDm2rxGgriI2KdzJgfBf6jE5BqSyh6tnnqF+wADSzN8M16gzif/5zXCNGoKgqpU2lLDq8iAX5CyhoKAi2O9GVyIVZFzItexrZ0dlQf9Qs+N3yCjS3DR+pNuh1HvQ4F7IngjuNykYvy/eUsyyvnM/zq/Adt0pvXLidCb0S6BPlo6KqkPW2ZvbGZVBviQ4eE2E0MMK3lZyCAzg3HcLfaPZUOMMjSOzWE1d0BgbJNNa6qS318cV/qQ6XlZRsN0kpNqJbjxJ2aBPeTbl49+/niwfb0tLMQDMwm7wuCmuUfHLLNwZncB2joNAlsgvZUZlk29xk6yrZLQ10qy0hrHLfl2Y7Hc/jSqUiLItDSjrbvKmsro9nly8ZD18uBo5wWOmRFPGlHpuESCkcFkJ8f0iA+cID8PlquPuZX9JnVQIJPQdR1+8NUlLy6dbtV2R1+8XXem1Na2XT5stpatqLO2owQ4a8gaqefA8eXdfJzc1l+fLl+P1+rFYrEydOZOTIkaiqiu4J0Ph5MU2rizG8ZtCwpYQTNbErzn5xaIEAWz9ayPp5b+Fr2/m5z9jxjLnyGtyJyTTWeNj1WTG7VxfjbTZXurU6LPQ+wxxeCvfXUP3cc9S9Pz+4Eq41IYHIc84h8txzcQ0bChYLu6p2seDgAj46/BENvoZg+/vG9WVa9jTO73Y+sdZwyFtgDi8d/cIO3wm9IftsM8xkjqFZt7FqfyVL88pZvqecBk/7/klhNgvjusfSxx2guqGIbS4ve2O60aS2bw0QbdRwhm87ORVHSDhSQcORJloazUCkKCrxXTOJSuiGxZ6KpyWO6hILmr/jP12LVSUxM5LkLk5iAuVEFG4lsHUDnry8YKg7xpqYSNiwYfj6Z5PXVWGN/cgJA80xCgppEWlkR2WQbY0iW4fslka61RTjqtoPjaUnPM9AoTUinXJHN/Yb6Wz1mPU1+7UUfHy5GDgu3N4h0PRKjqBHUiRRUjgshAhBEmC+8ABaW49y77Mz6bsmm6hePdEGvURsXDG9ez9AWuqVX/v1W1qOsHHTRQQCjXRJu5Zeve77r+fU1NSwcOFCCto2OkxPT2fatGkkJCQAoLf4aVxdTNOakvYgkxpO1KQMnH1iaamvY/Vbr7Fr5TKzN0FR6Np/EP0nnEOP4aMwsHAgt5ztnxb9f3tvHiXJVR/ofjf23LOWrqpeqnrV3pIA7RLII0tgPIwNHtvH9pEHje3xPAbwgH2ebZ4x2D5+MgLmeDAYY5vnscfHYAxzjA3YBgkJBAIhJCF1qxd1S70vtVdl5Z6x3Pv+iMi1sqqrRbe6W4qvT5yIuHEjMjKiOvPL3/3dG10j4U5cPci1d21iw0Cd+b/6K4pf/gqy3G5+0gcHydx9N5k3vYnUrbfgaYpvnfwWXzr0Jb598tv4KpIeYfCGTW/gJ7f/JHduuhNrZn/YtHToETj1dLuJCcIRfzffDjvuhu134w1dwZNHF3lw3zQP7p3i9FK755GuCW6cyHF1XlKon+T5nMeB7HaqWrunl1AB45zgsuAIlxUXmDgxi3FkgbnpaiuwkszlGdp0GXZ6E743QmE2Tb28/E95cEOKsYkUg9o82ak98Ox3qT/3HHheVz19YIDEDTcgd0wwN+pwbCBgf7LAC9WjHCocaj2LqheBYEN6A9szE2w3MmwPQrHZtniS5MzzUJ3vu58SOuX0ZibNLRxQm3iqOsp3SiMclaNd49U02ZBzWknDTcHZMRInDsfExFzcxALTcwHK5QP80ad+nSufuRlj2wZSN/8l6fQi11/3/zE8fNd5OYe5uUfYtftXAdi+7TfZvPn/OmMOg5SSH/zgBzz44IO4rouu69x1113cdttt6Hr4xSOrXhiR+c5pVPSMInNjmuw9EzhXDjJz5BDf/vv/3cqPgbCJ5crX/zt23vVGRrZs49TBArsfOcGR3XOttI3cSILr7trE5TcM4T/zJMUHH6T89YcJOh62qOVyZO66i8yPvYnUHXdQkGX+7ci/8aVDX2qN8guQs3O8ecubeev2t7JzeCeitghHHoUXHw6Fptg9NguZDVF05i7UtrvYWzB4cO8UD+6b5vmpUlfVK0fTXDOoqKspXszB8fQoi8bAsmuZUUvsUC9yWW2GHTMLDB+eo3C8RK0RypGmGwyNbyU1sBnEeipLg5QWln+5p/I267emGbJKZOeex9r7XerPPtMeVK8TTcMaH8fatg25eT0LoymODwXsz5Q50DjO4aXDLNQXlu8XsSG1ge2ZcbYbabb7sL26xLaFk6RmD0B9qe8+UjMpprZyytzM/mAjT1ZG+V5llBNqpGvcmibDaZsNeYcNuQQb8olwOR8t5xyG0zaaFufaxMTEXBhigem5AEtLz/I/P/EbbD94N/7mHGN3fArLqnPzTV8mk7n6vJ3H4SOf4MiRjwEwOvoTXHXlh5Y9ubofhUKBL3/5yxw6FA5/v2HDBt761rcyOtoepj+oeJS/fYryd0+h3DC6YW5KhxGZKwYozs6w99Gvs+ebX6c01+7WPLJlOzvvuoerXn8XjZrOc4+eZP93JnFrYSTFdHSuun091/7IJnKDJtUnQ5kpPfR1gvl2dEBLpUjfdReZN72R9BvewOH6Kb50+Ev8y6F/YabWblbZkt3C6ze+nhvHbuTG0RvJWVmYPQCHHg6F5th3wO+UAQEbXtuKzhxPXM2DB+Z5cN80Tx1dQHb89W0aSPCGbTlGUx7TjXlepMgxJ81pewxfdDehaCpggqNc5p3kssVFJo7Pox1eYn5xCUkogunBYfKj2zHsDdRrwyzNplCqWwJMR2dsS4bhdJ186QiJyQNw9ADuoUPIyvKeSU2MkRGs7dtQmzeyOJbi5BAcyFTYz2leXDq0qtisT61nW3ojO/QM230Zic0J0rMvtLu09yB1m8XUNk7om9nrb+B75RGerY8yqYb6Rmxa708XrM8leiQnwfq8w8Z8gvU5Jx7XJiYm5rwRC0zPBVhY+A5//rH/my0n3kphwuCKH/kUAG94/RNY1vB5Ow+lFKdOfYaDL/whSvlk0tdw7bWfIpHYuKZ9n332Wb761a/SaDTQNI0777yT22+/Hctq59QEFY/yt05Sfvx0S2Ss8QzZeyawLx9AKcnx53ax5xsP8eKTjxNEeS66abLjptu49q43Mbbjag5+f4bd3zjZ6oYN4fOWJnYOsWXnEGNbM7i7n6X4tQcpPfQQ/nS7d45IJEjfeSeZN72R5J1v4MnSHr506Es8fOxh6kF3pOKygcu4cTSUmRtGb2DISMKx74aRmUOPwMy+rvrYWdh6J+y4m8L6N/DQpMOD+6b59guz1D3ZVXVjPsE1GzJsTIOrKkwzwzFLcTwx2pUM3CSnFrlMHuayyjzbJwsMHZqnMFmk6oVRH8OyGNiwDSc9ThCMUlzI4bvLk2adlEl+NEE2p5ERZZK1GZy5I5jH9+MdfoFgdm7ZPk20bBZ761bYsoml9RlOD2sczFbZY87wQvEQ8/X+TUoAo8lRdqQ2ss1Is8OXbKsssX3hBJnZgz1S2EYhcJ11FK0R5vQRJhnimDfAC/Us+6tZTskhZsktG6Svk4xjsLEpNrkwgrOxY3ks58SPWYiJiXlJxALTcwFmZx/ibz76PiYWfp7ZrSWuuu0zgB6Nwnv+P2gXF7/Pc3vehectYJqDXLvzT1ftYt1JsVjkK1/5CgcPHgTAcRxuvPFGbr755q6bG5RdSt86ReXx06joi92ayJB+/UacKwfRLJ1aqcj+xx5lzzceZPbYkda+2XUjXPMj93DNnXezNGew+xsnOb5vodV7CcC0dcavGmTzziHGrx7AOH4glJkHH8Q71W4SEpZF6vWvJ/OmN6K/4Va+U3yWJ6ee5Knppzi8tLz78rbctlBoogjNOt8LRebFh+HwN6DWM8ru4HbYcTeNzXfxmH8FX3uhzA+OFzg0W17W6whgy1CSK0aS5Gyfoppnxi5xIpXltLm+61lNALry2KyOcVljkssXimw+uoA6UmS+MkegwhyY7PAY6aEtCH0D1eIg1XJqxZGchSbIDjvkhywyZp2Ut4CzdBLn9AHU4f34J0+2urIv29e2sbZsQWwZp7Qhy+SQzqF8nV2JOV6oHGW2Ntt3Pwh7ie1IbWCblmK7H7CjUmDb/Emy8y9C4K64XxOpmdScEZbMEWa1YU7LQQ67AxyoZXmxkee0GqJAGujf1CQEjGTsrqap9nIY3RlMWXG38JiYmGXEAtNzAaam/pnPfej/ZXPt5zh++XF2vu5fMM1R7nzDDz8K71qp10+z+7l3UCrtRQiDyy77XTZt/MU1fYgrpdizZw+PPPIIi9Gw+Zqmce2113LbbbcxNtbuqh2UXEqPnqTyxGRLZISl4Vw1RPK6dThXDIAumDlyiOe+8RDPP/ZNGtWo6UMINl/7Gnbe9UbGr7mByRcrHNszx7G9C9SK3V98w+NpNl8zxMTOQXLVU1S+/hClr30N99ixdiXTJHXrrWTe9EYyd93FUkrw9PTTPDX9FE9NP8ULiy8se6+bs5vbQrPudYwtTbabm04+Caqjd5BmwsStsONuquN38py7gd2TNXadLPDcqSWOzVeXHV8I2D6cYsuAgW7UWDIWmMnAieR6Stry/ywDap4d/jGuKC2y41SJgUOLFOYWKPvhfRCaRnpwhER2HaY9hCKP18hQKSUJvOSK99dOGuRHEmSTASm1RLI8hT3zIubRfQRHXkS5K4iGpmFu2oS2ZZzKxgGm15kczjfYnVpkf+NYV/NdLzkrx2hiiDEzw6iwGVWCUc9ltF5hrLLI6NIUydJkd8L1CgS6Q8UZY9FYxzTDnJCDHG7k2V/LctwfYFINUWHl5lLb0No5OLkE6/MJNua7RSdhxQnHMTGvNmKB6bkAx0/+PV+6/8/Ywk9x5Oo9XH3No2TS13HzzV98Wc8rCGrsf/53mJ4OR+Fdv/5nufKKP0DT1jaWh5SSAwcO8Pjjj3P8+PFW+datW7n99tvZvn07mhZGlIKSS/k7p6k+O0NQaA/yJmydxDVDJK5fh7Mjjx94vPj9x9nzjQc5vmd3q56TznDV6/8dO266jfU7rmBxusGxPfMc2zPP9NFi15htdtJg4upBJnYOMWoX8B97mNKDX6Pxwotd529u2kTi+uvD6TXXU9+6nh8s7uapqVBoDiwcaA2c12RTelMrOnPjwBVsnHkhSgZ+GArHu+qimWF37bGdMLqT8sCVPOeP84M5nd0nC+w+ucTk0vKmFUMTbB9OMpJW+OYSS06V6YEsk9Z6ZE90xVQuW+QxLqvNcvl8mc2Hi5inG5TdJUreAq7s6EFlWqQHR7GTw+jmAFLmaNTS1CopoP/AdEKED+fM5TQyepVkY47EwjGsE/vg0F5UqbRsn9b7WLcObcsEtY2DzIzYHB302JMusFueYHoVsekkY2UYdYYYNdKMaRajUjDmNhhtVBgtLzBanCJdXjn604lnZihZo8wbI0ypIY75A7zYyHGgluW0GmJKDdJg5SEGBpJm1EzVlptO0RnJOOhxwnFMzCuKWGB6LsBzh/+Gx+7/LBPJt3Dq2sfZvuNJ1q17E9dd+6mX/dyUUhw/8Ve8+OKHAUk2+1quu/aT2PboGfft5OTJkzz++OPs27eP5i0ZHh7mtttu47rrrsM0zdbruSdK1HbPUds9S9ARSdGSBolrhklcN4y9LU9xfpo93wwTf8vz7bwNw7TYcMVVTOy8nvFrriM3spmTB5Y4tmee43vnaVTbY7ggYHRLls07h1ifr2PvfpTyQw9R379/2WBxwrJwrrqKxGtCqfGu2sZz+iRPRVGa/Qv7kT3RgPWp9VEOzQ3caI8wPrkXcegROP44NIr0JbMeRnfC2E6WcleyT07wxNIAu06V2H1yifnK8miHZWhsHbRJJxo07CJLWY3T+VEq+vL/UMNqhi3+CdY3SmyoeWxaChid9jCnfSqVGiV/kbK32CU3ppMklR/FSgwhtDy+n6VWSRH4WYToL7RWwiA3aJJ1XFJBgWTxFNbUC5iHdqGm+48rA6BlMhhbNuONDVDJOxSzBnNpyWTC5YRT4ZCxyKnGNFV/ecSqH2kzxag9yKiZZlRYjErFmOsyWi+HkrM0Saa+tEIDUzd1a4iiFTVVqSGOegMcrGU55IZRnBnyBPSPxOiaYCzrsCHvMJZLMJSyGExZDKQsBpMWAymToZTNQMpkIGnFeTkxMZcAscD0XICH932aFx/4FzYO3sP8a7/GpvF9jI//Epdf9rsX7Bzn57/Nnr3vwfeXsKwRrrv2z8jlXnvWxykUCjzxxBM8/fTTuFGzQzKZ5Oabb+amm24ilWqPl6Kkwj1WpLp7ltpzc8hye2wTLW2S2DlM8rphjIk0J/bsYt9j3+T4c89SKXTnoViJBJuu2sn4NdcxfvV1BP4Ax/ctcmzvfNcjDAASWYvN1wwyvj3FsH8SuX83tWd3Udu1i6BQWPZ+9HXDrSgNV1/O/hGXp4p7eGr6KfbN7WuNO9NkJDHCDWM3hFKTWM/WcgExsxemnoPpPdEQ/30wEjByFWp0J0u5KzigNvPdynqengrYfbLQNbBe672YGhvzOqZTpp50WRzMMJ0eQ2n9v2CTqsKYmmLEX2CsUWVTxWNDQTE66aHPBZTrJUreIiV/AU+2o2ROOkcisw6j1SSVpl5JgZZHiD49iARkBmyyKRklEU/jzB7BOr4XcXQ/YoU8m67rPjiINjKMP5Sjnk9SzBnMp2E65XHCqXDEKnJUzVLy+vd6WnatjASj9gBjRppRYTIqYdRtMFYvMVqeZ2xpiqxbPaPkKKFRtddRMEaYEcOclIMccvMcqOY4KQeZVEPMk1k16bhJxjFCwUmGotOcwnWzVT6QshhKWWQdM+5SHhPzMhMLTM8F+NyTn6T0se8wuv5Oqjd9nnXrjnHZZb/LxPgvXdDzrFaPsfu5d1CpHEQIi/Hx+9g88atY1tCZd+6hXq/zgx/8gCeeeIKlaOwWwzC4/vrrufXWW1sD4jVRUtE4vERt9yy1PXPIjiiKlrVIXjtM4rp1mONpFk+f4vjeXZzYs5sTe3dTr3R/iTmZLBNXX8v4zusZHr+SpXmb43sXOLF/Aa/ezlkRmmBkc4Z1ExmGN6UZsCo4p5/H3bOb2q5d1J9/vjUacPtktPDBi9dfj77zSg6Pm3zfOMnTsz/gubnn8GT3AHMpM8W23Da25baxPb+d7cn1bPM8NhQm0Wb2wtSesKeTt0K0IT8RSk32Sg5qW/h+dQPfnk3y3OkSVTdYVj1laYzkwEq5BCmPsqOzlMqy6AyG7UErkFVLjMgZxrxFxuo1NlYkGxdCuZGLHmV3ibK3SMlfbMuNECQygzjpdWjGIEGQpVFLh1EbLds3Id20dXJ5jYxZJyHL2PVFzPIsxuIkxswx9MkjCHf5c6T6IWwbfWQdwXCexkCKct5iMS2YSQecdGocs4u8qC+wEKwQCevB0W1G7QFGjRRjwmQ0UIx6DUarJUYr84wuTTHgu2eUnEAzqVpDVPQcRZGjQJp5mWYmSDHpJjnlJllQaRZVhgWVYZHMqk1XTTQBA8nuiM5gyl4mO4MdQpS09Dg5OSbmhyAWmJ4L8Bff+GO0v9jL8JZb4Na/Ipud49qdf8bIyI9d6FPF9yvs2/9bzM5+FQBdT7Jp031snvgVTHP5AG1nIggC9u3bx+OPP87p06db5ePj41xxxRVcfvnlrFu3rutDVgWSxqElqrtmqe2dR3VEH/S8TeK6YZzLBrDGM2AJZo8d5fieXZzYs4uT+/fiNbrzStKDQ0xccx0br7oOO7WZuZOCY3vmWZxaLg2aLhjckGLdeIah9Q5Zb5bkqX14e56ltmsX/tTU8n2yWRLXXot57TWc3pLm6eES36vsYdfMLlzZP/nV0R225rayLb+N7dmtbNPTbG9U2bR4GmNmXxix6R1gr4mVQY1ew1L2Cg7pW3i6vpGvLwzz7JTb9TynTlK2zlBKx7I9sBq4TkDVMVlKZykl8+G34woMqHlGg1lGvCU2VBtsqig2zCtGTgd4pTplr0DJW6TsLeApN7qOOonMMGZiGKEP4LsZGrUUQhsAkV75S1VAImmQSEDC9HGoY3kl7PoCZnEGY+EUxvQx9LmTaGtI7gXQBgdR6wbwBjNU8jaFjMZcSnI60eC4U+GQuchJsbiq4DWxNDOUHD0ZRnICxZhXDyWnPMdocYZBGawh/tKNryeoGTkqepYlkaVAhrkgzXSQYspLMukmWSTTIT3pvs+oWna+hhbJTjuqM9QUnY7oT1uATGwjTlaOiWkSC0zPBfiTL99P8u9OkdvxGjKv/ziWXeOmG79INnvdhT5VIMxTmZ//JoePfIxSaQ8Aup5mfPw+JsZ/BdPMvaRjHj9+nO9+97scOHCga1s+n+fyyy/n8ssvZ8uWLRhGu1lC+ZL6wcUwMrNvoTXaLwACzLEU1uYs1uYs9kQGsgbTh1/kxJ5dHN+7m9MH9xP0DLufH13PxM7rGd58Jaa9meK8YPZEidkTpdbzmboQMDCaZHg8w2BekalNkjz5HMGeH1Dfs7fvKLjWli3Y111L9fJNnNqc4sBAlUOV4xxaOsTRpaPLIjVNTM1kc3ZzFK3ZwDalsb1aZvPiSczpvTD7/ApdjwVqaAfF3BUcNbbxjLuR75bX82whyUx59a7KmoB8UieZ8BGWh+9IagmTYjpLNZkBU+v75S5UwBDzjPpzjLol1lddNpUEG+cUQ6clbr3SEpuSt4gfyY1uWjiZdZhWHrQUSibwfQev4QBJhJYCsXJ38OY9SSR1Eo7C0T0cVcV2i5jVeaylaYy5E+jTxzCrC2sSHeE4MDyIP5Shmk9QzBrMZ2Ay6XLSqXDYXOKwuUign1lyDGEwZGXI6wlymkkOnbyCfBCQ8z1ybp18o0K+XiJbLZD3XbJSrjKcX398zaFm5ihrkfSoNLMyzYyf4rSbZDZIs0g6FJ5IemrYrNTdvEnaNsLoTle0p7eJqy1E+aQVJy/HvGKJBabnAnzoHz7AyD+WMK/YzsZ/9zEA3vD677+kpprziVKKubmHOXzkY5TL+wEwjAzj47/MxPgvYRiZMxyhP4VCgYMHD3Lw4EGOHDlC0PGgQtM02b59e0to0ul0+3y8gPqBRWp752kcK7aeit2JljZDmYmkRgybTB4+wIm9uzm+ZxdTh15A9eRgDI9vZsMVVzG0aTPJ/HqUHGhJzdzxEpWl/gKQGXJYtylFPlEnUz5B8tgu1O4nu7tut05Mw1y/HmvzZozN41RHc0wPahzNNthnz/NC+QhHi0ep+bW+r6ULnfHMONtzW9lm5tkeKLZXCmyZP44zvQ8qK/TqSQwg81upJMZYMEaZFOs46g9woJpnVynD3oJOw1/9v5BjClIJieF4BI6injQpJ7M0kilUQu8bvdGVzzo1y4i/wGijxIZqwKaiYOO0YGDap+4VKXsFakGZelChHlS68m4ATCeF6WTRjTRCT6FUEukn8FwbRRKhpUPZYZUxXAQkEhoJW+JoLk5QxmosYVbmMAtTmLPHMeZPYrklBGf+KBGDeYKhPPWBJKWcyUJGMJPyOenUOGItcdgsUHbUmqI5vWSMJDk9QV63yGGQR5APJLnAJ+c1yDeq5OslcrUiOb9BPpCklFpTcnITX7OpGqH0FEWWBZVmTqaZ8sJIz5xMR5GedCQ9GapnkB4hIJ8wu2Un2c7d6Yz+DCQtMo5B2jHiSE/MJUEsMD0X4Pf+96+z/asO3rUOW279W8DiR+/ad9G2VSslmZ19iMNHPkalEg5gZxhZJib+C+Ob7sMw0mc4wsq4rsvhw4dbQlMud+ezbNy4sSUzY2NjXdcoKLo0jhVxjxVxjxdxT5Uh6Plz0AXWxnRLahgxmTx5kON7dnF8z66uAfQ6SebyDI9PMDS+mczwRjR9GM/NUZjymT1RojjXf2TZZNZieL1DziiRWTpG4sgzsPvxVbsbo2mYGzZgbp7A27COxXU2p3KSg+kSz5kzvFA+TMXr/1gAgWBTZhPbUhvZpifZ7vlsLy2wdfYwqbkXusep6YMyUwSZjVQS61kwRjjNOo56AzxfjwSnlFqx1w2EX14pR2I6ASqhaCQsKskMXtJBJfW+0RtTuayTs4x6C2F36HqD4ZpgqGowVNbJLxoYFYUf1KgFFeotyam2loOO5GlNN7ESWXQzjdDTQJLAT+B7DogkQqSiqE5yxYEihQAnIUiYAY6oY/tlrPoiVmkWY/E0xsxxrOr82kTHtlGDOYKUg5u2aSQNakmdsg1LjmTJ9pk3XeaMGjNGlWmjSjkBrsFZi48hdLJGgrzmkNcMckojp1QY7fFc8m6NfL1Mrl4KJUhK8kGwhoybNr5mUdXbkZ55lWYuSDHlp5j2kq08nqbwLKgzSw+ApWukbJ20Y5C2TTJ2KDYp2yBtG6Ho2B2T055n7KieY5CyjDgCFHPeiAWm5wL8zqd/leu+NU7xtXNsf82/YJrj3PmGb17o0zwjSklmZr/K4cN/QrUajqlimgNMTPwqmzb+IoaROsMRVkdKydTUFAcPHuTAgQNMTnZ3xc1kMi2Z2bp1a9cjDACUJ3FPlXCPlUKxOV7s6tnURB90sCcyWJuzyGGNqbkXmD56mPmTx5k7cYyl6eV5Lq1zGFrH0PgE+bFNWIlRlBqkVk4xf6pBoeNp053YSYOhUZuBtEdWLuIsncKaPow48QLesWOoWv+oS3iyOubGDbBxjNJohpkhg6PZOvsTBZ7RT7Lgr5ygOpYcZXtilG1mlm3KYINbZ6RaYKQ0S6ZwCrFS1KYDJXT81BgVZ4x5Y5TTDHPEG+RALcezpQxHvEGqOCvubxoK2wkgofCSFtVkmiBpoRLGitEbgLQqkVVLZGSFrF8l5zXIuR4DDRiq6gxXDQaLOtlFDd3zaPjVSHa6p0ZQQdKMuAlMJ41hZdD0NIpk2HzlOWGTVVNytDRC9H++khDgOJAwPBxVw/JL2NUFzOI0xvwpzMXT2I0lTK+8pohOz8VCZdL4mQReKhSfalKj7AhKtqRg+yyYLrNmjVm9xrzlUk5A1QZ1ll/gCc0ipzvkNYscOrlWE5dL3muQa1TI10qtSE9eSjJSrqKy3QTCpBJFegpkmFcZ5oIUs36Cgm9TxqGiEpRxKJNoLVdUgkpU1u/hnyuRsvSW/GQ6Zcc2SXdIUpf8dEpSNLcN7aL9IRlzYbjoBOaTn/wkH/3oR5mamuL666/nE5/4BDfffHPfup/+9Kf527/9W/bsCXNBbrjhBv7oj/5oxfr96L0A/8/H7+WGZ65l/qZ97LjyO2Qyt3DzTZ8927dxwVAqYHr6Xzhy9ONUq2EEwzQH2bjxFxhZ92Ok01efkw+BYrHICy+8wMGDBzl8+DBeRy6LYRhs3bqVyy+/nImJCYaHh1tPyG6fpyJYqLejNMdKeNMVer9XhKVjTWSwJjJhlGbYYHFhkvlTJ5g7cYz5E8eYO3m8ayya7gMI8iNjDG6cIJEba0VrSgsJFqfqyN6oUIRuaGSGHNIZjZTRIOEv4ZSnseaOY5w6CEcPwBnkRt+wnsb6QRbXOZzKS17MVNhtzXAwUUCu8qWWMBKMJNYxYmUZ0RxGlMZo4DPSqDFSWWS0OMNQ4RTmCrk6nfh2nrKznnl9hFMMc8Qb4Plann2VLKfUOubI0v/XuMJ2JFoCSGh4jknDShDYBlg6ytJQlrZiDg6EeTgZSuRkkUzQlB2PXCNgsC4YrukMlQ0GlwzSJYny6zSCUHYaQSWUHj+UHVfWWgMX6qYdio6RBlJImSDwHYRIgZbqiOr0HwAQwDTBMiSmFmDiYsoGpl/F8Cro9RJ6dQm9vIBemsdolDC8KqZfw/CraGeInHVfBIFKJQgyCdyUTSNlUnU0KglB0ZEUrFB85q0G03qVkiMpO1BOgGes/f+pQJDRbfKaTV4YZBXkpSIfeORcl5wbNXF5LnkZkJOSfCBJnmUzVxNXc2hoSWoiQVUkqSiHUjQtBTaLgUNJOi3hKTflRzlUiORIJaiQwGVtD/w0NNEhP91yk+mQopStR+tmd/2O5Tgq9MrgohKYf/iHf+Dtb387f/7nf84tt9zCxz72Mb7whS9w4MABRkZGltW/9957ueOOO7j99ttxHIcPf/jDfPGLX2Tv3r1s3HjmhyDC8gvwgQ+/ldcdupPFWx9j85bdjI7+NDuv+cjZvI2LAil9pqe/zJGjH6dWa49C69gbGF73RkbWvYlc7kY07WzTE5fjeR5Hjx5tNTU1u2Y3MQyD0dFRxsbGWL9+PevXr2dkZKQ1gF7rnOs+7okS7rFiFKUpoRrLvyxEwsAcTmCsS2AMh5NMQ6E6w/zk8Va0Zu7EMWrFpWX7Q9gTJz+2gczQBszEKEoN0KjlqFeSVApu32hN13uydTI5g5Ttk1RlErV5rMIpzMlDGMf2YVQWV9lZR46tozKWZXZQ51jO42imzoF0iaOJ8qpy07oGCIbsAdZZGUY1mxGlMeL7jDSqjFYKjJSmGakWyMjVv6ACzaLsjIWCo4Y57A3yfC3LUX+IU2qYSTWEt0oKq0BhWgrdUmCBNHU828S1bJRltEXH0lG2BrroKzyG8siqIllZIhtUyHgNcq7LgCsZrAmGqlYoO4sayZofNWGVW8JT75OvI4SGYWfQjTSIJFIlI9FJhgMACjucazZCOCBswDyj4Ou6wjIklvAx8TBkPZQftxzJTwG9tIhRXcTwa5H8VEP5kd6ahUHZFkHaCSM+KZNqQqfsQMlRrYjPnFlnzqxTdgTlBFQcqNmg1vgjxRBa2MQlTNJCI6UgKRVJGZAKApKBR8r3SHg1Ul6dZOCTUoqUlCSkIqUkyWhuqTM1TPUnEAb1fjIkHQoyFKLOCFApEp9KJEGhIIVitJbmMYCkpfeJCK3SHNZaN6Nokk7GNnHMOCp0IbmoBOaWW27hpptu4k//9E+BsNlifHycX/u1X+N973vfGfcPgoCBgQH+9E//lLe//e1res3eC/CHv/9Gds6+heod/8rY2CG2bX0vW7f+2tm8jYsKKX1mZv6Vmdl/Y37+W8jOEV7NAYaHfpR1697I4ODr0fWVn0ezVpRSzMzMcPDgQV588UUmJydbg+Z1omka69at65KasbExbLvd/VRJhT9T7YjSFPEX6suiNJ3oObtLbIKkpNiYZ27xBPMnw2jN/Ilj7Wc69e5vmgxu2ER23QZMJ4+mZ1Gk8d0EjapDuSCoFs8c+bAcjVQSUlqNhFfALk1hzR7FPHkQuziJEawwnophIAbz+AMZ6jmHcsakkIK5pM+kXeekVeaoUWA+GdCwzvzBmdBtRswMIy3J8RitVxmpLDJSnmPU9xkKghV/AysEFWuIBWOUGW0dMzLHVNST5nRP9+EC6TP+mtY0hW4qhAXS0vAtE9+2I8nRUJ3RHUsPhacHW9XDJqwoqpP1G+QbHvk6DNV11lUsBos6A0tgug28nqhOI6jgygaerOPJBr7quJ9CQzccNN1B6KHgKGWjlIUMrG7xEQ5C61w/swBpIpQfU/iYNDCDOoZfxWiUMWpF9GoBrbyA6YXC0yk/etBYkyAoTRCkEvgpm3rKpJbQKCcEJUdSsIKW+CzZPuWEaEV8yg5r6sm1EgYaCc0gJQySaKTQSCoVTlKSCnySgUfSc0l5DZJBg5QMt7XqdEhRQq1lyMFuJAJXC0WoSjvqU4xkqBUVisSn0ooEOaE4EQpUmVCIVssxg3CE57XIT2dzWMo2SJh6OFnh3ImWHUPDiEeBXjMXjcC4rksymeT//J//w9ve9rZW+X333UehUOCf//mfz3iMUqnEyMgIX/jCF/gP/+E/9K3TaDRoNNpfHsVikfHx8dYFeOC37+Ly+tuQd36egYEprr7qI6xf/9NrfRsXNUFQY2HhO8zOPsjc/CN4XjtKoGkJhobewLrhNzI8/KOYZv6cvKaUksXFRSYnJ5mcnGRqaorJyUmq1f6Dww0NDXVJzfr160kmk63tygvw5+t4szX8uSr+bA1/roY3W0PV+nSzbqILjKGm2DgESUnJW2C+dIrZyWPMnTzG/Mnj+GcYqM2wbNKDQySyg1iJPLqZBdIEQYpGzaFWtqiXz/yrzLYhZXkkZBmnOou9eAJz8hBOaQqnvoC+huYhlXAIBrM0cgnKGYNCWjCf8Jl0QtE5bdcopGApxapRHYFgyEhGkiMY9TxG6pVQctw6o4HPuiA4YzQHwNVTVI0cpWZ+hUwzE6SZdBPMBOlWUmnYfThNgcyqER5ND2UHS+BbJoFlgt0WnU7pwdSW5e0kVYVs1ISV82tkPJd8I2CgDgM1jcG6Rq5uka4apOoK05cgfVTg4UsXTzZwI9EJl9vi40Zlnmygmrk8nQIUSY1SFlJagP1DCZBAYeoSS3iYqhFGfrwqeqOMUVtCrxTQ60thU1ckQE35Mfz6mvJ+lKETOCaBbeI5Bp6t41qCuiWom1AzFVVTUjECyrpPyfAo6R51i3AyRTSnVeaaa48I9ZIQnUIkSChIKUkqkCSbQuS7YXRIypb8hMsqjA51CNLaGqvauMKi1iFDJZWgKB2WpN2K+nRFhVRPk1lHWQOTtUSHLF3DMbVlctOUHqdjOWFF202dRLSPs4Icde7zSsknumgE5vTp02zcuJHvfve73Hbbba3y3/qt3+LRRx/liSeeOOMx3vnOd/K1r32NvXv34jj9kxd///d/nz/4gz9YVr60tEQmk+Z//vqb2Ky/FeuuvyKZLPG6136GgYFb1/o2Lhmk9FlaeprZ2QeZnXuIer09MJsQOvn8zawbfiPr1r0Rx9lwTl9bKUWxWGxJTVNsisX+Sa+5XK4VoWlKTSaTWfYfMKh4+HO1SGpCufHmavjzNVilO7JwjChq4xAkJOWgwFJ9lkJ1muLiDKX5OUrzc1SXCmt6f6bjkMwNYacGMK0cQksTyDCKUyvbeG5yxUTUJgkHkpaHQx3Tr2DWi5jVBfSlGYyFSYzyPJZXwvTKZxxLRQmBzCap55JUsiZLKZhLBkw5DU5ZFeaTAYW0oJAKmyBWymlJCCPMyREGIxIGfZ+sVyfXqJJrlMgGAblAkpOS7Fkkltb1FGUtx5LIsKDSzAZh1+E5GQpOc6yUhSjaUyCFv4L0CFOBpRFYRiQ2PaJjR8umDuby5ixH1UioGo6qk5A1HOmSCFwSgUciCEh4kpSnSHmClKuTbehkGxbpmkG6qpHyJCLwEYGPLxstCeoVoW4harTG4UFoaLqDpofNWgoLFclPp+icvQCpMN9H+GG+T1AL832a8lMtYHhhlCecXHTptpejck26ax6ksJOWFNk6nq3TsASNSIqqnVJkhFJUM1SHFBEKVI8YvZRokYlGSuikhEZCiVaTWUoGoRD5LinfjZrK2jKUVCqMFinZFTVyziKHyEenLhJURYI6FjVlU1MmVWVRkSZ1LOrKooYdbseiocJ5s349Wg7Xozk2ddUudzFYiyh1S06PMK0iQN0CpS2r31nnfD9T7BUjMA888AAf+chH+OY3v8l116086NxqEZhUyuDP3vkzjOZ/nIE3fhxNk9x+2zdIJCbW+jYuSZRSlMv7mJ19iNnZBylXugezy2R2Mjz0o2Sz15HJXottDZ+X86hUKsukZmFhoW/dVCrF2NgYIyMj5PP5rqmzGQrCpqig0IjkphpKTSQ6wVLjDE1SFsa6JHrORqR0PN2lIatUvSKl6gJLpRmKC23JqZdX6ZLdgZVI4WQGsZwcmp5FqjSBl6BedQiCVNTbZm35SZapsPUASzSwggpmo4RVXUAvzWEUpjHrxZbsnEl4pKHj5hJUsiaFlGA+GTDtNJh2XAppWEwJCukwquOaZ/6QzGgWOWGSRZCTkAsCsr5Lzq2Rc6tkg4CsjKQnkp+clDhn+OioaimKIssiGeajUXG7RSfdIz3p5c0BQkVioyFNHQwNDIHSo7nRf46hoaJ5b8RHKEmCKglVIyHrJFQDJ2jgBD5J3yPhByR9RcqDlCtIuwbZhkGmZpGq6WQbCtv3UYEPgUfQigI1pac3+tOWorBHl4ZmRGKDDVgo1Ss+KwmQAxhn/GWuCYlOgIGPjo8uvbbgeHV0r4bmVtEavUIUzrWggRE00PpI0tn0DpO6RmAbYaTI0mnYnVIkqZqKiu5TNnwqpuySn04papjtsoZ1dtEiDUgKnSQayUiIUlKSbAmRRypwW5GgRBQJcpTCjgTIVgpbquVlL6EJDUCi0RAWjaYIRXJTlRZVZVLHpo7ZEqKmINVVVB4JUQ2LRkuczLZYqagcGw+d1WTJ0MQy6QmXtT4RJH2NEaS2NHn1CuvXDZ0XgTmr7NBmT5Xp6emu8unpacbGxlbd93/8j//BAw88wNe//vVV5QXAtu1lX3BN/KCK7jkEiRqaJlFKYNurv/YrASEEmcw1ZDLXsG3be6lWjzE393VmZh9kaelpSqU9rVF/AWx7jExmJ9nMTjKZnedMalKpFDt27GDHjh2tsnq93mp2akrN7OwslUqFQ4cOcejQoWXHSSaTLZkZGBhoy81AnvzWEdIdycMrNUn5czVk1SdYcgl6BssTQAqDFCOMMYKWNNA3WGhXWIikjm/6uKpOzS9RbhQolmdZXJqKRGcWt1bDrVVwaxXgxIrXw0pmcFIDmE4O3cyBSCGlReDbeA0Tr26ASNBwHVxhEP6XS4EYgdR2SAF9/nwtPcAWLpasYXolzFoBozSPUZ7DckPRsWdKjLtltvqVFYXHS5jUsg6VrEExpVGyJEuWz5Lhsmh6VB2o2HWqToOSDdMOVOzwywLLglVGPLHQyAmDHIKsVGT9cMC4rFcnJ8NIT1YukZOL5APJ5ijqk14l6lMW6ajbcBjlKZBhIciwWA1Fp6DSLKp0q5dMs/twHYsVP6QFLblpCo5naHiGzpKRASMbluuRAJmRADm9IkQrGmQojwS1MAKk6jiBS0I2SAQ+Cd8n6clIggTphkbaNcg1LDI1nXRNkHEDNN+HwEdJF1+6HdGfGp4shOtBvyiQQGgmQjNBGAhMFCYoA4RBGOExe5ZNQvExwTLBziEYDsuFiSCq36q3So4QAQYBOl4oRk3B8epoXg3NraF7tVY0qCtCVHZJyQbZjoiRHgg0qdCDYM1y5FsGvqPjWjquLWiYgpoJNVNSNSXlKFLUjgrJtgRZsGAKTltQtzTqpk3dsfH19v09GywENhoOAhuwFaHkSBlKTuDjyCCcK9kSH0cqbCVxVA1bVbtEaahDkMI67X3W1sjVJkCjgU0jEpxmRKkZMWpgUZMW9ZpFrdaMErUFqSlEc/3Ko4hUHZsaVt+oq2ys8Jy6c8BLSuK9+eab+cQnPgGE+RMTExO8+93vXjGJ9yMf+Qj3338/X/va17j11rNv6ukMQRnGIn/7jveRuWYbYzd9DlSeu+9++qyP+UrCdeeYnXuYwuITFEt7qFYP0y9k0ZSaTrGx7XXLD3hOzsllZmaGyclJFhYWWFxcpFAoUCgUqPd5dEAv6XR6WdSmKTq5XK71uITOJqmg2CAouQRFFxnNg5K7fHC+VdCSBlomlJzACvCES11WqLpLlGoLFIpTLCyeZmluGt9b/REDvRiWHY2jkkQ3U2iaAyKBUjaBb+P7Jl7DRIhE9Ms7waqj5LZQWMLHUjUsv4JZX8Ioz2PWl1qyY3klTDeae5UzflEoIfCSJo2EQc3RqDqCsiUpWgEF06NsK6p22JxVjaSnaotw3V59bBUBZNDJKsJoju+R9VxyMor0BLId8YnWm8srRX0kGlWRoBYlhDaTQis4reTPZq5DMyeilQjaTBA9kxAJUM0ojy7AjORGb0tOS3ZWig7potXLy1b1sDlM1qIoULMpzA+jQJ4i6UHKE2QaOhk3jAJlagYZT5HwfDQZgJThgItBQKA8pAoIpIevPALl40uPQK2wLj185RN0LCtkKDqaiSCUGqWMDtGJxCmSHUQkSdG2bnlqSlGvSPX/29AJ0JUXTtJF9xtofiOMGLWaz9yuJjNdhhGjdlk7ktRuVjtzzzKpC3xLx41yijxD4BrhAImurmjokrqhqGuShiFxDfAMcA0RzvUwl8jTm+XNKdzeWdZcfinSJCCSJYGtwGkKk5LYUmIHAY702wIkuyNGTqcU9d3WlKtw3VJqzZGOAC2KHoXSU1MWsw2dOz6y58I3IUHYjfq+++7jL/7iL7j55pv52Mc+xuc//3mef/55RkdHefvb387GjRv50Ic+BMCHP/xhPvjBD/LZz36WO+64o3WcdDrdNcz9anQKjBCn+Nw7Pop9S4KNO7+KqV/BnT/yr2fzFl7x+H6ZUnl/GJUp7omk5hB9pcYaJZPtlZrl3eHPJbVajaWlpS6pKRQKrfV+PaJ6yWQyy6M30Xo2m22NaaOUQlb9ttBEUhMUG2FZyWuJz2o5OL0IR0ekDJSj8HWfBjWqXomqW6TiFijXFliqzFEtLVIvl1EvIScBQGg6pp3CsJJoRhKhOYCDlE3psRBEsiOctvysMGJuiMISHhYNjKCO4dfRo0RTrV7GcCsYQR3dr3fNDb+G3qwf1M+YZ+E6OnVHD+XHhrIVULSCtuQ4gkpTgmyoOKItRXb//AlbQU5BVsown8f3SEtFoqNXTLLZDKDCbsRJpUhE+RAJ2S5frQdNgEYVh2rUXXi56HQLUTmq2zuYXLOsU4gULGvmWjbXzyBChkBoCkc0sFUDU3mYysOK5qb0sWQQzSVmILECiR0oLAm2H02BhuNpJHydhG/guDqOZ5DwBI4nsf0AQ0mUDBAyCB8jIn2kCvCV15KhQHn40o/EyCeQbluMuraFoiSF1paeSIKa0aRWVGiZIPXKktEhRb2y1Pu3o9CVH4lR1Jzm18PmNL/e1YymybYkacpHkz6a9KLJ75n3K/PXHElSAnxDIzA1fEPgmSIUHl3RaIqTIanrskuQ2iIkemSJDlkSXbLUK1BrGQqiiQHYKowwtSUnEiYZRDIk+8qQqga88/2TF74JCeDnfu7nmJ2d5YMf/CBTU1O85jWv4atf/Sqjo6MAHD9+HE1rfyx86lOfwnVdfuZnfqbrOL/3e7/H7//+75/1Cdf8CraWRznhaK924twmr74SMIw0A/mbGMjf1Crz/Qrl8n6Kpee6pKbhTtOYm2Zu7uFWXcsaaclMMrkFJ7GJhLMJy1p3hi/GtZFIJEgkEn2bHZVS1Gq1ZVLTOXmeR6lUolQqceLE8uYdIQTZbJZ8Pk86nSaVSpFMJtvz4RTJZJZUKkUikUDTNJRSqJrfiuA0RUe2hCdaL7koT6LqAaoejn+jA0lskthARzOdDSKroyWNKClVIXVJoAX4hHkTDb9Gw69Qq5eo1ItUq4uUK4uUivO4Xg0lA9xaEbe28ojB/dDNBLqZRDcSIBKgbAJpI/0wjyLQEtQi4RFGHswxROrs+n/o+BjKw5CNUGzcKrpbQXcrLclpztO1Ovlyra8MrTTonGtp1O1IfmxJ2Q7HT6k4TcnRqdo6880IUCRAVTvMlXDNMyeROgoSikhuApJBQCISnkTUQyahqiRlpUuO1knJhFpZjnqvpI/WkpmW6AQJKoFDuZGgquyzFiKFQBqCmqFR1QXoJuhWGPnSBWgiFKHmui66t9kd2zSB0mmtd9YzND/qVeW2BUl5WNLHVD5W4GOqACuSJFOqUJR8sAOwA4HjayQ8ge0bpD0D29NxXI2Er7D9AMsLMGQAMgAlQQZI6SGVHwmRh6+q3REk6RF0iFFYHq57KKTSeqSoz7Jlgt2MJqVA5HtkSQd0EDph5Ki5boRz9L7RJKECdBUglI+ufLQglB0RuGiBu4oItdcN6WO5Ptl6rygtr6tJP5Itr0OiVifQBL4pQnHqkJ2GrmgYioaulklPe725j45n6LgGLBkw15QsQ7SEq6YFwOQZzualcck9SuBk8Rl2vf/r+Hc/w4ZN+xkdeTs7d/7ehT7FS5IuqSntoVTaS6VyCOj/y1rTLBxnI44TCo3jbMJJbCThjOMkNmGZQ+e9259Sikql0jdy05w6H265FpLJZLfgRPOVyjSPVhQnKHmh5PRpvlLeS4u6tDA1hKWFg8/pkkAP8PHwlIsb1Gj4VepumWqjRLW6RLm6QKW8iNvZY+YsEJqBbthouhX2sNFMwv7RJkqZKGkipYGSRlguTERzjtVTZrGWZFMATfkY0g2Fxquhe8sFyPBrXWVG0ClDUUSoTzNBoAlcS+CagoYJDUNRMxSNSHAazcmgVVY3RXub0a7TOkbHtpUkyeiUm5UiRGss71y2ox41vgojRG3BsbtEp64sGoS9ZhqYNDrXlRmWLVu3qHfUbU4+BkrQX4p61jvFp2tdFyiN/tuiuS4CTN3DFB62aEqSh6nCSFIzmmTJpiwprEBh+wI7AMcX2L4WyZKB4+vYrobjgeNLHD/A8nz0qNlNqQACH6n8UIxazWiRCEVNalKFEadABUgVIPFby+FcIRUEhGMyiUh2EE3B6V4P5Uk/q/V+xxFNiWrW65GpVvSoQ6Ca8iQi+dFfghidORoVrgsVIIByEHDziy9cHBGYC81MsYhhOmhO+NDCTGbzBT6jSxfDSJHP30g+f2OrLAiqlEr7QqEp76dWO0G9fpJ6fRIpXarVI63HH/SiaU4oN4mNOM44CWdjK3rjOJswzYEfWnCEEK3mx02bNi3bLqWkUqm0pKZSqVCtVvvOm7k41WqVarXK3NwKjzrowbbt5VGdVIrkUHM9H5YZDrY00D2BqvnIuo+sNacgLKt5yHrQUe6j6n7YzuDJUIIqUR4qAgMLBwvoaX51ommweaEASyCNKOojfDxcPFmPxKdCzS1RrRWpVBdpeFVc2cD163huofVIgB8OgWZYaJqF0KyWDLWFqC1CbiQ9wjDBtNq/hIUV/SJuytEqD8ZUAUbQQPdrYYSnK9rTTCj1MKWHLT30modWdqMPcrf1Ia9LN/qwb37Ah/sJ1f9Xra+Hv0gbpuoSnobZFCdBw9SWSdL8S5AkTfWTHY+kdEmoYl8JGookyIxyJayeyVYKUylsRVd588vBV1qH0FjUA5NGYNHwIgnqEZ62AFl9t3etLxOtsLwmLKqaha9boGmrSBLdUSVDgLVCxCma61qAqXmYzciS8DHxsVUYUTKDoEOUVChLUVTJDBRmIDClwAgEViCwAg0z0EhIHdPXMH2B6QksBUagsIIAM5AYgUSTMowwKRXmLhFGm5SSKOUjlUQqj0DVu8WJIBKtTnHyu5bDeiCVIgA8FQqVQuuOIAkdjLYMdcuSicBp1W9v75alXnnqK1OApjwa9SK8+HM/7IdJXy45gVkoLKBMC9sOR2lNpsYv8Bm9stD15DKpAZDSo9GYolY/Sb12ilo9FJta7ST1+kkajWmkrFOtvth6UOXyY6dwnI2h0HSITSKxCcsexTTyP/RjEzRNI5PJkMlkmJhYvWt9EAQteVlNdJp1qtUqSqlWN/+Vuo/3Yppm32hOYl0C27axLCvqeZcNly0bEx1T6hi+Bg2J6pKfbtnpLSNQoQA1FFoDNDQMLOxe8dGj1T6paMoAZYIyFFILCAgIhN8K4ftBNHaKX8f167hejYZbo9Go0GhUun7N+r5HoIrdo+m+RIQwoh444XgqTRkikh03kh2hW2A0BSgbzjFfcpQovCgKTXnoQfOXrBsKTuB2/Jptio+LLT2SgYvmee3tnXUDd83C5GsdkmNEg88ZoRi5pt5qMmvKUMkUzK0YZRKt+s1E0s6p2UVZ65AbK5IfMyqzlItJI1pmmRRZrXqK1BrFqXVsFJYM57oUKGXi+pEQKbOv8HSvWy15WjXa1CFSdUzqmk1Fs2joDnVh4elmKEGrRJwwBMoiXBci7KmmReIkovJoWWki7N6uSXQRYIgATfgYmgx7dYmwd5chJCYBupIYMsxBMpRClwGGVNEURvnMQGFIgSnBCMCUoVQZkVg15cr0wfA1LD+s2xQqKwgwpUT3A4RSYTJ4JFlKSVA+SrkEqhbJUihNLXGSfodg9W5XoUx5P/z/+5W45ARmvjRPzjRwIoFx7DgH5uVA00wSiXESiXEYWL5dSpd6/TT1+qlIck5Qq59qzV13hiCoUKkcpFI5uOLrGEYW0xzEMgcwrUFMcwDLDOemOYhpDYTbzEFMcxDDWD5Y3lrRdb0lO2tBSkm9Xj+j7HTOpZR4nsfS0tKy50+tFdM0e0SnYznTW57E0k0sYWCqSIICDcPXMD0N0ZCh6LSiPh6q1o4AKTdsfhN+OIFAx8AM0/hWOcloOsMD1ZUOSlMoTSKFJBDhh19LeKQbdh0OGpEY1Wm4FTy/0dV7pr1c7kgkXWWU59UQeiRGzVC9QRh/0MMTbv0Cbf4iNdq/UHUdYTS3GVFTWrK1j+i7b8f+rDLaqpIdsuOtKExOEI5jonlelIS6gjBF6/2EqTNvwtdCkfEiofE6JMfTBZ4u8HXRLuva3t6vpIOvRz1weiSp2VPH18A3iI7ZXa+53TMATWEIhQWYSmKrGpaq9pWnpiw1I05Jpcg3xairXo84dU1gSIXyQstTyiCQFtFjRftElkI58tHxMPCac9Vebm8z8FTHcphNhouBH5W7wsAT4byqGbiaiYeFp5l4QsfTTHzNCGVKRJLU7OavRc11IopGNaWqQ7CacqU69gvlKkAXoWCFU1hm0JYuU0lMEQqWiQoFS0p0qTClwogiT6YEQ4Is1+ALn3lp/zfPwCUnMMXFOTL2IKYVDnR3rkegjXlpaJpFMrmFZHJL3+1B0KBePxVGbeonqdeieRTF8bwFQOH7RXy/SK12dE2vK4SBaeZbgmNFktMWn0h+rPa6rvcfAfrM71Fr5cGshWa0ZrUmrEajgeu6rahO57KUYQ6N53ldTxL/YTAMY7kIpe32umlhagaWZmKhY6pIhAjnljIwlYYe6Og+KDcIk5rdAOlKlBegXBmVR8sduUAiABEIQEdHx1xp4HiNcAgaizNKUROFQmkKKSRSRFGjlvC4eIGL5zejRrUwktRPilplDSTtX5pheN8nWCFH7CUjjHYIvilOoi1TrbwHETUBmP1kKtUtSM3tYpXjYXQn5XcKk/TCPAkV9j7SlL9srssAQ/kkpETzfTQ3TFrVZL95/2N0z4OO1wznmvTDfaO5LwJ8XRHoYRLpMulpSpMOQSRBze01HYo99fyO7cuPEyaj+ka7LNAUQncRmoumlRE66EJhaMvFqdUUp8KkbiOSJoOwzGgtg6lUa9mImu+MaF+D5nZ69lMoVwvvq9KQKrynSmooQumSKlz2MFty1ClYPjquasuV2xStPuLVKVhdkqaF2xqaQVkzcWkLlisM6u7504xLTmD8xTlIh4NrycDCMM5tUlDM+UHXbVKpbaRS2/puVyrA8wp43iKut4jnLeC54by17i3iuYu40XIQVFDKx3XncN215a9A+EwpK5KalvSYA9GUR9dT6EYSQ0+j68lwXU9hGCl0PYmmrTy4WydCCBzHwXEcBgcHz7xDD77v9xWbs11uNBqtxGbf9/F9f8XnXJ0Nuq63ZMg0TQzDwEgYGBkDwzDaZYaBoenoQscQOjpaa66r5iQwpI4mQQ8EutTQAtB9Dd0HzRdoHghfhXIUyZJ0g1b3d4FASIGGRt+Ptmbz/CqBpLWgUCAUUiiUUChk9K+Z5CnDULoMWsmgQRCJUeDhB14UapddCaEr5ThImjkRnTIVdO0nX7JUhdlV3YITCU9nBEno4UNMu/IctEiaNJr5E+3y6Iu1Yzmch1OYy7RaeTNvo1neI1p9ZKdbuELpSaiApPTRggDh9dTrI0hax7HC9baE9RUvJdGkj1IBUvMJRNTUqgdI4ePpQShSLTkK5cpvRp90aGhQ1UBqYZmMyqUI63eVL9tO3+2BFgpcICSB7oJoIISKbo1CiPCyCxEua5qKWrvCSFenXHUKVUIpMiwXqeay2StgKFwlufcl/nWeiUtOYNRSDZJhl1IV5F8RD7uKASF0LGsIyxpa6w9ugqCB5y/iuQuR+PRKTrTeUaaUh5Q16o0a9cbpl3iuFrqebAmNrqcx9CS6kWrLjh5tM5rLzfWobocUaVqi799x88s/lVrrFVmZIAjOiQy5rtuKBgVBQK1Wo1ar/dDnt1Y0TWtdF9OJBElvS5Kh6ehNWdJ0DCJhQouESUeXAl2FkqRLgRZo6AHovkDzRTgyrKfQPIERgBYINCkQUUaKQIAS6GfKcxbRtIJPnWuaGqVoSlRbigIZ9qbxA69LlDrzFvr1sllex+2uI4NlMtU+ztn1Bjwz3XLUFpvO8rYUCb23vEO4WnIUJZKjRXLWe7xOCesUrpXKO48TrSsi0VFotKVIlwFmjxQJP4h6/YR1hJRhbx4l0VR7WbyUZdZWV6kwgqm09t9ToElU1OSrhCTQoiinkEhNEujhuDKdAlWL5uUgALpH7z9XXHICYy75sCkUGEM7vwOuxVzc6LqNro/hrPFREkopgqDcjvK4Cx0RnmjdXyIIqgRBhcCv4DeXgwpSNqLjuPi+i+8XztE7EW3pMZJ9Jah7PYwMGXoSTbMRmoUezTXNbi3rmo0QUZlunFXz12oEQdCSmua8GdnxPK+1fDZlq9VpNqVBmIfkuu6aBjs8JzSDDYRCaRomhqGj6wambqDrOoZmYOg6Ghq60NCEhoYIv8pEuKQp0SrTVMe6FOGyIoweKYEmo2UZLQcCIUELFCIQaEHYFBfuJwhfOYw8hV+fJgYilGLR8T4uAIowQqWa/1TzS1Eim1+WrSkIIxnNOlEX5bBu0LF/EA5QSXP/cJtUkbxF+zSP3XotOo/nduzTPpfVjhfWCcJ303Eened3ZjplqkO2mlJl9EjYsmhUb3mz6TEyZRHORc96lOgSveZKdZbvI3r2WV6neSwV/g2rsN+TaC4rid6oAr/8w/4p9eWSExhR1RCJUGDi/JeYs0EIgWFkMIzMS3r4p5ReS278oBIu+2WCoNp/vXdbEK37ldZxwu5CKtpWhvP2vayhaTZaJDndy/3mFpoIx4PRNDtcXqGu4/Q7XnJZ3VCmzv4jR0r5ksTnXEhUJ/3KLggdUrUamhYJlRZKla7poVwJEa53yVZ7rkdfXKFsiT6yFTXVKcLIlGxLl95Zv+e4mhLha6N1bDfREBiqp260vRn1ulToFaKWiDWlp0vE5CrzHhE707xL1Lw11JEt+WqeTzjvLGtqZ2/dzrKmwCn8nrpN6nEvpDZOzUY48wCk4jFgYl5GNM1E03KYZu6cHE8piZT1SGjCqXO5LULRtg4JakaHlHSRykUGDaRshMuygZQuqqtXjkTKGlK+fM09/RBCb8lMZ9RomTx1LAthokWJrkKL5sJolRmGgWmFCa6aMFvbhdauI4SF0JJd+3Uer7dMi37ZBuEjhpASfF8RBMGKMhQEAVLKrnm/stW2vdSyfuORShl+eZ3zlpx+NH/kn49Di7CnlhBtARIilBstmgvRFp5WWXO9ay1sAtRUx3q0HJZF66pZTns5KtdU7z7t43a/Hu11JTq204zPoYWxsvY2Rauk73k319Uqr9VTdiGRSlJslPhdHjwvx7/0BEamMJwwApPNb73AZxMT89IRQotyaJLAuX+oplIBUraFpnver8xdJkFnrCvdUJakSxDVbS6r6Diq41EBSgUEQRWochHEMs4a0Sk6wkDTjL5lumFgmD1y1Ee+lpX1HC8ss5bv1yVfOkoJpAKUCIfxQKCkQEoVbpNEcxUth1LWmpQiHKBWIaUgkAolIQgkQaCieuG8XXbuhExK2dVU2IlUst9j3F5+mjlNlxDhKWsdUtdPdLrXtahOp3SF+b+iQ+o69+s9JqDa5bXz+KPpkhMYU6SxojFgsrktF/ZkYmIuYoTQ0fUEup64oOchpR/JzFqFqFumVJSAqpqT9KIQe2eZH41k2l0mVVg3LIv2k+1lpXyk7KzTnvqx2rZXPAI0PZxMaD0XSIhmMm3Yiyicmsms4dD6zcTW9rYooVa0ex6JzmRbOnMswhyPZt6HQnTUi+oo0d7Ws6zQornonkfbVCR+qlUWzlEglYi2ay3xU1FZKIQibDKJ5DAURxWJI0gV7dMljqpDIFWrrHu5uywImttkz/bw1ijVaVdtywobqGUotz/kve859JppBI0f8sVX5pITGM2wW6PwppLxKLwxMRc7Yd6LEUWaLg3CL6Ue8emQJ6W8SJaCqCzqStsUo96yqKtte7/Vxat9rE4ZW2m/AKK8iXD01KCVO6FU+HBERRAtq2i5WT98gk9r32a9NV2jIKp/Hm/ExUIkb5cGUWKtED3LzdhKbxJuc5n2dqIB8lS7nmrV7bAZ1VPeu46gUj5/wn9JCYxSCiOj0DSFkgLLinshxcTEnHvCnIto0Dde2sCHlzJNgaNDdtqiE5URtIac75UoRSRSLTGSXdIUbpcdy+39WtLVtW/7NXtFrPU60fnRdX7ROXe+j36vg6Rb/jreW9c1WC6GdCS1NrdD2OzVep/Ijnoyupayoz5d+3Yf62wNUdErlhdSMjsTes81l5TANBoLqGzYnuY3Uj/0c3NiYmJiYpbTLXAxF5JQZtrCQ9QTiBVkSKE6pElGx5DL6rflSnVJ1pmO11xv11Nd4tUrbcVSGc7TUHaX1F/nQ1/8vxh8XTggjmzEI/DGxMTExLyyaTYFdY1GfAmRjAaePR9cUgKT2fAClhPexKT/hgt8NjExMTExMTEXiktKYAovvA7XMUEO8tPvuv9Cn05MTExMTEzMBeKSEpi3/Ze/JpuNm45iYmJiYmJe7VyajWoxMTExMTExr2pigYmJiYmJiYm55IgFJiYmJiYmJuaSIxaYmJiYmJiYmEuOWGBiYmJiYmJiLjligYmJiYmJiYm55IgFJiYmJiYmJuaSIxaYmJiYmJiYmEuOWGBiYmJiYmJiLjligYmJiYmJiYm55IgFJiYmJiYmJuaSIxaYmJiYmJiYmEuOWGBiYmJiYmJiLjkuiadRK6UAKBaLF/hMYmJiYmJiYtZK83u7+T1+LrkkBGZ+fh6A8fHxC3wmMTExMTExMWfL/Pw8uVzunB7zkhCYwcFBAI4fP37OL0DM2VEsFhkfH+fEiRNks9kLfTqvauJ7cfEQ34uLi/h+XDwsLS0xMTHR+h4/l1wSAqNpYapOLpeL/xgvErLZbHwvLhLie3HxEN+Li4v4flw8NL/Hz+kxz/kRY2JiYmJiYmLOM7HAxMTExMTExFxyXBICY9s2v/d7v4dt2xf6VF71xPfi4iG+FxcP8b24uIjvx8XD+bwXQp2Pvk0xMTExMTExMeeRSyICExMTExMTExPTSSwwMTExMTExMZccscDExMTExMTEXHLEAhMTExMTExNzyXHRC8wnP/lJtmzZguM43HLLLXz/+9+/0Kf0iudDH/oQN910E5lMhpGREd72trdx4MCBrjr1ep13vetdDA0NkU6n+emf/mmmp6cv0Bm/enjggQcQQvDe9763VRbfi5eXU6dO8Yu/+IsMDQ2RSCS49tpreeqpp1rblVJ88IMfZP369SQSCe655x5eeOGFC3jGr0yCIOADH/gAW7duJZFIsH37dv7wD/+w65k78b04P3zrW9/iJ37iJ9iwYQNCCP7pn/6pa/tarvvCwgL33nsv2WyWfD7Pr/zKr1Aul8/uRNRFzOc+9zllWZb6X//rf6m9e/eqX/3VX1X5fF5NT09f6FN7RfNjP/Zj6q//+q/Vnj171LPPPqv+/b//92piYkKVy+VWnXe84x1qfHxcPfzww+qpp55St956q7r99tsv4Fm/8vn+97+vtmzZoq677jr1nve8p1Ue34uXj4WFBbV582b1n//zf1ZPPPGEOnz4sPra176mXnzxxVadBx54QOVyOfVP//RPateuXeonf/In1datW1WtVruAZ/7K4/7771dDQ0PqK1/5ijpy5Ij6whe+oNLptPqTP/mTVp34Xpwf/vVf/1W9//3vV//4j/+oAPXFL36xa/tarvub3/xmdf3116vvfe976tvf/rbasWOH+oVf+IWzOo+LWmBuvvlm9a53vau1HgSB2rBhg/rQhz50Ac/q1cfMzIwC1KOPPqqUUqpQKCjTNNUXvvCFVp39+/crQD3++OMX6jRf0ZRKJXXZZZephx56SP3Ij/xIS2Die/Hy8tu//dvq9a9//YrbpZRqbGxMffSjH22VFQoFZdu2+vu///uX4xRfNbzlLW9Rv/zLv9xV9h//439U9957r1IqvhcvF70Cs5brvm/fPgWoJ598slXn3/7t35QQQp06dWrNr33RNiG5rsvTTz/NPffc0yrTNI177rmHxx9//AKe2auPpaUloP1QzaeffhrP87ruzZVXXsnExER8b84T73rXu3jLW97Sdc0hvhcvN1/60pe48cYb+dmf/VlGRkZ47Wtfy6c//enW9iNHjjA1NdV1P3K5HLfcckt8P84xt99+Ow8//DAHDx4EYNeuXTz22GP8+I//OBDfiwvFWq77448/Tj6f58Ybb2zVueeee9A0jSeeeGLNr3XRPsxxbm6OIAgYHR3tKh8dHeX555+/QGf16kNKyXvf+17uuOMOdu7cCcDU1BSWZZHP57vqjo6OMjU1dQHO8pXN5z73OX7wgx/w5JNPLtsW34uXl8OHD/OpT32K3/iN3+B3fud3ePLJJ/nv//2/Y1kW9913X+ua9/vciu/HueV973sfxWKRK6+8El3XCYKA+++/n3vvvRcgvhcXiLVc96mpKUZGRrq2G4bB4ODgWd2bi1ZgYi4O3vWud7Fnzx4ee+yxC30qr0pOnDjBe97zHh566CEcx7nQp/OqR0rJjTfeyB/90R8B8NrXvpY9e/bw53/+59x3330X+OxeXXz+85/nM5/5DJ/97Ge55pprePbZZ3nve9/Lhg0b4nvxKuGibUIaHh5G1/VlvSmmp6cZGxu7QGf16uLd7343X/nKV/jGN77Bpk2bWuVjY2O4rkuhUOiqH9+bc8/TTz/NzMwMr3vd6zAMA8MwePTRR/n4xz+OYRiMjo7G9+JlZP369Vx99dVdZVdddRXHjx8HaF3z+HPr/PObv/mbvO997+Pnf/7nufbaa/lP/+k/8eu//ut86EMfAuJ7caFYy3UfGxtjZmama7vv+ywsLJzVvbloBcayLG644QYefvjhVpmUkocffpjbbrvtAp7ZKx+lFO9+97v54he/yCOPPMLWrVu7tt9www2Yptl1bw4cOMDx48fje3OOufvuu3nuued49tlnW9ONN97Ivffe21qO78XLxx133LFsSIGDBw+yefNmALZu3crY2FjX/SgWizzxxBPx/TjHVKtVNK37K0zXdaSUQHwvLhRrue633XYbhUKBp59+ulXnkUceQUrJLbfcsvYX+6FTkM8jn/vc55Rt2+pv/uZv1L59+9R//a//VeXzeTU1NXWhT+0VzX/7b/9N5XI59c1vflNNTk62pmq12qrzjne8Q01MTKhHHnlEPfXUU+q2225Tt9122wU861cPnb2QlIrvxcvJ97//fWUYhrr//vvVCy+8oD7zmc+oZDKp/u7v/q5V54EHHlD5fF798z//s9q9e7d661vfGnfdPQ/cd999auPGja1u1P/4j/+ohoeH1W/91m+16sT34vxQKpXUM888o5555hkFqD/+4z9WzzzzjDp27JhSam3X/c1vfrN67Wtfq5544gn12GOPqcsuu+yV1Y1aKaU+8YlPqImJCWVZlrr55pvV9773vQt9Sq94gL7TX//1X7fq1Go19c53vlMNDAyoZDKpfuqnfkpNTk5euJN+FdErMPG9eHn58pe/rHbu3Kls21ZXXnml+su//Muu7VJK9YEPfECNjo4q27bV3XffrQ4cOHCBzvaVS7FYVO95z3vUxMSEchxHbdu2Tb3//e9XjUajVSe+F+eHb3zjG32/I+677z6l1Nqu+/z8vPqFX/gFlU6nVTabVb/0S7+kSqXSWZ2HUKpj2MKYmJiYmJiYmEuAizYHJiYmJiYmJiZmJWKBiYmJiYmJibnkiAUmJiYmJiYm5pIjFpiYmJiYmJiYS45YYGJiYmJiYmIuOWKBiYmJiYmJibnkiAUmJiYmJiYm5pIjFpiYmJiYmJiYS45YYGJiYmJiYmIuOWKBiYmJiYmJibnkiAUmJiYmJiYm5pIjFpiYmJiYmJiYS47/H2iObLpY1ZA0AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_funcs([sol.ShareFuncAdj for sol in portfolio_agent.solution[:-1:5]], 0, 100)\n", + "# add Morton-Samuelson\n", + "# plot all data\n", + "# Title: Portfolio Share Converges to Merton-Samuelson at top and bottom\n", + "# add reference" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the variables we want to keep track of.\n", + "portfolio_agent.track_vars = [\"aNrm\", \"cNrm\", \"pLvl\", \"t_age\", \"mNrm\", \"Share\"]\n", + "\n", + "\n", + "portfolio_agent.T_sim = portfolio_agent.T_cycle\n", + "# Run the simulations\n", + "portfolio_agent.initialize_sim()\n", + "history = portfolio_agent.simulate()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "raw_data = {\n", + " \"Age\": portfolio_agent.history[\"t_age\"].flatten() + 25,\n", + " \"pIncome\": portfolio_agent.history[\"pLvl\"].flatten(),\n", + " \"nrmM\": portfolio_agent.history[\"mNrm\"].flatten(),\n", + " \"nrmC\": portfolio_agent.history[\"cNrm\"].flatten(),\n", + " \"Share\": portfolio_agent.history[\"Share\"].flatten(),\n", + "}\n", + "\n", + "Data = pd.DataFrame(raw_data)\n", + "Data[\"Cons\"] = Data.nrmC * Data.pIncome\n", + "Data[\"M\"] = Data.nrmM * Data.pIncome\n", + "\n", + "# Find the mean of each variable at every age\n", + "AgeMeans = Data.groupby([\"Age\"]).median().reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from estimark.estimation import get_weighted_moments\n", + "from estimark.parameters import age_mapping\n", + "from estimark.scf import scf_data\n", + "\n", + "moments = get_weighted_moments(\n", + " data=scf_data,\n", + " variable=\"wealth_income_ratio\",\n", + " weights=\"weight\",\n", + " groups=\"age_group\",\n", + " mapping=age_mapping,\n", + ")\n", + "moments\n", + "\n", + "moments_values = []\n", + "for key in moments:\n", + " moments_values.append([np.mean(age_mapping[key]), moments[key][0]])\n", + "\n", + "moments_values = np.asarray(moments_values).T" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(25.0, 95.0)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(AgeMeans.Age, AgeMeans.nrmM, label=\"Market resources\")\n", + "plt.plot(AgeMeans.Age, AgeMeans.nrmC, label=\"Consumption\")\n", + "plt.plot(moments_values[0], moments_values[1], label=\"SCF\")\n", + "plt.legend()\n", + "plt.xlabel(\"Age\")\n", + "plt.title(\"TRP Wealth Medians vs. SCF data\")\n", + "plt.grid()\n", + "plt.xlim([25, 95])\n", + "\n", + "# show these figures for other models too\n", + "\n", + "# try all on same graph; make TRP prediction thicker" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(25.0, 95.0)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(AgeMeans.Age, AgeMeans.Share, label=\"Portfolio Share\")\n", + "plt.plot(\n", + " snp_data_full[\"age\"],\n", + " snp_data_full[\"share\"],\n", + " label=\"S&P 500\",\n", + " linestyle=\"--\",\n", + ")\n", + "plt.plot(snp_data[\"age\"], snp_data[\"share\"], label=\"S&P 500 (retirement)\")\n", + "plt.legend()\n", + "plt.xlabel(\"Age\")\n", + "plt.ylabel(\"Fraction of Savings\")\n", + "plt.title(\"TRP Portfolio Share Median Conditional on Survival\")\n", + "plt.ylim(0, 1)\n", + "plt.grid()\n", + "plt.xlim(25, 95)\n", + "\n", + "# same graph for other two\n", + "\n", + "# also try all on same graph" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/src/notebooks/median_share.pdf b/src/notebooks/median_share.pdf new file mode 100644 index 0000000..ac15a8c Binary files /dev/null and b/src/notebooks/median_share.pdf differ diff --git a/src/notebooks/median_share.svg b/src/notebooks/median_share.svg new file mode 100644 index 0000000..17b2ce5 --- /dev/null +++ b/src/notebooks/median_share.svg @@ -0,0 +1,1541 @@ + + + + + + + + 2024-09-20T16:06:42.447774 + image/svg+xml + + + Matplotlib v3.9.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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b/src/notebooks/median_wealth.pdf differ diff --git a/src/notebooks/median_wealth.svg b/src/notebooks/median_wealth.svg new file mode 100644 index 0000000..b41a049 --- /dev/null +++ b/src/notebooks/median_wealth.svg @@ -0,0 +1,1664 @@ + + + + + + + + 2024-09-20T16:06:42.215842 + image/svg+xml + + + Matplotlib v3.9.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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moments.\n", + "Calculated moments covariance matrix.\n", + "Estimating MSM...\n", + "MSM estimation complete.\n" + ] + } + ], + "source": [ + "res = estimate_msm(\n", + " \"IndShock\",\n", + " params={\"CRRA\": 2.0, \"DiscFac\": 0.96},\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'CRRA': 1.7685648638137668, 'DiscFac': 0.9709190706648813}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res.params" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'CRRA': {'CRRA': array(0.01253294), 'DiscFac': array(-0.00020467)},\n", + " 'DiscFac': {'CRRA': array(-0.00020467), 'DiscFac': array(3.3853946e-06)}}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res.cov(method=\"robust\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'CRRA': 0.11195060766200018, 'DiscFac': 0.0018399441849149213}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res.se()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "MomentsResult(_params={'CRRA': 1.7685648638137668, 'DiscFac': 0.9709190706648813}, _internal_estimates=InternalParams(values=array([1.76856486, 0.97091907]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20., 1.]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True])), _free_estimates=FreeParams(values=array([1.76856486, 0.97091907]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac']), _weights={'(25,30]': {'(25,30]': array(2027.28563602), '(30,35]': array(0.), 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15.71527224435591, '(90,95]': 17.99554082928189}, _has_constraints=False, _jacobian={'(25,30]': {'CRRA': array(0.32146506), 'DiscFac': array(15.34256936)}, '(30,35]': {'CRRA': array(0.66774082), 'DiscFac': array(41.16402998)}, '(35,40]': {'CRRA': array(1.13409539), 'DiscFac': array(67.04892712)}, '(40,45]': {'CRRA': array(2.40277415), 'DiscFac': array(135.1470847)}, '(45,50]': {'CRRA': array(1.61454588), 'DiscFac': array(114.95345702)}, '(50,55]': {'CRRA': array(1.99774008), 'DiscFac': array(143.40169664)}, '(55,60]': {'CRRA': array(2.30237639), 'DiscFac': array(161.17576083)}, '(70,75]': {'CRRA': array(2.77258387), 'DiscFac': array(180.92268951)}, '(75,80]': {'CRRA': array(2.75745812), 'DiscFac': array(166.41114592)}, '(80,85]': {'CRRA': array(2.36995711), 'DiscFac': array(139.5298569)}, '(85,90]': {'CRRA': array(1.57196747), 'DiscFac': array(75.57445843)}, '(90,95]': {'CRRA': array(0.3472485), 'DiscFac': array(19.21066924)}}, _no_jacobian_reason=None, _cache={})" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "estimatingmicrodsops", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/notebooks/parse_tables.ipynb b/src/notebooks/parse_tables.ipynb new file mode 100644 index 0000000..b008d3c --- /dev/null +++ b/src/notebooks/parse_tables.ipynb @@ -0,0 +1,206 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import annotations\n", + "\n", + "from pathlib import Path\n", + "\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "csv_file_dir = Path(\"../../content/tables/TRP/\")\n", + "params_to_keep = set(\n", + " [\"CRRA\", \"BeqFac\", \"BeqShift\", \"WealthShare\", \"WealthShift\", \"criterion\"],\n", + ")\n", + "\n", + "# Get all files in the directory\n", + "files = [f for f in csv_file_dir.iterdir() if f.is_file()]\n", + "\n", + "parameters = []\n", + "\n", + "# Iterate over each file\n", + "for file in files:\n", + " file_name = file.stem.replace(\"_estimate_results\", \"\")\n", + " # Read the CSV file and convert it to a dictionary\n", + " res = pd.read_csv(file, header=None)\n", + " res = res.set_index(res.columns[0])[res.columns[1]].to_dict()\n", + "\n", + " # Create a new dictionary for this file\n", + " file_parameters = {}\n", + "\n", + " labor = 0\n", + " stock = 0\n", + "\n", + " file_parameters[\"Name\"] = file_name\n", + "\n", + " # Iterate over each parameter we want to keep\n", + " for param in params_to_keep:\n", + " # If the parameter is in the dictionary, add it to the file's parameters\n", + " if param in res:\n", + " file_parameters[param] = res[param]\n", + "\n", + " # Add the file's parameters to the overall parameters dictionary\n", + " parameters.append(file_parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NamecriterionCRRAWealthShareBeqFacBeqShift
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" + ], + "text/plain": [ + " Name criterion CRRA WealthShare BeqFac BeqShift\n", + "0 Portfolio 0.642 9.252 \n", + "1 WarmGlowPortfolio 0.641 9.207 23.051 45.643\n", + "2 WealthPortfolio 0.242 5.336 0.171 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def format_df(df):\n", + " for col in df.columns:\n", + " if col == \"Name\":\n", + " continue\n", + " # Check if column is of float type\n", + " if col in params_to_keep:\n", + " df[col] = df[col].astype(float).round(3).fillna(\"\")\n", + " # Check if column contains only 0 and 1\n", + " else:\n", + " df[col] = df[col].map({0: \"\", 1: \"✔️\"})\n", + " return df\n", + "\n", + "\n", + "# Define the order of columns\n", + "column_order = [\"Name\", \"criterion\", \"CRRA\", \"WealthShare\", \"BeqFac\", \"BeqShift\"]\n", + "\n", + "df = pd.DataFrame(parameters)\n", + "formatted_df = format_df(df)[column_order].sort_index()\n", + "formatted_df\n", + "\n", + "\n", + "# Life cycle portfolio choice\n", + "# Bequest portfolio Choice\n", + "# TRP Life cycle portfolio choice\n", + "# leave out wealth shift" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "formatted_df.to_latex(\"../../content/tables/parameters.tex\", index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "estimatingmicrodsops", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/notebooks/testing_notebook.ipynb b/src/notebooks/testing_notebook.ipynb new file mode 100644 index 0000000..2eb0ffe --- /dev/null +++ b/src/notebooks/testing_notebook.ipynb @@ -0,0 +1,117 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "ImportError", + "evalue": "cannot import name 'init_consumer_objects' from 'estimark.parameters' (/mnt/c/Users/alujan/GitHub/alanlujan91/EstimatingMicroDSOPs/code/estimark/parameters.py)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mestimark\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01magents\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m IndShkLifeCycleConsumerType\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mestimark\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mparameters\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 3\u001b[0m init_consumer_objects,\n\u001b[1;32m 4\u001b[0m init_subjective_labor,\n\u001b[1;32m 5\u001b[0m )\n", + "\u001b[0;31mImportError\u001b[0m: cannot import name 'init_consumer_objects' from 'estimark.parameters' (/mnt/c/Users/alujan/GitHub/alanlujan91/EstimatingMicroDSOPs/code/estimark/parameters.py)" + ] + } + ], + "source": [ + "from __future__ import annotations\n", + "\n", + "from estimark.agents import IndShkLifeCycleConsumerType\n", + "from estimark.parameters import (\n", + " init_consumer_objects,\n", + " init_subjective_labor,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "agent = IndShkLifeCycleConsumerType(**init_consumer_objects)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pre = agent.PermShkDstn[0].atoms" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "agent.PermShkStd = init_subjective_labor[\"PermShkStd\"]\n", + "agent.update_income_process()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "post = agent.PermShkDstn[0].atoms" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.29788637, -0.18268043, -0.10486472, -0.0302585 , 0.05262022,\n", + " 0.16142052, 0.40164928]])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pre - post" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "estimatingmicrodsops", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/run_all.py b/src/run_all.py new file mode 100644 index 0000000..39dda9d --- /dev/null +++ b/src/run_all.py @@ -0,0 +1,33 @@ +from __future__ import annotations + +from estimark.estimation import estimate +from estimark.options import low_resource + +agent_names = [ + "Portfolio", + "WealthPortfolio", + "WarmGlowPortfolio", +] + + +# Ask the user which replication to run, and run it: +def run_replication(): + for agent_name in agent_names: + for sub_stock in [0]: + temp_agent_name = agent_name + if sub_stock: + temp_agent_name += "Sub(Stock)Market" + + replication_specs = low_resource.copy() + replication_specs["agent_name"] = temp_agent_name + replication_specs["save_dir"] = "content/tables/TRP" + + print("Model: ", replication_specs["agent_name"]) + + estimate(**replication_specs) + + print("All replications complete.") + + +if __name__ == "__main__": + run_replication() diff --git a/src/run_all_msm.py b/src/run_all_msm.py new file mode 100644 index 0000000..ae35064 --- /dev/null +++ b/src/run_all_msm.py @@ -0,0 +1,66 @@ +from __future__ import annotations + +import itertools + +import dask +from dask.distributed import Client + +from estimark.estimation import estimate, get_empirical_moments, get_moments_cov +from estimark.options import low_resource + +agent_names = [ + "IndShock", + "Portfolio", + "WarmGlow", + "WarmGlowPortfolio", + "WealthPortfolio", +] + + +def run_replication(): + inds_emp_moments = get_empirical_moments("IndShock") + port_emp_moments = get_empirical_moments("Porfolio") + + inds_moments_cov = get_moments_cov("IndShock", inds_emp_moments) + port_moments_cov = get_moments_cov("Porfolio", port_emp_moments) + + client = Client(threads_per_worker=10, n_workers=20) + + lazy_results = [] + + for agent_name in agent_names: + for sub_stock, sub_labor in itertools.product(range(2), repeat=2): + temp_agent_name = agent_name + if sub_stock or sub_labor: + temp_agent_name += "Sub" + if sub_stock: + temp_agent_name += "(Stock)" + if sub_labor: + temp_agent_name += "(Labor)" + temp_agent_name += "Market" + + replication_specs = low_resource.copy() + replication_specs["agent_name"] = temp_agent_name + replication_specs["save_dir"] = "content/tables/msm" + + print("Model: ", replication_specs["agent_name"]) + + if "Portfolio" in replication_specs["agent_name"]: + replication_specs["emp_moments"] = port_emp_moments + replication_specs["moments_cov"] = port_moments_cov + else: + replication_specs["emp_moments"] = inds_emp_moments + replication_specs["moments_cov"] = inds_moments_cov + + replication_specs["estimate_method"] = "msm" + + lazy_result = dask.delayed(estimate)(**replication_specs) + lazy_results.append(lazy_result) + + dask.compute(*lazy_results) + + client.close() + + +if __name__ == "__main__": + run_replication() diff --git a/src/stata/AppendDataUsingSCF1992_2007.do b/src/stata/AppendDataUsingSCF1992_2007.do new file mode 100644 index 0000000..91c8f5c --- /dev/null +++ b/src/stata/AppendDataUsingSCF1992_2007.do @@ -0,0 +1,34 @@ +* AppendDataUsingSCF1992_2007.do +* This file gives selected varaibles of the Population +clear + +cd $basePath/$logPath +cap log close +cap log using ./AppendDataUsingSCF1992_2007.log, replace +cd $basePath/$stataPath + +** Construct data +do "SelectVarsUsingSCF1992.do" +do "SelectVarsUsingSCF1995.do" +do "SelectVarsUsingSCF1998.do" +do "SelectVarsUsingSCF2001.do" +do "SelectVarsUsingSCF2004.do" +do "SelectVarsUsingSCF2007.do" + +cd ../../Data/Constructed +append using SCF2004_population +append using SCF2001_population +append using SCF1998_population +append using SCF1995_population +append using SCF1992_population +drop if AGE<26 +drop if AGE>65 + +** Save data +save SCF1992_2007_population, replace + +/* Note: In the waves between 1995 and 2004, levels of normal income are reported. I interpret the level of normal income as being permanent income. Levels of normal income are not reported in the 1992 wave. Instead, in this wave there is a variable which reports whether the level of income is normal or not. Regarding the 1992 wave, only observations which report that the level of income is normal are used, and the levels of income of remaining observations in the 1992 wave are interpreted as the levels of permanent income. */ + +cd $basePath/$stataPath /* When program ends, make sure working directory is the program's directory */ + +log close diff --git a/src/stata/ReadMe.txt b/src/stata/ReadMe.txt new file mode 100644 index 0000000..5875f83 --- /dev/null +++ b/src/stata/ReadMe.txt @@ -0,0 +1,67 @@ + + +In order for the files in this directory to work properly, the Federal +Reserve's SCF datasets +that the programs use must be located in +appropriate directories that are accessible to the +programs. For +example, the 1992 SCF needs to be located at + + + +../../../Downloads/SCF/1992 + + + +An all the required SCF datasets in the appropriate directory +structure can be downloaded from + + + +ftp://llorracc.net/VaultPub/Data/SCF + + + +or the entire set of SCF's (warning: it is very large) from + + + + ftp://llorracc.net/VaultPub/Data/SCF.zip + + + +which has SCF files downloaded on 2011/08/06. + +Alternatively, the latest versions of the individual +files can be +obtained directly from the Fed's website: + + + +http://www.federalreserve.gov/pubs/oss/oss2/scfindex.html + + + +but if you download them one-by-one you need to make sure that you put them in a directory structure +corresponding +to the structure at + +ftp://llorracc.net/VaultPub/Data/SCF + + +(you don't need to download the extra files like codebooks etc; all that is needed for the programs to +work is the +Stata scf files, like scf92.dta for the 1992 SCF, which should be in a directory 1992/scf92.dta) + +*********************************************************************************************************** + +doAll.do file runs all the programs. + +In Particular: + +1) SelectVarsUsingSCFXXXX.do: Selects the variables from the SCF raw data and construct the Permanent income, + wealth and the weights of each household in the population for the year XXXX. +2) AppendDataUsingSCF1992_2007.do: Appends the outcomes of the SelectVarsUsingSCFXXXX. +3) WIRatioPopulation.do: Constructs the Wealth to after tax permanent income ratio of each households. And save the + output "SCFdata.txt" in the folder "./Code/Mathematica/StructuralEstimation" which + is used by the Mathematica programs to estimate the structural parameters. diff --git a/src/stata/SelectVarsUsingSCF1992.do b/src/stata/SelectVarsUsingSCF1992.do new file mode 100644 index 0000000..6ef4de5 --- /dev/null +++ b/src/stata/SelectVarsUsingSCF1992.do @@ -0,0 +1,267 @@ +* This file selects variables, using SCF92 +* This file closely follows codes written for SAS which create summary variables. +* (These codes are available at http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt) + + +clear + +** Set memeory +set memory 32m + +global startDir "`c(pwd)'" +global scfFldr 1992 +global scfFile scf92 +global SuffixForConstructedFile "_population" + +cd $startDir + +cd ../../../Downloads/SCF/$scfFldr + +cap confirm file $scfFile.dta +if _rc~=0 { + display "File $scfFile is not in the Downloads/SCF/$scfFldr folder; please see ReadMe.txt for instructions." + exit +} + + +** Load data and pick up necessary vars from original data +use y1 x42000 x42001 x5729 x5751 x7650 /// + x3506 x3507 x3510 x3511 x3514 x3515 x3518 x3519 x3522 x3523 x3526 x3527 x3529 /// + x3804 x3807 x3810 x3813 x3816 x3818 /// + x3506 x3507 x9113 x3510 x3511 x9114 x3514 x3515 x9115 x3518 x3519 x9116 x3522 x3523 x9117 x3526 x3527 x9118 x3529 x3706 x9131 x3711 x9132 x3716 x9133 x3718 /// + x3930 /// + x3721 x3821 x3822 x3823 x3824 x3825 x3826 x3827 x3828 x3829 x3830 x3915 x3910 x3906 x3908 x7634 x7633 /// + x3610 x3620 x3630 x3631 /// + x4216 x4316 x4416 x4816 x4916 x5016 x4226 x4326 x4426 x4826 x4926 x5026 x4227 x4327 x4427 x4827 x4927 x5027 x4231 x4331 x4431 x4831 x4931 x5031 x4234 x4334 x4434 x4834 x4934 x5034 x4436 x5036 /// + x3902 x4006 x3942 x3947 x4018 x4022 x4020 x4026 x4024 x4030 x4028 /// + x8166 x8167 x8168 x2422 x2506 x2606 x2623 /// + x507 x604 x614 x623 x716 x513 x526 /// + x1405 x1409 x1505 x1509 x1605 x1609 x1619 x1703 x1706 x1705 x1803 x1806 x1805 x1903 x1906 x1905 x2002 x1715 x1815 x1915 x2016 x2012 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 /// + x3129 x3124 x3126 x3127 x3121 x3122 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 x3229 x3224 x3226 x3227 x3221 x3222 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 x3329 x3324 x3326 x3327 x3321 x3322 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 x3335 x507 x513 x526 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 x3408 x3412 x3416 x3420 x3424 x3428 /// + x4022 x4026 x4030 /// + x805 x905 x1005 x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// + x1417 x1517 x1617 x1621 x2006 /// + x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// + x427 x413 x421 x430 x424 x7575 /// + x2218 x2318 x2418 x2424 x2519 x2619 x2625 x7824 x7847 x7870 x7924 x7947 x7970 x1044 x1215 x1219 /// + x4229 x4230 x4329 x4330 x4429 x4430 x4829 x4830 x4929 x4930 x5029 x5030 /// + x4010 x3932 x4032 /// + x14 x19 x8020 x8023 x5902 x5904 x6102 x6104 /// + using "scf92.dta" + + +** Generate variables +* ID +gen ID = y1 /* ID # */ +gen HHID = (y1-mod(y1,10))/10 /* HH ID # */ +gen YEAR = 1992 /* Indicates data is from which wave */ + +* Weight +gen WGT = x42001 + +* Income +gen INCOME = x5729 /* Income before tax */ +scalar CPIBASE = 2116 /* September 1992 consumer price index level, the numbers can be found in http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt*/ +scalar CPIADJ = CPIBASE/2116 /* Adjust with CPI (adjusted to 1992$ price) */ +scalar CPILAG = 2103/2051 /* Income is the previous year's income level, CPILAG adjust income to survey year */ +replace INCOME = INCOME*CPILAG*CPIADJ /* Adjust with CPI (adjusted to 1992$ price) */ +gen D_NORMINC = (x7650==3) /* D_NORMINC = 1 if inc level is normal */ +keep if D_NORMINC == 1 /* Keep if inc level is normal */ + +* Asset +gen CHECKING = max(0,x3506)*(x3507==5)+max(0,x3510)*(x3511==5) /// + +max(0,x3514)*(x3515==5)+max(0,x3518)*(x3519==5) /// + +max(0,x3522)*(x3523==5)+max(0,x3526)*(x3527==5) /// + +max(0,x3529)*(x3527==5) +gen SAVING = max(0,x3804)+max(0,x3807)+max(0,x3810)+max(0,x3813) /// + +max(0,x3816)+max(0,x3818) + gen MMDA = max(0,x3506)*((x3507==1)*(11<=x9113 & x9113<=13)) /// + +max(0,x3510)*((x3511==1)*(11<=x9114 & x9114<=13)) /// + +max(0,x3514)*((x3515==1)*(11<=x9115 & x9115<=13)) /// + +max(0,x3518)*((x3519==1)*(11<=x9116 & x9116<=13)) /// + +max(0,x3522)*((x3523==1)*(11<=x9117 & x9117<=13)) /// + +max(0,x3526)*((x3527==1)*(11<=x9118 & x9118<=13)) /// + +max(0,x3529)*((x3527==1)*(11<=x9118 & x9118<=13)) /// + +max(0,x3706)*(11<=x9131 & x9131<=13) /// + +max(0,x3711)*(11<=x9132 & x9132<=13) /// + +max(0,x3716)*(11<=x9133 & x9133<=13) /// + +max(0,x3718)*(11<=x9133 & x9133<=13) + gen MMMF = max(0,x3506)*(x3507==1)*(x9113<11|x9113>13) /// + +max(0,x3510)*(x3511==1)*(x9114<11|x9114>13) /// + +max(0,x3514)*(x3515==1)*(x9115<11|x9115>13) /// + +max(0,x3518)*(x3519==1)*(x9116<11|x9116>13) /// + +max(0,x3522)*(x3523==1)*(x9117<11|x9117>13) /// + +max(0,x3526)*(x3527==1)*(x9118<11|x9118>13) /// + +max(0,x3529)*(x3527==1)*(x9118<11|x9118>13) /// + +max(0,x3706)*(x9131<11|x9131>13) /// + +max(0,x3711)*(x9132<11|x9132>13) /// + +max(0,x3716)*(x9133<11|x9133>13) /// + +max(0,x3718)*(x9133<11|x9133>13) +gen MMA = MMDA+MMMF +gen CALL = max(0,x3930) +gen LIQ = CHECKING+SAVING+MMA+CALL + +gen CDS = max(0,x3721) + gen STMUTF = (x3821==1)*max(0,x3822) + gen TFBMUTF = (x3823==1)*max(0,x3824) + gen GBMUTF = (x3825==1)*max(0,x3826) + gen OBMUTF = (x3827==1)*max(0,x3828) + gen COMUTF = (x3829==1)*max(0,x3830) + gen SNMMF = TFBMUTF+GBMUTF+OBMUTF+(.5*(COMUTF)) + gen RNMMF = STMUTF + (.5*(COMUTF)) +gen NMMF = SNMMF + RNMMF +gen STOCKS = max(0,x3915) + gen NOTXBND = x3910 + gen MORTBND = x3906 + gen GOVTBND = x3908 + gen OBND = x7634+x7633 +gen BOND = NOTXBND + MORTBND + GOVTBND + OBND + gen IRAKH = max(0,x3610)+max(0,x3620)+max(0,x3630) + gen THRIFT = max(0,x4226)*(x4216==1|x4216==2|x4227==1|x4231==1) /// + +max(0,x4326)*(x4316==1|x4316==2|x4327==1|x4331==1) /// + +max(0,x4426)*(x4416==1|x4416==2|x4427==1|x4431==1) /// + +max(0,x4826)*(x4816==1|x4816==2|x4827==1|x4831==1) /// + +max(0,x4926)*(x4916==1|x4916==2|x4927==1|x4931==1) /// + +max(0,x5026)*(x5016==1|x5016==2|x5027==1|x5031==1) + gen PMOP = x4436 + replace PMOP = 0 if x4436<=0 + replace PMOP = 0 if x4216!=0 & x4316!=0 & x4416!=0 & x4231!=0 & x4331!=0 & x4431!=0 + replace THRIFT = THRIFT + PMOP + replace PMOP = x5036 + replace PMOP = 0 if x5036<=0 + replace PMOP = 0 if x4816!=0 & x4916!=0 & x5016!=0 & x4831!=0 & x4931!=0 & x5031!=0 + replace THRIFT = THRIFT + PMOP +gen RETQLIQ = IRAKH + THRIFT +gen SAVBND = x3902 +gen CASHLI = max(0,x4006) + gen ROTHMA = 0 + gen SOTHMA = 0 + gen COTHMA = 0 + replace ROTHMA = x3942 if x3947==1 | x3947==3 + replace SOTHMA = x3942 if x3947==2 | x3947==7 + replace COTHMA = x3942 if x3947==5 | x3947==6 | x3947==8 | x3947==9 | x3947==-7 +gen OTHMA = ROTHMA+SOTHMA+COTHMA +gen OTHFIN = x4018+x4022*(x4020==61|x4020==62|x4020==63|x4020==64|x4020==66|x4020==71|x4020==73|x4020==74) /// + +x4026*(x4024==61|x4024==62|x4024==63|x4024==64|x4024==66|x4024==71|x4024==73|x4024==74) /// + +x4030*(x4028==61|x4028==62|x4028==63|x4028==64|x4028==66|x4028==71|x4028==73|x4028==74) +gen FIN = LIQ+CDS+NMMF+STOCKS+BOND+RETQLIQ+SAVBND+CASHLI+OTHMA+OTHFIN /* Total fin asset */ + +gen VEHIC = max(0,x8166)+max(0,x8167)+max(0,x8168) /// + +max(0,x2422)+max(0,x2506)+max(0,x2606)+max(0,x2623) +replace x507 = 9000 if x507 > 9000 +gen HOUSES = (x604+x614+x623+x716) + ((10000-x507)/10000)*(x513+x526) +gen ORESRE = max(x1405,x1409)+max(x1505,x1509)+max(x1605,x1609)+max(0,x1619) /// + +(x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// + *max(0,x1706)*(x1705/10000) /// + +(x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// + *max(0,x1806)*(x1805/10000) /// + +(x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// + *max(0,x1906)*(x1905/10000) /// + +max(0,x2002) +gen NNRESRE = (x1703==1|x1703==2|x1703==3|x1703==4|x1703==5|x1703==6|x1703==7|x1703==10|x1703==11|x1703==13|x1703==15|x1703==24|x1703==45|x1703==46|x1703==47|x1703==48|x1703==51|x1703==-7) /// + *(max(0,x1706)*(x1705/10000)-x1715*(x1705/10000)) /// + +(x1803==1|x1803==2|x1803==3|x1803==4|x1803==5|x1803==6|x1803==7|x1803==10|x1803==11|x1803==13|x1803==15|x1803==24|x1803==45|x1803==46|x1803==47|x1803==48|x1803==51|x1803==-7) /// + *(max(0,x1806)*(x1805/10000)-x1815*(x1805/10000)) /// + +(x1903==1|x1903==2|x1903==3|x1903==4|x1903==5|x1903==6|x1903==7|x1903==10|x1903==11|x1903==13|x1903==15|x1903==24|x1903==45|x1903==46|x1903==47|x1903==48|x1903==51|x1903==-7) /// + *(max(0,x1906)*(x1905/10000)-x1915*(x1905/10000)) /// + +max(0,x2012)-x2016 +replace NNRESRE = NNRESRE-x2723*(x2710==78)-x2740*(x2727==78)-x2823*(x2810==78) /// + -x2840*(x2827==78)-x2923*(x2910==78)-x2940*(x2927==78) if NNRESRE!=0 +gen FLAG781 = (NNRESRE!=0) +gen BUS = max(0,x3129)+max(0,x3124)-max(0,x3126)*(x3127==5)+max(0,x3121)*(x3122==1) /// + +max(0,x2723*(x2710==71))+max(0,x2740*(x2727==71)) /// + +max(0,x2823*(x2810==71))+max(0,x2840*(x2827==71)) /// + +max(0,x2923*(x2910==71))+max(0,x2940*(x2927==71)) /// + +max(0,x3229)+max(0,x3224)-max(0,x3226)*(x3227==5) /// + +max(0,x3221)*(x3222==1)+max(0,x2723*(x2710==72))+max(0,x2740*(x2727==72)) /// + +max(0,x2823*(x2810==72))+max(0,x2840*(x2827==72)) /// + +max(0,x2923*(x2910==72))+max(0,x2940*(x2927==72)) /// + +max(0,x3329)+max(0,x3324)-max(0,x3326)*(x3327==5) /// + +max(0,x3321)*(x3322==1)+max(0,x2723*(x2710==73))+max(0,x2740*(x2727==73)) /// + +max(0,x2823*(x2810==73))+max(0,x2840*(x2827==73)) /// + +max(0,x2923*(x2910==73))+max(0,x2940*(x2927==73)) /// + +max(0,x3335)+(x507/10000)*(x513+x526) /// + +max(0,x2723*(x2710==74))+max(0,x2740*(x2727==74)) /// + +max(0,x2823*(x2810==74))+max(0,x2840*(x2827==74)) /// + +max(0,x2923*(x2910==74))+max(0,x2940*(x2927==74)) /// + +max(0,x3408)+max(0,x3412)+max(0,x3416)+max(0,x3420)+max(0,x3424)+max(0,x3428) +gen OTHNFIN = x4022 + x4026 + x4030 - OTHFIN + x4018 +gen NFIN = VEHIC+HOUSES+ORESRE+NNRESRE+BUS+OTHNFIN +gen ASSET = FIN+NFIN /* Total asset */ + +* Debt +gen MRTHEL = x805+x905+x1005+x1108*(x1103==1)+x1119*(x1114==1) /// + +x1130*(x1125==1)+max(0,x1136)*(x1108*(x1103==1)+x1119*(x1114==1) /// + +x1130*(x1125==1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 +replace MRTHEL = x805+x905+x1005+.5*(max(0,x1136)) if (x1108+x1119+x1130)<1 + gen MORT1 = (x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// + *x1715*(x1705/10000) + gen MORT2 = (x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// + *x1815*(x1805/10000) + gen MORT3 = (x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// + *x1915*(x1905/10000) +gen RESDBT = x1417+x1517+x1617+x1621+MORT1+MORT2+MORT3+x2006 + gen FLAG782 = (FLAG781!=1 & ORESRE>0) +replace RESDBT = RESDBT+x2723*(x2710==78)+x2740*(x2727==78)+x2823*(x2810==78)+x2840*(x2827==78) /// + +x2923*(x2910==78)+x2940*(x2927==78) if FLAG781!=1 & ORESRE>0 + gen FLAG67 = (ORESRE>0) +replace RESDBT= RESDBT+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67)+x2840*(x2827==67) /// + +x2923*(x2910==67)+x2940*(x2927==67) if ORESRE>0 +gen OTHLOC = x1108*(x1103!=1)+x1119*(x1114!=1)+x1130*(x1125!=1) /// + +max(0,x1136)*(x1108*(x1103!=1)+x1119*(x1114!=1) /// + +x1130*(x1125!=1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 +replace OTHLOC = .5*(max(0,x1136)) if (x1108+x1119+x1130)<1 +gen CCBAL = max(0,x427)+max(0,x413)+max(0,x421)+max(0,x430)+max(0,x424)+max(0,x7575) +gen INSTALL = x2218+x2318+x2418+x2424+x2519+x2619+x2625+x7824 /// + +x7847+x7870+x7924+x7947+x7970+x1044+x1215+x1219 +replace INSTALL = INSTALL+x2723*(x2710==78)+x2740*(x2727==78) /// + +x2823*(x2810==78)+x2840*(x2827==78)+x2923*(x2910==78)+x2940*(x2927==78) /// + if FLAG781==0 & FLAG782==0 +replace INSTALL = INSTALL+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67) /// + +x2840*(x2827==67)+x2923*(x2910==67)+x2940*(x2927==67) if FLAG67==0 +replace INSTALL = INSTALL+x2723*(x2710!=67&x2710!=78)+x2740*(x2727!=67&x2727!=78) /// + +x2823*(x2810!=67&x2810!=78)+x2840*(x2827!=67&x2827!=78)+x2923*(x2910!=67&x2910!=78) /// + +x2940*(x2927!=67&x2927!=78) +gen PENDBT = max(0,x4229)*(x4230==5)+max(0,x4329)*(x4330==5) /// + +max(0,x4429)*(x4430==5)+max(0,x4829)*(x4830==5) /// + +max(0,x4929)*(x4930==5)+max(0,x5029)*(x5030==5) +gen CASHLIDB = max(0,x4010) +gen CALLDBT = max(0,x3932) +gen ODEBT = max(0,x4032) +gen DEBT = MRTHEL+RESDBT+OTHLOC+CCBAL+INSTALL+PENDBT+CASHLIDB+CALLDBT+ODEBT /* Total debt */ + +* Net worth +gen NETW = ASSET-DEBT +replace NETW = NETW*CPIADJ + +* Ratio of net worth to income +gen WIRATIO = NETW/INCOME + + +* Demographic vars +gen AGE = x14 /* Age */ +gen MARITST = x8023 /* Marital status */ +keep if MARITST == 1 /* Keep if married following Cagetti(2003) */ + +gen EDUC = 0 +replace EDUC = 1 if x5902!=1 /* No high school deg */ +replace EDUC = 2 if x5902==1 /* High school deg */ +replace EDUC = 3 if x5904==1 /* College deg */ +* keep if EDUC == 3 /* Keep college graduates only */ + +* Correct time effects. The base is set at 25 yrs old in 1980 (0 yrs old in 1955) +replace INCOME = INCOME/exp(0.016*(YEAR-1955-AGE)) +drop if INCOME<=0 +replace NETW = NETW/exp(0.016*(YEAR-1955-AGE)) + + +** Keep necessary vars +keep HHID YEAR WGT INCOME NETW WIRATIO AGE + +cd "$startDir" +cd ../../Data/Constructed +** Save data +save "./SCF$scfFldr$SuffixForConstructedFile", replace + +** End in the same directory you started from +cd "$startDir" diff --git a/src/stata/SelectVarsUsingSCF1995.do b/src/stata/SelectVarsUsingSCF1995.do new file mode 100644 index 0000000..bb9aea2 --- /dev/null +++ b/src/stata/SelectVarsUsingSCF1995.do @@ -0,0 +1,264 @@ +* This file selects variables, using SCF95 +* This file closely follows codes written for SAS which create summary variables. +* (These codes are available at http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt) + +clear + +** Set memeory +set memory 32m + +global startDir "`c(pwd)'" +global scfFldr 1995 +global scfFile scf95 +global SuffixForConstructedFile "_population" + +cd ../../../Downloads/SCF/$scfFldr + +cap confirm file $scfFile.dta +if _rc~=0 { + display "File $scfFile is not in the Downloads/SCF/$scfFldr folder; please see ReadMe.txt for instructions." + exit +} + + + +** Load data and pick up necessary vars from original data +use y1 x42000 x42001 x5729 x7362 x5751 x7650 /// + x3506 x3507 x3510 x3511 x3514 x3515 x3518 x3519 x3522 x3523 x3526 x3527 x3529 /// + x3804 x3807 x3810 x3813 x3816 x3818 /// + x3506 x3507 x9113 x3510 x3511 x9114 x3514 x3515 x9115 x3518 x3519 x9116 x3522 x3523 x9117 x3526 x3527 x9118 x3529 x3706 x9131 x3711 x9132 x3716 x9133 x3718 /// + x3930 /// + x3721 x3821 x3822 x3823 x3824 x3825 x3826 x3827 x3828 x3829 x3830 x3915 x3910 x3906 x3908 x7634 x7633 /// + x3610 x3620 x3630 x3631 /// + x4216 x4316 x4416 x4816 x4916 x5016 x4226 x4326 x4426 x4826 x4926 x5026 x4227 x4327 x4427 x4827 x4927 x5027 x4231 x4331 x4431 x4831 x4931 x5031 x4234 x4334 x4434 x4834 x4934 x5034 x4436 x5036 /// + x3902 x4006 x3942 x3947 x4018 x4022 x4020 x4026 x4024 x4030 x4028 /// + x8166 x8167 x8168 x8188 x2422 x2506 x2606 x2623 /// + x507 x604 x614 x623 x716 x513 x526 x7134 x716 x701 x7133 /// + x1405 x1409 x1505 x1509 x1605 x1609 x1619 x1703 x1706 x1705 x1803 x1806 x1805 x1903 x1906 x1905 x2002 x2012 x1715 x1815 x1915 x2016 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 /// + x3129 x3124 x3126 x3127 x3121 x3122 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 x3229 x3224 x3226 x3227 x3221 x3222 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 x3329 x3324 x3326 x3327 x3321 x3322 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 x3335 x507 x513 x526 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 x3408 x3412 x3416 x3420 x3424 x3428 /// + x4022 x4026 x4030 /// + x805 x905 x1005 x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// + x1417 x1517 x1617 x1621 x2006 /// + x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// + x427 x413 x421 x430 x424 x7575 /// + x2218 x2318 x2418 x7169 x2424 x2519 x2619 x2625 x7183 x7824 x7847 x7870 x7924 x7947 x7970 x7179 x1044 x1215 x1219 /// + x4229 x4230 x4329 x4330 x4429 x4430 x4829 x4830 x4929 x4930 x5029 x5030 /// + x4010 x3932 x7194 x4032 /// + x14 x19 x8020 x8023 x5902 x5904 x6102 x6104 /// + using "scf95.dta" + +** Generate variables +* ID +gen ID = y1 /* ID # */ +gen HHID = (y1-mod(y1,10))/10 /* HH ID # */ +gen YEAR = 1995 /* Indicates data is from which wave */ + +* Weight +gen WGT = x42001 + +* Income +gen INCOME = x5729 /* Income before tax */ +replace INCOME = x7362 if x7650!=3 +scalar CPIBASE = 2116 /* September 1992 consumer price index level, the numbers can be found in http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt*/ +scalar CPIADJ = CPIBASE/2265 /* Adjust with CPI (adjusted to 1992$ price) */ +scalar CPILAG = 2254/2201 /* Income is the previous year's income level, CPILAG adjust income to survey year */ +replace INCOME = INCOME*CPILAG*CPIADJ /* Adjust with CPI (adjusted to 1992$ price) */ + +* Asset +gen CHECKING = max(0,x3506)*(x3507==5)+max(0,x3510)*(x3511==5) /// + +max(0,x3514)*(x3515==5)+max(0,x3518)*(x3519==5) /// + +max(0,x3522)*(x3523==5)+max(0,x3526)*(x3527==5) /// + +max(0,x3529)*(x3527==5) +gen SAVING = max(0,x3804)+max(0,x3807)+max(0,x3810)+max(0,x3813) /// + +max(0,x3816)+max(0,x3818) + gen MMDA = max(0,x3506)*((x3507==1)*(11<=x9113 & x9113<=13)) /// + +max(0,x3510)*((x3511==1)*(11<=x9114 & x9114<=13)) /// + +max(0,x3514)*((x3515==1)*(11<=x9115 & x9115<=13)) /// + +max(0,x3518)*((x3519==1)*(11<=x9116 & x9116<=13)) /// + +max(0,x3522)*((x3523==1)*(11<=x9117 & x9117<=13)) /// + +max(0,x3526)*((x3527==1)*(11<=x9118 & x9118<=13)) /// + +max(0,x3529)*((x3527==1)*(11<=x9118 & x9118<=13)) /// + +max(0,x3706)*(11<=x9131 & x9131<=13) /// + +max(0,x3711)*(11<=x9132 & x9132<=13) /// + +max(0,x3716)*(11<=x9133 & x9133<=13) /// + +max(0,x3718)*(11<=x9133 & x9133<=13) + gen MMMF = max(0,x3506)*(x3507==1)*(x9113<11|x9113>13) /// + +max(0,x3510)*(x3511==1)*(x9114<11|x9114>13) /// + +max(0,x3514)*(x3515==1)*(x9115<11|x9115>13) /// + +max(0,x3518)*(x3519==1)*(x9116<11|x9116>13) /// + +max(0,x3522)*(x3523==1)*(x9117<11|x9117>13) /// + +max(0,x3526)*(x3527==1)*(x9118<11|x9118>13) /// + +max(0,x3529)*(x3527==1)*(x9118<11|x9118>13) /// + +max(0,x3706)*(x9131<11|x9131>13) /// + +max(0,x3711)*(x9132<11|x9132>13) /// + +max(0,x3716)*(x9133<11|x9133>13) /// + +max(0,x3718)*(x9133<11|x9133>13) +gen MMA = MMDA+MMMF +gen CALL = max(0,x3930) +gen LIQ = CHECKING+SAVING+MMA+CALL + +gen CDS = max(0,x3721) + gen STMUTF = (x3821==1)*max(0,x3822) + gen TFBMUTF = (x3823==1)*max(0,x3824) + gen GBMUTF = (x3825==1)*max(0,x3826) + gen OBMUTF = (x3827==1)*max(0,x3828) + gen COMUTF = (x3829==1)*max(0,x3830) + gen SNMMF = TFBMUTF+GBMUTF+OBMUTF+(.5*(COMUTF)) + gen RNMMF = STMUTF + (.5*(COMUTF)) +gen NMMF = SNMMF + RNMMF +gen STOCKS = max(0,x3915) + gen NOTXBND = x3910 + gen MORTBND = x3906 + gen GOVTBND = x3908 + gen OBND = x7634+x7633 +gen BOND = NOTXBND + MORTBND + GOVTBND + OBND + gen IRAKH = max(0,x3610)+max(0,x3620)+max(0,x3630) + gen THRIFT = max(0,x4226)*(x4216==1|x4216==2|x4227==1|x4231==1) /// + +max(0,x4326)*(x4316==1|x4316==2|x4327==1|x4331==1) /// + +max(0,x4426)*(x4416==1|x4416==2|x4427==1|x4431==1) /// + +max(0,x4826)*(x4816==1|x4816==2|x4827==1|x4831==1) /// + +max(0,x4926)*(x4916==1|x4916==2|x4927==1|x4931==1) /// + +max(0,x5026)*(x5016==1|x5016==2|x5027==1|x5031==1) + gen PMOP = x4436 + replace PMOP = 0 if x4436<=0 + replace PMOP = 0 if x4216!=0 & x4316!=0 & x4416!=0 & x4231!=0 & x4331!=0 & x4431!=0 + replace THRIFT = THRIFT + PMOP + replace PMOP = x5036 + replace PMOP = 0 if x5036<=0 + replace PMOP = 0 if x4816!=0 & x4916!=0 & x5016!=0 & x4831!=0 & x4931!=0 & x5031!=0 + replace THRIFT = THRIFT + PMOP +gen RETQLIQ = IRAKH + THRIFT +gen SAVBND = x3902 +gen CASHLI = max(0,x4006) + gen ROTHMA = 0 + gen SOTHMA = 0 + gen COTHMA = 0 + replace ROTHMA = x3942 if x3947==1 | x3947==3 + replace SOTHMA = x3942 if x3947==2 | x3947==7 + replace COTHMA = x3942 if x3947==5 | x3947==6 | x3947==8 | x3947==9 | x3947==-7 +gen OTHMA = ROTHMA+SOTHMA+COTHMA +gen OTHFIN = x4018+x4022*(x4020==62|x4020==63|x4020==64|x4020==66|x4020==71|x4020==73|x4020==74|x4020==-7) /// + +x4026*(x4024==62|x4024==63|x4024==64|x4024==66|x4024==71|x4024==73|x4024==74|x4024==-7) /// + +x4030*(x4028==62|x4028==63|x4028==64|x4028==66|x4028==71|x4028==73|x4028==74|x4028==-7) +gen FIN = LIQ+CDS+NMMF+STOCKS+BOND+RETQLIQ+SAVBND+CASHLI+OTHMA+OTHFIN /* Total fin asset */ + +gen VEHIC = max(0,x8166)+max(0,x8167)+max(0,x8168)+max(0,x8188) /// + +max(0,x2422)+max(0,x2506)+max(0,x2606)+max(0,x2623) +replace x507 = 9000 if x507 > 9000 +gen HOUSES = (x604+x614+x623+x716) + ((10000-x507)/10000)*(x513+x526) + * replace HOUSES = (x7134/10000)*x716 if x701==-7 & x7133==1 +gen ORESRE = max(x1405,x1409)+max(x1505,x1509)+max(x1605,x1609)+max(0,x1619) /// + +(x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// + *max(0,x1706)*(x1705/10000) /// + +(x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// + *max(0,x1806)*(x1805/10000) /// + +(x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// + *max(0,x1906)*(x1905/10000) /// + +max(0,x2002) +gen NNRESRE = (x1703==1|x1703==2|x1703==3|x1703==4|x1703==5|x1703==6|x1703==7|x1703==10|x1703==11|x1703==13|x1703==15|x1703==24|x1703==45|x1703==46|x1703==47|x1703==48|x1703==51|x1703==-7) /// + *(max(0,x1706)*(x1705/10000)-x1715*(x1705/10000)) /// + +(x1803==1|x1803==2|x1803==3|x1803==4|x1803==5|x1803==6|x1803==7|x1803==10|x1803==11|x1803==13|x1803==15|x1803==24|x1803==45|x1803==46|x1803==47|x1803==48|x1803==51|x1803==-7) /// + *(max(0,x1806)*(x1805/10000)-x1815*(x1805/10000)) /// + +(x1903==1|x1903==2|x1903==3|x1903==4|x1903==5|x1903==6|x1903==7|x1903==10|x1903==11|x1903==13|x1903==15|x1903==24|x1903==45|x1903==46|x1903==47|x1903==48|x1903==51|x1903==-7) /// + *(max(0,x1906)*(x1905/10000)-x1915*(x1905/10000)) /// + +max(0,x2012)-x2016 +replace NNRESRE = NNRESRE-x2723*(x2710==78)-x2740*(x2727==78)-x2823*(x2810==78) /// + -x2840*(x2827==78)-x2923*(x2910==78)-x2940*(x2927==78) if NNRESRE!=0 +gen FLAG781 = (NNRESRE!=0) +gen BUS = max(0,x3129)+max(0,x3124)-max(0,x3126)*(x3127==5)+max(0,x3121)*(x3122==1|x3222==6) /// + +max(0,x2723*(x2710==71))+max(0,x2740*(x2727==71)) /// + +max(0,x2823*(x2810==71))+max(0,x2840*(x2827==71)) /// + +max(0,x2923*(x2910==71))+max(0,x2940*(x2927==71)) /// + +max(0,x3229)+max(0,x3224)-max(0,x3226)*(x3227==5) /// + +max(0,x3221)*(x3222==1|x3222==6)+max(0,x2723*(x2710==72))+max(0,x2740*(x2727==72)) /// + +max(0,x2823*(x2810==72))+max(0,x2840*(x2827==72)) /// + +max(0,x2923*(x2910==72))+max(0,x2940*(x2927==72)) /// + +max(0,x3329)+max(0,x3324)-max(0,x3326)*(x3327==5) /// + +max(0,x3321)*(x3322==1|x3222==6)+max(0,x2723*(x2710==73))+max(0,x2740*(x2727==73)) /// + +max(0,x2823*(x2810==73))+max(0,x2840*(x2827==73)) /// + +max(0,x2923*(x2910==73))+max(0,x2940*(x2927==73)) /// + +max(0,x3335)+(x507/10000)*(x513+x526) /// + +max(0,x2723*(x2710==74))+max(0,x2740*(x2727==74)) /// + +max(0,x2823*(x2810==74))+max(0,x2840*(x2827==74)) /// + +max(0,x2923*(x2910==74))+max(0,x2940*(x2927==74)) /// + +max(0,x3408)+max(0,x3412)+max(0,x3416)+max(0,x3420)+max(0,x3424)+max(0,x3428) +gen OTHNFIN = x4022 + x4026 + x4030 - OTHFIN + x4018 +gen NFIN = VEHIC+HOUSES+ORESRE+NNRESRE+BUS+OTHNFIN +gen ASSET = FIN+NFIN /* Total asset */ + +* Debt +gen MRTHEL = x805+x905+x1005+x1108*(x1103==1)+x1119*(x1114==1) /// + +x1130*(x1125==1)+max(0,x1136)*(x1108*(x1103==1)+x1119*(x1114==1) /// + +x1130*(x1125==1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 +replace MRTHEL = x805+x905+x1005+.5*(max(0,x1136)) if (x1108+x1119+x1130)<1 + gen MORT1 = (x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// + *x1715*(x1705/10000) + gen MORT2 = (x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// + *x1815*(x1805/10000) + gen MORT3 = (x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// + *x1915*(x1905/10000) +gen RESDBT = x1417+x1517+x1617+x1621+MORT1+MORT2+MORT3+x2006 + gen FLAG782 = (FLAG781!=1 & ORESRE>0) +replace RESDBT = RESDBT+x2723*(x2710==78)+x2740*(x2727==78)+x2823*(x2810==78)+x2840*(x2827==78) /// + +x2923*(x2910==78)+x2940*(x2927==78) if FLAG781!=1 & ORESRE>0 + gen FLAG67 = (ORESRE>0) +replace RESDBT= RESDBT+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67)+x2840*(x2827==67) /// + +x2923*(x2910==67)+x2940*(x2927==67) if ORESRE>0 +gen OTHLOC = x1108*(x1103!=1)+x1119*(x1114!=1)+x1130*(x1125!=1) /// + +max(0,x1136)*(x1108*(x1103!=1)+x1119*(x1114!=1) /// + +x1130*(x1125!=1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 +replace OTHLOC = .5*(max(0,x1136)) if (x1108+x1119+x1130)<1 +gen CCBAL = max(0,x427)+max(0,x413)+max(0,x421)+max(0,x430)+max(0,x424)+max(0,x7575) +gen INSTALL = x2218+x2318+x2418+x7169+x2424+x2519+x2619+x2625+x7183 /// + +x7824+x7847+x7870+x7924+x7947+x7970+x7179+x1044+x1215+x1219 +replace INSTALL = INSTALL+x2723*(x2710==78)+x2740*(x2727==78) /// + +x2823*(x2810==78)+x2840*(x2827==78)+x2923*(x2910==78)+x2940*(x2927==78) /// + if FLAG781==0 & FLAG782==0 +replace INSTALL = INSTALL+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67) /// + +x2840*(x2827==67)+x2923*(x2910==67)+x2940*(x2927==67) if FLAG67==0 +replace INSTALL = INSTALL+x2723*(x2710!=67&x2710!=78)+x2740*(x2727!=67&x2727!=78) /// + +x2823*(x2810!=67&x2810!=78)+x2840*(x2827!=67&x2827!=78)+x2923*(x2910!=67&x2910!=78) /// + +x2940*(x2927!=67&x2927!=78) +gen PENDBT = max(0,x4229)*(x4230==5)+max(0,x4329)*(x4330==5) /// + +max(0,x4429)*(x4430==5)+max(0,x4829)*(x4830==5) /// + +max(0,x4929)*(x4930==5)+max(0,x5029)*(x5030==5) +gen CASHLIDB = max(0,x4010) +gen CALLDBT = max(0,x3932)*(x7194==5) +gen ODEBT = max(0,x4032) +gen DEBT = MRTHEL+RESDBT+OTHLOC+CCBAL+INSTALL+PENDBT+CASHLIDB+CALLDBT+ODEBT /* Total debt */ + +* Net worth +gen NETW = ASSET-DEBT +replace NETW = NETW*CPIADJ + +* Ratio of net worth to income +gen WIRATIO = NETW/INCOME + + +* Demographic vars +gen AGE = x14 /* Age */ +gen MARITST = x8023 /* Marital status */ +keep if MARITST == 1 /* Keep if married following Cagetti(2003) */ + +gen EDUC = 0 +replace EDUC = 1 if /* RTORESP==1 & */ x5902!=1 /* No high school deg */ +replace EDUC = 2 if /* RTORESP==1 & */ x5902==1 /* High school deg */ +replace EDUC = 3 if /* RTORESP==1 & */ x5904==1 /* College deg */ +* keep if EDUC == 3 /* Keep college graduates only */ + +* Correct time effects. The base is set at 25 yrs old in 1980 (0 yrs old in 1955) +replace INCOME = INCOME/exp(0.016*(YEAR-1955-AGE)) +drop if INCOME<=0 +replace NETW = NETW/exp(0.016*(YEAR-1955-AGE)) + + +** Keep necessary vars +keep HHID YEAR WGT INCOME NETW WIRATIO AGE + +cd "$startDir" +cd ../../Data/Constructed +** Save data +save "./SCF$scfFldr$SuffixForConstructedFile", replace + +** End in the same directory you started from +cd "$startDir" diff --git a/src/stata/SelectVarsUsingSCF1998.do b/src/stata/SelectVarsUsingSCF1998.do new file mode 100644 index 0000000..ad81634 --- /dev/null +++ b/src/stata/SelectVarsUsingSCF1998.do @@ -0,0 +1,265 @@ +* This file selects variables, using SCF98 +* This file closely follows codes written for SAS which create summary variables. +* (These codes are available at http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt) + +clear + +** Set memeory +set memory 32m + +global startDir "`c(pwd)'" +global scfFldr 1998 +global scfFile scf98 +global SuffixForConstructedFile "_population" + +cd ../../../Downloads/SCF/$scfFldr + +cap confirm file $scfFile.dta +if _rc~=0 { + display "File $scfFile is not in the Downloads/SCF/$scfFldr folder; please see ReadMe.txt for instructions." + exit +} + + +** Load data and pick up necessary vars from original data +use y1 x42000 x42001 x5729 x7362 x5751 x7650 /// + x3506 x3507 x3510 x3511 x3514 x3515 x3518 x3519 x3522 x3523 x3526 x3527 x3529 /// + x3804 x3807 x3810 x3813 x3816 x3818 /// + x3506 x3507 x9113 x3510 x3511 x9114 x3514 x3515 x9115 x3518 x3519 x9116 x3522 x3523 x9117 x3526 x3527 x9118 x3529 x3706 x9131 x3711 x9132 x3716 x9133 x3718 /// + x3930 /// + x3721 x3821 x3822 x3823 x3824 x3825 x3826 x3827 x3828 x3829 x3830 x3915 x3910 x3906 x3908 x7634 x7633 /// + x3610 x3620 x3630 x3631 /// + x4216 x4316 x4416 x4816 x4916 x5016 x4226 x4326 x4426 x4826 x4926 x5026 x4227 x4327 x4427 x4827 x4927 x5027 x4231 x4331 x4431 x4831 x4931 x5031 x4234 x4334 x4434 x4834 x4934 x5034 x4436 x5036 /// + x3902 x4006 x6820 x6826 x6835 x6841 x4018 x4022 x4020 x4026 x4024 x4030 x4028 /// + x8166 x8167 x8168 x8188 x2422 x2506 x2606 x2623 /// + x507 x604 x614 x623 x716 x513 x526 x7134 x716 x701 x7133 /// + x1405 x1409 x1505 x1509 x1605 x1609 x1619 x1703 x1706 x1705 x1803 x1806 x1805 x1903 x1906 x1905 x2002 x2012 x1715 x1815 x1915 x2016 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 /// + x3129 x3124 x3126 x3127 x3121 x3122 x3122 x3229 x3224 x3226 x3227 x3221 x3222 x3222 x3329 x3324 x3326 x3327 x3321 x3322 x3322 x3335 x507 x513 x526 x3408 x3412 x3416 x3420 x3424 x3428 /// + x4022 x4026 x4030 /// + x805 x905 x1005 x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// + x1417 x1517 x1617 x1621 x2006 /// + x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// + x427 x413 x421 x430 x424 x7575 /// + x2218 x2318 x2418 x7169 x2424 x2519 x2619 x2625 x7183 x7824 x7847 x7870 x7924 x7947 x7970 x7179 x1044 x1215 x1219 /// + x4229 x4230 x4329 x4330 x4429 x4430 x4829 x4830 x4929 x4930 x5029 x5030 /// + x4010 x3932 x4032 /// + x14 x19 x8020 x8023 x5902 x5904 x6102 x6104 /// + using "scf98.dta" + +** Generate variables +* ID +gen ID = y1 /* ID # */ +gen HHID = (y1-mod(y1,10))/10 /* HH ID # */ +gen YEAR = 1998 /* Indicates data is from which wave */ + +* Weight +gen WGT = x42001 + +* Income +gen INCOME = x5729 /* Income before tax */ +replace INCOME = x7362 if x7650!=3 +scalar CPIBASE = 2116 /* September 1992 consumer price index level, the numbers can be found in http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt*/ +scalar CPIADJ = CPIBASE/2405 /* Adjust with CPI (adjusted to 1992$ price) */ +scalar CPILAG = 2397/2364 /* Income is the previous year's income level, CPILAG adjust income to survey year */ +replace INCOME = INCOME*CPILAG*CPIADJ /* Adjust with CPI (adjusted to 1992$ price) */ + +* Asset +gen CHECKING = max(0,x3506)*(x3507==5)+max(0,x3510)*(x3511==5) /// + +max(0,x3514)*(x3515==5)+max(0,x3518)*(x3519==5) /// + +max(0,x3522)*(x3523==5)+max(0,x3526)*(x3527==5) /// + +max(0,x3529)*(x3527==5) +gen SAVING = max(0,x3804)+max(0,x3807)+max(0,x3810)+max(0,x3813) /// + +max(0,x3816)+max(0,x3818) + gen MMDA = max(0,x3506)*((x3507==1)*(11<=x9113 & x9113<=13)) /// + +max(0,x3510)*((x3511==1)*(11<=x9114 & x9114<=13)) /// + +max(0,x3514)*((x3515==1)*(11<=x9115 & x9115<=13)) /// + +max(0,x3518)*((x3519==1)*(11<=x9116 & x9116<=13)) /// + +max(0,x3522)*((x3523==1)*(11<=x9117 & x9117<=13)) /// + +max(0,x3526)*((x3527==1)*(11<=x9118 & x9118<=13)) /// + +max(0,x3529)*((x3527==1)*(11<=x9118 & x9118<=13)) /// + +max(0,x3706)*(11<=x9131 & x9131<=13) /// + +max(0,x3711)*(11<=x9132 & x9132<=13) /// + +max(0,x3716)*(11<=x9133 & x9133<=13) /// + +max(0,x3718)*(11<=x9133 & x9133<=13) + gen MMMF = max(0,x3506)*(x3507==1)*(x9113<11|x9113>13) /// + +max(0,x3510)*(x3511==1)*(x9114<11|x9114>13) /// + +max(0,x3514)*(x3515==1)*(x9115<11|x9115>13) /// + +max(0,x3518)*(x3519==1)*(x9116<11|x9116>13) /// + +max(0,x3522)*(x3523==1)*(x9117<11|x9117>13) /// + +max(0,x3526)*(x3527==1)*(x9118<11|x9118>13) /// + +max(0,x3529)*(x3527==1)*(x9118<11|x9118>13) /// + +max(0,x3706)*(x9131<11|x9131>13) /// + +max(0,x3711)*(x9132<11|x9132>13) /// + +max(0,x3716)*(x9133<11|x9133>13) /// + +max(0,x3718)*(x9133<11|x9133>13) +gen MMA = MMDA+MMMF +gen CALL = max(0,x3930) +gen LIQ = CHECKING+SAVING+MMA+CALL + +gen CDS = max(0,x3721) + gen STMUTF = (x3821==1)*max(0,x3822) + gen TFBMUTF = (x3823==1)*max(0,x3824) + gen GBMUTF = (x3825==1)*max(0,x3826) + gen OBMUTF = (x3827==1)*max(0,x3828) + gen COMUTF = (x3829==1)*max(0,x3830) + gen SNMMF = TFBMUTF+GBMUTF+OBMUTF+(.5*(COMUTF)) + gen RNMMF = STMUTF + (.5*(COMUTF)) +gen NMMF = SNMMF + RNMMF +gen STOCKS = max(0,x3915) + gen NOTXBND = x3910 + gen MORTBND = x3906 + gen GOVTBND = x3908 + gen OBND = x7634+x7633 +gen BOND = NOTXBND + MORTBND + GOVTBND + OBND + gen IRAKH = max(0,x3610)+max(0,x3620)+max(0,x3630) + gen THRIFT = max(0,x4226)*(x4216==1|x4216==2|x4227==1|x4231==1) /// + +max(0,x4326)*(x4316==1|x4316==2|x4327==1|x4331==1) /// + +max(0,x4426)*(x4416==1|x4416==2|x4427==1|x4431==1) /// + +max(0,x4826)*(x4816==1|x4816==2|x4827==1|x4831==1) /// + +max(0,x4926)*(x4916==1|x4916==2|x4927==1|x4931==1) /// + +max(0,x5026)*(x5016==1|x5016==2|x5027==1|x5031==1) + gen PMOP = x4436 + replace PMOP = 0 if x4436<=0 + replace PMOP = 0 if x4216!=0 & x4316!=0 & x4416!=0 & x4231!=0 & x4331!=0 & x4431!=0 + replace THRIFT = THRIFT + PMOP + replace PMOP = x5036 + replace PMOP = 0 if x5036<=0 + replace PMOP = 0 if x4816!=0 & x4916!=0 & x5016!=0 & x4831!=0 & x4931!=0 & x5031!=0 + replace THRIFT = THRIFT + PMOP +gen RETQLIQ = IRAKH + THRIFT +gen SAVBND = x3902 +gen CASHLI = max(0,x4006) + gen RANNUIT = 0 + gen SANNUIT = 0 + gen CANNUIT = 0 + gen RTRUST = 0 + gen STRUST = 0 + gen CTRUST = 0 + replace RANNUIT = x6820 if x6826== 1|x6826==3 + replace SANNUIT = x6820 if x6826== 2|x6826==7 + replace CANNUIT = x6820 if x6826== 5|x6826==6|x6826==8|x6826==9|x6826==-7 + replace RTRUST = x6835 if x6841==1|x6841==3 + replace STRUST = x6835 if x6841==2|x6841==7 + replace CTRUST = x6835 if x6841==5|x6841==6|x6841==8|x6841==9|x6841==-7 + gen ROTHMA = max(0,(RANNUIT + RTRUST)) + gen SOTHMA = max(0,(SANNUIT + STRUST)) + gen COTHMA = max(0,(CANNUIT + CTRUST)) +gen OTHMA = ROTHMA+SOTHMA+COTHMA +gen OTHFIN = x4018+x4022*(x4020==62|x4020==63|x4020==64|x4020==66|x4020==71|x4020==73|x4020==74|x4020==-7) /// + +x4026*(x4024==62|x4024==63|x4024==64|x4024==66|x4024==71|x4024==73|x4024==74|x4024==-7) /// + +x4030*(x4028==62|x4028==63|x4028==64|x4028==66|x4028==71|x4028==73|x4028==74|x4028==-7) +gen FIN = LIQ+CDS+NMMF+STOCKS+BOND+RETQLIQ+SAVBND+CASHLI+OTHMA+OTHFIN /* Total fin asset */ + +gen VEHIC = max(0,x8166)+max(0,x8167)+max(0,x8168)+max(0,x8188) /// + +max(0,x2422)+max(0,x2506)+max(0,x2606)+max(0,x2623) +replace x507 = 9000 if x507 > 9000 +gen HOUSES = (x604+x614+x623+x716) + ((10000-max(0,x507))/10000)*(x513+x526) + * replace HOUSES = (x7134/10000)*x716 if x701==-7 & x7133==1 +gen ORESRE = max(x1405,x1409)+max(x1505,x1509)+max(x1605,x1609)+max(0,x1619) /// + +(x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// + *max(0,x1706)*(x1705/10000) /// + +(x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// + *max(0,x1806)*(x1805/10000) /// + +(x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// + *max(0,x1906)*(x1905/10000) /// + +max(0,x2002) +gen NNRESRE = (x1703==1|x1703==2|x1703==3|x1703==4|x1703==5|x1703==6|x1703==7|x1703==10|x1703==11|x1703==13|x1703==15|x1703==24|x1703==45|x1703==46|x1703==47|x1703==48|x1703==51|x1703==-7) /// + *(max(0,x1706)*(x1705/10000)-x1715*(x1705/10000)) /// + +(x1803==1|x1803==2|x1803==3|x1803==4|x1803==5|x1803==6|x1803==7|x1803==10|x1803==11|x1803==13|x1803==15|x1803==24|x1803==45|x1803==46|x1803==47|x1803==48|x1803==51|x1803==-7) /// + *(max(0,x1806)*(x1805/10000)-x1815*(x1805/10000)) /// + +(x1903==1|x1903==2|x1903==3|x1903==4|x1903==5|x1903==6|x1903==7|x1903==10|x1903==11|x1903==13|x1903==15|x1903==24|x1903==45|x1903==46|x1903==47|x1903==48|x1903==51|x1903==-7) /// + *(max(0,x1906)*(x1905/10000)-x1915*(x1905/10000)) /// + +max(0,x2012)-x2016 +replace NNRESRE = NNRESRE-x2723*(x2710==78)-x2740*(x2727==78)-x2823*(x2810==78) /// + -x2840*(x2827==78)-x2923*(x2910==78)-x2940*(x2927==78) if NNRESRE!=0 +gen FLAG781 = (NNRESRE!=0) +gen BUS = max(0,x3129)+max(0,x3124)-max(0,x3126)*(x3127==5) /// + +max(0,x3121)*(x3122==1|x3122==6) /// + +max(0,x3229)+max(0,x3224)-max(0,x3226)*(x3227==5) /// + +max(0,x3221)*(x3222==1|x3222==6) /// + +max(0,x3329)+max(0,x3324)-max(0,x3326)*(x3327==5) /// + +max(0,x3321)*(x3322==1|x3322==6) /// + +max(0,x3335)+(x507/10000)*(x513+x526) /// + +max(0,x3408)+max(0,x3412)+max(0,x3416)+max(0,x3420) /// + +max(0,x3424)+max(0,x3428) +gen OTHNFIN = x4022 + x4026 + x4030 - OTHFIN + x4018 +gen NFIN = VEHIC+HOUSES+ORESRE+NNRESRE+BUS+OTHNFIN +gen ASSET = FIN+NFIN /* Total asset */ + +* Debt +gen MRTHEL = x805+x905+x1005+x1108*(x1103==1)+x1119*(x1114==1) /// + +x1130*(x1125==1)+max(0,x1136)*(x1108*(x1103==1)+x1119*(x1114==1) /// + +x1130*(x1125==1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 +replace MRTHEL = x805+x905+x1005+.5*(max(0,x1136)) if (x1108+x1119+x1130)<1 + gen MORT1 = (x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// + *x1715*(x1705/10000) + gen MORT2 = (x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// + *x1815*(x1805/10000) + gen MORT3 = (x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// + *x1915*(x1905/10000) +gen RESDBT = x1417+x1517+x1617+x1621+MORT1+MORT2+MORT3+x2006 + gen FLAG782 = (FLAG781!=1 & ORESRE>0) +replace RESDBT = RESDBT+x2723*(x2710==78)+x2740*(x2727==78)+x2823*(x2810==78)+x2840*(x2827==78) /// + +x2923*(x2910==78)+x2940*(x2927==78) if FLAG781!=1 & ORESRE>0 + gen FLAG67 = (ORESRE>0) +replace RESDBT= RESDBT+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67)+x2840*(x2827==67) /// + +x2923*(x2910==67)+x2940*(x2927==67) if ORESRE>0 +gen OTHLOC = x1108*(x1103!=1)+x1119*(x1114!=1)+x1130*(x1125!=1) /// + +max(0,x1136)*(x1108*(x1103!=1)+x1119*(x1114!=1) /// + +x1130*(x1125!=1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 +replace OTHLOC = .5*(max(0,x1136)) if (x1108+x1119+x1130)<1 +gen CCBAL = max(0,x427)+max(0,x413)+max(0,x421)+max(0,x430)+max(0,x424)+max(0,x7575) +gen INSTALL = x2218+x2318+x2418+x7169+x2424+x2519+x2619+x2625+x7183 /// + +x7824+x7847+x7870+x7924+x7947+x7970+x7179+x1044+x1215+x1219 +replace INSTALL = INSTALL+x2723*(x2710==78)+x2740*(x2727==78) /// + +x2823*(x2810==78)+x2840*(x2827==78)+x2923*(x2910==78)+x2940*(x2927==78) /// + if FLAG781==0 & FLAG782==0 +replace INSTALL = INSTALL+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67) /// + +x2840*(x2827==67)+x2923*(x2910==67)+x2940*(x2927==67) if FLAG67==0 +replace INSTALL = INSTALL+x2723*(x2710!=67&x2710!=78)+x2740*(x2727!=67&x2727!=78) /// + +x2823*(x2810!=67&x2810!=78)+x2840*(x2827!=67&x2827!=78)+x2923*(x2910!=67&x2910!=78) /// + +x2940*(x2927!=67&x2927!=78) +gen PENDBT = max(0,x4229)*(x4230==5)+max(0,x4329)*(x4330==5) /// + +max(0,x4429)*(x4430==5)+max(0,x4829)*(x4830==5) /// + +max(0,x4929)*(x4930==5)+max(0,x5029)*(x5030==5) +gen CASHLIDB = max(0,x4010) +gen CALLDBT = max(0,x3932) +gen ODEBT = max(0,x4032) +gen DEBT = MRTHEL+RESDBT+OTHLOC+CCBAL+INSTALL+PENDBT+CASHLIDB+CALLDBT+ODEBT /* Total debt */ + +* Net worth +gen NETW = ASSET-DEBT +replace NETW = NETW*CPIADJ + +* Ratio of net worth to income +gen WIRATIO = NETW/INCOME + + +* Demographic vars +gen AGE = x14 /* Age */ +gen MARITST = x8023 /* Marital status */ +keep if MARITST == 1 /* Keep if married following Cagetti(2003) */ + +gen EDUC = 0 +replace EDUC = 1 if x5902!=1 /* No high school deg */ +replace EDUC = 2 if x5902==1 /* High school deg */ +replace EDUC = 3 if x5904==1 /* College deg */ +* keep if EDUC == 3 /* Keep college graduates only */ + +* Correct time effects. The base is set at 25 yrs old in 1980 (0 yrs old in 1955) +replace INCOME = INCOME/exp(0.016*(YEAR-1955-AGE)) +drop if INCOME<=0 +replace NETW = NETW/exp(0.016*(YEAR-1955-AGE)) + + +** Keep necessary vars +keep HHID YEAR WGT INCOME NETW WIRATIO AGE + +** Save data +cd "$startDir" +cd ../../Data/Constructed +** Save data +save "./SCF$scfFldr$SuffixForConstructedFile", replace + +** End in the same directory you started from +cd "$startDir" diff --git a/src/stata/SelectVarsUsingSCF2001.do b/src/stata/SelectVarsUsingSCF2001.do new file mode 100644 index 0000000..f90f244 --- /dev/null +++ b/src/stata/SelectVarsUsingSCF2001.do @@ -0,0 +1,254 @@ +* This file selects variables, using SCF2001 +* This file closely follows codes written for SAS which create summary variables. +* (These codes are available at http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt) + +clear + +** Set memeory +set memory 32m + +global startDir "`c(pwd)'" +global scfFldr 2001 +global scfFile scf2001 +global SuffixForConstructedFile "_population" + +cd ../../../Downloads/SCF/$scfFldr + +cap confirm file $scfFile.dta +if _rc~=0 { + display "File $scfFile is not in the Downloads/SCF/$scfFldr folder; please see ReadMe.txt for instructions." + exit +} + +** Load data and pick up necessary vars from original data +use y1 x42000 x42001 x5729 x7362 x5751 x7650 /// + x3506 x3507 x3510 x3511 x3514 x3515 x3518 x3519 x3522 x3523 x3526 x3527 x3529 /// + x3804 x3807 x3810 x3813 x3816 x3818 /// + x3506 x3507 x9113 x3510 x3511 x9114 x3514 x3515 x9115 x3518 x3519 x9116 x3522 x3523 x9117 x3526 x3527 x9118 x3529 x3706 x9131 x3711 x9132 x3716 x9133 x3718 /// + x3930 /// + x3721 x3821 x3822 x3823 x3824 x3825 x3826 x3827 x3828 x3829 x3830 x3915 x3910 x3906 x3908 x7634 x7633 /// + x3610 x3620 x3630 x3631 /// + x4216 x4316 x4416 x4816 x4916 x5016 x4226 x4326 x4426 x4826 x4926 x5026 x4227 x4327 x4427 x4827 x4927 x5027 x4231 x4331 x4431 x4831 x4931 x5031 x4234 x4334 x4434 x4834 x4934 x5034 x4436 x5036 /// + x3902 x4006 x6820 x6835 x4018 x4022 x4020 x4026 x4024 x4030 x4028 /// + x8166 x8167 x8168 x8188 x2422 x2506 x2606 x2623 /// + x507 x604 x614 x623 x716 x513 x526 x7134 x716 x701 x7133 /// + x1405 x1409 x1505 x1509 x1605 x1609 x1619 x1703 x1706 x1705 x1803 x1806 x1805 x1903 x1906 x1905 x2002 x2012 x1715 x1815 x1915 x2016 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 /// + x3129 x3124 x3126 x3127 x3121 x3122 x3122 x3229 x3224 x3226 x3227 x3221 x3222 x3222 x3329 x3324 x3326 x3327 x3321 x3322 x3322 x3335 x507 x513 x526 x3408 x3412 x3416 x3420 x3424 x3428 /// + x4022 x4026 x4030 /// + x805 x905 x1005 x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// + x1417 x1517 x1617 x1621 x2006 /// + x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// + x427 x413 x421 x430 x424 x7575 /// + x2218 x2318 x2418 x7169 x2424 x2519 x2619 x2625 x7183 x7824 x7847 x7870 x7924 x7947 x7970 x7179 x1044 x1215 x1219 /// + x4229 x4230 x4329 x4330 x4429 x4430 x4829 x4830 x4929 x4930 x5029 x5030 /// + x4010 x3932 x4032 /// + x14 x19 x8020 x8023 x5902 x5904 x6102 x6104 /// + using "scf2001.dta" + +** Generate variables +* ID +gen ID = y1 /* ID # */ +gen HHID = (y1-mod(y1,10))/10 /* HH ID # */ +gen YEAR = 2001 /* Indicates data is from which wave */ + +* Weight +gen WGT = x42001 + +* Income +gen INCOME = x5729 /* Income before tax */ +replace INCOME = x7362 if x7650!=3 +scalar CPIBASE = 2116 /* September 1992 consumer price index level, the numbers can be found in http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt*/ +scalar CPIADJ = CPIBASE/2618 /* Adjust with CPI (adjusted to 1992$ price) */ +scalar CPILAG = 2600/2529 /* Income is the previous year's income level, CPILAG adjust income to survey year */ +replace INCOME = INCOME*CPILAG*CPIADJ /* Adjust with CPI (adjusted to 1992$ price) */ + +* Asset +gen CHECKING = max(0,x3506)*(x3507==5)+max(0,x3510)*(x3511==5) /// + +max(0,x3514)*(x3515==5)+max(0,x3518)*(x3519==5) /// + +max(0,x3522)*(x3523==5)+max(0,x3526)*(x3527==5) /// + +max(0,x3529)*(x3527==5) +gen SAVING = max(0,x3804)+max(0,x3807)+max(0,x3810)+max(0,x3813) /// + +max(0,x3816)+max(0,x3818) + gen MMDA = max(0,x3506)*((x3507==1)*(11<=x9113 & x9113<=13)) /// + +max(0,x3510)*((x3511==1)*(11<=x9114 & x9114<=13)) /// + +max(0,x3514)*((x3515==1)*(11<=x9115 & x9115<=13)) /// + +max(0,x3518)*((x3519==1)*(11<=x9116 & x9116<=13)) /// + +max(0,x3522)*((x3523==1)*(11<=x9117 & x9117<=13)) /// + +max(0,x3526)*((x3527==1)*(11<=x9118 & x9118<=13)) /// + +max(0,x3529)*((x3527==1)*(11<=x9118 & x9118<=13)) /// + +max(0,x3706)*(11<=x9131 & x9131<=13) /// + +max(0,x3711)*(11<=x9132 & x9132<=13) /// + +max(0,x3716)*(11<=x9133 & x9133<=13) /// + +max(0,x3718)*(11<=x9133 & x9133<=13) + gen MMMF = max(0,x3506)*(x3507==1)*(x9113<11|x9113>13) /// + +max(0,x3510)*(x3511==1)*(x9114<11|x9114>13) /// + +max(0,x3514)*(x3515==1)*(x9115<11|x9115>13) /// + +max(0,x3518)*(x3519==1)*(x9116<11|x9116>13) /// + +max(0,x3522)*(x3523==1)*(x9117<11|x9117>13) /// + +max(0,x3526)*(x3527==1)*(x9118<11|x9118>13) /// + +max(0,x3529)*(x3527==1)*(x9118<11|x9118>13) /// + +max(0,x3706)*(x9131<11|x9131>13) /// + +max(0,x3711)*(x9132<11|x9132>13) /// + +max(0,x3716)*(x9133<11|x9133>13) /// + +max(0,x3718)*(x9133<11|x9133>13) +gen MMA = MMDA+MMMF +gen CALL = max(0,x3930) +gen LIQ = CHECKING+SAVING+MMA+CALL + +gen CDS = max(0,x3721) + gen STMUTF = (x3821==1)*max(0,x3822) + gen TFBMUTF = (x3823==1)*max(0,x3824) + gen GBMUTF = (x3825==1)*max(0,x3826) + gen OBMUTF = (x3827==1)*max(0,x3828) + gen COMUTF = (x3829==1)*max(0,x3830) + gen SNMMF = TFBMUTF+GBMUTF+OBMUTF+(.5*(COMUTF)) + gen RNMMF = STMUTF + (.5*(COMUTF)) +gen NMMF = SNMMF + RNMMF +gen STOCKS = max(0,x3915) + gen NOTXBND = x3910 + gen MORTBND = x3906 + gen GOVTBND = x3908 + gen OBND = x7634+x7633 +gen BOND = NOTXBND + MORTBND + GOVTBND + OBND + gen IRAKH = max(0,x3610)+max(0,x3620)+max(0,x3630) + gen THRIFT = max(0,x4226)*(x4216==1|x4216==2|x4227==1|x4231==1) /// + +max(0,x4326)*(x4316==1|x4316==2|x4327==1|x4331==1) /// + +max(0,x4426)*(x4416==1|x4416==2|x4427==1|x4431==1) /// + +max(0,x4826)*(x4816==1|x4816==2|x4827==1|x4831==1) /// + +max(0,x4926)*(x4916==1|x4916==2|x4927==1|x4931==1) /// + +max(0,x5026)*(x5016==1|x5016==2|x5027==1|x5031==1) + gen PMOP = x4436 + replace PMOP = 0 if x4436<=0 + replace PMOP = 0 if x4216!=0 & x4316!=0 & x4416!=0 & x4231!=0 & x4331!=0 & x4431!=0 + replace THRIFT = THRIFT + PMOP + replace PMOP = x5036 + replace PMOP = 0 if x5036<=0 + replace PMOP = 0 if x4816!=0 & x4916!=0 & x5016!=0 & x4831!=0 & x4931!=0 & x5031!=0 + replace THRIFT = THRIFT + PMOP +gen RETQLIQ = IRAKH + THRIFT +gen SAVBND = x3902 +gen CASHLI = max(0,x4006) + gen ANNUIT = max(0,x6820) + gen TRUSTS = max(0,x6835) +gen OTHMA = ANNUIT+TRUSTS +gen OTHFIN = x4018 /// + +x4022*(x4020==61|x4020==62|x4020==63|x4020==64|x4020==65|x4020==66|x4020==71| /// + x4020==72|x4020==73|x4020==74|x4020==77|x4020==80|x4020==81|x4020==-7) /// + +x4026*(x4024==61|x4024==62|x4024==63|x4024==64|x4024==65|x4024==66|x4024==71| /// + x4024==72|x4024==73|x4024==74|x4024==77|x4024==80|x4024==81|x4024==-7) /// + +x4030*(x4028==61|x4028==62|x4028==63|x4028==64|x4028==65|x4028==66|x4028==71| /// + x4028==72|x4028==73|x4028==74|x4028==77|x4028==80|x4028==81|x4028==-7) +gen FIN = LIQ+CDS+NMMF+STOCKS+BOND+RETQLIQ+SAVBND+CASHLI+OTHMA+OTHFIN /* Total fin asset */ + +gen VEHIC = max(0,x8166)+max(0,x8167)+max(0,x8168)+max(0,x8188) /// + +max(0,x2422)+max(0,x2506)+max(0,x2606)+max(0,x2623) +replace x507 = 9000 if x507 > 9000 +gen HOUSES = (x604+x614+x623+x716) + ((10000-x507)/10000)*(x513+x526) + * replace HOUSES = (x7134/10000)*x716 if x701==-7 & x7133==1 +gen ORESRE = max(x1405,x1409)+max(x1505,x1509)+max(x1605,x1609)+max(0,x1619) /// + +(x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// + *max(0,x1706)*(x1705/10000) /// + +(x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// + *max(0,x1806)*(x1805/10000) /// + +(x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// + *max(0,x1906)*(x1905/10000) /// + +max(0,x2002) +gen NNRESRE = (x1703==1|x1703==2|x1703==3|x1703==4|x1703==5|x1703==6|x1703==7|x1703==10|x1703==11|x1703==13|x1703==15|x1703==24|x1703==45|x1703==46|x1703==47|x1703==48|x1703==51|x1703==-7) /// + *(max(0,x1706)*(x1705/10000)-x1715*(x1705/10000)) /// + +(x1803==1|x1803==2|x1803==3|x1803==4|x1803==5|x1803==6|x1803==7|x1803==10|x1803==11|x1803==13|x1803==15|x1803==24|x1803==45|x1803==46|x1803==47|x1803==48|x1803==51|x1803==-7) /// + *(max(0,x1806)*(x1805/10000)-x1815*(x1805/10000)) /// + +(x1903==1|x1903==2|x1903==3|x1903==4|x1903==5|x1903==6|x1903==7|x1903==10|x1903==11|x1903==13|x1903==15|x1903==24|x1903==45|x1903==46|x1903==47|x1903==48|x1903==51|x1903==-7) /// + *(max(0,x1906)*(x1905/10000)-x1915*(x1905/10000)) /// + +max(0,x2012)-x2016 +replace NNRESRE = NNRESRE-x2723*(x2710==78)-x2740*(x2727==78)-x2823*(x2810==78) /// + -x2840*(x2827==78)-x2923*(x2910==78)-x2940*(x2927==78) if NNRESRE!=0 +gen FLAG781 = (NNRESRE!=0) +gen BUS = max(0,x3129)+max(0,x3124)-max(0,x3126)*(x3127==5) /// + +max(0,x3121)*(x3122==1|x3122==6) /// + +max(0,x3229)+max(0,x3224)-max(0,x3226)*(x3227==5) /// + +max(0,x3221)*(x3222==1|x3222==6) /// + +max(0,x3329)+max(0,x3324)-max(0,x3326)*(x3327==5) /// + +max(0,x3321)*(x3322==1|x3322==6) /// + +max(0,x3335)+(x507/10000)*(x513+x526) /// + +max(0,x3408)+max(0,x3412)+max(0,x3416)+max(0,x3420) /// + +max(0,x3424)+max(0,x3428) +gen OTHNFIN = x4022 + x4026 + x4030 - OTHFIN + x4018 +gen NFIN = VEHIC+HOUSES+ORESRE+NNRESRE+BUS+OTHNFIN +gen ASSET = FIN+NFIN /* Total asset */ + +* Debt +gen MRTHEL = x805+x905+x1005+x1108*(x1103==1)+x1119*(x1114==1) /// + +x1130*(x1125==1)+max(0,x1136)*(x1108*(x1103==1)+x1119*(x1114==1) /// + +x1130*(x1125==1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 +replace MRTHEL = x805+x905+x1005+.5*(max(0,x1136)) if (x1108+x1119+x1130)<1 + gen MORT1 = (x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// + *x1715*(x1705/10000) + gen MORT2 = (x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// + *x1815*(x1805/10000) + gen MORT3 = (x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// + *x1915*(x1905/10000) +gen RESDBT = x1417+x1517+x1617+x1621+MORT1+MORT2+MORT3+x2006 + gen FLAG782 = (FLAG781!=1 & ORESRE>0) +replace RESDBT = RESDBT+x2723*(x2710==78)+x2740*(x2727==78)+x2823*(x2810==78)+x2840*(x2827==78) /// + +x2923*(x2910==78)+x2940*(x2927==78) if FLAG781!=1 & ORESRE>0 + gen FLAG67 = (ORESRE>0) +replace RESDBT= RESDBT+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67)+x2840*(x2827==67) /// + +x2923*(x2910==67)+x2940*(x2927==67) if ORESRE>0 +gen OTHLOC = x1108*(x1103!=1)+x1119*(x1114!=1)+x1130*(x1125!=1) /// + +max(0,x1136)*(x1108*(x1103!=1)+x1119*(x1114!=1) /// + +x1130*(x1125!=1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 +replace OTHLOC = .5*(max(0,x1136)) if (x1108+x1119+x1130)<1 +gen CCBAL = max(0,x427)+max(0,x413)+max(0,x421)+max(0,x430)+max(0,x424)+max(0,x7575) +gen INSTALL = x2218+x2318+x2418+x7169+x2424+x2519+x2619+x2625+x7183 /// + +x7824+x7847+x7870+x7924+x7947+x7970+x7179+x1044+x1215+x1219 +replace INSTALL = INSTALL+x2723*(x2710==78)+x2740*(x2727==78) /// + +x2823*(x2810==78)+x2840*(x2827==78)+x2923*(x2910==78)+x2940*(x2927==78) /// + if FLAG781==0 & FLAG782==0 +replace INSTALL = INSTALL+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67) /// + +x2840*(x2827==67)+x2923*(x2910==67)+x2940*(x2927==67) if FLAG67==0 +replace INSTALL = INSTALL+x2723*(x2710!=67&x2710!=78)+x2740*(x2727!=67&x2727!=78) /// + +x2823*(x2810!=67&x2810!=78)+x2840*(x2827!=67&x2827!=78)+x2923*(x2910!=67&x2910!=78) /// + +x2940*(x2927!=67&x2927!=78) +gen PENDBT = max(0,x4229)*(x4230==5)+max(0,x4329)*(x4330==5) /// + +max(0,x4429)*(x4430==5)+max(0,x4829)*(x4830==5) /// + +max(0,x4929)*(x4930==5)+max(0,x5029)*(x5030==5) +gen CASHLIDB = max(0,x4010) +gen CALLDBT = max(0,x3932) +gen ODEBT = max(0,x4032) +gen DEBT = MRTHEL+RESDBT+OTHLOC+CCBAL+INSTALL+PENDBT+CASHLIDB+CALLDBT+ODEBT /* Total debt */ + +* Net worth +gen NETW = ASSET-DEBT +replace NETW = NETW*CPIADJ + +* Ratio of net worth to income +gen WIRATIO = NETW/INCOME + + +* Demographic vars +gen AGE = x14 /* Age */ +gen MARITST = x8023 /* Marital status */ +keep if MARITST == 1 /* Keep if married following Cagetti(2003) */ + +gen EDUC = 0 +replace EDUC = 1 if x5902!=1 /* No high school deg */ +replace EDUC = 2 if x5902==1 /* High school deg */ +replace EDUC = 3 if x5904==1 /* College deg */ +* keep if EDUC == 3 /* Keep college graduates only */ + +* Correct time effects. The base is set at 25 yrs old in 1980 (0 yrs old in 1955) +replace INCOME = INCOME/exp(0.016*(YEAR-1955-AGE)) +drop if INCOME<=0 +replace NETW = NETW/exp(0.016*(YEAR-1955-AGE)) + + +** Keep necessary vars +keep HHID YEAR WGT INCOME NETW WIRATIO AGE + +** Save data +cd "$startDir" +cd ../../Data/Constructed +save "./SCF$scfFldr$SuffixForConstructedFile", replace + +** End in the same directory you started from +cd "$startDir" diff --git a/src/stata/SelectVarsUsingSCF2004.do b/src/stata/SelectVarsUsingSCF2004.do new file mode 100644 index 0000000..b4730f4 --- /dev/null +++ b/src/stata/SelectVarsUsingSCF2004.do @@ -0,0 +1,272 @@ +* This file selects variables, using SCF2004 +* This file closely follows codes written for SAS which create summary variables. +* (These codes are available at http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt) + +clear + +** Set memeory +set memory 32m + +global startDir "`c(pwd)'" +global scfFldr 2004 +global scfFile scf2004 +global SuffixForConstructedFile "_population" + +cd ../../../Downloads/SCF/$scfFldr + +cap confirm file $scfFile.dta +if _rc~=0 { + display "File $scfFile is not in the Downloads/SCF/$scfFldr folder; please see ReadMe.txt for instructions." + exit +} + + +** Load data and pick up necessary vars from original data +use y1 x42000 x42001 x5729 x6558 x6566 x6574 x6464 x6469 x6474 x6479 x6484 x6489 x6965 x6971 x6977 x6983 x6989 x6995 x7362 x5751 x7650 /// + x3506 x3507 x3510 x3511 x3514 x3515 x3518 x3519 x3522 x3523 x3526 x3527 x3529 /// + x3730 x3732 x3732 x3736 x3738 x3738 x3742 x3744 x3744 x3748 x3750 x3750 x3754 x3756 x3756 x3760 x3762 x3762 x3765 /// + x3506 x3507 x9113 x9113 x3510 x3511 x9114 x9114 x3514 x3515 x9115 x9115 x3518 x3519 x9116 x9116 x3522 x3523 x9117 x9117 x3526 x3527 x9118 x9118 x3529 x3527 x9118 x9118 x3730 x3732 x9259 x9259 x3736 x3738 x9260 x9260 x3742 x3744 x9261 x9261 x3748 x3750 x9262 x9262 x3754 x3756 x9263 x9263 x3760 x3762 x9264 x9264 /// + x3930 /// + x3721 x3821 x3822 x3823 x3824 x3825 x3826 x3827 x3828 x3829 x3830 x7785 x7787 /// + x3915 x3910 x3906 x3908 x7634 x7633 /// + x6551 x6559 x6567 x6552 x6560 x6568 x6553 x6561 x6569 x6554 x6562 x6570 /// + x11032 x11000 x11001 x11025 x11031 x11132 x11100 x11101 x11125 x11131 x11232 x11200 x11201 x11225 x11231 x11332 x11300 x11301 x11325 x11331 x11432 x11400 x11401 x11425 x11431 x11532 x11500 x11501 x11525 x11531 /// + x11259 x11559 /// + x3902 x4006 x6577 x6587 x4018 x4022 x4020 x4026 x4024 x4030 x4028 /// + x8166 x8167 x8168 x8188 x2422 x2506 x2606 x2623 /// + x507 x604 x614 x623 x716 x513 x526 x7134 x716 x701 x7133 /// + x1405 x1409 x1505 x1509 x1605 x1609 x1619 x1703 x1706 x1705 x1803 x1806 x1805 x1903 x1906 x1905 x2002 x2012 x1715 x1815 x1915 x2016 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 /// + x3129 x3124 x3126 x3127 x3121 x3122 x3122 x3229 x3224 x3226 x3227 x3221 x3222 x3222 x3329 x3324 x3326 x3327 x3321 x3322 x3322 x3335 x507 x513 x526 x3408 x3412 x3416 x3420 x3424 x3428 /// + x4022 x4026 x4030 /// + x805 x905 x1005 x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// + x1417 x1517 x1617 x1621 x2006 /// + x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// + x427 x413 x421 x430 x424 x7575 /// + x2218 x2318 x2418 x7169 x2424 x2519 x2619 x2625 x7183 x7824 x7847 x7870 x7924 x7947 x7970 x7179 x1044 x1215 x1219 /// + x11027 x11070 x11127 x11170 x11227 x11270 x11327 x11370 x11427 x11470 x11527 x11570 /// + x4010 x3932 x4032 /// + x14 x19 x8020 x8023 x5902 x5904 x6102 x6104 /// + using "scf2004.dta" + +** Generate variables +* ID +gen ID = y1 /* ID # */ +gen HHID = (y1-mod(y1,10))/10 /* HH ID # */ +gen YEAR = 2004 /* Indicates data is from which wave */ + +* Weight +gen WGT = x42001 + +* Income +gen INCOME = max(0,x5729)+x6558+x6566+x6574+max(0,x6464)+max(0,x6469) /// + +max(0,x6474)+max(0,x6479)+max(0,x6484)+max(0,x6489) /// + +max(0,x6965)+max(0,x6971)+max(0,x6977)+max(0,x6983) /// + +max(0,x6989)+max(0,x6995) /* Income before tax */ +replace INCOME = x7362 if x7650!=3 +scalar CPIBASE = 2116 /* September 1992 consumer price index level, the numbers can be found in http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt*/ +scalar CPIADJ = CPIBASE/2788 /* Adjust with CPI (adjusted to 1992$ price) */ +scalar CPILAG = 2774/2701 /* Income is the previous year's income level, CPILAG adjust income to survey year */ +replace INCOME = INCOME*CPILAG*CPIADJ /* Adjust with CPI (adjusted to 1992$ price) */ + * gen INCOMEAT = x5751 /* After tax income */ +* gen D_NORMINC = (x7650==3) /* D_NORMINC = 1 if inc level is normal */ +* keep if D_NORMINC == 1 /* Keep if inc level is normal */ + +* Asset +gen CHECKING = max(0,x3506)*(x3507==5)+max(0,x3510)*(x3511==5) /// + +max(0,x3514)*(x3515==5)+max(0,x3518)*(x3519==5) /// + +max(0,x3522)*(x3523==5)+max(0,x3526)*(x3527==5) /// + +max(0,x3529)*(x3527==5) +gen SAVING = max(0,x3730*(x3732!=4 & x3732!=30)) /// + +max(0,x3736*(x3738!=4 & x3738!=30)) /// + +max(0,x3742*(x3744!=4 & x3744!=30)) /// + +max(0,x3748*(x3750!=4 & x3750!=30)) /// + +max(0,x3754*(x3756!=4 & x3756!=30)) /// + +max(0,x3760*(x3762!=4 & x3762!=30))+max(0,x3765) +gen MMDA = max(0,x3506)*((x3507==1)*(x9113>=11 & x9113<=13)) /// + +max(0,x3510)*((x3511==1)*(x9114>=11 & x9114<=13)) /// + +max(0,x3514)*((x3515==1)*(x9115>=11 & x9115<=13)) /// + +max(0,x3518)*((x3519==1)*(x9116>=11 & x9116<=13)) /// + +max(0,x3522)*((x3523==1)*(x9117>=11 & x9117<=13)) /// + +max(0,x3526)*((x3527==1)*(x9118>=11 & x9118<=13)) /// + +max(0,x3529)*((x3527==1)*(x9118>=11 & x9118<=13)) /// + +max(0,x3730*(x3732==4|x3732==30)*(x9259>=11 & x9259<=13)) /// + +max(0,x3736*(x3738==4|x3738==30)*(x9260>=11 & x9260<=13)) /// + +max(0,x3742*(x3744==4|x3744==30)*(x9261>=11 & x9261<=13)) /// + +max(0,x3748*(x3750==4|x3750==30)*(x9262>=11 & x9262<=13)) /// + +max(0,x3754*(x3756==4|x3756==30)*(x9263>=11 & x9263<=13)) /// + +max(0,x3760*(x3762==4|x3762==30)*(x9264>=11 & x9264<=13)) +gen MMMF = max(0,x3506)*((x3507==1)*(x9113<11 | x9113>13)) /// + +max(0,x3510)*((x3511==1)*(x9114<11 | x9114>13)) /// + +max(0,x3514)*((x3515==1)*(x9115<11 | x9115>13)) /// + +max(0,x3518)*((x3519==1)*(x9116<11 | x9116>13)) /// + +max(0,x3522)*((x3523==1)*(x9117<11 | x9117>13)) /// + +max(0,x3526)*((x3527==1)*(x9118<11 | x9118>13)) /// + +max(0,x3529)*((x3527==1)*(x9118<11 | x9118>13)) /// + +max(0,x3730*(x3732==4|x3732==30)*(x9259<11 | x9259>13)) /// + +max(0,x3736*(x3738==4|x3738==30)*(x9260<11 | x9260>13)) /// + +max(0,x3742*(x3744==4|x3744==30)*(x9261<11 | x9261>13)) /// + +max(0,x3748*(x3750==4|x3750==30)*(x9262<11 | x9262>13)) /// + +max(0,x3754*(x3756==4|x3756==30)*(x9263<11 | x9263>13)) /// + +max(0,x3760*(x3762==4|x3762==30)*(x9264<11 | x9264>13)) +gen MMA = MMDA+MMMF +gen CALL = max(0,x3930) +gen LIQ = CHECKING+SAVING+MMA+CALL + +gen CDS = max(0,x3721) + gen STMUTF = (x3821==1)*max(0,x3822) + gen TFBMUTF = (x3823==1)*max(0,x3824) + gen GBMUTF = (x3825==1)*max(0,x3826) + gen OBMUTF = (x3827==1)*max(0,x3828) + gen COMUTF = (x3829==1)*max(0,x3830) + gen OMUTF = (x7785==1)*max(0,x7787) + gen SNMMF = TFBMUTF+GBMUTF+OBMUTF+(.5*(COMUTF)) + gen RNMMF = STMUTF + (.5*(COMUTF)) +gen NMMF = SNMMF + RNMMF + OMUTF +gen STOCKS = max(0,x3915) + gen NOTXBND = x3910 + gen MORTBND = x3906 + gen GOVTBND = x3908 + gen OBND = x7634+x7633 +gen BOND = NOTXBND + MORTBND + GOVTBND + OBND + gen IRAKH = x6551+x6559+x6567+x6552+x6560+x6568+x6553+x6561+x6569+x6554+x6562+x6570 + gen THRIFT = max(0,x11032)*(x11000==5|x11000==6|x11000==10|x11001==2|x11001==3|x11001==4|x11001==6|x11025==1|x11031==1) /// + +max(0,x11132)*(x11100==5|x11100==6|x11100==10|x11101==2|x11101==3|x11101==4|x11101==6|x11125==1|x11131==1) /// + +max(0,x11232)*(x11200==5|x11200==6|x11200==10|x11201==2|x11201==3|x11201==4|x11201==6|x11225==1|x11231==1) /// + +max(0,x11332)*(x11300==5|x11300==6|x11300==10|x11301==2|x11301==3|x11301==4|x11301==6|x11325==1|x11331==1) /// + +max(0,x11432)*(x11400==5|x11400==6|x11400==10|x11401==2|x11401==3|x11401==4|x11401==6|x11425==1|x11431==1) /// + +max(0,x11532)*(x11500==5|x11500==6|x11500==10|x11501==2|x11501==3|x11501==4|x11501==6|x11525==1|x11531==1) + gen PMOP = x11259 + replace PMOP = 0 if x11259<=0 + replace PMOP = 0 if x11000!=0 & x11100!=0 & x11200!=0 & x11031!=0 & x11131!=0 & x11231!=0 + replace THRIFT = THRIFT + PMOP + replace PMOP = x11559 + replace PMOP = 0 if x11559<=0 + replace PMOP = 0 if x11300!=0 & x11400!=0 & x11500!=0 & x11331!=0 & x11431!=0 & x11531!=0 + replace THRIFT = THRIFT + PMOP +gen RETQLIQ = IRAKH+THRIFT +gen SAVBND = x3902 +gen CASHLI = max(0,x4006) + gen ANNUIT = max(0,x6577) + gen TRUSTS = max(0,x6587) +gen OTHMA = ANNUIT+TRUSTS +gen OTHFIN = x4018 /// + +x4022*(x4020==61|x4020==62|x4020==63|x4020==64|x4020==65|x4020==66|x4020==71| /// + x4020==72|x4020==73|x4020==74|x4020==77|x4020==80|x4020==81|x4020==-7) /// + +x4026*(x4024==61|x4024==62|x4024==63|x4024==64|x4024==65|x4024==66|x4024==71| /// + x4024==72|x4024==73|x4024==74|x4024==77|x4024==80|x4024==81|x4024==-7) /// + +x4030*(x4028==61|x4028==62|x4028==63|x4028==64|x4028==65|x4028==66|x4028==71| /// + x4028==72|x4028==73|x4028==74|x4028==77|x4028==80|x4028==81|x4028==-7) +gen FIN = LIQ+CDS+NMMF+STOCKS+BOND+RETQLIQ+SAVBND+CASHLI+OTHMA+OTHFIN /* Total fin asset */ + +gen VEHIC = max(0,x8166)+max(0,x8167)+max(0,x8168)+max(0,x8188) /// + +max(0,x2422)+max(0,x2506)+max(0,x2606)+max(0,x2623) +replace x507 = 9000 if x507 > 9000 +gen HOUSES = (x604+x614+x623+x716) + ((10000-x507)/10000)*(x513+x526) + * replace HOUSES = (x7134/10000)*x716 if x701==-7 & x7133==1 +gen ORESRE = max(x1405,x1409)+max(x1505,x1509)+max(x1605,x1609)+max(0,x1619) /// + +(x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// + *max(0,x1706)*(x1705/10000) /// + +(x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// + *max(0,x1806)*(x1805/10000) /// + +(x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// + *max(0,x1906)*(x1905/10000) /// + +max(0,x2002) +gen NNRESRE = (x1703==1|x1703==2|x1703==3|x1703==4|x1703==5|x1703==6|x1703==7|x1703==10|x1703==11|x1703==13|x1703==15|x1703==24|x1703==45|x1703==46|x1703==47|x1703==48|x1703==51|x1703==-7) /// + *(max(0,x1706)*(x1705/10000)-x1715*(x1705/10000)) /// + +(x1803==1|x1803==2|x1803==3|x1803==4|x1803==5|x1803==6|x1803==7|x1803==10|x1803==11|x1803==13|x1803==15|x1803==24|x1803==45|x1803==46|x1803==47|x1803==48|x1803==51|x1803==-7) /// + *(max(0,x1806)*(x1805/10000)-x1815*(x1805/10000)) /// + +(x1903==1|x1903==2|x1903==3|x1903==4|x1903==5|x1903==6|x1903==7|x1903==10|x1903==11|x1903==13|x1903==15|x1903==24|x1903==45|x1903==46|x1903==47|x1903==48|x1903==51|x1903==-7) /// + *(max(0,x1906)*(x1905/10000)-x1915*(x1905/10000)) /// + +max(0,x2012)-x2016 +replace NNRESRE = NNRESRE-x2723*(x2710==78)-x2740*(x2727==78)-x2823*(x2810==78) /// + -x2840*(x2827==78)-x2923*(x2910==78)-x2940*(x2927==78) if NNRESRE!=0 +gen FLAG781 = (NNRESRE!=0) +gen BUS = max(0,x3129)+max(0,x3124)-max(0,x3126)*(x3127==5) /// + +max(0,x3121)*(x3122==1|x3122==6) /// + +max(0,x3229)+max(0,x3224)-max(0,x3226)*(x3227==5) /// + +max(0,x3221)*(x3222==1|x3222==6) /// + +max(0,x3329)+max(0,x3324)-max(0,x3326)*(x3327==5) /// + +max(0,x3321)*(x3322==1|x3322==6) /// + +max(0,x3335)+(x507/10000)*(x513+x526) /// + +max(0,x3408)+max(0,x3412)+max(0,x3416)+max(0,x3420) /// + +max(0,x3424)+max(0,x3428) +gen OTHNFIN = x4022 + x4026 + x4030 - OTHFIN + x4018 +gen NFIN = VEHIC+HOUSES+ORESRE+NNRESRE+BUS+OTHNFIN +gen ASSET = FIN+NFIN /* Total asset */ + +* Debt +gen MRTHEL = x805+x905+x1005+x1108*(x1103==1)+x1119*(x1114==1) /// + +x1130*(x1125==1)+max(0,x1136)*(x1108*(x1103==1)+x1119*(x1114==1) /// + +x1130*(x1125==1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 +replace MRTHEL = x805+x905+x1005+.5*(max(0,x1136)) if (x1108+x1119+x1130)<1 + gen MORT1 = (x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// + *x1715*(x1705/10000) + gen MORT2 = (x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// + *x1815*(x1805/10000) + gen MORT3 = (x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// + *x1915*(x1905/10000) +gen RESDBT = x1417+x1517+x1617+x1621+MORT1+MORT2+MORT3+x2006 + gen FLAG782 = (FLAG781!=1 & ORESRE>0) +replace RESDBT = RESDBT+x2723*(x2710==78)+x2740*(x2727==78)+x2823*(x2810==78)+x2840*(x2827==78) /// + +x2923*(x2910==78)+x2940*(x2927==78) if FLAG781!=1 & ORESRE>0 + gen FLAG67 = (ORESRE>0) +replace RESDBT= RESDBT+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67)+x2840*(x2827==67) /// + +x2923*(x2910==67)+x2940*(x2927==67) if ORESRE>0 +gen OTHLOC = x1108*(x1103!=1)+x1119*(x1114!=1)+x1130*(x1125!=1) /// + +max(0,x1136)*(x1108*(x1103!=1)+x1119*(x1114!=1) /// + +x1130*(x1125!=1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 +replace OTHLOC = .5*(max(0,x1136)) if (x1108+x1119+x1130)<1 +gen CCBAL = max(0,x427)+max(0,x413)+max(0,x421)+max(0,x430)+max(0,x424)+max(0,x7575) +gen INSTALL = x2218+x2318+x2418+x7169+x2424+x2519+x2619+x2625+x7183 /// + +x7824+x7847+x7870+x7924+x7947+x7970+x7179+x1044+x1215+x1219 +replace INSTALL = INSTALL+x2723*(x2710==78)+x2740*(x2727==78) /// + +x2823*(x2810==78)+x2840*(x2827==78)+x2923*(x2910==78)+x2940*(x2927==78) /// + if FLAG781==0 & FLAG782==0 +replace INSTALL = INSTALL+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67) /// + +x2840*(x2827==67)+x2923*(x2910==67)+x2940*(x2927==67) if FLAG67==0 +replace INSTALL = INSTALL+x2723*(x2710!=67&x2710!=78)+x2740*(x2727!=67&x2727!=78) /// + +x2823*(x2810!=67&x2810!=78)+x2840*(x2827!=67&x2827!=78)+x2923*(x2910!=67&x2910!=78) /// + +x2940*(x2927!=67&x2927!=78) +gen PENDBT = max(0,x11027)*(x11070==5)+max(0,x11127)*(x11170==5) /// + +max(0,x11227)*(x11270==5)+max(0,x11327)*(x11370==5) /// + +max(0,x11427)*(x11470==5)+max(0,x11527)*(x11570==5) +gen CASHLIDB = max(0,x4010) +gen CALLDBT = max(0,x3932) +gen ODEBT = max(0,x4032) +gen DEBT = MRTHEL+RESDBT+OTHLOC+CCBAL+INSTALL+PENDBT+CASHLIDB+CALLDBT+ODEBT /* Total debt */ + +* Net worth +gen NETW = ASSET-DEBT +replace NETW = NETW*CPIADJ + +* Ratio of net worth to income +gen WIRATIO = NETW/INCOME + + +* Demographic vars +gen AGE = x14 /* Age */ +gen MARITST = x8023 /* Marital status */ +keep if MARITST == 1 /* Keep if married following Cagetti(2003) */ + +gen EDUC = 0 +replace EDUC = 1 if x5902!=1 /* No high school deg */ +replace EDUC = 2 if x5902==1 /* High school deg */ +replace EDUC = 3 if x5904==1 /* College deg */ +* keep if EDUC == 3 /* Keep college graduates only */ + +* Correct time effects. The base is set at 25 yrs old in 1980 (0 yrs old in 1955) +replace INCOME = INCOME/exp(0.016*(YEAR-1955-AGE)) +drop if INCOME<=0 +replace NETW = NETW/exp(0.016*(YEAR-1955-AGE)) + + +** Keep necessary vars +keep HHID YEAR WGT INCOME NETW WIRATIO AGE + +** Save data +cd "$startDir" +cd ../../Data/Constructed +save "./SCF$scfFldr$SuffixForConstructedFile", replace + +** End in the same directory you started from +cd "$startDir" diff --git a/src/stata/SelectVarsUsingSCF2007.do b/src/stata/SelectVarsUsingSCF2007.do new file mode 100644 index 0000000..705a20d --- /dev/null +++ b/src/stata/SelectVarsUsingSCF2007.do @@ -0,0 +1,271 @@ +* This file selects variables, using SCF2007 +* This file closely follows codes written for SAS which create summary variables. +* (These codes are available at http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt) + +clear + +** Set memeory +set memory 32m + +global startDir "`c(pwd)'" +global scfFldr 2007 +global scfFile scf2007 +global SuffixForConstructedFile "_population" + +cd ../../../Downloads/SCF/$scfFldr + +cap confirm file $scfFile.dta +if _rc~=0 { + display "File $scfFile is not in the Downloads/SCF/$scfFldr folder; please see ReadMe.txt for instructions." + exit +} + +** Load data and pick up necessary vars from original data +use y1 x42000 x42001 x5729 x6558 x6566 x6574 x6464 x6469 x6474 x6479 x6484 x6489 x6965 x6971 x6977 x6983 x6989 x6995 x7362 x5751 x7650 /// + x3506 x3507 x3510 x3511 x3514 x3515 x3518 x3519 x3522 x3523 x3526 x3527 x3529 /// + x3730 x3732 x3732 x3736 x3738 x3738 x3742 x3744 x3744 x3748 x3750 x3750 x3754 x3756 x3756 x3760 x3762 x3762 x3765 /// + x3506 x3507 x9113 x9113 x3510 x3511 x9114 x9114 x3514 x3515 x9115 x9115 x3518 x3519 x9116 x9116 x3522 x3523 x9117 x9117 x3526 x3527 x9118 x9118 x3529 x3527 x9118 x9118 x3730 x3732 x9259 x9259 x3736 x3738 x9260 x9260 x3742 x3744 x9261 x9261 x3748 x3750 x9262 x9262 x3754 x3756 x9263 x9263 x3760 x3762 x9264 x9264 /// + x3930 /// + x3721 x3821 x3822 x3823 x3824 x3825 x3826 x3827 x3828 x3829 x3830 x7785 x7787 /// + x3915 x3910 x3906 x3908 x7634 x7633 /// + x6551 x6559 x6567 x6552 x6560 x6568 x6553 x6561 x6569 x6554 x6562 x6570 /// + x11032 x11000 x11001 x11025 x11031 x11132 x11100 x11101 x11125 x11131 x11232 x11200 x11201 x11225 x11231 x11332 x11300 x11301 x11325 x11331 x11432 x11400 x11401 x11425 x11431 x11532 x11500 x11501 x11525 x11531 /// + x11259 x11559 /// + x3902 x4006 x6577 x6587 x4018 x4022 x4020 x4026 x4024 x4030 x4028 /// + x8166 x8167 x8168 x8188 x2422 x2506 x2606 x2623 /// + x507 x604 x614 x623 x716 x513 x526 x7134 x716 x701 x7133 /// + x1405 x1409 x1505 x1509 x1605 x1609 x1619 x1703 x1706 x1705 x1803 x1806 x1805 x1903 x1906 x1905 x2002 x2012 x1715 x1815 x1915 x2016 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 /// + x3129 x3124 x3126 x3127 x3121 x3122 x3122 x3229 x3224 x3226 x3227 x3221 x3222 x3222 x3329 x3324 x3326 x3327 x3321 x3322 x3322 x3335 x507 x513 x526 x3408 x3412 x3416 x3420 x3424 x3428 /// + x4022 x4026 x4030 /// + x805 x905 x1005 x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// + x1417 x1517 x1617 x1621 x2006 /// + x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// + x427 x413 x421 x430 x424 x7575 /// + x2218 x2318 x2418 x7169 x2424 x2519 x2619 x2625 x7183 x7824 x7847 x7870 x7924 x7947 x7970 x7179 x1044 x1215 x1219 /// + x11027 x11070 x11127 x11170 x11227 x11270 x11327 x11370 x11427 x11470 x11527 x11570 /// + x4010 x3932 x4032 /// + x14 x19 x8020 x8023 x5902 x5904 x6102 x6104 /// + using "scf2007.dta" + +** Generate variables +* ID +gen ID = y1 /* ID # */ +gen HHID = (y1-mod(y1,10))/10 /* HH ID # */ +gen YEAR = 2007 /* Indicates data is from which wave */ + +* Weight +gen WGT = x42001 + +* Income +gen INCOME = max(0,x5729)+x6558+x6566+x6574+max(0,x6464)+max(0,x6469) /// + +max(0,x6474)+max(0,x6479)+max(0,x6484)+max(0,x6489) /// + +max(0,x6965)+max(0,x6971)+max(0,x6977)+max(0,x6983) /// + +max(0,x6989)+max(0,x6995) /* Income before tax */ +replace INCOME = x7362 if x7650!=3 +scalar CPIBASE = 2116 /* September 1992 consumer price index level, the numbers can be found in http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt*/ +scalar CPIADJ = CPIBASE/3062 /* Adjust with CPI (adjusted to 1992$ price) */ +scalar CPILAG = 3045/2961 /* Income is the previous year's income level, CPILAG adjust income to survey year */ +replace INCOME = INCOME*CPILAG*CPIADJ /* Adjust with CPI (adjusted to 1992$ price) */ + * gen INCOMEAT = x5751 /* After tax income */ +* gen D_NORMINC = (x7650==3) /* D_NORMINC = 1 if inc level is normal */ +* keep if D_NORMINC == 1 /* Keep if inc level is normal */ + +* Asset +gen CHECKING = max(0,x3506)*(x3507==5)+max(0,x3510)*(x3511==5) /// + +max(0,x3514)*(x3515==5)+max(0,x3518)*(x3519==5) /// + +max(0,x3522)*(x3523==5)+max(0,x3526)*(x3527==5) /// + +max(0,x3529)*(x3527==5) +gen SAVING = max(0,x3730*(x3732!=4 & x3732!=30)) /// + +max(0,x3736*(x3738!=4 & x3738!=30)) /// + +max(0,x3742*(x3744!=4 & x3744!=30)) /// + +max(0,x3748*(x3750!=4 & x3750!=30)) /// + +max(0,x3754*(x3756!=4 & x3756!=30)) /// + +max(0,x3760*(x3762!=4 & x3762!=30))+max(0,x3765) +gen MMDA = max(0,x3506)*((x3507==1)*(x9113>=11 & x9113<=13)) /// + +max(0,x3510)*((x3511==1)*(x9114>=11 & x9114<=13)) /// + +max(0,x3514)*((x3515==1)*(x9115>=11 & x9115<=13)) /// + +max(0,x3518)*((x3519==1)*(x9116>=11 & x9116<=13)) /// + +max(0,x3522)*((x3523==1)*(x9117>=11 & x9117<=13)) /// + +max(0,x3526)*((x3527==1)*(x9118>=11 & x9118<=13)) /// + +max(0,x3529)*((x3527==1)*(x9118>=11 & x9118<=13)) /// + +max(0,x3730*(x3732==4|x3732==30)*(x9259>=11 & x9259<=13)) /// + +max(0,x3736*(x3738==4|x3738==30)*(x9260>=11 & x9260<=13)) /// + +max(0,x3742*(x3744==4|x3744==30)*(x9261>=11 & x9261<=13)) /// + +max(0,x3748*(x3750==4|x3750==30)*(x9262>=11 & x9262<=13)) /// + +max(0,x3754*(x3756==4|x3756==30)*(x9263>=11 & x9263<=13)) /// + +max(0,x3760*(x3762==4|x3762==30)*(x9264>=11 & x9264<=13)) +gen MMMF = max(0,x3506)*((x3507==1)*(x9113<11 | x9113>13)) /// + +max(0,x3510)*((x3511==1)*(x9114<11 | x9114>13)) /// + +max(0,x3514)*((x3515==1)*(x9115<11 | x9115>13)) /// + +max(0,x3518)*((x3519==1)*(x9116<11 | x9116>13)) /// + +max(0,x3522)*((x3523==1)*(x9117<11 | x9117>13)) /// + +max(0,x3526)*((x3527==1)*(x9118<11 | x9118>13)) /// + +max(0,x3529)*((x3527==1)*(x9118<11 | x9118>13)) /// + +max(0,x3730*(x3732==4|x3732==30)*(x9259<11 | x9259>13)) /// + +max(0,x3736*(x3738==4|x3738==30)*(x9260<11 | x9260>13)) /// + +max(0,x3742*(x3744==4|x3744==30)*(x9261<11 | x9261>13)) /// + +max(0,x3748*(x3750==4|x3750==30)*(x9262<11 | x9262>13)) /// + +max(0,x3754*(x3756==4|x3756==30)*(x9263<11 | x9263>13)) /// + +max(0,x3760*(x3762==4|x3762==30)*(x9264<11 | x9264>13)) +gen MMA = MMDA+MMMF +gen CALL = max(0,x3930) +gen LIQ = CHECKING+SAVING+MMA+CALL + +gen CDS = max(0,x3721) + gen STMUTF = (x3821==1)*max(0,x3822) + gen TFBMUTF = (x3823==1)*max(0,x3824) + gen GBMUTF = (x3825==1)*max(0,x3826) + gen OBMUTF = (x3827==1)*max(0,x3828) + gen COMUTF = (x3829==1)*max(0,x3830) + gen OMUTF = (x7785==1)*max(0,x7787) + gen SNMMF = TFBMUTF+GBMUTF+OBMUTF+(.5*(COMUTF)) + gen RNMMF = STMUTF + (.5*(COMUTF)) +gen NMMF = SNMMF + RNMMF + OMUTF +gen STOCKS = max(0,x3915) + gen NOTXBND = x3910 + gen MORTBND = x3906 + gen GOVTBND = x3908 + gen OBND = x7634+x7633 +gen BOND = NOTXBND + MORTBND + GOVTBND + OBND + gen IRAKH = x6551+x6559+x6567+x6552+x6560+x6568+x6553+x6561+x6569+x6554+x6562+x6570 + gen THRIFT = max(0,x11032)*(x11000==5|x11000==6|x11000==10|x11001==2|x11001==3|x11001==4|x11001==6|x11025==1|x11031==1) /// + +max(0,x11132)*(x11100==5|x11100==6|x11100==10|x11101==2|x11101==3|x11101==4|x11101==6|x11125==1|x11131==1) /// + +max(0,x11232)*(x11200==5|x11200==6|x11200==10|x11201==2|x11201==3|x11201==4|x11201==6|x11225==1|x11231==1) /// + +max(0,x11332)*(x11300==5|x11300==6|x11300==10|x11301==2|x11301==3|x11301==4|x11301==6|x11325==1|x11331==1) /// + +max(0,x11432)*(x11400==5|x11400==6|x11400==10|x11401==2|x11401==3|x11401==4|x11401==6|x11425==1|x11431==1) /// + +max(0,x11532)*(x11500==5|x11500==6|x11500==10|x11501==2|x11501==3|x11501==4|x11501==6|x11525==1|x11531==1) + gen PMOP = x11259 + replace PMOP = 0 if x11259<=0 + replace PMOP = 0 if x11000!=0 & x11100!=0 & x11200!=0 & x11031!=0 & x11131!=0 & x11231!=0 + replace THRIFT = THRIFT + PMOP + replace PMOP = x11559 + replace PMOP = 0 if x11559<=0 + replace PMOP = 0 if x11300!=0 & x11400!=0 & x11500!=0 & x11331!=0 & x11431!=0 & x11531!=0 + replace THRIFT = THRIFT + PMOP +gen RETQLIQ = IRAKH+THRIFT +gen SAVBND = x3902 +gen CASHLI = max(0,x4006) + gen ANNUIT = max(0,x6577) + gen TRUSTS = max(0,x6587) +gen OTHMA = ANNUIT+TRUSTS +gen OTHFIN = x4018 /// + +x4022*(x4020==61|x4020==62|x4020==63|x4020==64|x4020==65|x4020==66|x4020==71| /// + x4020==72|x4020==73|x4020==74|x4020==77|x4020==80|x4020==81|x4020==-7) /// + +x4026*(x4024==61|x4024==62|x4024==63|x4024==64|x4024==65|x4024==66|x4024==71| /// + x4024==72|x4024==73|x4024==74|x4024==77|x4024==80|x4024==81|x4024==-7) /// + +x4030*(x4028==61|x4028==62|x4028==63|x4028==64|x4028==65|x4028==66|x4028==71| /// + x4028==72|x4028==73|x4028==74|x4028==77|x4028==80|x4028==81|x4028==-7) +gen FIN = LIQ+CDS+NMMF+STOCKS+BOND+RETQLIQ+SAVBND+CASHLI+OTHMA+OTHFIN /* Total fin asset */ + +gen VEHIC = max(0,x8166)+max(0,x8167)+max(0,x8168)+max(0,x8188) /// + +max(0,x2422)+max(0,x2506)+max(0,x2606)+max(0,x2623) +replace x507 = 9000 if x507 > 9000 +gen HOUSES = (x604+x614+x623+x716) + ((10000-x507)/10000)*(x513+x526) + * replace HOUSES = (x7134/10000)*x716 if x701==-7 & x7133==1 +gen ORESRE = max(x1405,x1409)+max(x1505,x1509)+max(x1605,x1609)+max(0,x1619) /// + +(x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// + *max(0,x1706)*(x1705/10000) /// + +(x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// + *max(0,x1806)*(x1805/10000) /// + +(x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// + *max(0,x1906)*(x1905/10000) /// + +max(0,x2002) +gen NNRESRE = (x1703==1|x1703==2|x1703==3|x1703==4|x1703==5|x1703==6|x1703==7|x1703==10|x1703==11|x1703==13|x1703==15|x1703==24|x1703==45|x1703==46|x1703==47|x1703==48|x1703==51|x1703==-7) /// + *(max(0,x1706)*(x1705/10000)-x1715*(x1705/10000)) /// + +(x1803==1|x1803==2|x1803==3|x1803==4|x1803==5|x1803==6|x1803==7|x1803==10|x1803==11|x1803==13|x1803==15|x1803==24|x1803==45|x1803==46|x1803==47|x1803==48|x1803==51|x1803==-7) /// + *(max(0,x1806)*(x1805/10000)-x1815*(x1805/10000)) /// + +(x1903==1|x1903==2|x1903==3|x1903==4|x1903==5|x1903==6|x1903==7|x1903==10|x1903==11|x1903==13|x1903==15|x1903==24|x1903==45|x1903==46|x1903==47|x1903==48|x1903==51|x1903==-7) /// + *(max(0,x1906)*(x1905/10000)-x1915*(x1905/10000)) /// + +max(0,x2012)-x2016 +replace NNRESRE = NNRESRE-x2723*(x2710==78)-x2740*(x2727==78)-x2823*(x2810==78) /// + -x2840*(x2827==78)-x2923*(x2910==78)-x2940*(x2927==78) if NNRESRE!=0 +gen FLAG781 = (NNRESRE!=0) +gen BUS = max(0,x3129)+max(0,x3124)-max(0,x3126)*(x3127==5) /// + +max(0,x3121)*(x3122==1|x3122==6) /// + +max(0,x3229)+max(0,x3224)-max(0,x3226)*(x3227==5) /// + +max(0,x3221)*(x3222==1|x3222==6) /// + +max(0,x3329)+max(0,x3324)-max(0,x3326)*(x3327==5) /// + +max(0,x3321)*(x3322==1|x3322==6) /// + +max(0,x3335)+(x507/10000)*(x513+x526) /// + +max(0,x3408)+max(0,x3412)+max(0,x3416)+max(0,x3420) /// + +max(0,x3424)+max(0,x3428) +gen OTHNFIN = x4022 + x4026 + x4030 - OTHFIN + x4018 +gen NFIN = VEHIC+HOUSES+ORESRE+NNRESRE+BUS+OTHNFIN +gen ASSET = FIN+NFIN /* Total asset */ + +* Debt +gen MRTHEL = x805+x905+x1005+x1108*(x1103==1)+x1119*(x1114==1) /// + +x1130*(x1125==1)+max(0,x1136)*(x1108*(x1103==1)+x1119*(x1114==1) /// + +x1130*(x1125==1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 +replace MRTHEL = x805+x905+x1005+.5*(max(0,x1136)) if (x1108+x1119+x1130)<1 + gen MORT1 = (x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// + *x1715*(x1705/10000) + gen MORT2 = (x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// + *x1815*(x1805/10000) + gen MORT3 = (x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// + *x1915*(x1905/10000) +gen RESDBT = x1417+x1517+x1617+x1621+MORT1+MORT2+MORT3+x2006 + gen FLAG782 = (FLAG781!=1 & ORESRE>0) +replace RESDBT = RESDBT+x2723*(x2710==78)+x2740*(x2727==78)+x2823*(x2810==78)+x2840*(x2827==78) /// + +x2923*(x2910==78)+x2940*(x2927==78) if FLAG781!=1 & ORESRE>0 + gen FLAG67 = (ORESRE>0) +replace RESDBT= RESDBT+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67)+x2840*(x2827==67) /// + +x2923*(x2910==67)+x2940*(x2927==67) if ORESRE>0 +gen OTHLOC = x1108*(x1103!=1)+x1119*(x1114!=1)+x1130*(x1125!=1) /// + +max(0,x1136)*(x1108*(x1103!=1)+x1119*(x1114!=1) /// + +x1130*(x1125!=1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 +replace OTHLOC = .5*(max(0,x1136)) if (x1108+x1119+x1130)<1 +gen CCBAL = max(0,x427)+max(0,x413)+max(0,x421)+max(0,x430)+max(0,x424)+max(0,x7575) +gen INSTALL = x2218+x2318+x2418+x7169+x2424+x2519+x2619+x2625+x7183 /// + +x7824+x7847+x7870+x7924+x7947+x7970+x7179+x1044+x1215+x1219 +replace INSTALL = INSTALL+x2723*(x2710==78)+x2740*(x2727==78) /// + +x2823*(x2810==78)+x2840*(x2827==78)+x2923*(x2910==78)+x2940*(x2927==78) /// + if FLAG781==0 & FLAG782==0 +replace INSTALL = INSTALL+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67) /// + +x2840*(x2827==67)+x2923*(x2910==67)+x2940*(x2927==67) if FLAG67==0 +replace INSTALL = INSTALL+x2723*(x2710!=67&x2710!=78)+x2740*(x2727!=67&x2727!=78) /// + +x2823*(x2810!=67&x2810!=78)+x2840*(x2827!=67&x2827!=78)+x2923*(x2910!=67&x2910!=78) /// + +x2940*(x2927!=67&x2927!=78) +gen PENDBT = max(0,x11027)*(x11070==5)+max(0,x11127)*(x11170==5) /// + +max(0,x11227)*(x11270==5)+max(0,x11327)*(x11370==5) /// + +max(0,x11427)*(x11470==5)+max(0,x11527)*(x11570==5) +gen CASHLIDB = max(0,x4010) +gen CALLDBT = max(0,x3932) +gen ODEBT = max(0,x4032) +gen DEBT = MRTHEL+RESDBT+OTHLOC+CCBAL+INSTALL+PENDBT+CASHLIDB+CALLDBT+ODEBT /* Total debt */ + +* Net worth +gen NETW = ASSET-DEBT +replace NETW = NETW*CPIADJ + +* Ratio of net worth to income +gen WIRATIO = NETW/INCOME + + +* Demographic vars +gen AGE = x14 /* Age */ +gen MARITST = x8023 /* Marital status */ +keep if MARITST == 1 /* Keep if married following Cagetti(2003) */ + +gen EDUC = 0 +replace EDUC = 1 if x5902!=1 /* No high school deg */ +replace EDUC = 2 if x5902==1 /* High school deg */ +replace EDUC = 3 if x5904==1 /* College deg */ +* keep if EDUC == 3 /* Keep college graduates only */ + +* Correct time effects. The base is set at 25 yrs old in 1980 (0 yrs old in 1955) +replace INCOME = INCOME/exp(0.016*(YEAR-1955-AGE)) +drop if INCOME<=0 +replace NETW = NETW/exp(0.016*(YEAR-1955-AGE)) + + +** Keep necessary vars +keep HHID YEAR WGT INCOME NETW WIRATIO AGE + +** Save data +cd "$startDir" +cd ../../Data/Constructed +save "./SCF$scfFldr$SuffixForConstructedFile", replace + +** End in the same directory you started from +cd "$startDir" diff --git a/src/stata/WIRatioPopulation.do b/src/stata/WIRatioPopulation.do new file mode 100644 index 0000000..9b9c810 --- /dev/null +++ b/src/stata/WIRatioPopulation.do @@ -0,0 +1,720 @@ +/* This program gives the Summary statistics for Income, Net Worth and +Wealth/Income Ratio of the Married Households whose ages are between +31 and 55. +The program builts on the results obtained by doAll.do file, so run this +file after running the doAll.do file. +*/ + +cd $basePath/$stataPath + +cd ../../Data/Constructed + +*************************************************************************************************** +/* Specifies the list of percentiles of INCOME, NETW and WIRATIO. p50 represents 50th percentile: median. + Modify the list if you want to obtain results for different percentiles.*/ +global percentiles = "p50" + +scalar AgeRange1 = `"26-30"' +scalar AgeRange2 = `"31-35"' +scalar AgeRange3 = `"36-40"' +scalar AgeRange4 = `"41-45"' +scalar AgeRange5 = `"46-50"' +scalar AgeRange6 = `"51-55"' +scalar AgeRange7 = `"56-60"' + +*************************************************************************************************** +************************************* 1992 Survey Summary ****************************************** + +use $basePath/Data/Constructed/SCF1992_2007_population, clear +keep if YEAR == 1992 +xtset HHID +sort HHID +by HHID: gen OBS=_n +xtset HHID OBS + +egen AVGWGT = sum(WGT), by(HHID) /* Generates one Weight for each Household */ +egen AVGINC = sum(INCOME*WGT), by(HHID) /* This line and the following line Generate the Average Income for each Household */ +replace AVGINC = AVGINC/AVGWGT + +egen AVGNETW = sum(NETW*WGT), by(HHID) /* This line and the following line Generate the Average Net Worth for each Household */ +replace AVGNETW = AVGNETW/AVGWGT + +gen AVGWIRATIO = AVGNETW/AVGINC /* Generates the Average Wealth/Income Ratio for each Household */ + +************************************** Age Range Selection ***************************** + +xtsum HHID +keep if OBS==1 +drop OBS + +keep if AGE >= 26 & AGE <= 60 /* Constructs 5 year period age groups: 31-35, ...., 51-55. */ +gen AGEID = int((AGE-26)/5)+1 +xtset AGEID +sort AGEID HHID + +***************** Before Tax Permanent Income / After Tax Permanent Income RATIO ***************** + +/* This section gives the ratio: Before Tax Permanent Income / After Tax Permanent Income + This adjustment is necessary; we need to rescale WIRATIO properly, since WIRATIO obtained + using STATA is the ratio of wealth to before tax permanent income, not to after tax permanent income. + (Note that the work in the MICRODSOP lecture notes takes parameters from Cagetti (2003) + which is based on after tax income.) + */ + + /* Income and IncomeRatio are calculated using data in Cagetti (2003) and SCF data */ + matrix input RawMat = (1.1758, 39497 \ 1.221, 49262 \ 1.2874, 61057 \ 1.2594, 68224 \ 1.4432, 86353 /// + \ 1.5055, 96983 \ 1.5509, 98786 \ 1.5663, 1.0223e+005 \ 1.5663, 1e+010 ) +svmat RawMat +rename RawMat1 RAWIRATIO +rename RawMat2 RAWI + +gen TXIRATIO =. /* := Before Tax Permanent Income / After Tax Permanent Income */ +local N=_N + +qui forvalues i=1/`N' { /* Gives the Before Tax Permanent Income / After Tax Permanent Income RATIO */ + replace RAWI = AVGINC[`i'] in 10 + ipolate RAWIRATIO RAWI, gen(TEMP) epolate + replace TXIRATIO = TEMP[10] in `i' + drop TEMP + } +replace RAWI =. in 10 +replace TXIRATIO = 1 if TXIRATIO < 1 + +/* AfterTax adjustment of Wealth/Income Ratio: AVGWIRATIO represents Net Worth/Before Tax Permanent Income for each HH, +thus multiplying AVGWIRATIO by TXIRATIO (Bef. Tax Inc / After Tax Inc) gives AVGWIRATIO= Net Worth/After Tax Inc, which is desired. */ + +replace AVGWIRATIO = AVGWIRATIO*TXIRATIO + +************************************************************************************************** + +qui: sum AGEID, d +local size=r(max) /* size is used as an index number in the following loops*/ + +gen AVGINCBYAGE = . +gen OBSBYAGE = . + +qui foreach p in $percentiles { + gen `p'INCBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Average Income for each Age Group defined by AGEID */ + sum AVGINC [aweight = AVGWGT] if AGEID==`k', d + replace AVGINCBYAGE = r(mean) if AGEID==`k' + replace `p'INCBYAGE = r(`p') if AGEID==`k' + replace OBSBYAGE = r(N) if AGEID==`k' + } + } + +gen AVGNETWBYAGE = . + +qui foreach p in $percentiles { + gen `p'NETWBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Net Worth for each Age Group defined by AGEID */ + sum AVGNETW [aweight = AVGWGT] if AGEID==`k', d + replace AVGNETWBYAGE = r(mean) if AGEID==`k' + replace `p'NETWBYAGE = r(`p') if AGEID==`k' + } + } + +gen AVGWIRATIOBYAGE = . + +qui foreach p in $percentiles { + gen `p'WIRATIOBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Wealth/Income Ratio for each Age Group defined by AGEID */ + sum AVGWIRATIO [aweight = AVGWGT] if AGEID==`k', d + replace AVGWIRATIOBYAGE = r(mean) if AGEID==`k' + replace `p'WIRATIOBYAGE = r(`p') if AGEID==`k' + } + } + +gen AGERANGE= `""' +qui forvalues k=1/`size' { + replace AGERANGE = AgeRange`k' if AGEID==`k' /* Generates string values for each Age Group which are used in graphs for illustrative purposes */ + } + +by AGEID: gen OBS=_n +save "$basePath/Data/Constructed/All1992", replace + +************************************* 1995 Survey Summary ****************************************** + +use $basePath/Data/Constructed/SCF1992_2007_population, clear +keep if YEAR == 1995 + +xtset HHID +sort HHID +by HHID: gen OBS=_n +xtset HHID OBS + +egen AVGWGT = sum(WGT), by(HHID) /* Generates one Weight for each Household */ +egen AVGINC = sum(INCOME*WGT), by(HHID) /* This line and the following line Generate the Average Income for each Household */ +replace AVGINC = AVGINC/AVGWGT + +egen AVGNETW = sum(NETW*WGT), by(HHID) /* This line and the following line Generate the Average Net Worth for each Household */ +replace AVGNETW = AVGNETW/AVGWGT + +gen AVGWIRATIO = AVGNETW/AVGINC /* Generates the Average Wealth/Income Ratio for each Household */ + +************************************** Age Range Selection ***************************** + +xtsum HHID +keep if OBS==1 +drop OBS + +keep if AGE >= 26 & AGE <= 60 /* Constructs 5 year period age groups: 31-35, ...., 51-55. */ +gen AGEID = int((AGE-26)/5)+1 +xtset AGEID +sort AGEID HHID + +***************** Before Tax Permanent Income / After Tax Permanent Income RATIO ***************** + +/* This section gives the ratio: Before Tax Permanent Income / After Tax Permanent Income + This adjustment is necessary; we need to rescale WIRATIO properly, since WIRATIO obtained + using STATA is the ratio of wealth to before tax permanent income, not to after tax permanent income. + (Note that the work in the MICRODSOP lecture notes takes parameters from Cagetti (2003) + which is based on after tax income.) + */ + + /* Income and IncomeRatio are calculated using data in Cagetti (2003) and SCF data */ + matrix input RawMat = (1.1758, 39497 \ 1.221, 49262 \ 1.2874, 61057 \ 1.2594, 68224 \ 1.4432, 86353 /// + \ 1.5055, 96983 \ 1.5509, 98786 \ 1.5663, 1.0223e+005 \ 1.5663, 1e+010 ) +svmat RawMat +rename RawMat1 RAWIRATIO +rename RawMat2 RAWI + +gen TXIRATIO =. /* := Before Tax Permanent Income / After Tax Permanent Income */ +local N=_N + +qui forvalues i=1/`N' { /* Gives the Before Tax Permanent Income / After Tax Permanent Income RATIO */ + replace RAWI = AVGINC[`i'] in 10 + ipolate RAWIRATIO RAWI, gen(TEMP) epolate + replace TXIRATIO = TEMP[10] in `i' + drop TEMP + } +replace RAWI =. in 10 +replace TXIRATIO = 1 if TXIRATIO < 1 + +/* AfterTax adjustment of Wealth/Income Ratio: AVGWIRATIO represents Net Worth/Before Tax Permanent Income for each HH, +thus multiplying AVGWIRATIO by TXIRATIO (Bef. Tax Inc / After Tax Inc) gives AVGWIRATIO= Net Worth/After Tax Inc, which is desired. */ + +replace AVGWIRATIO = AVGWIRATIO*TXIRATIO + +************************************************************************************************** + +qui: sum AGEID, d +local size=r(max) /* size is used as an index number in the following loops*/ + +gen AVGINCBYAGE = . +gen OBSBYAGE = . + +qui foreach p in $percentiles { + gen `p'INCBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Average Income for each Age Group defined by AGEID */ + sum AVGINC [aweight = AVGWGT] if AGEID==`k', d + replace AVGINCBYAGE = r(mean) if AGEID==`k' + replace `p'INCBYAGE = r(`p') if AGEID==`k' + replace OBSBYAGE = r(N) if AGEID==`k' + } + } + +gen AVGNETWBYAGE = . + +qui foreach p in $percentiles { + gen `p'NETWBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Net Worth for each Age Group defined by AGEID */ + sum AVGNETW [aweight = AVGWGT] if AGEID==`k', d + replace AVGNETWBYAGE = r(mean) if AGEID==`k' + replace `p'NETWBYAGE = r(`p') if AGEID==`k' + } + } + +gen AVGWIRATIOBYAGE = . + +qui foreach p in $percentiles { + gen `p'WIRATIOBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Wealth/Income Ratio for each Age Group defined by AGEID */ + sum AVGWIRATIO [aweight = AVGWGT] if AGEID==`k', d + replace AVGWIRATIOBYAGE = r(mean) if AGEID==`k' + replace `p'WIRATIOBYAGE = r(`p') if AGEID==`k' + } + } + +gen AGERANGE= `""' +qui forvalues k=1/`size' { + replace AGERANGE = AgeRange`k' if AGEID==`k' /* Generates string values for each Age Group which are used in graphs for illustrative purposes */ + } + +by AGEID: gen OBS=_n +save "$basePath/Data/Constructed/All1995", replace + +************************************* 1998 Survey Summary ****************************************** + +use $basePath/Data/Constructed/SCF1992_2007_population, clear +keep if YEAR == 1998 + +xtset HHID +sort HHID +by HHID: gen OBS=_n +xtset HHID OBS + +egen AVGWGT = sum(WGT), by(HHID) /* Generates one Weight for each Household */ +egen AVGINC = sum(INCOME*WGT), by(HHID) /* This line and the following line Generate the Average Income for each Household */ +replace AVGINC = AVGINC/AVGWGT + +egen AVGNETW = sum(NETW*WGT), by(HHID) /* This line and the following line Generate the Average Net Worth for each Household */ +replace AVGNETW = AVGNETW/AVGWGT + +gen AVGWIRATIO = AVGNETW/AVGINC /* Generates the Average Wealth/Income Ratio for each Household */ + +************************************** Age Range Selection ***************************** + +xtsum HHID +keep if OBS==1 +drop OBS + +keep if AGE >= 26 & AGE <= 60 /* Constructs 5 year period age groups: 31-35, ...., 51-55. */ +gen AGEID = int((AGE-26)/5)+1 +xtset AGEID +sort AGEID HHID + +***************** Before Tax Permanent Income / After Tax Permanent Income RATIO ***************** + +/* This section gives the ratio: Before Tax Permanent Income / After Tax Permanent Income + This adjustment is necessary; we need to rescale WIRATIO properly, since WIRATIO obtained + using STATA is the ratio of wealth to before tax permanent income, not to after tax permanent income. + (Note that the work in the MICRODSOP lecture notes takes parameters from Cagetti (2003) + which is based on after tax income.) + */ + + /* Income and IncomeRatio are calculated using data in Cagetti (2003) and SCF data */ + matrix input RawMat = (1.1758, 39497 \ 1.221, 49262 \ 1.2874, 61057 \ 1.2594, 68224 \ 1.4432, 86353 /// + \ 1.5055, 96983 \ 1.5509, 98786 \ 1.5663, 1.0223e+005 \ 1.5663, 1e+010 ) +svmat RawMat +rename RawMat1 RAWIRATIO +rename RawMat2 RAWI + +gen TXIRATIO =. /* := Before Tax Permanent Income / After Tax Permanent Income */ +local N=_N + +qui forvalues i=1/`N' { /* Gives the Before Tax Permanent Income / After Tax Permanent Income RATIO */ + replace RAWI = AVGINC[`i'] in 10 + ipolate RAWIRATIO RAWI, gen(TEMP) epolate + replace TXIRATIO = TEMP[10] in `i' + drop TEMP + } +replace RAWI =. in 10 +replace TXIRATIO = 1 if TXIRATIO < 1 + +/* AfterTax adjustment of Wealth/Income Ratio: AVGWIRATIO represents Net Worth/Before Tax Permanent Income for each HH, +thus multiplying AVGWIRATIO by TXIRATIO (Bef. Tax Inc / After Tax Inc) gives AVGWIRATIO= Net Worth/After Tax Inc, which is desired. */ + +replace AVGWIRATIO = AVGWIRATIO*TXIRATIO + +************************************************************************************************** + +qui: sum AGEID, d +local size=r(max) /* size is used as an index number in the following loops*/ + +gen AVGINCBYAGE = . +gen OBSBYAGE = . + +qui foreach p in $percentiles { + gen `p'INCBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Average Income for each Age Group defined by AGEID */ + sum AVGINC [aweight = AVGWGT] if AGEID==`k', d + replace AVGINCBYAGE = r(mean) if AGEID==`k' + replace `p'INCBYAGE = r(`p') if AGEID==`k' + replace OBSBYAGE = r(N) if AGEID==`k' + } + } + +gen AVGNETWBYAGE = . + +qui foreach p in $percentiles { + gen `p'NETWBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Net Worth for each Age Group defined by AGEID */ + sum AVGNETW [aweight = AVGWGT] if AGEID==`k', d + replace AVGNETWBYAGE = r(mean) if AGEID==`k' + replace `p'NETWBYAGE = r(`p') if AGEID==`k' + } + } + +gen AVGWIRATIOBYAGE = . + +qui foreach p in $percentiles { + gen `p'WIRATIOBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Wealth/Income Ratio for each Age Group defined by AGEID */ + sum AVGWIRATIO [aweight = AVGWGT] if AGEID==`k', d + replace AVGWIRATIOBYAGE = r(mean) if AGEID==`k' + replace `p'WIRATIOBYAGE = r(`p') if AGEID==`k' + } + } + +gen AGERANGE= `""' +qui forvalues k=1/`size' { + replace AGERANGE = AgeRange`k' if AGEID==`k' /* Generates string values for each Age Group which are used in graphs for illustrative purposes */ + } + +by AGEID: gen OBS=_n +save "$basePath/Data/Constructed/All1998", replace + +************************************* 2001 Survey Summary ****************************************** + +use $basePath/Data/Constructed/SCF1992_2007_population, clear +keep if YEAR == 2001 + +xtset HHID +sort HHID +by HHID: gen OBS=_n +xtset HHID OBS + +egen AVGWGT = sum(WGT), by(HHID) /* Generates one Weight for each Household */ +egen AVGINC = sum(INCOME*WGT), by(HHID) /* This line and the following line Generate the Average Income for each Household */ +replace AVGINC = AVGINC/AVGWGT + +egen AVGNETW = sum(NETW*WGT), by(HHID) /* This line and the following line Generate the Average Net Worth for each Household */ +replace AVGNETW = AVGNETW/AVGWGT + +gen AVGWIRATIO = AVGNETW/AVGINC /* Generates the Average Wealth/Income Ratio for each Household */ + +************************************** Age Range Selection ***************************** + +xtsum HHID +keep if OBS==1 +drop OBS + +keep if AGE >= 26 & AGE <= 60 /* Constructs 5 year period age groups: 31-35, ...., 51-55. */ +gen AGEID = int((AGE-26)/5)+1 +xtset AGEID +sort AGEID HHID + +***************** Before Tax Permanent Income / After Tax Permanent Income RATIO ***************** + +/* This section gives the ratio: Before Tax Permanent Income / After Tax Permanent Income + This adjustment is necessary; we need to rescale WIRATIO properly, since WIRATIO obtained + using STATA is the ratio of wealth to before tax permanent income, not to after tax permanent income. + (Note that the work in the MICRODSOP lecture notes takes parameters from Cagetti (2003) + which is based on after tax income.) + */ + + /* Income and IncomeRatio are calculated using data in Cagetti (2003) and SCF data */ + matrix input RawMat = (1.1758, 39497 \ 1.221, 49262 \ 1.2874, 61057 \ 1.2594, 68224 \ 1.4432, 86353 /// + \ 1.5055, 96983 \ 1.5509, 98786 \ 1.5663, 1.0223e+005 \ 1.5663, 1e+010 ) +svmat RawMat +rename RawMat1 RAWIRATIO +rename RawMat2 RAWI + +gen TXIRATIO =. /* := Before Tax Permanent Income / After Tax Permanent Income */ +local N=_N + +qui forvalues i=1/`N' { /* Gives the Before Tax Permanent Income / After Tax Permanent Income RATIO */ + replace RAWI = AVGINC[`i'] in 10 + ipolate RAWIRATIO RAWI, gen(TEMP) epolate + replace TXIRATIO = TEMP[10] in `i' + drop TEMP + } +replace RAWI =. in 10 +replace TXIRATIO = 1 if TXIRATIO < 1 + +/* AfterTax adjustment of Wealth/Income Ratio: AVGWIRATIO represents Net Worth/Before Tax Permanent Income for each HH, +thus multiplying AVGWIRATIO by TXIRATIO (Bef. Tax Inc / After Tax Inc) gives AVGWIRATIO= Net Worth/After Tax Inc, which is desired. */ + +replace AVGWIRATIO = AVGWIRATIO*TXIRATIO + +************************************************************************************************** + +qui: sum AGEID, d +local size=r(max) /* size is used as an index number in the following loops*/ + +gen AVGINCBYAGE = . +gen OBSBYAGE = . + +qui foreach p in $percentiles { + gen `p'INCBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Average Income for each Age Group defined by AGEID */ + sum AVGINC [aweight = AVGWGT] if AGEID==`k', d + replace AVGINCBYAGE = r(mean) if AGEID==`k' + replace `p'INCBYAGE = r(`p') if AGEID==`k' + replace OBSBYAGE = r(N) if AGEID==`k' + } + } + +gen AVGNETWBYAGE = . + +qui foreach p in $percentiles { + gen `p'NETWBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Net Worth for each Age Group defined by AGEID */ + sum AVGNETW [aweight = AVGWGT] if AGEID==`k', d + replace AVGNETWBYAGE = r(mean) if AGEID==`k' + replace `p'NETWBYAGE = r(`p') if AGEID==`k' + } + } + +gen AVGWIRATIOBYAGE = . + +qui foreach p in $percentiles { + gen `p'WIRATIOBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Wealth/Income Ratio for each Age Group defined by AGEID */ + sum AVGWIRATIO [aweight = AVGWGT] if AGEID==`k', d + replace AVGWIRATIOBYAGE = r(mean) if AGEID==`k' + replace `p'WIRATIOBYAGE = r(`p') if AGEID==`k' + } + } + +gen AGERANGE= `""' +qui forvalues k=1/`size' { + replace AGERANGE = AgeRange`k' if AGEID==`k' /* Generates string values for each Age Group which are used in graphs for illustrative purposes */ + } + +by AGEID: gen OBS=_n +save "$basePath/Data/Constructed/All2001", replace + +************************************* 2004 Survey Summary ****************************************** + +use $basePath/Data/Constructed/SCF1992_2007_population, clear +keep if YEAR == 2004 + +xtset HHID +sort HHID +by HHID: gen OBS=_n +xtset HHID OBS + +egen AVGWGT = sum(WGT), by(HHID) /* Generates one Weight for each Household */ +egen AVGINC = sum(INCOME*WGT), by(HHID) /* This line and the following line Generate the Average Income for each Household */ +replace AVGINC = AVGINC/AVGWGT + +egen AVGNETW = sum(NETW*WGT), by(HHID) /* This line and the following line Generate the Average Net Worth for each Household */ +replace AVGNETW = AVGNETW/AVGWGT + +gen AVGWIRATIO = AVGNETW/AVGINC /* Generates the Average Wealth/Income Ratio for each Household */ + +************************************** Age Range Selection ***************************** + +xtsum HHID +keep if OBS==1 +drop OBS + +keep if AGE >= 26 & AGE <= 60 /* Constructs 5 year period age groups: 31-35, ...., 51-55. */ +gen AGEID = int((AGE-26)/5)+1 +xtset AGEID +sort AGEID HHID + +***************** Before Tax Permanent Income / After Tax Permanent Income RATIO ***************** + +/* This section gives the ratio: Before Tax Permanent Income / After Tax Permanent Income + This adjustment is necessary; we need to rescale WIRATIO properly, since WIRATIO obtained + using STATA is the ratio of wealth to before tax permanent income, not to after tax permanent income. + (Note that the work in the MICRODSOP lecture notes takes parameters from Cagetti (2003) + which is based on after tax income.) + */ + + /* Income and IncomeRatio are calculated using data in Cagetti (2003) and SCF data */ + matrix input RawMat = (1.1758, 39497 \ 1.221, 49262 \ 1.2874, 61057 \ 1.2594, 68224 \ 1.4432, 86353 /// + \ 1.5055, 96983 \ 1.5509, 98786 \ 1.5663, 1.0223e+005 \ 1.5663, 1e+010 ) +svmat RawMat +rename RawMat1 RAWIRATIO +rename RawMat2 RAWI + +gen TXIRATIO =. /* := Before Tax Permanent Income / After Tax Permanent Income */ +local N=_N + +qui forvalues i=1/`N' { /* Gives the Before Tax Permanent Income / After Tax Permanent Income RATIO */ + replace RAWI = AVGINC[`i'] in 10 + ipolate RAWIRATIO RAWI, gen(TEMP) epolate + replace TXIRATIO = TEMP[10] in `i' + drop TEMP + } +replace RAWI =. in 10 +replace TXIRATIO = 1 if TXIRATIO < 1 + +/* AfterTax adjustment of Wealth/Income Ratio: AVGWIRATIO represents Net Worth/Before Tax Permanent Income for each HH, +thus multiplying AVGWIRATIO by TXIRATIO (Bef. Tax Inc / After Tax Inc) gives AVGWIRATIO= Net Worth/After Tax Inc, which is desired. */ + +replace AVGWIRATIO = AVGWIRATIO*TXIRATIO + +************************************************************************************************** + +qui: sum AGEID, d +local size=r(max) /* size is used as an index number in the following loops*/ + +gen AVGINCBYAGE = . +gen OBSBYAGE = . + +qui foreach p in $percentiles { + gen `p'INCBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Average Income for each Age Group defined by AGEID */ + sum AVGINC [aweight = AVGWGT] if AGEID==`k', d + replace AVGINCBYAGE = r(mean) if AGEID==`k' + replace `p'INCBYAGE = r(`p') if AGEID==`k' + replace OBSBYAGE = r(N) if AGEID==`k' + } + } + +gen AVGNETWBYAGE = . + +qui foreach p in $percentiles { + gen `p'NETWBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Net Worth for each Age Group defined by AGEID */ + sum AVGNETW [aweight = AVGWGT] if AGEID==`k', d + replace AVGNETWBYAGE = r(mean) if AGEID==`k' + replace `p'NETWBYAGE = r(`p') if AGEID==`k' + } + } + +gen AVGWIRATIOBYAGE = . + +qui foreach p in $percentiles { + gen `p'WIRATIOBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Wealth/Income Ratio for each Age Group defined by AGEID */ + sum AVGWIRATIO [aweight = AVGWGT] if AGEID==`k', d + replace AVGWIRATIOBYAGE = r(mean) if AGEID==`k' + replace `p'WIRATIOBYAGE = r(`p') if AGEID==`k' + } + } + +gen AGERANGE= `""' +qui forvalues k=1/`size' { + replace AGERANGE = AgeRange`k' if AGEID==`k' /* Generates string values for each Age Group which are used in graphs for illustrative purposes */ + } + +by AGEID: gen OBS=_n +save "$basePath/Data/Constructed/All2004", replace + +************************************* 2007 Survey Summary ****************************************** + +use $basePath/Data/Constructed/SCF1992_2007_population, clear +keep if YEAR == 2007 + +xtset HHID +sort HHID +by HHID: gen OBS=_n +xtset HHID OBS + +egen AVGWGT = sum(WGT), by(HHID) /* Generates one Weight for each Household */ +egen AVGINC = sum(INCOME*WGT), by(HHID) /* This line and the following line Generate the Average Income for each Household */ +replace AVGINC = AVGINC/AVGWGT + +egen AVGNETW = sum(NETW*WGT), by(HHID) /* This line and the following line Generate the Average Net Worth for each Household */ +replace AVGNETW = AVGNETW/AVGWGT + +gen AVGWIRATIO = AVGNETW/AVGINC /* Generates the Average Wealth/Income Ratio for each Household */ + +************************************** Age Range Selection ***************************** + +xtsum HHID +keep if OBS==1 +drop OBS + +keep if AGE >= 26 & AGE <= 60 /* Constructs 5 year period age groups: 31-35, ...., 51-55. */ +gen AGEID = int((AGE-26)/5)+1 +xtset AGEID +sort AGEID HHID + +***************** Before Tax Permanent Income / After Tax Permanent Income RATIO ***************** + +/* This section gives the ratio: Before Tax Permanent Income / After Tax Permanent Income + This adjustment is necessary; we need to rescale WIRATIO properly, since WIRATIO obtained + using STATA is the ratio of wealth to before tax permanent income, not to after tax permanent income. + (Note that the work in the MICRODSOP lecture notes takes parameters from Cagetti (2003) + which is based on after tax income.) + */ + + /* Income and IncomeRatio are calculated using data in Cagetti (2003) and SCF data */ + matrix input RawMat = (1.1758, 39497 \ 1.221, 49262 \ 1.2874, 61057 \ 1.2594, 68224 \ 1.4432, 86353 /// + \ 1.5055, 96983 \ 1.5509, 98786 \ 1.5663, 1.0223e+005 \ 1.5663, 1e+010 ) +svmat RawMat +rename RawMat1 RAWIRATIO +rename RawMat2 RAWI + +gen TXIRATIO =. /* := Before Tax Permanent Income / After Tax Permanent Income */ +local N=_N + +qui forvalues i=1/`N' { /* Gives the Before Tax Permanent Income / After Tax Permanent Income RATIO */ + replace RAWI = AVGINC[`i'] in 10 + ipolate RAWIRATIO RAWI, gen(TEMP) epolate + replace TXIRATIO = TEMP[10] in `i' + drop TEMP + } +replace RAWI =. in 10 +replace TXIRATIO = 1 if TXIRATIO < 1 + +/* AfterTax adjustment of Wealth/Income Ratio: AVGWIRATIO represents Net Worth/Before Tax Permanent Income for each HH, +thus multiplying AVGWIRATIO by TXIRATIO (Bef. Tax Inc / After Tax Inc) gives AVGWIRATIO= Net Worth/After Tax Inc, which is desired. */ + +replace AVGWIRATIO = AVGWIRATIO*TXIRATIO + +************************************************************************************************** + +qui: sum AGEID, d +local size=r(max) /* size is used as an index number in the following loops*/ + +gen AVGINCBYAGE = . +gen OBSBYAGE = . + +qui foreach p in $percentiles { + gen `p'INCBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Average Income for each Age Group defined by AGEID */ + sum AVGINC [aweight = AVGWGT] if AGEID==`k', d + replace AVGINCBYAGE = r(mean) if AGEID==`k' + replace `p'INCBYAGE = r(`p') if AGEID==`k' + replace OBSBYAGE = r(N) if AGEID==`k' + } + } + +gen AVGNETWBYAGE = . + +qui foreach p in $percentiles { + gen `p'NETWBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Net Worth for each Age Group defined by AGEID */ + sum AVGNETW [aweight = AVGWGT] if AGEID==`k', d + replace AVGNETWBYAGE = r(mean) if AGEID==`k' + replace `p'NETWBYAGE = r(`p') if AGEID==`k' + } + } + +gen AVGWIRATIOBYAGE = . + +qui foreach p in $percentiles { + gen `p'WIRATIOBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Wealth/Income Ratio for each Age Group defined by AGEID */ + sum AVGWIRATIO [aweight = AVGWGT] if AGEID==`k', d + replace AVGWIRATIOBYAGE = r(mean) if AGEID==`k' + replace `p'WIRATIOBYAGE = r(`p') if AGEID==`k' + } + } + +gen AGERANGE= `""' +qui forvalues k=1/`size' { + replace AGERANGE = AgeRange`k' if AGEID==`k' /* Generates string values for each Age Group which are used in graphs for illustrative purposes */ + } + +by AGEID: gen OBS=_n +save "$basePath/Data/Constructed/All2007", replace + +************************************ 2001-2007 Population WIRATIO , AGEID and WEIGHT ******************************* +cd $basePath/Data/Constructed/ + +use All2007, clear +append using All2004 +append using All2001 +append using All1998 +append using All1995 +append using All1992 + +keep HHID YEAR AGEID AGERANGE AVGWIRATIO AVGWGT +sort AGEID AVGWIRATIO + +bysort AGEID: gen N=_N +egen SUMAVGWGT = sum(AVGWGT), by(AGEID) +gen WGTPOP = (AVGWGT/SUMAVGWGT)*N +gen WIRATIOPOP = AVGWIRATIO + +order WIRATIOPOP AGEID WGTPOP +keep WIRATIOPOP AGEID WGTPOP + +save "./WIRATIO_Population", replace +save"./SCFdata",replace + +cd $basePath +outfile using "./Code/Mathematica/StructuralEstimation/SCFdata.txt", replace + +************************************************************************************************** + +cd $basePath/$stataPath /* When program ends, make sure working directory is the program's directory */ diff --git a/src/stata/doAll.do b/src/stata/doAll.do new file mode 100644 index 0000000..ab395bc --- /dev/null +++ b/src/stata/doAll.do @@ -0,0 +1,13 @@ +* Assuming existence of all 'raw' data files in appropriate locations, create processed files containing needed data + +set linesize 200 + +* make paths +global basePath "/Volumes/Data/Notes/NumericalMethods/EstimatingMicroDSOPs/Latest" +global stataPath "Code/Stata" +global logPath "Code/Stata" + +cd $basePath/$stataPath + +do AppendDataUsingSCF1992_2007.do +do WIRatioPopulation.do diff --git a/src/tests.py b/src/tests.py new file mode 100644 index 0000000..56493be --- /dev/null +++ b/src/tests.py @@ -0,0 +1,34 @@ +from __future__ import annotations + +from estimark.min import estimate_min +from estimark.options import low_resource, medium_resource + + +def test_low_resource(): + print("Running low-resource replication...") + estimate_min(**low_resource) + + +def test_medium_resource(): + print("Running medium-resource replication...") + estimate_min(**medium_resource) + + +def test_portfolio_low_resource(): + print("Running medium-resource replication...") + estimate_min(**low_resource, agent_name="Portfolio") + + +def test_warmglow_low_resource(): + print("Running medium-resource replication...") + estimate_min(**low_resource, agent_name="WarmGlow") + + +def test_warmglowportfolio_low_resource(): + print("Running medium-resource replication...") + estimate_min(**low_resource, agent_name="WarmGlowPortfolio") + + +def test_wealthportfolio_low_resource(): + print("Running medium-resource replication...") + estimate_min(**low_resource, agent_name="WealthPortfolio") diff --git a/tests/test_package.py b/tests/test_package.py new file mode 100644 index 0000000..8026afa --- /dev/null +++ b/tests/test_package.py @@ -0,0 +1,9 @@ +from __future__ import annotations + +import importlib.metadata + +import estimark as m + + +def test_version(): + assert importlib.metadata.version("estimark") == m.__version__