From dbe2ecaa6a8407a444871261e6ec965edce535e2 Mon Sep 17 00:00:00 2001 From: Jeff Hanson Date: Mon, 27 May 2024 09:39:09 +1200 Subject: [PATCH] fix #21, fix compatibility issues, fix broken urls + badges (PR #19) - Fix aliasing for package manual entry (#21). - Fix compatibility with updates to _ggplot2_ package. - Fix compatibility with updates to package citation format. - Fix broken URLs in package documentation. - Fix broken badges in README. --- .github/workflows/R-CMD-check-macos.yaml | 65 ++- .github/workflows/R-CMD-check-ubuntu.yaml | 83 ++-- .github/workflows/R-CMD-check-windows.yaml | 55 +-- DESCRIPTION | 8 +- NAMESPACE | 1 - NEWS.md | 8 + R/add_gurobi_solver.R | 8 +- R/add_lpsolveapi_solver.R | 2 +- R/package.R | 15 +- R/plot_feature_persistence.R | 60 ++- R/plot_phylo_persistence.R | 64 ++- R/solvers.R | 2 +- README.Rmd | 8 +- README.md | 8 +- docs/404.html | 4 +- docs/articles/index.html | 4 +- docs/articles/oppr.html | 355 +++++++++-------- .../figure-html/unnamed-chunk-12-1.png | Bin 99757 -> 99099 bytes .../figure-html/unnamed-chunk-19-1.png | Bin 107409 -> 107564 bytes .../figure-html/unnamed-chunk-26-1.png | Bin 24675 -> 22089 bytes .../figure-html/unnamed-chunk-27-1.png | Bin 22228 -> 22102 bytes .../figure-html/unnamed-chunk-32-1.png | Bin 99586 -> 98951 bytes .../figure-html/unnamed-chunk-33-1.png | Bin 38134 -> 38145 bytes .../figure-html/unnamed-chunk-40-1.png | Bin 96020 -> 95264 bytes .../figure-html/unnamed-chunk-43-1.png | Bin 91670 -> 91631 bytes .../figure-html/unnamed-chunk-45-1.png | Bin 91342 -> 91306 bytes docs/authors.html | 12 +- docs/index.html | 18 +- docs/news/index.html | 14 +- docs/pkgdown.yml | 4 +- docs/reference/ArrayParameter-class.html | 4 +- docs/reference/Collection-class.html | 4 +- docs/reference/Constraint-class.html | 4 +- docs/reference/Decision-class.html | 4 +- docs/reference/MiscParameter-class.html | 4 +- docs/reference/Objective-class.html | 4 +- docs/reference/OptimizationProblem-class.html | 4 +- .../OptimizationProblem-methods.html | 4 +- docs/reference/Parameter-class.html | 4 +- docs/reference/Parameters-class.html | 4 +- docs/reference/ProjectModifier-class.html | 4 +- docs/reference/ProjectProblem-class.html | 4 +- docs/reference/Rplot001.png | Bin 3030 -> 13476 bytes docs/reference/Rplot002.png | Bin 11050 -> 10845 bytes docs/reference/Rplot003.png | Bin 11669 -> 10730 bytes docs/reference/Rplot004.png | Bin 11274 -> 10292 bytes docs/reference/Rplot005.png | Bin 11049 -> 10046 bytes docs/reference/ScalarParameter-class.html | 4 +- docs/reference/Solver-class.html | 4 +- docs/reference/Target-class.html | 4 +- docs/reference/Weight-class.html | 4 +- docs/reference/action_names.html | 4 +- docs/reference/add_absolute_targets-1.png | Bin 33325 -> 32416 bytes docs/reference/add_absolute_targets-2.png | Bin 31767 -> 30746 bytes docs/reference/add_absolute_targets-3.png | Bin 31767 -> 30746 bytes docs/reference/add_absolute_targets.html | 103 +++-- docs/reference/add_binary_decisions-1.png | Bin 34256 -> 33242 bytes docs/reference/add_binary_decisions.html | 33 +- docs/reference/add_default_solver-1.png | Bin 34256 -> 33242 bytes docs/reference/add_default_solver.html | 33 +- docs/reference/add_feature_weights-1.png | Bin 34256 -> 33242 bytes docs/reference/add_feature_weights-2.png | Bin 38178 -> 37194 bytes docs/reference/add_feature_weights-3.png | Bin 38178 -> 37194 bytes docs/reference/add_feature_weights.html | 91 +++-- docs/reference/add_gurobi_solver-1.png | Bin 34256 -> 33242 bytes docs/reference/add_gurobi_solver.html | 72 ++-- docs/reference/add_heuristic_solver-1.png | Bin 34256 -> 33242 bytes docs/reference/add_heuristic_solver.html | 18 +- docs/reference/add_locked_in_constraints.html | 64 ++- .../reference/add_locked_out_constraints.html | 64 ++- docs/reference/add_lpsolveapi_solver-1.png | Bin 34256 -> 33242 bytes docs/reference/add_lpsolveapi_solver.html | 20 +- docs/reference/add_lsymphony_solver-1.png | Bin 34256 -> 33242 bytes docs/reference/add_lsymphony_solver.html | 18 +- .../add_manual_locked_constraints.html | 33 +- docs/reference/add_manual_targets.html | 33 +- .../add_max_phylo_div_objective-2.png | Bin 33059 -> 30573 bytes .../add_max_phylo_div_objective-3.png | Bin 33053 -> 30514 bytes .../add_max_phylo_div_objective.html | 103 +++-- .../add_max_richness_objective-1.png | Bin 34256 -> 33242 bytes .../add_max_richness_objective-2.png | Bin 38178 -> 37194 bytes .../reference/add_max_richness_objective.html | 62 ++- .../add_max_targets_met_objective-1.png | Bin 35614 -> 34493 bytes .../add_max_targets_met_objective-2.png | Bin 38919 -> 37573 bytes .../add_max_targets_met_objective.html | 75 ++-- docs/reference/add_min_set_objective-1.png | Bin 35788 -> 34845 bytes docs/reference/add_min_set_objective.html | 33 +- docs/reference/add_random_solver-1.png | Bin 34011 -> 33371 bytes docs/reference/add_random_solver-2.png | Bin 22090 -> 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docs/reference/matrix_parameters.html | 26 +- docs/reference/misc_parameter.html | 12 +- docs/reference/new_id.html | 8 +- docs/reference/new_optimization_problem.html | 4 +- docs/reference/new_waiver.html | 4 +- docs/reference/number_of_actions.html | 4 +- docs/reference/number_of_features.html | 4 +- docs/reference/number_of_projects.html | 4 +- docs/reference/objectives-1.png | Bin 34256 -> 33242 bytes docs/reference/objectives-2.png | Bin 33059 -> 30573 bytes docs/reference/objectives-3.png | Bin 34256 -> 33242 bytes docs/reference/objectives-4.png | Bin 33325 -> 32416 bytes docs/reference/objectives.html | 142 ++++--- docs/reference/oppr-1.png | Bin 39992 -> 38949 bytes docs/reference/oppr.html | 70 ++-- docs/reference/parameters.html | 6 +- docs/reference/pipe.html | 4 +- docs/reference/plot.ProjectProblem-1.png | Bin 39992 -> 38949 bytes docs/reference/plot.ProjectProblem-2.png | Bin 34258 -> 31520 bytes docs/reference/plot.ProjectProblem.html | 47 ++- 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docs/reference/scalar_parameters.html | 14 +- docs/reference/show.html | 4 +- docs/reference/sim_data.html | 25 +- docs/reference/simulate_ppp_data.html | 24 +- docs/reference/simulate_ptm_data.html | 27 +- docs/reference/solution_statistics.html | 40 +- docs/reference/solve.html | 54 +-- docs/reference/solvers.html | 36 +- docs/reference/targets-1.png | Bin 33325 -> 32416 bytes docs/reference/targets-2.png | Bin 30931 -> 30073 bytes docs/reference/targets.html | 68 ++-- docs/reference/tee.html | 6 +- docs/reference/tibble-methods.html | 4 +- docs/reference/weights-1.png | Bin 38178 -> 37194 bytes docs/reference/weights.html | 33 +- inst/CITATION | 12 +- inst/doc/oppr.Rmd | 4 +- inst/doc/oppr.html | 369 +++++++++--------- man/add_gurobi_solver.Rd | 8 +- man/add_lpsolveapi_solver.Rd | 2 +- man/figures/README-readme-plot-1.png | Bin 17666 -> 17363 bytes man/oppr.Rd | 21 +- man/solvers.Rd | 2 +- .../testthat/test_plot_feature_persistence.R | 2 +- tests/testthat/test_plot_phylo_persistence.R | 2 +- vignettes/oppr.Rmd | 4 +- 172 files changed, 1936 insertions(+), 1316 deletions(-) diff --git a/.github/workflows/R-CMD-check-macos.yaml b/.github/workflows/R-CMD-check-macos.yaml index 13ec387..c790aaf 100644 --- a/.github/workflows/R-CMD-check-macos.yaml +++ b/.github/workflows/R-CMD-check-macos.yaml @@ -37,41 +37,32 @@ jobs: steps: - uses: actions/checkout@v2 - - uses: r-lib/actions/setup-r@v1 + - uses: r-lib/actions/setup-pandoc@v2 + + - uses: r-lib/actions/setup-r@v2 with: r-version: ${{ matrix.config.r }} + use-public-rspm: true - - uses: r-lib/actions/setup-pandoc@v1 - - - name: Query dependencies - run: | - install.packages('remotes') - saveRDS(remotes::dev_package_deps(dependencies = TRUE), ".github/depends.Rds", version = 2) - writeLines(sprintf("R-%i.%i", getRversion()$major, getRversion()$minor), ".github/R-version") - shell: Rscript {0} - - - name: Cache R packages - if: runner.os != 'Windows' - uses: actions/cache@v2 + - uses: r-lib/actions/setup-r-dependencies@v2 with: - path: ${{ env.R_LIBS_USER }} - key: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-${{ hashFiles('.github/depends.Rds') }} - restore-keys: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1- - - - name: Install system dependencies - if: runner.os == 'Linux' + cache-version: 1 + extra-packages: | + cran/XML + any::remotes + any::covr + any::rcmdcheck + gurobi=?ignore + Rsymphony=?ignore + lpsymphony=?ignore + ggtree=?ignore + + - name: Install additional packages run: | - while read -r cmd - do - eval sudo $cmd - done < <(Rscript -e 'writeLines(remotes::system_requirements("ubuntu", "20.04"))') - - - name: Install dependencies - run: | - options("install.packages.compile.from.source" = "never") - remotes::install_deps(dependencies = TRUE) - remotes::install_cran("rcmdcheck") - remotes::install_cran("covr") + options(repos = "https://cloud.r-project.org/") + remotes::install_cran("Rsymphony") + remotes::install_bioc("lpsymphony") + remotes::install_bioc("ggtree") shell: Rscript {0} - name: Session information @@ -81,19 +72,11 @@ jobs: sessioninfo::session_info(pkgs, include_base = TRUE) shell: Rscript {0} - - name: Check - run: | - rcmdcheck::rcmdcheck(args = c("--no-manual", "--as-cran", "--no-build-vignettes"), error_on = "warning", check_dir = "check") - shell: Rscript {0} + - uses: r-lib/actions/check-r-package@v2 + with: + upload-snapshots: true - name: Show testthat output if: always() run: find check -name 'testthat.Rout*' -exec cat '{}' \; || true shell: bash - - - name: Upload check results - if: failure() - uses: actions/upload-artifact@main - with: - name: ${{ runner.os }}-r${{ matrix.config.r }}-results - path: check diff --git a/.github/workflows/R-CMD-check-ubuntu.yaml b/.github/workflows/R-CMD-check-ubuntu.yaml index 08ffced..9336816 100644 --- a/.github/workflows/R-CMD-check-ubuntu.yaml +++ b/.github/workflows/R-CMD-check-ubuntu.yaml @@ -25,8 +25,8 @@ jobs: matrix: config: - {os: ubuntu-20.04, r: 'release', pkgs: 'all'} - - {os: ubuntu-20.04, r: 'devel', pkgs: 'all'} - - {os: ubuntu-20.04, r: 'devel', pkgs: 'depends only'} + # - {os: ubuntu-20.04, r: 'devel', pkgs: 'all'} + # - {os: ubuntu-20.04, r: 'devel', pkgs: 'depends only'} env: _R_REMOTES_NO_ERRORS_FROM_WARNINGS_: true @@ -39,40 +39,36 @@ jobs: steps: - uses: actions/checkout@v2 - - uses: r-lib/actions/setup-r@v1 - with: - r-version: ${{ matrix.config.r }} - - - uses: r-lib/actions/setup-pandoc@v1 - - - name: Query dependencies - run: | - install.packages('remotes') - saveRDS(remotes::dev_package_deps(dependencies = TRUE), ".github/depends.Rds", version = 2) - writeLines(sprintf("R-%i.%i", getRversion()$major, getRversion()$minor), ".github/R-version") - shell: Rscript {0} + - uses: r-lib/actions/setup-pandoc@v2 - - name: Cache R packages - if: runner.os != 'Windows' - uses: actions/cache@v2 + - uses: r-lib/actions/setup-r@v2 with: - path: ${{ env.R_LIBS_USER }} - key: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-cache-${{ hashFiles('.github/depends.Rds') }} - restore-keys: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-cache- + r-version: ${{ matrix.config.r }} + use-public-rspm: true - name: Install system dependencies run: | - sudo apt-get -y install libcurl4-openssl-dev - while read -r cmd - do - eval sudo $cmd - done < <(Rscript -e 'writeLines(remotes::system_requirements("ubuntu", "20.04"))') + sudo apt-get install libxml2 coinor-libsymphony-dev - - name: Install dependencies + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + cache-version: 4 + extra-packages: | + cran/XML + any::remotes + any::covr + any::rcmdcheck + gurobi=?ignore + Rsymphony=?ignore + lpsymphony=?ignore + ggtree=?ignore + + - name: Install additional packages run: | - remotes::install_deps(dependencies = TRUE) - remotes::install_cran("rcmdcheck") - remotes::install_cran("covr") + options(repos = "https://cloud.r-project.org/") + remotes::install_cran("Rsymphony") + remotes::install_bioc("lpsymphony") + remotes::install_bioc("ggtree") shell: Rscript {0} - name: Session information @@ -87,17 +83,9 @@ jobs: run: | echo "_R_CHECK_DEPENDS_ONLY_=true" >> $GITHUB_ENV - - name: Check - run: | - rcmdcheck::rcmdcheck(args = c("--no-manual", "--as-cran", "--no-build-vignettes"), error_on = "warning", check_dir = "check") - shell: Rscript {0} - - - name: Upload check results - if: failure() - uses: actions/upload-artifact@main + - uses: r-lib/actions/check-r-package@v2 with: - name: ${{ runner.os }}-r${{ matrix.config.r }}-results - path: check + upload-snapshots: true - name: Show testthat output if: always() @@ -106,5 +94,20 @@ jobs: - name: Test coverage if: runner.os == 'Linux' && matrix.config.r == 'release' - run: covr::codecov() + run: | + cov <- covr::package_coverage( + quiet = FALSE, + clean = FALSE, + install_path = file.path(normalizePath(Sys.getenv("RUNNER_TEMP"), winslash = "/"), "package") + ) + covr::to_cobertura(cov) shell: Rscript {0} + + - uses: codecov/codecov-action@v4 + if: runner.os == 'Linux' && matrix.config.r == 'release' + with: + fail_ci_if_error: ${{ github.event_name != 'pull_request' && true || false }} + file: ./cobertura.xml + plugin: noop + disable_search: true + token: ${{ secrets.CODECOV_TOKEN }} diff --git a/.github/workflows/R-CMD-check-windows.yaml b/.github/workflows/R-CMD-check-windows.yaml index be4da68..4aac9fe 100644 --- a/.github/workflows/R-CMD-check-windows.yaml +++ b/.github/workflows/R-CMD-check-windows.yaml @@ -35,27 +35,40 @@ jobs: CI: true steps: + # configure git to prevent it from automatically converting line-endings + # this is important to avoid WARNINGS during CRAN checks + # that are false-positives, see: + # https://msmith.de/2020/03/12/r-cmd-check-github-actions.html + - name: Configure git + run: git config --global core.autocrlf false + - uses: actions/checkout@v2 - - uses: r-lib/actions/setup-r@v1 + - uses: r-lib/actions/setup-pandoc@v2 + + - uses: r-lib/actions/setup-r@v2 with: r-version: ${{ matrix.config.r }} + use-public-rspm: true - - uses: r-lib/actions/setup-pandoc@v1 - - - name: Query dependencies - run: | - install.packages('remotes') - saveRDS(remotes::dev_package_deps(dependencies = TRUE), ".github/depends.Rds", version = 2) - writeLines(sprintf("R-%i.%i", getRversion()$major, getRversion()$minor), ".github/R-version") - shell: Rscript {0} + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + cache-version: 1 + extra-packages: | + cran/XML + any::remotes + any::rcmdcheck + gurobi=?ignore + Rsymphony=?ignore + lpsymphony=?ignore + ggtree=?ignore - - name: Install dependencies + - name: Install additional packages run: | - options("install.packages.compile.from.source" = "never", pkgType = "binary") - remotes::install_deps(dependencies = TRUE) - remotes::install_cran("rcmdcheck") - remotes::install_cran("covr") + options(repos = "https://cloud.r-project.org/") + remotes::install_cran("Rsymphony") + remotes::install_bioc("lpsymphony") + remotes::install_bioc("ggtree") shell: Rscript {0} - name: Session information @@ -65,19 +78,11 @@ jobs: sessioninfo::session_info(pkgs, include_base = TRUE) shell: Rscript {0} - - name: Check - run: | - rcmdcheck::rcmdcheck(args = c("--no-manual", "--as-cran", "--no-build-vignettes"), error_on = "warning", check_dir = "check") - shell: Rscript {0} + - uses: r-lib/actions/check-r-package@v2 + with: + upload-snapshots: true - name: Show testthat output if: always() run: find check -name 'testthat.Rout*' -exec cat '{}' \; || true shell: bash - - - name: Upload check results - if: failure() - uses: actions/upload-artifact@main - with: - name: ${{ runner.os }}-r${{ matrix.config.r }}-results - path: check diff --git a/DESCRIPTION b/DESCRIPTION index 9d1008a..db53665 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: oppr Type: Package -Version: 1.0.4 +Version: 1.0.4.1 Title: Optimal Project Prioritization Description: A decision support tool for prioritizing conservation projects. Prioritizations can be developed by maximizing expected feature richness, @@ -41,10 +41,11 @@ Imports: tibble (>= 2.0.0), ape (>= 5.2), tidytree (>= 0.3.3), - ggplot2 (>= 3.0.0), + ggplot2 (>= 3.5.0), viridisLite (>= 0.3.0), lpSolveAPI (>= 5.5.2.0.17), withr (>= 2.4.1), + rlang (>= 1.1.3) Suggests: testthat (>= 2.0.0), knitr (>= 1.20), @@ -65,11 +66,10 @@ LinkingTo: RcppProgress (>= 0.4.1) License: GPL-3 LazyData: true -SystemRequirements: C++11 URL: https://prioritizr.github.io/oppr/ BugReports: https://github.com/prioritizr/oppr/issues VignetteBuilder: knitr -RoxygenNote: 7.2.1 +RoxygenNote: 7.3.1 Encoding: UTF-8 Language: en-US Collate: diff --git a/NAMESPACE b/NAMESPACE index 4a51698..12b3faf 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -16,7 +16,6 @@ S3method(print,ScalarParameter) S3method(print,Solver) export("%>%") export("%T>%") -export() export(ArrayParameter) export(Collection) export(Constraint) diff --git a/NEWS.md b/NEWS.md index 66b1e4b..18da998 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,11 @@ +# oppr 1.0.4.1 + +- Fix aliasing for package manual entry (#21). +- Fix compatibility with updates to _ggplot2_ package. +- Fix compatibility with updates to package citation format. +- Fix broken URLs in package documentation. +- Fix broken badges in README. + # oppr 1.0.4 - CRAN release. diff --git a/R/add_gurobi_solver.R b/R/add_gurobi_solver.R index 8cc9e08..bf09d85 100644 --- a/R/add_gurobi_solver.R +++ b/R/add_gurobi_solver.R @@ -71,16 +71,12 @@ NULL #' #' To install the \pkg{gurobi} package, the #' [Gurobi](https://www.gurobi.com) optimization suite will first need to -#' be installed (see instructions for [Linux](https://www.gurobi.com/documentation/8.1/quickstart_linux/software_installation_guid.html), -#' [Mac OSX](https://www.gurobi.com/documentation/8.1/quickstart_mac/software_installation_guid.html), and -#' [Windows](https://www.gurobi.com/documentation/8.1/quickstart_windows/software_installation_guid.html) operating systems). Although +#' be installed (see for instructions). Although #' [Gurobi](https://www.gurobi.com) is a commercial software, academics #' can obtain a #' [special license for no cost](https://www.gurobi.com/downloads/end-user-license-agreement-academic/). After installing the #' [Gurobi](https://www.gurobi.com) optimization suite, the \pkg{gurobi} -#' package can then be installed (see instructions for [Linux](https://www.gurobi.com/documentation/8.1/quickstart_linux/r_installing_the_r_package.html), -#' [Mac OSX](https://www.gurobi.com/documentation/8.1/quickstart_mac/r_installing_the_r_package.html), and -#' [Windows](https://www.gurobi.com/documentation/8.1/quickstart_windows/r_installing_the_r_package.html) operating systems). +#' package can then be installed (see for instructions). #' #' @return [ProjectProblem-class] object with the solver added #' to it. diff --git a/R/add_lpsolveapi_solver.R b/R/add_lpsolveapi_solver.R index 8ce2f7e..5e7f6d5 100644 --- a/R/add_lpsolveapi_solver.R +++ b/R/add_lpsolveapi_solver.R @@ -14,7 +14,7 @@ NULL #' #' @inheritParams add_gurobi_solver #' -#' @details [*lp_solve*](http://lpsolve.sourceforge.net/5.5/) is an +#' @details [*lp_solve*](https://lpsolve.sourceforge.net/5.5/) is an #' open-source integer programming solver. #' Although this solver is the slowest currently supported solver, #' it is also the only exact algorithm solver that can be installed on all diff --git a/R/package.R b/R/package.R index 1e9f9c4..effccf0 100644 --- a/R/package.R +++ b/R/package.R @@ -37,23 +37,17 @@ NULL #' If the installation process fails, please consult the #' [package's online documentation](https://bioconductor.org/packages/release/bioc/html/ggtree.html). To install the \pkg{gurobi} package, the #' [Gurobi](https://www.gurobi.com) optimization suite will first need to -#' be installed (see instructions for [Linux](https://www.gurobi.com/documentation/8.1/quickstart_linux/software_installation_guid.html), -#' [Mac OSX](https://www.gurobi.com/documentation/8.1/quickstart_mac/software_installation_guid.html), and -#' [Windows](https://www.gurobi.com/documentation/8.1/quickstart_windows/software_installation_guid.html) operating systems). Although +#' be installed (see for instructions). Although #' [Gurobi](https://www.gurobi.com) is a commercial software, academics #' can obtain a #' [special license for no cost](https://www.gurobi.com/downloads/end-user-license-agreement-academic/). After installing the #' [Gurobi](https://www.gurobi.com) optimization suite, the \pkg{gurobi} -#' package can then be installed (see instructions for [Linux](https://www.gurobi.com/documentation/8.1/quickstart_linux/r_installing_the_r_package.html), -#' [Mac OSX](https://www.gurobi.com/documentation/8.1/quickstart_mac/r_installing_the_r_package.html), and -#' [Windows](https://www.gurobi.com/documentation/8.1/quickstart_windows/r_installing_the_r_package.html) operating systems). +#' package can then be installed (see for instructions). #' #' @seealso Please refer to the package vignette for more information and worked #' examples. This can be accessed using the code #' `vignette("oppr")`. #' -#' @name oppr -#' #' @examples #' # load data #' data(sim_projects, sim_features, sim_actions) @@ -94,5 +88,8 @@ NULL #' # visualize solution #' plot(p, s) #' } +#' +#' @name oppr #' @docType package -NULL +#' @aliases oppr-package +"_PACKAGE" diff --git a/R/plot_feature_persistence.R b/R/plot_feature_persistence.R index b0959a5..c6fb706 100644 --- a/R/plot_feature_persistence.R +++ b/R/plot_feature_persistence.R @@ -144,31 +144,49 @@ plot_feature_persistence <- function(x, solution, n = 1, symbol_hjust = 0.007, d$status[d$name %in% partially_funded_fts] <- "Partially Funded" d <- d[rev(seq_len(nrow(d))), ] # re-order data d$name <- factor(d$name, levels = unique(d$name)) + d$status <- factor(d$status, levels = c("Funded", "Partially Funded")) # Main processing ## prepare outputs if (!isTRUE(return_data)) { - o <- ggplot2::ggplot(d, ggplot2::aes_string(x = "name", y = "prob", - fill = "weight")) + - ggplot2::geom_col() + - ggplot2::geom_point(data = d[!is.na(d$status), , drop = FALSE], - ggplot2::aes_string(shape = "status"), - size = 3, color = "black", - position = ggplot2::position_nudge( - y = symbol_hjust)) + - ggplot2::scale_y_continuous(name = "Probability of persistence", - limits = c(0, 1)) + - ggplot2::xlab("") + - ggplot2::scale_fill_gradientn(name = "Weight", - colors = viridisLite::inferno( - 150, begin = 0, end = 0.9, - direction = -1)) + - ggplot2::scale_shape_manual(name = "Projects", - values = c("Funded" = 8, - "Partially Funded" = 1), - na.translate = FALSE) + - ggplot2::theme(legend.position = "right") + - ggplot2::coord_flip() + o <- + ggplot2::ggplot( + d, + ggplot2::aes( + x = !!rlang::sym("name"), + y = !!rlang::sym("prob"), + fill = !!rlang::sym("weight") + ) + ) + + ggplot2::geom_col() + + + ggplot2::scale_y_continuous( + name = "Probability of persistence", limits = c(0, 1) + ) + + ggplot2::xlab("") + + ggplot2::scale_fill_gradientn( + name = "Weight", + colors = viridisLite::inferno( + 150, begin = 0, end = 0.9, direction = -1 + ) + ) + + ggplot2::theme(legend.position = "right") + + ggplot2::coord_flip() + if (any(!is.na(d$status))) { + o <- + o + + ggplot2::geom_point( + data = d[!is.na(d$status), , drop = FALSE], + mapping = ggplot2::aes(shape = !!rlang::sym("status")), + size = 3, color = "black", + position = ggplot2::position_nudge(y = symbol_hjust)) + + ggplot2::scale_shape_manual( + name = "Projects", + values = c("Funded" = 8, "Partially Funded" = 1), + na.translate = FALSE, + drop = FALSE + ) + } } else { o <- d } diff --git a/R/plot_phylo_persistence.R b/R/plot_phylo_persistence.R index ab6ffcf..0eecac9 100644 --- a/R/plot_phylo_persistence.R +++ b/R/plot_phylo_persistence.R @@ -193,33 +193,55 @@ plot_phylo_persistence <- function(x, solution, n = 1, symbol_hjust = 0.007, paste0(tree2$parent, "_", tree2$node), paste0(tree$edge[, 1], "_", tree$edge[, 2]))] tree2$label <- paste0(" ", tree2$label) + any_nonmissing_status <- any(!is.na(tree2$status)) tree2 <- suppressMessages(suppressWarnings(tidytree::as.treedata(tree2))) ## calculate padding for points - point_padding <- max(rowSums(as.matrix(branch_matrix(tree)) * - matrix(tree$edge.length, ncol = nrow(tree$edge), - nrow = length(tree$tip.label)))) * - symbol_hjust + point_padding <- + max(rowSums( + as.matrix(branch_matrix(tree)) * + matrix( + tree$edge.length, + ncol = nrow(tree$edge), + nrow = length(tree$tip.label) + ) + )) * symbol_hjust ## prepare outputs if (!isTRUE(return_data)) { - o <- ggtree::ggtree(tree2, ggplot2::aes_string(color = "prob"), - size = 1.1) + - ggtree::geom_tippoint( - ggplot2::aes_string(x = "x + point_padding", - subset = "!is.na(status)", - shape = "status"), color = "black") + - ggtree::geom_tiplab(color = "black", size = 2.5) + - ggplot2::scale_color_gradientn(name = "Probability of\npersistence", - colors = viridisLite::inferno( - 150, begin = 0, end = 0.9, - direction = -1), - limits = c(0, 1)) + - ggplot2::scale_shape_manual(name = "Projects", - values = c("Funded" = 8, - "Partially Funded" = 1), - na.translate = FALSE) + - ggplot2::theme(legend.position = "right") + o <- + ggtree::ggtree( + tree2, + ggplot2::aes(color = !!rlang::sym("prob")), + size = 1.1 + ) + + ggtree::geom_tiplab(color = "black", size = 2.5) + + ggplot2::scale_color_gradientn( + name = "Probability of\npersistence", + colors = viridisLite::inferno( + 150, begin = 0, end = 0.9, direction = -1 + ), + limits = c(0, 1) + ) + + ggplot2::theme(legend.position = "right") + if (isTRUE(any_nonmissing_status)) { + o <- + o + + ggtree::geom_tippoint( + mapping = ggplot2::aes( + x = !!rlang::expr(!!rlang::sym("x") + point_padding), + subset = !!rlang::expr(!is.na(!!rlang::sym("status"))), + shape = !!rlang::sym("status") + ), + color = "black" + ) + + ggplot2::scale_shape_manual( + name = "Projects", + values = c("Funded" = 8, "Partially Funded" = 1), + na.translate = FALSE, + drop = FALSE + ) + } } else { o <- tree2 } diff --git a/R/solvers.R b/R/solvers.R index d7edd51..e976cd7 100644 --- a/R/solvers.R +++ b/R/solvers.R @@ -42,7 +42,7 @@ NULL #' Max OSX systems than the \pkg{Rsymphony} package.} #' #' \item{[add_lpsolveapi_solver()]}{ -#' [*lp_solve*](http://lpsolve.sourceforge.net/5.5/) is an +#' [*lp_solve*](https://lpsolve.sourceforge.net/5.5/) is an #' open-source integer programming solver. The \pkg{lpSolveAPI} package #' provides an interface to this solver and is available on CRAN. #' Although this solver is the slowest currently supported solver, diff --git a/README.Rmd b/README.Rmd index 94df5af..c9175e8 100644 --- a/README.Rmd +++ b/README.Rmd @@ -9,10 +9,10 @@ output: # oppr: Optimal Project Prioritization [![lifecycle](https://img.shields.io/badge/Lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html) -[![R-CMD-check-Ubuntu](https://img.shields.io/github/workflow/status/prioritizr/oppr/Ubuntu/master.svg?label=Ubuntu)](https://github.com/prioritizr/oppr/actions) -[![R-CMD-check-Windows](https://img.shields.io/github/workflow/status/prioritizr/oppr/Windows/master.svg?label=Windows)](https://github.com/prioritizr/oppr/actions) -[![R-CMD-check-macOS](https://img.shields.io/github/workflow/status/prioritizr/oppr/macOS/master.svg?label=macOS)](https://github.com/prioritizr/oppr/actions) -[![Coverage Status](https://codecov.io/github/prioritizr/oppr/coverage.svg?branch=master)](https://codecov.io/github/prioritizr/oppr?branch=master) +[![R-CMD-check-Ubuntu](https://img.shields.io/github/actions/workflow/status/prioritizr/oppr/R-CMD-check-ubuntu.yaml?branch=master&label=Ubuntu)](https://github.com/prioritizr/oppr/actions) +[![R-CMD-check-Windows](https://img.shields.io/github/actions/workflow/status/prioritizr/oppr/R-CMD-check-windows.yaml?branch=master&label=Windows)](https://github.com/prioritizr/oppr/actions) +[![R-CMD-check-macOS](https://img.shields.io/github/actions/workflow/status/prioritizr/oppr/R-CMD-check-macos.yaml?branch=master&label=macOS)](https://github.com/prioritizr/oppr/actions) +[![Coverage Status](https://img.shields.io/codecov/c/github/prioritizr/oppr?label=Coverage)](https://app.codecov.io/gh/prioritizr/oppr/branch/master) [![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/oppr)](https://CRAN.R-project.org/package=oppr) ```{r, include = FALSE} diff --git a/README.md b/README.md index ec6fd40..82e9d5a 100644 --- a/README.md +++ b/README.md @@ -4,11 +4,11 @@ # oppr: Optimal Project Prioritization [![lifecycle](https://img.shields.io/badge/Lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html) -[![R-CMD-check-Ubuntu](https://img.shields.io/github/workflow/status/prioritizr/oppr/Ubuntu/master.svg?label=Ubuntu)](https://github.com/prioritizr/oppr/actions) -[![R-CMD-check-Windows](https://img.shields.io/github/workflow/status/prioritizr/oppr/Windows/master.svg?label=Windows)](https://github.com/prioritizr/oppr/actions) -[![R-CMD-check-macOS](https://img.shields.io/github/workflow/status/prioritizr/oppr/macOS/master.svg?label=macOS)](https://github.com/prioritizr/oppr/actions) +[![R-CMD-check-Ubuntu](https://img.shields.io/github/actions/workflow/status/prioritizr/oppr/R-CMD-check-ubuntu.yaml?branch=master&label=Ubuntu)](https://github.com/prioritizr/oppr/actions) +[![R-CMD-check-Windows](https://img.shields.io/github/actions/workflow/status/prioritizr/oppr/R-CMD-check-windows.yaml?branch=master&label=Windows)](https://github.com/prioritizr/oppr/actions) +[![R-CMD-check-macOS](https://img.shields.io/github/actions/workflow/status/prioritizr/oppr/R-CMD-check-macos.yaml?branch=master&label=macOS)](https://github.com/prioritizr/oppr/actions) [![Coverage -Status](https://codecov.io/github/prioritizr/oppr/coverage.svg?branch=master)](https://codecov.io/github/prioritizr/oppr?branch=master) +Status](https://img.shields.io/codecov/c/github/prioritizr/oppr?label=Coverage)](https://app.codecov.io/gh/prioritizr/oppr/branch/master) [![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version/oppr)](https://CRAN.R-project.org/package=oppr) The *oppr R* package is decision support tool for prioritizing diff --git a/docs/404.html b/docs/404.html index 9d9937d..32b454f 100644 --- a/docs/404.html +++ b/docs/404.html @@ -32,7 +32,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -91,7 +91,7 @@

Page not found (404)

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/docs/articles/index.html b/docs/articles/index.html index 906679a..3b6fe8b 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -17,7 +17,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -65,7 +65,7 @@

All vignettes

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/docs/articles/oppr.html b/docs/articles/oppr.html index 68b11ec..633c7e5 100644 --- a/docs/articles/oppr.html +++ b/docs/articles/oppr.html @@ -33,7 +33,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -149,13 +149,13 @@

Data simulation# 8 project… 0.932 0.593 NA NA NA NA 0.803 0.580 0.666 NA # 9 project… 0.900 NA NA NA 0.572 0.547 NA NA NA NA # 10 project… 0.952 NA NA NA NA NA NA NA NA NA -# # … with 61 more rows, and 62 more variables: F10 <dbl>, F11 <dbl>, F12 <dbl>, -# # F13 <dbl>, F14 <dbl>, F15 <dbl>, F16 <dbl>, F17 <dbl>, F18 <dbl>, -# # F19 <dbl>, F20 <dbl>, F21 <dbl>, F22 <dbl>, F23 <dbl>, F24 <dbl>, -# # F25 <dbl>, F26 <dbl>, F27 <dbl>, F28 <dbl>, F29 <dbl>, F30 <dbl>, -# # F31 <dbl>, F32 <dbl>, F33 <dbl>, F34 <dbl>, F35 <dbl>, F36 <dbl>, -# # F37 <dbl>, F38 <dbl>, F39 <dbl>, F40 <dbl>, action_1 <lgl>, action_2 <lgl>, -# # action_3 <lgl>, action_4 <lgl>, action_5 <lgl>, action_6 <lgl>, … +# # ℹ 61 more rows +# # ℹ 62 more variables: F10 <dbl>, F11 <dbl>, F12 <dbl>, F13 <dbl>, F14 <dbl>, +# # F15 <dbl>, F16 <dbl>, F17 <dbl>, F18 <dbl>, F19 <dbl>, F20 <dbl>, +# # F21 <dbl>, F22 <dbl>, F23 <dbl>, F24 <dbl>, F25 <dbl>, F26 <dbl>, +# # F27 <dbl>, F28 <dbl>, F29 <dbl>, F30 <dbl>, F31 <dbl>, F32 <dbl>, +# # F33 <dbl>, F34 <dbl>, F35 <dbl>, F36 <dbl>, F37 <dbl>, F38 <dbl>, +# # F39 <dbl>, F40 <dbl>, action_1 <lgl>, action_2 <lgl>, action_3 <lgl>, … @@ -225,7 +225,7 @@

Prioritizations with exact algori # solver: default -

After building the problem, we can solve it. Although backwards heuristic algorithms have conventionally been used to solve project prioritization problems (e.g. Joseph et al. 2009; Bennett et al. 2014; Gerber et al. 2018), here we will use exact algorithms to develop a prioritization. Exact algorithms are superior to heuristic algorithms because they can provide guarantees on solution quality (Underhill 1994; Rodrigues & Gaston 2002). In other words, if you specify that you want an optimal solution when using exact algorithms, then you are guaranteed to get an optimal solution. Heuristic algorithms—and even more advanced meta-heuristic algorithms such as simulated annealing that are commonly to guide conservation decision making (Kirkpatrick et al. 1983)—provide no such guarantees and can actually deliver remarkably poor solutions (e.g. Beyer et al. 2016). Later on we will experiment with heuristic algorithms, but for now, we will use exact algorithms. Since we haven’t explicitly stated which solver we would like to use, the oppr R package will identify the best exact algorithm solver currently installed on your system. This is typically the lpSolveAPI R package unless you have manually installed the gurobi R package and the Gurobi optimization suite. Although not shown here, when you try solving the problem you will see information printed to your screen as the solver tries to find an optimal solution. Please do not be alarmed, this is normal behavior and some of the information can actually be useful (e.g. to estimate how long it might take to solve the problem).

+

After building the problem, we can solve it. Although backwards heuristic algorithms have conventionally been used to solve project prioritization problems (e.g. Joseph et al. 2009; Bennett et al. 2014; Gerber et al. 2018), here we will use exact algorithms to develop a prioritization. Exact algorithms are superior to heuristic algorithms because they can provide guarantees on solution quality (Underhill 1994; Rodrigues & Gaston 2002). In other words, if you specify that you want an optimal solution when using exact algorithms, then you are guaranteed to get an optimal solution. Heuristic algorithms—and even more advanced meta-heuristic algorithms such as simulated annealing that are commonly to guide conservation decision making (Kirkpatrick et al. 1983)—provide no such guarantees and can actually deliver remarkably poor solutions (e.g. Beyer et al. 2016). Later on we will experiment with heuristic algorithms, but for now, we will use exact algorithms. Since we haven’t explicitly stated which solver we would like to use, the oppr R package will identify the best exact algorithm solver currently installed on your system. This is typically the lpSolveAPI R package unless you have manually installed the gurobi R package and the Gurobi optimization suite. Although not shown here, when you try solving the problem you will see information printed to your screen as the solver tries to find an optimal solution. Please do not be alarmed, this is normal behavior and some of the information can actually be useful (e.g. to estimate how long it might take to solve the problem).

 # solve problem
 s1 <- solve(p1)
@@ -234,16 +234,16 @@

Prioritizations with exact algori print(s1)
# # A tibble: 1 × 146
-#   solution status    obj  cost action_1 action_2 actio…¹ actio…² actio…³ actio…⁴
-#      <int> <chr>   <dbl> <dbl>    <dbl>    <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
-# 1        1 OPTIMAL  31.0  993.        1        1       0       0       0       0
-# # … with 136 more variables: action_7 <dbl>, action_8 <dbl>, action_9 <dbl>,
-# #   action_10 <dbl>, action_11 <dbl>, action_12 <dbl>, action_13 <dbl>,
-# #   action_14 <dbl>, action_15 <dbl>, action_16 <dbl>, action_17 <dbl>,
-# #   action_18 <dbl>, action_19 <dbl>, action_20 <dbl>, action_21 <dbl>,
-# #   action_22 <dbl>, action_23 <dbl>, action_24 <dbl>, action_25 <dbl>,
-# #   action_26 <dbl>, action_27 <dbl>, action_28 <dbl>, action_29 <dbl>,
-# #   action_30 <dbl>, baseline_action <dbl>, project_1 <dbl>, project_2 <dbl>, …
+#   solution status    obj  cost action_1 action_2 action_3 action_4 action_5
+#      <int> <chr>   <dbl> <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
+# 1        1 OPTIMAL  31.0  993.        1        1        0        0        0
+# # ℹ 137 more variables: action_6 <dbl>, action_7 <dbl>, action_8 <dbl>,
+# #   action_9 <dbl>, action_10 <dbl>, action_11 <dbl>, action_12 <dbl>,
+# #   action_13 <dbl>, action_14 <dbl>, action_15 <dbl>, action_16 <dbl>,
+# #   action_17 <dbl>, action_18 <dbl>, action_19 <dbl>, action_20 <dbl>,
+# #   action_21 <dbl>, action_22 <dbl>, action_23 <dbl>, action_24 <dbl>,
+# #   action_25 <dbl>, action_26 <dbl>, action_27 <dbl>, action_28 <dbl>,
+# #   action_29 <dbl>, action_30 <dbl>, baseline_action <dbl>, project_1 <dbl>, …
 

The s1 table contains the solution (i.e. a prioritization) and also various statistics associated with the solution. Here, each row corresponds to a different solution. By default only one solution will be returned, and so the table has one row. The "solution" column contains an integer identifier for the solution (this is when multiple solutions are output), the "obj" column contains the objective value (i.e. the expected feature richness in this case), the "cost" column stores the cost of the solution, and the "status" column contains information from the solver about the solution (i.e. it found an optimal solution). Additionally, it contains columns for each action ("action_1", "action_2", "action_3", …, "baseline_action") which indicate their status in the solution. In other words, these columns tell us if each action was prioritized for funding in the solution (i.e. a value of one) or not (i.e. a value of zero). Additionally, it contains columns for each project ("project_1", "project_2", "project_3", …, "baseline_project") that indicate if the project was completely funded or not. Finally, this table contains columns for each feature ("F1, "F2", "F3, …) which indicate the probability that each feature is expected to persist into the future if the solution was implemented (for information on how this is calculated see ?add_max_richness_objective).

@@ -269,7 +269,7 @@

Prioritizations with exact algori # 8 1 project_8 0 # 9 1 project_9 0 # 10 1 project_10 0 -# # … with 61 more rows +# # ℹ 61 more rows
@@ -301,7 +301,7 @@ 

Prioritizations with exact algori # 8 project_8 2761. 33.1 22.3 0.00809 44 # 9 project_9 1786. 15.7 4.95 0.00277 65 # 10 project_10 2977. 34.7 24.0 0.00806 46.5 -# # … with 61 more rows +# # ℹ 61 more rows

The p1_pce table contains the cost-effectiveness of each project. Specifically, the "project" column contains the name of each project. The "cost" column contains the total cost of all the management actions associated with the project. The "obj" column contains the objective value associated with a solution that contains all of the management actions for each project, and also the actions associated with a zero cost. The "benefit" column contains the difference between (i) the objective value associated with each project and also any zero cost actions (as per the "obj" column), and (ii) the objective value associated with the baseline project. The "ce" column contains the cost-effectiveness of each project. For a given project, this is calculated by dividing its benefit by its cost (as per the "benefit" and "cost" columns). The "rank" column contains the rank for each project, with the most cost-effective project having a rank of one.

@@ -345,7 +345,7 @@

Prioritizations with exact algori # 8 F8 108301. # 9 F9 3.62 # 10 F10 6956. -# # … with 30 more rows +# # ℹ 30 more rows
@@ -388,16 +388,16 @@ 

Prioritizations with exact algori print(s2)

# # A tibble: 1 × 146
-#   solution status      obj  cost actio…¹ actio…² actio…³ actio…⁴ actio…⁵ actio…⁶
-#      <int> <chr>     <dbl> <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
-# 1        1 OPTIMAL  6.29e6  987.       0       1       1       0       0       0
-# # … with 136 more variables: action_7 <dbl>, action_8 <dbl>, action_9 <dbl>,
-# #   action_10 <dbl>, action_11 <dbl>, action_12 <dbl>, action_13 <dbl>,
-# #   action_14 <dbl>, action_15 <dbl>, action_16 <dbl>, action_17 <dbl>,
-# #   action_18 <dbl>, action_19 <dbl>, action_20 <dbl>, action_21 <dbl>,
-# #   action_22 <dbl>, action_23 <dbl>, action_24 <dbl>, action_25 <dbl>,
-# #   action_26 <dbl>, action_27 <dbl>, action_28 <dbl>, action_29 <dbl>,
-# #   action_30 <dbl>, baseline_action <dbl>, project_1 <dbl>, project_2 <dbl>, …
+#   solution status       obj  cost action_1 action_2 action_3 action_4 action_5
+#      <int> <chr>      <dbl> <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
+# 1        1 OPTIMAL 6290606.  987.        0        1        1        0        0
+# # ℹ 137 more variables: action_6 <dbl>, action_7 <dbl>, action_8 <dbl>,
+# #   action_9 <dbl>, action_10 <dbl>, action_11 <dbl>, action_12 <dbl>,
+# #   action_13 <dbl>, action_14 <dbl>, action_15 <dbl>, action_16 <dbl>,
+# #   action_17 <dbl>, action_18 <dbl>, action_19 <dbl>, action_20 <dbl>,
+# #   action_21 <dbl>, action_22 <dbl>, action_23 <dbl>, action_24 <dbl>,
+# #   action_25 <dbl>, action_26 <dbl>, action_27 <dbl>, action_28 <dbl>,
+# #   action_29 <dbl>, action_30 <dbl>, baseline_action <dbl>, project_1 <dbl>, …
 
@@ -432,7 +432,7 @@ 

Prioritizations with exact algori
# [1] 6
 

-

Earlier, we talked about the oppr R package using the default exact algorithm solver. Although you should have the lpSolveAPI solver automatically installed, we strongly recommend installing the gurobi R package and the Gurobi optimization suite. This is because Gurobi can solve optimization problems much faster than any other software currently supported and it can also easily generate multiple solutions. Unfortunately, you will have to install Gurobi manually, but please see the documentation for the solver for instructions (i.e. enter the command ?add_gurobi_solver into the console). If you have Gurobi installed, let’s try using the Gurobi solver to generate multiple solutions. Here, we will manually specify the Gurobi solver and request 1,000 solutions (though we will probably obtain less than 1,000 solutions, so if we actually wanted 1,000 solutions then we would need to specify a much larger number).

+

Earlier, we talked about the oppr R package using the default exact algorithm solver. Although you should have the lpSolveAPI solver automatically installed, we strongly recommend installing the gurobi R package and the Gurobi optimization suite. This is because Gurobi can solve optimization problems much faster than any other software currently supported and it can also easily generate multiple solutions. Unfortunately, you will have to install Gurobi manually, but please see the documentation for the solver for instructions (i.e. enter the command ?add_gurobi_solver into the console). If you have Gurobi installed, let’s try using the Gurobi solver to generate multiple solutions. Here, we will manually specify the Gurobi solver and request 1,000 solutions (though we will probably obtain less than 1,000 solutions, so if we actually wanted 1,000 solutions then we would need to specify a much larger number).

 # create new problem, with Gurobi solver added and request multiple solutions
 # note that we set the gap to 0.5 because we are not necessarily interested
@@ -447,29 +447,29 @@ 

Prioritizations with exact algori # print solution print(s3)

-
# # A tibble: 85 × 146
-#    solution status     obj  cost actio…¹ actio…² actio…³ actio…⁴ actio…⁵ actio…⁶
-#       <int> <chr>    <dbl> <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
-#  1        1 OPTIMAL 6.29e6  974.       0       1       1       0       0       0
-#  2        2 OPTIMAL 6.29e6  969.       0       1       1       0       0       0
-#  3        3 OPTIMAL 6.29e6  967.       0       1       1       0       0       0
-#  4        4 OPTIMAL 6.29e6  974.       0       1       1       0       0       0
-#  5        5 OPTIMAL 6.29e6  977.       0       1       1       0       0       0
-#  6        6 OPTIMAL 6.29e6  874.       0       1       1       0       0       0
-#  7        7 OPTIMAL 6.29e6  980.       0       1       1       0       0       0
-#  8        8 OPTIMAL 6.29e6  977.       0       1       1       1       0       0
-#  9        9 OPTIMAL 6.29e6  987.       0       1       1       0       0       0
-# 10       10 OPTIMAL 6.29e6  970.       0       1       1       0       1       0
-# # … with 75 more rows, 136 more variables: action_7 <dbl>, action_8 <dbl>,
+
# # A tibble: 39 × 146
+#    solution status        obj  cost action_1 action_2 action_3 action_4 action_5
+#       <int> <chr>       <dbl> <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
+#  1        1 OPTIMAL    6.29e6  971.        0        1        1        0        0
+#  2        2 OPTIMAL    6.29e6  977.        0        1        1        0        0
+#  3        3 OPTIMAL    6.29e6  987.        0        1        1        0        0
+#  4        4 OPTIMAL    6.29e6  874.        0        1        1        0        0
+#  5        5 OPTIMAL    6.29e6  974.        0        1        1        0        0
+#  6        6 OPTIMAL    6.29e6  980.        0        1        1        0        0
+#  7        7 OPTIMAL    6.29e6  977.        0        1        1        1        0
+#  8        8 OPTIMAL    6.29e6  971.        0        1        1        0        0
+#  9        9 SUBOPTIMAL 6.29e6  974.        0        1        1        0        0
+# 10       10 SUBOPTIMAL 6.10e6  976.        0        0        1        0        0
+# # ℹ 29 more rows
+# # ℹ 137 more variables: action_6 <dbl>, action_7 <dbl>, action_8 <dbl>,
 # #   action_9 <dbl>, action_10 <dbl>, action_11 <dbl>, action_12 <dbl>,
 # #   action_13 <dbl>, action_14 <dbl>, action_15 <dbl>, action_16 <dbl>,
 # #   action_17 <dbl>, action_18 <dbl>, action_19 <dbl>, action_20 <dbl>,
 # #   action_21 <dbl>, action_22 <dbl>, action_23 <dbl>, action_24 <dbl>,
-# #   action_25 <dbl>, action_26 <dbl>, action_27 <dbl>, action_28 <dbl>,
-# #   action_29 <dbl>, action_30 <dbl>, baseline_action <dbl>, project_1 <dbl>, …
+# #   action_25 <dbl>, action_26 <dbl>, action_27 <dbl>, action_28 <dbl>, …
 
-

We obtained 85 solutions. Let’s briefly explore them.

+

We obtained 39 solutions. Let’s briefly explore them.

 # plot histogram of objective values, and add red dashed line to indicate the
 # objective value for the optimal solution
@@ -491,26 +491,26 @@ 

Assessing action importance# print the solution object to remind ourselves what it looks like print(s3)

-
# # A tibble: 85 × 146
-#    solution status     obj  cost actio…¹ actio…² actio…³ actio…⁴ actio…⁵ actio…⁶
-#       <int> <chr>    <dbl> <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
-#  1        1 OPTIMAL 6.29e6  974.       0       1       1       0       0       0
-#  2        2 OPTIMAL 6.29e6  969.       0       1       1       0       0       0
-#  3        3 OPTIMAL 6.29e6  967.       0       1       1       0       0       0
-#  4        4 OPTIMAL 6.29e6  974.       0       1       1       0       0       0
-#  5        5 OPTIMAL 6.29e6  977.       0       1       1       0       0       0
-#  6        6 OPTIMAL 6.29e6  874.       0       1       1       0       0       0
-#  7        7 OPTIMAL 6.29e6  980.       0       1       1       0       0       0
-#  8        8 OPTIMAL 6.29e6  977.       0       1       1       1       0       0
-#  9        9 OPTIMAL 6.29e6  987.       0       1       1       0       0       0
-# 10       10 OPTIMAL 6.29e6  970.       0       1       1       0       1       0
-# # … with 75 more rows, 136 more variables: action_7 <dbl>, action_8 <dbl>,
+
# # A tibble: 39 × 146
+#    solution status        obj  cost action_1 action_2 action_3 action_4 action_5
+#       <int> <chr>       <dbl> <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
+#  1        1 OPTIMAL    6.29e6  971.        0        1        1        0        0
+#  2        2 OPTIMAL    6.29e6  977.        0        1        1        0        0
+#  3        3 OPTIMAL    6.29e6  987.        0        1        1        0        0
+#  4        4 OPTIMAL    6.29e6  874.        0        1        1        0        0
+#  5        5 OPTIMAL    6.29e6  974.        0        1        1        0        0
+#  6        6 OPTIMAL    6.29e6  980.        0        1        1        0        0
+#  7        7 OPTIMAL    6.29e6  977.        0        1        1        1        0
+#  8        8 OPTIMAL    6.29e6  971.        0        1        1        0        0
+#  9        9 SUBOPTIMAL 6.29e6  974.        0        1        1        0        0
+# 10       10 SUBOPTIMAL 6.10e6  976.        0        0        1        0        0
+# # ℹ 29 more rows
+# # ℹ 137 more variables: action_6 <dbl>, action_7 <dbl>, action_8 <dbl>,
 # #   action_9 <dbl>, action_10 <dbl>, action_11 <dbl>, action_12 <dbl>,
 # #   action_13 <dbl>, action_14 <dbl>, action_15 <dbl>, action_16 <dbl>,
 # #   action_17 <dbl>, action_18 <dbl>, action_19 <dbl>, action_20 <dbl>,
 # #   action_21 <dbl>, action_22 <dbl>, action_23 <dbl>, action_24 <dbl>,
-# #   action_25 <dbl>, action_26 <dbl>, action_27 <dbl>, action_28 <dbl>,
-# #   action_29 <dbl>, action_30 <dbl>, baseline_action <dbl>, project_1 <dbl>, …
+# #   action_25 <dbl>, action_26 <dbl>, action_27 <dbl>, action_28 <dbl>, …
 
# # A tibble: 1 × 146
-#   solution status      obj  cost actio…¹ actio…² actio…³ actio…⁴ actio…⁵ actio…⁶
-#      <int> <chr>     <dbl> <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
-# 1        1 OPTIMAL  6.29e6  987.       0       1       1       0       0       0
-# # … with 136 more variables: action_7 <dbl>, action_8 <dbl>, action_9 <dbl>,
-# #   action_10 <dbl>, action_11 <dbl>, action_12 <dbl>, action_13 <dbl>,
-# #   action_14 <dbl>, action_15 <dbl>, action_16 <dbl>, action_17 <dbl>,
-# #   action_18 <dbl>, action_19 <dbl>, action_20 <dbl>, action_21 <dbl>,
-# #   action_22 <dbl>, action_23 <dbl>, action_24 <dbl>, action_25 <dbl>,
-# #   action_26 <dbl>, action_27 <dbl>, action_28 <dbl>, action_29 <dbl>,
-# #   action_30 <dbl>, baseline_action <dbl>, project_1 <dbl>, project_2 <dbl>, …
+#   solution status       obj  cost action_1 action_2 action_3 action_4 action_5
+#      <int> <chr>      <dbl> <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
+# 1        1 OPTIMAL 6290606.  987.        0        1        1        0        0
+# # ℹ 137 more variables: action_6 <dbl>, action_7 <dbl>, action_8 <dbl>,
+# #   action_9 <dbl>, action_10 <dbl>, action_11 <dbl>, action_12 <dbl>,
+# #   action_13 <dbl>, action_14 <dbl>, action_15 <dbl>, action_16 <dbl>,
+# #   action_17 <dbl>, action_18 <dbl>, action_19 <dbl>, action_20 <dbl>,
+# #   action_21 <dbl>, action_22 <dbl>, action_23 <dbl>, action_24 <dbl>,
+# #   action_25 <dbl>, action_26 <dbl>, action_27 <dbl>, action_28 <dbl>,
+# #   action_29 <dbl>, action_30 <dbl>, baseline_action <dbl>, project_1 <dbl>, …
 

The r1 table contains the replacement costs for each action and also various statistics associated with the solutions obtained when locking out each action. Here, each row corresponds to a different action. The "action" column contains the name of the actions (as per the actions table used when building the problem), the "cost" column contains the cost of the solutions obtained when each action was locked out, the "obj" column contains the objective value of the solutions when each action was locked out (i.e. the expected feature richness in this case), and the "rep_cost" column contains the replacement costs for the actions. Actions associated with larger replacement cost values are more irreplaceable, actions associated with missing (NA) replacement cost values were not selected for funding in the input solution (i.e. s2), and actions associated with infinite (Inf) replacement cost values are absolutely critical for meeting the constraints (though infinite values should only problems with minimum set objectives and potentially the baseline project).

@@ -639,17 +639,17 @@

Assessing action importance# # A tibble: 31 × 6 # name cost locked_in locked_out sel_freq rep_cost # <chr> <dbl> <lgl> <lgl> <dbl> <dbl> -# 1 action_14 99.4 FALSE FALSE 97.6 345636. -# 2 action_2 94.0 FALSE FALSE 71.8 187698. -# 3 action_22 93.9 FALSE FALSE 68.2 187698. -# 4 action_3 100. FALSE FALSE 27.1 167620. -# 5 action_17 101. FALSE FALSE 32.9 167620. -# 6 action_19 89.8 FALSE FALSE 74.1 167620. -# 7 action_27 91.1 FALSE FALSE 48.2 167620. -# 8 action_28 102. FALSE FALSE 56.5 167620. -# 9 action_29 103. FALSE FALSE 41.2 167620. -# 10 baseline_action 0 FALSE FALSE 97.6 167620. -# # … with 21 more rows +# 1 action_14 99.4 FALSE FALSE 97.4 345636. +# 2 action_2 94.0 FALSE FALSE 69.2 187698. +# 3 action_22 93.9 FALSE FALSE 69.2 187698. +# 4 action_3 100. FALSE FALSE 61.5 167620. +# 5 action_17 101. FALSE FALSE 66.7 167620. +# 6 action_19 89.8 FALSE FALSE 76.9 167620. +# 7 action_27 91.1 FALSE FALSE 82.1 167620. +# 8 action_28 102. FALSE FALSE 89.7 167620. +# 9 action_29 103. FALSE FALSE 66.7 167620. +# 10 baseline_action 0 FALSE FALSE 94.9 167620. +# # ℹ 21 more rows
# # A tibble: 1 × 146
-#   solution status    obj  cost action_1 action_2 actio…¹ actio…² actio…³ actio…⁴
-#      <int> <chr>   <dbl> <dbl>    <dbl>    <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
-# 1        1 OPTIMAL    36  993.        1        1       0       0       0       0
-# # … with 136 more variables: action_7 <dbl>, action_8 <dbl>, action_9 <dbl>,
-# #   action_10 <dbl>, action_11 <dbl>, action_12 <dbl>, action_13 <dbl>,
-# #   action_14 <dbl>, action_15 <dbl>, action_16 <dbl>, action_17 <dbl>,
-# #   action_18 <dbl>, action_19 <dbl>, action_20 <dbl>, action_21 <dbl>,
-# #   action_22 <dbl>, action_23 <dbl>, action_24 <dbl>, action_25 <dbl>,
-# #   action_26 <dbl>, action_27 <dbl>, action_28 <dbl>, action_29 <dbl>,
-# #   action_30 <dbl>, baseline_action <dbl>, project_1 <dbl>, project_2 <dbl>, …
+#   solution status    obj  cost action_1 action_2 action_3 action_4 action_5
+#      <int> <chr>   <dbl> <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
+# 1        1 OPTIMAL    36  993.        1        1        0        0        0
+# # ℹ 137 more variables: action_6 <dbl>, action_7 <dbl>, action_8 <dbl>,
+# #   action_9 <dbl>, action_10 <dbl>, action_11 <dbl>, action_12 <dbl>,
+# #   action_13 <dbl>, action_14 <dbl>, action_15 <dbl>, action_16 <dbl>,
+# #   action_17 <dbl>, action_18 <dbl>, action_19 <dbl>, action_20 <dbl>,
+# #   action_21 <dbl>, action_22 <dbl>, action_23 <dbl>, action_24 <dbl>,
+# #   action_25 <dbl>, action_26 <dbl>, action_27 <dbl>, action_28 <dbl>,
+# #   action_29 <dbl>, action_30 <dbl>, baseline_action <dbl>, project_1 <dbl>, …
 
@@ -800,8 +800,20 @@ 

Complementarity in project p # attempt to solve problem, but this will throw an error s5 <- solve(p5)

-
# Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64)
+
# Set parameter Username
+# Set parameter TimeLimit to value 2147483647
+# Set parameter MIPGap to value 0
+# Set parameter NumericFocus to value 3
+# Set parameter Presolve to value 2
+# Set parameter Threads to value 1
+# Set parameter PoolSolutions to value 1
+# Set parameter PoolSearchMode to value 2
+# Academic license - for non-commercial use only - expires 2025-04-21
+# Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS")
+# 
+# CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512]
 # Thread count: 4 physical cores, 8 logical processors, using up to 1 threads
+# 
 # Optimize a model with 4068 rows, 2942 columns and 10690 nonzeros
 # Model fingerprint: 0xa60a2b7f
 # Variable types: 0 continuous, 2942 integer (2942 binary)
@@ -812,7 +824,7 @@ 

Complementarity in project p # RHS range [1e+00, 1e+00] # Presolve time: 0.00s # -# Explored 0 nodes (0 simplex iterations) in 0.01 seconds (0.00 work units) +# Explored 0 nodes (0 simplex iterations) in 0.00 seconds (0.00 work units) # Thread count was 1 (of 8 available processors) # # Solution count 0 @@ -859,16 +871,16 @@

Complementarity in project p print(s6)

# # A tibble: 1 × 146
-#   solution status    obj  cost action_1 action_2 actio…¹ actio…² actio…³ actio…⁴
-#      <int> <chr>   <dbl> <dbl>    <dbl>    <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
-# 1        1 OPTIMAL  972.  972.        0        1       0       0       0       0
-# # … with 136 more variables: action_7 <dbl>, action_8 <dbl>, action_9 <dbl>,
-# #   action_10 <dbl>, action_11 <dbl>, action_12 <dbl>, action_13 <dbl>,
-# #   action_14 <dbl>, action_15 <dbl>, action_16 <dbl>, action_17 <dbl>,
-# #   action_18 <dbl>, action_19 <dbl>, action_20 <dbl>, action_21 <dbl>,
-# #   action_22 <dbl>, action_23 <dbl>, action_24 <dbl>, action_25 <dbl>,
-# #   action_26 <dbl>, action_27 <dbl>, action_28 <dbl>, action_29 <dbl>,
-# #   action_30 <dbl>, baseline_action <dbl>, project_1 <dbl>, project_2 <dbl>, …
+#   solution status    obj  cost action_1 action_2 action_3 action_4 action_5
+#      <int> <chr>   <dbl> <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
+# 1        1 OPTIMAL  972.  972.        0        1        0        0        0
+# # ℹ 137 more variables: action_6 <dbl>, action_7 <dbl>, action_8 <dbl>,
+# #   action_9 <dbl>, action_10 <dbl>, action_11 <dbl>, action_12 <dbl>,
+# #   action_13 <dbl>, action_14 <dbl>, action_15 <dbl>, action_16 <dbl>,
+# #   action_17 <dbl>, action_18 <dbl>, action_19 <dbl>, action_20 <dbl>,
+# #   action_21 <dbl>, action_22 <dbl>, action_23 <dbl>, action_24 <dbl>,
+# #   action_25 <dbl>, action_26 <dbl>, action_27 <dbl>, action_28 <dbl>,
+# #   action_29 <dbl>, action_30 <dbl>, baseline_action <dbl>, project_1 <dbl>, …
 
@@ -901,25 +913,25 @@ 

Benchmarking conventional algorith print(s7)

# # A tibble: 100 × 147
-#    solution status     obj  cost actio…¹ actio…² actio…³ actio…⁴ actio…⁵ actio…⁶
-#       <int> <chr>    <dbl> <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
-#  1        1 NA      2.28e6   0         0       0       0       0       0       0
-#  2        1 NA      2.28e6   0         0       0       0       0       0       0
-#  3        1 NA      2.28e6   0         0       0       0       0       0       0
-#  4        1 NA      2.28e6   0         0       0       0       0       0       0
-#  5        1 NA      4.51e6  99.4       0       0       0       0       0       0
-#  6        1 NA      4.51e6  99.4       0       0       0       0       0       0
-#  7        1 NA      4.51e6  99.4       0       0       0       0       0       0
-#  8        1 NA      4.51e6  99.4       0       0       0       0       0       0
-#  9        1 NA      4.51e6  99.4       0       0       0       0       0       0
-# 10        1 NA      4.51e6  99.4       0       0       0       0       0       0
-# # … with 90 more rows, 137 more variables: action_7 <dbl>, action_8 <dbl>,
+#    solution status      obj  cost action_1 action_2 action_3 action_4 action_5
+#       <int> <chr>     <dbl> <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
+#  1        1 NA     2284593.   0          0        0        0        0        0
+#  2        1 NA     2284593.   0          0        0        0        0        0
+#  3        1 NA     2284593.   0          0        0        0        0        0
+#  4        1 NA     2284593.   0          0        0        0        0        0
+#  5        1 NA     4513353.  99.4        0        0        0        0        0
+#  6        1 NA     4513353.  99.4        0        0        0        0        0
+#  7        1 NA     4513353.  99.4        0        0        0        0        0
+#  8        1 NA     4513353.  99.4        0        0        0        0        0
+#  9        1 NA     4513353.  99.4        0        0        0        0        0
+# 10        1 NA     4513353.  99.4        0        0        0        0        0
+# # ℹ 90 more rows
+# # ℹ 138 more variables: action_6 <dbl>, action_7 <dbl>, action_8 <dbl>,
 # #   action_9 <dbl>, action_10 <dbl>, action_11 <dbl>, action_12 <dbl>,
 # #   action_13 <dbl>, action_14 <dbl>, action_15 <dbl>, action_16 <dbl>,
 # #   action_17 <dbl>, action_18 <dbl>, action_19 <dbl>, action_20 <dbl>,
 # #   action_21 <dbl>, action_22 <dbl>, action_23 <dbl>, action_24 <dbl>,
-# #   action_25 <dbl>, action_26 <dbl>, action_27 <dbl>, action_28 <dbl>,
-# #   action_29 <dbl>, action_30 <dbl>, baseline_action <dbl>, project_1 <dbl>, …
+# #   action_25 <dbl>, action_26 <dbl>, action_27 <dbl>, action_28 <dbl>, …
 

Now let’s generate a portfolio of solutions using random processes.

@@ -954,7 +966,7 @@

Benchmarking conventional algorith # 8 211. 2284651. 4513353. # 9 241. 2284651. 4513353. # 10 271. 2284651. 4513353. -# # … with 90 more rows +# # ℹ 90 more rows

Now we can visualize how well the solutions identified using the heuristic algorithm compare to solutions generated using random processes. In the plot below, the orange line shows the performance of solutions generated using the heuristic algorithm, and the blue ribbon shows the performance of the best and worst solutions generated using random processes.

@@ -989,25 +1001,25 @@

Benchmarking conventional algorith print(s9)
# # A tibble: 100 × 147
-#    solution status     obj  cost actio…¹ actio…² actio…³ actio…⁴ actio…⁵ actio…⁶
-#       <int> <chr>    <dbl> <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
-#  1        1 OPTIMAL 2.28e6   0         0       0       0       0       0       0
-#  2        1 OPTIMAL 2.28e6   0         0       0       0       0       0       0
-#  3        1 OPTIMAL 2.28e6   0         0       0       0       0       0       0
-#  4        1 OPTIMAL 2.28e6   0         0       0       0       0       0       0
-#  5        1 OPTIMAL 4.51e6  99.4       0       0       0       0       0       0
-#  6        1 OPTIMAL 4.51e6  99.4       0       0       0       0       0       0
-#  7        1 OPTIMAL 4.51e6  99.4       0       0       0       0       0       0
-#  8        1 OPTIMAL 4.51e6  99.4       0       0       0       0       0       0
-#  9        1 OPTIMAL 4.51e6  99.4       0       0       0       0       0       0
-# 10        1 OPTIMAL 4.51e6  99.4       0       0       0       0       0       0
-# # … with 90 more rows, 137 more variables: action_7 <dbl>, action_8 <dbl>,
+#    solution status       obj  cost action_1 action_2 action_3 action_4 action_5
+#       <int> <chr>      <dbl> <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
+#  1        1 OPTIMAL 2284593.   0          0        0        0        0        0
+#  2        1 OPTIMAL 2284593.   0          0        0        0        0        0
+#  3        1 OPTIMAL 2284593.   0          0        0        0        0        0
+#  4        1 OPTIMAL 2284593.   0          0        0        0        0        0
+#  5        1 OPTIMAL 4513353.  99.4        0        0        0        0        0
+#  6        1 OPTIMAL 4513353.  99.4        0        0        0        0        0
+#  7        1 OPTIMAL 4513353.  99.4        0        0        0        0        0
+#  8        1 OPTIMAL 4513353.  99.4        0        0        0        0        0
+#  9        1 OPTIMAL 4513353.  99.4        0        0        0        0        0
+# 10        1 OPTIMAL 4513353.  99.4        0        0        0        0        0
+# # ℹ 90 more rows
+# # ℹ 138 more variables: action_6 <dbl>, action_7 <dbl>, action_8 <dbl>,
 # #   action_9 <dbl>, action_10 <dbl>, action_11 <dbl>, action_12 <dbl>,
 # #   action_13 <dbl>, action_14 <dbl>, action_15 <dbl>, action_16 <dbl>,
 # #   action_17 <dbl>, action_18 <dbl>, action_19 <dbl>, action_20 <dbl>,
 # #   action_21 <dbl>, action_22 <dbl>, action_23 <dbl>, action_24 <dbl>,
-# #   action_25 <dbl>, action_26 <dbl>, action_27 <dbl>, action_28 <dbl>,
-# #   action_29 <dbl>, action_30 <dbl>, baseline_action <dbl>, project_1 <dbl>, …
+# #   action_25 <dbl>, action_26 <dbl>, action_27 <dbl>, action_28 <dbl>, …
 

Now let’s redraw the previous graph and add a red line to the plot to represent the solutions generated using the exact algorithm solver.

@@ -1037,16 +1049,15 @@

ConclusionCitation

-

-To cite the oppr package in publications, please use:
+
To cite the oppr package in publications, please use:
 
   Hanson JO, Schuster R, Strimas-Mackey M & Bennett JR (2019)
   Optimality in prioritizing conservation projects. Methods in Ecology
   & Evolution, 10: 1655--1663.
 
-  Hanson JO, Schuster R, Strimas-Mackey M, Bennett J (2022) oppr:
-  Optimal Project Prioritization R package version 1.0.4.
-  https://CRAN.R-project.org/package=oppr
+  Hanson JO, Schuster R, Strimas-Mackey M, Bennett J (2024) oppr:
+  Optimal Project Prioritization R package version 1.0.4.1.
+  https://github.com/prioritizr/oppr
 
 To see these entries in BibTeX format, use 'print(<citation>,
 bibtex=TRUE)', 'toBibtex(.)', or set
@@ -1133,7 +1144,7 @@ 

References

-

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    Citation

    pages = {1655--1663}, doi = {10.1111/2041-210X.13264}, } -

    Hanson JO, Schuster R, Strimas-Mackey M, Bennett J (2022) oppr: Optimal Project Prioritization R package version 1.0.4. https://CRAN.R-project.org/package=oppr

    +

    Hanson JO, Schuster R, Strimas-Mackey M, Bennett J (2024) oppr: Optimal Project Prioritization R package version 1.0.4.1. https://github.com/prioritizr/oppr

    @Manual{,
       author = {Jeffrey O Hanson and Richard Schuster and Matthew Strimas-Mackey and Joseph Bennett},
       title = {oppr: Optimal Project Prioritization},
    -  year = {2022},
    -  note = {R package version 1.0.4},
    -  url = {https://CRAN.R-project.org/package=oppr},
    +  year = {2024},
    +  note = {R package version 1.0.4.1},
    +  url = {https://github.com/prioritizr/oppr},
     }
    @@ -107,7 +107,7 @@

    Citation

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/index.html b/docs/index.html index 967b6dd..d515435 100644 --- a/docs/index.html +++ b/docs/index.html @@ -45,7 +45,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -83,7 +83,7 @@
    - +

    lifecycle R-CMD-check-Ubuntu R-CMD-check-Windows R-CMD-check-macOS Coverage Status CRAN_Status_Badge

    The oppr R package is decision support tool for prioritizing conservation projects. Prioritizations can be developed by maximizing expected feature richness, expected phylogenetic diversity, the number of features that meet persistence targets, or identifying a set of projects that meet persistence targets for minimal cost. Constraints (e.g. lock in specific actions) and feature weights can also be specified to further customize prioritizations. After defining a project prioritization problem, solutions can be obtained using exact algorithms, heuristic algorithms, or random processes. In particular, it is recommended to install the ‘Gurobi’ optimizer because it can identify optimal solutions very quickly. Finally, methods are provided for comparing different prioritizations and evaluating their benefits.

    Installation @@ -262,17 +262,7 @@

    Developers

    -
    -

    Dev status

    -
      -
    • lifecycle
    • -
    • R-CMD-check-Ubuntu
    • -
    • R-CMD-check-Windows
    • -
    • R-CMD-check-macOS
    • -
    • Coverage Status
    • -
    • CRAN_Status_Badge
    • -
    -
    +
    @@ -285,7 +275,7 @@

    Dev status

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/news/index.html b/docs/news/index.html index 23556a0..8dd3df7 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -17,7 +17,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -51,7 +51,15 @@

    Changelog

    - + +
    • Fix aliasing for package manual entry (#21).
    • +
    • Fix compatibility with updates to ggplot2 package.
    • +
    • Fix compatibility with updates to package citation format.
    • +
    • Fix broken URLs in package documentation.
    • +
    • Fix broken badges in README.
    • +
    +
    +
    • CRAN release.
    • Fix compiler warnings raised by CRAN checks.
    • Remove references to the ggtree package in documentation.
    • @@ -222,7 +230,7 @@
    diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 963e48e..68048d8 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -1,9 +1,9 @@ pandoc: '2.13' -pkgdown: 2.0.6 +pkgdown: 2.0.7 pkgdown_sha: ~ articles: oppr: oppr.html -last_built: 2022-09-06T22:14Z +last_built: 2024-05-26T07:08Z urls: reference: https://prioritizr.github.io/oppr/reference article: https://prioritizr.github.io/oppr/articles diff --git a/docs/reference/ArrayParameter-class.html b/docs/reference/ArrayParameter-class.html index a825f32..7dbfdef 100644 --- a/docs/reference/ArrayParameter-class.html +++ b/docs/reference/ArrayParameter-class.html @@ -19,7 +19,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -195,7 +195,7 @@

    See also

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/Collection-class.html b/docs/reference/Collection-class.html index 81ae2e3..6243fe0 100644 --- a/docs/reference/Collection-class.html +++ b/docs/reference/Collection-class.html @@ -18,7 +18,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -170,7 +170,7 @@

    See also

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/Constraint-class.html b/docs/reference/Constraint-class.html index 3afe289..2b2bd14 100644 --- a/docs/reference/Constraint-class.html +++ b/docs/reference/Constraint-class.html @@ -21,7 +21,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -81,7 +81,7 @@

    See also

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/Decision-class.html b/docs/reference/Decision-class.html index 64849a2..8c4b481 100644 --- a/docs/reference/Decision-class.html +++ b/docs/reference/Decision-class.html @@ -22,7 +22,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -83,7 +83,7 @@

    See also

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/MiscParameter-class.html b/docs/reference/MiscParameter-class.html index 7fc725f..69ef949 100644 --- a/docs/reference/MiscParameter-class.html +++ b/docs/reference/MiscParameter-class.html @@ -18,7 +18,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -165,7 +165,7 @@

    See also

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/Objective-class.html b/docs/reference/Objective-class.html index e82ed92..0209a1a 100644 --- a/docs/reference/Objective-class.html +++ b/docs/reference/Objective-class.html @@ -21,7 +21,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -77,7 +77,7 @@

    Objective prototype

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/OptimizationProblem-class.html b/docs/reference/OptimizationProblem-class.html index 85e9e59..c78f239 100644 --- a/docs/reference/OptimizationProblem-class.html +++ b/docs/reference/OptimizationProblem-class.html @@ -19,7 +19,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -222,7 +222,7 @@

    Details

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/OptimizationProblem-methods.html b/docs/reference/OptimizationProblem-methods.html index e237bef..5d7ec07 100644 --- a/docs/reference/OptimizationProblem-methods.html +++ b/docs/reference/OptimizationProblem-methods.html @@ -18,7 +18,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -247,7 +247,7 @@

    Details

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/Parameter-class.html b/docs/reference/Parameter-class.html index 73f0134..db72879 100644 --- a/docs/reference/Parameter-class.html +++ b/docs/reference/Parameter-class.html @@ -19,7 +19,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -142,7 +142,7 @@

    See also

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/Parameters-class.html b/docs/reference/Parameters-class.html index 0912aa3..10b8b81 100644 --- a/docs/reference/Parameters-class.html +++ b/docs/reference/Parameters-class.html @@ -19,7 +19,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -169,7 +169,7 @@

    Details

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/ProjectModifier-class.html b/docs/reference/ProjectModifier-class.html index 84698ff..47b8180 100644 --- a/docs/reference/ProjectModifier-class.html +++ b/docs/reference/ProjectModifier-class.html @@ -23,7 +23,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -224,7 +224,7 @@

    Details

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

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    Details

    -

    Site built with pkgdown 2.0.6.

    +

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    Examples

    # \dontrun{ # solve problems s1 <- solve(p1) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 46 rows, 42 columns and 92 nonzeros #> Model fingerprint: 0xde05e947 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -216,12 +228,25 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 497.767 +#> No other solutions better than 497.767 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 4.977671458279e+02, best bound 4.977671458279e+02, gap 0.0000% s2 <- solve(p2) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 46 rows, 42 columns and 92 nonzeros #> Model fingerprint: 0x4e7d2c3e #> Variable types: 0 continuous, 42 integer (42 binary) @@ -235,26 +260,39 @@

    Examples

    #> Presolve time: 0.00s #> Presolved: 15 rows, 28 columns, 30 nonzeros #> Variable types: 0 continuous, 28 integer (28 binary) -#> Root relaxation presolved: 15 rows, 28 columns, 30 nonzeros +#> Root relaxation presolved: 6 rows, 19 columns, 12 nonzeros #> #> -#> Root relaxation: objective 3.023765e+02, 4 iterations, 0.00 seconds (0.00 work units) +#> Root relaxation: objective 3.023765e+02, 0 iterations, 0.00 seconds (0.00 work units) #> #> Nodes | Current Node | Objective Bounds | Work #> Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time #> #> * 0 0 0 302.3764827 302.37648 0.00% - 0s #> -#> Explored 1 nodes (4 simplex iterations) in 0.00 seconds (0.00 work units) +#> Explored 1 nodes (0 simplex iterations) in 0.00 seconds (0.00 work units) #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 302.376 +#> No other solutions better than 302.376 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 3.023764827378e+02, best bound 3.023764827378e+02, gap 0.0000% s3 <- solve(p3) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 46 rows, 42 columns and 92 nonzeros #> Model fingerprint: 0x4e7d2c3e #> Variable types: 0 continuous, 42 integer (42 binary) @@ -268,20 +306,21 @@

    Examples

    #> Presolve time: 0.00s #> Presolved: 15 rows, 28 columns, 30 nonzeros #> Variable types: 0 continuous, 28 integer (28 binary) -#> Root relaxation presolved: 15 rows, 28 columns, 30 nonzeros +#> Root relaxation presolved: 6 rows, 19 columns, 12 nonzeros #> #> -#> Root relaxation: objective 3.023765e+02, 4 iterations, 0.00 seconds (0.00 work units) +#> Root relaxation: objective 3.023765e+02, 0 iterations, 0.00 seconds (0.00 work units) #> #> Nodes | Current Node | Objective Bounds | Work #> Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time #> #> * 0 0 0 302.3764827 302.37648 0.00% - 0s #> -#> Explored 1 nodes (4 simplex iterations) in 0.00 seconds (0.00 work units) +#> Explored 1 nodes (0 simplex iterations) in 0.00 seconds (0.00 work units) #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 302.376 +#> No other solutions better than 302.376 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 3.023764827378e+02, best bound 3.023764827378e+02, gap 0.0000% @@ -289,34 +328,28 @@

    Examples

    # print solutions print(s1) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 498. 498. 1 1 1 1 1 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 498. 498. 1 1 1 1 1 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> print(s2) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 302. 302. 0 0 1 1 1 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 302. 302. 0 0 1 1 1 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> print(s3) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 302. 302. 0 0 1 1 1 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 302. 302. 0 0 1 1 1 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # plot solutions plot(p1, s1) @@ -340,7 +373,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

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+119,20 @@

    Examples

    # \dontrun{ # solve problem s <- solve(p) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0x193cb636 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -149,6 +161,7 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 2.19038 +#> No other solutions better than 2.19038 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 2.190380737245e+00, best bound 2.190380737245e+00, gap 0.0000% @@ -156,14 +169,12 @@

    Examples

    # print solution print(s) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 2.19 195. 1 1 0 0 0 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 2.19 195. 1 1 0 0 0 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # plot solution plot(p, s) @@ -183,7 +194,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

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@@

    Examples

    # \dontrun{ # solve problem s <- solve(p) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0x193cb636 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -148,6 +160,7 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 2.19038 +#> No other solutions better than 2.19038 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 2.190380737245e+00, best bound 2.190380737245e+00, gap 0.0000% @@ -155,14 +168,12 @@

    Examples

    # print solution print(s) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 2.19 195. 1 1 0 0 0 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 2.19 195. 1 1 0 0 0 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # plot solution plot(p, s) @@ -182,7 +193,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

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    Examples

    # \dontrun{ # solve the problems s1 <- solve(p1) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0x193cb636 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -229,12 +241,25 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 2.19038 +#> No other solutions better than 2.19038 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 2.190380737245e+00, best bound 2.190380737245e+00, gap 0.0000% s2 <- solve(p2) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0x40fa7344 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -263,12 +288,25 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 1.51123 +#> No other solutions better than 1.51123 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 1.511229665304e+00, best bound 1.511229665304e+00, gap 0.0000% s3 <- solve(p3) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0x40fa7344 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -297,6 +335,7 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 1.51123 +#> No other solutions better than 1.51123 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 1.511229665304e+00, best bound 1.511229665304e+00, gap 0.0000% @@ -304,34 +343,28 @@

    Examples

    # print solutions print(s1) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 2.19 195. 1 1 0 0 0 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 2.19 195. 1 1 0 0 0 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> print(s2) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 1.51 199. 0 0 0 1 1 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 1.51 199. 0 0 0 1 1 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> print(s3) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 1.51 199. 0 0 0 1 1 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 1.51 199. 0 0 0 1 1 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # plot solutions plot(p1, s1) @@ -355,7 +388,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

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    Details

    This solver uses the gurobi package to solve problems.

    To install the gurobi package, the Gurobi optimization suite will first need to -be installed (see instructions for Linux, -Mac OSX, and -Windows operating systems). Although +be installed (see https://support.gurobi.com/hc/en-us/articles/4534161999889-How-do-I-install-Gurobi-Optimizer for instructions). Although Gurobi is a commercial software, academics can obtain a special license for no cost. After installing the Gurobi optimization suite, the gurobi -package can then be installed (see instructions for Linux, -Mac OSX, and -Windows operating systems).

    +package can then be installed (see https://www.gurobi.com/documentation/current/refman/r_ins_the_r_package.html for instructions).

    @@ -359,7 +377,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

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+253,12 @@

    Examples

    # print solution print(s1) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_act…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 NA 2.19 195. 1 1 0 0 0 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 NA 2.19 195. 1 1 0 0 0 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # plot solution plot(p1, s1) @@ -280,7 +278,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/add_locked_in_constraints.html b/docs/reference/add_locked_in_constraints.html index e62e701..720cd35 100644 --- a/docs/reference/add_locked_in_constraints.html +++ b/docs/reference/add_locked_in_constraints.html @@ -20,7 +20,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -226,8 +226,20 @@

    Examples

    # \dontrun{ # solve problems s1 <- solve(p1) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0xf97d9094 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -256,12 +268,25 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 1.68015 +#> No other solutions better than 1.68015 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 1.680145013696e+00, best bound 1.680145013696e+00, gap 0.0000% s2 <- solve(p2) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0xaace6374 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -281,12 +306,25 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 1.44561 +#> No other solutions better than 1.44561 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 1.445609277954e+00, best bound 1.445609277954e+00, gap 0.0000% s3 <- solve(p3) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0xaace6374 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -306,12 +344,25 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 1.44561 +#> No other solutions better than 1.44561 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 1.445609277954e+00, best bound 1.445609277954e+00, gap 0.0000% s4 <- solve(p4) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0xaace6374 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -331,6 +382,7 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 1.44561 +#> No other solutions better than 1.44561 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 1.445609277954e+00, best bound 1.445609277954e+00, gap 0.0000% @@ -371,7 +423,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/add_locked_out_constraints.html b/docs/reference/add_locked_out_constraints.html index f7c889f..59ea90b 100644 --- a/docs/reference/add_locked_out_constraints.html +++ b/docs/reference/add_locked_out_constraints.html @@ -20,7 +20,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -190,8 +190,20 @@

    Examples

    # \dontrun{ # solve problems s1 <- solve(p1) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0xf97d9094 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -220,12 +232,25 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 1.68015 +#> No other solutions better than 1.68015 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 1.680145013696e+00, best bound 1.680145013696e+00, gap 0.0000% s2 <- solve(p2) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0x80197d20 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -254,12 +279,25 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 1.57544 +#> No other solutions better than 1.57544 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 1.575440809243e+00, best bound 1.575440809243e+00, gap 0.0000% s3 <- solve(p3) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0x80197d20 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -288,12 +326,25 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 1.57544 +#> No other solutions better than 1.57544 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 1.575440809243e+00, best bound 1.575440809243e+00, gap 0.0000% s4 <- solve(p4) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0x80197d20 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -322,6 +373,7 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 1.57544 +#> No other solutions better than 1.57544 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 1.575440809243e+00, best bound 1.575440809243e+00, gap 0.0000% @@ -362,7 +414,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

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-101,7 +101,7 @@

    Value

    Details

    -

    lp_solve is an +

    lp_solve is an open-source integer programming solver. Although this solver is the slowest currently supported solver, it is also the only exact algorithm solver that can be installed on all @@ -181,14 +181,12 @@

    Examples

    # print solution print(s) #> # A tibble: 1 × 21 -#> solution status obj cost F1_ac…¹ F2_ac…² F3_ac…³ F4_ac…⁴ F5_ac…⁵ basel…⁶ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 optimal … 2.19 195. 1 1 0 0 0 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F1_action, ²​F2_action, ³​F3_action, -#> # ⁴​F4_action, ⁵​F5_action, ⁶​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 optima… 2.19 195. 1 1 0 0 0 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # plot solution plot(p, s) @@ -207,7 +205,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

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+167,12 @@

    Examples

    # print solution print(s) #> # A tibble: 1 × 21 -#> solution status obj cost F1_ac…¹ F2_ac…² F3_ac…³ F4_ac…⁴ F5_ac…⁵ basel…⁶ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 TM_OPTIM… 2.19 195. 1 1 0 0 0 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F1_action, ²​F2_action, ³​F3_action, -#> # ⁴​F4_action, ⁵​F5_action, ⁶​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 TM_OPT… 2.19 195. 1 1 0 0 0 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # plot solution plot(p, s) @@ -194,7 +192,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/add_manual_locked_constraints.html b/docs/reference/add_manual_locked_constraints.html index f96d012..986419b 100644 --- a/docs/reference/add_manual_locked_constraints.html +++ b/docs/reference/add_manual_locked_constraints.html @@ -20,7 +20,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -151,8 +151,20 @@

    Examples

    # \dontrun{ # solve problem s <- solve(p) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0x54473ab0 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -182,6 +194,7 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 2.91025 +#> No other solutions better than 2.91025 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 2.910245655750e+00, best bound 2.910245655750e+00, gap 0.0000% @@ -189,14 +202,12 @@

    Examples

    # print solution print(s) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 2.91 403. 0 1 1 1 1 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 2.91 403. 0 1 1 1 1 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # }

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    See also

    @@ -216,8 +212,20 @@

    Examples

    # solve problem s2 <- solve(p2) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0x193cb636 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -246,6 +254,7 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 2.19038 +#> No other solutions better than 2.19038 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 2.190380737245e+00, best bound 2.190380737245e+00, gap 0.0000% @@ -253,14 +262,12 @@

    Examples

    # print solution print(s2) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 2.19 195. 1 1 0 0 0 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 2.19 195. 1 1 0 0 0 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # plot solution plot(p2, s2) @@ -289,8 +296,20 @@

    Examples

    # solve problem s3 <- solve(p3) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 100 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0x193cb636 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -325,10 +344,11 @@

    Examples

    #> 0 0 - 0 - 2.19038 - - 0s #> 0 2 - 0 - 2.19038 - - 0s #> -#> Explored 240 nodes (37 simplex iterations) in 0.01 seconds (0.00 work units) +#> Explored 218 nodes (35 simplex iterations) in 0.00 seconds (0.00 work units) #> Thread count was 1 (of 8 available processors) #> #> Solution count 100: 2.19038 2.19038 2.19038 ... 2.19038 +#> No other solutions better than 2.19038 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 2.190380737245e+00, best bound 2.190380737245e+00, gap 0.0000% @@ -336,14 +356,12 @@

    Examples

    # print solutions print(s3) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 2.19 195. 1 1 0 0 0 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 2.19 195. 1 1 0 0 0 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # }
    @@ -213,7 +224,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/add_manual_targets.html b/docs/reference/add_manual_targets.html index 3e32370..d9c731a 100644 --- a/docs/reference/add_manual_targets.html +++ b/docs/reference/add_manual_targets.html @@ -23,7 +23,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -184,8 +184,20 @@

    Examples

    # \dontrun{ # solve problem s <- solve(p) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 46 rows, 42 columns and 92 nonzeros #> Model fingerprint: 0x5a3c452c #> Variable types: 0 continuous, 42 integer (42 binary) @@ -213,6 +225,7 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 103.226 +#> No other solutions better than 103.226 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 1.032258290263e+02, best bound 1.032258290263e+02, gap 0.0000% @@ -220,14 +233,12 @@

    Examples

    # print solution print(s) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 103. 103. 0 0 1 0 0 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 103. 103. 0 0 1 0 0 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # } @@ -243,7 +254,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

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    Examples

    # \dontrun{ # solve problem s1 <- solve(p1) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 50 rows, 50 columns and 123 nonzeros -#> Model fingerprint: 0x3d5512d7 +#> Model fingerprint: 0x8b6e070d #> Model has 3 piecewise-linear objective terms #> Variable types: 3 continuous, 42 integer (42 binary) #> Semi-Variable types: 5 continuous, 0 integer @@ -258,27 +270,28 @@

    Examples

    #> Found heuristic solution: objective 1.7230501 #> Presolve removed 16 rows and 12 columns #> Presolve time: 0.00s -#> Presolved: 37 rows, 2547 columns, 2589 nonzeros -#> Variable types: 2512 continuous, 35 integer (35 binary) -#> Root relaxation presolve removed 3 rows and 3 columns -#> Root relaxation presolved: 34 rows, 2544 columns, 2583 nonzeros +#> Presolved: 37 rows, 2530 columns, 2572 nonzeros +#> Variable types: 2495 continuous, 35 integer (35 binary) +#> Root relaxation presolved: 34 rows, 2527 columns, 2566 nonzeros #> #> -#> Root relaxation: objective 2.638346e+00, 24 iterations, 0.01 seconds (0.00 work units) +#> Root relaxation: objective 2.638346e+00, 23 iterations, 0.00 seconds (0.01 work units) #> #> Nodes | Current Node | Objective Bounds | Work #> Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time #> #> 0 0 2.63835 0 6 1.72305 2.63835 53.1% - 0s -#> H 0 0 1.9924877 2.63835 32.4% - 0s -#> H 0 0 2.1724148 2.63835 21.4% - 0s -#> H 0 0 2.5726722 2.63835 2.55% - 0s -#> 0 2 2.63696 0 6 2.57267 2.63696 2.50% - 0s +#> H 0 0 1.9924876 2.62002 31.5% - 0s +#> H 0 0 2.0656903 2.62002 26.8% - 0s +#> H 0 0 2.1724148 2.62002 20.6% - 0s +#> H 0 0 2.5726722 2.62002 1.84% - 0s +#> 0 0 infeasible 0 2.57267 2.57267 0.00% - 0s #> -#> Explored 3 nodes (28 simplex iterations) in 0.06 seconds (0.02 work units) +#> Explored 1 nodes (28 simplex iterations) in 0.02 seconds (0.04 work units) #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 2.57267 +#> No other solutions better than 2.57267 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 2.572672188340e+00, best bound 2.572672188340e+00, gap 0.0000% @@ -286,14 +299,12 @@

    Examples

    # print solution print(s1) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 2.57 194. 1 0 0 0 1 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 2.57 194. 1 0 0 0 1 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # plot solution plot(p1, s1) @@ -305,10 +316,22 @@

    Examples

    # solve problem with feature weights s2 <- solve(p2) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 50 rows, 50 columns and 123 nonzeros -#> Model fingerprint: 0x5feb5267 +#> Model fingerprint: 0x6f543abe #> Model has 3 piecewise-linear objective terms #> Variable types: 3 continuous, 42 integer (42 binary) #> Semi-Variable types: 5 continuous, 0 integer @@ -322,39 +345,39 @@

    Examples

    #> Found heuristic solution: objective 2.3885146 #> Presolve removed 16 rows and 12 columns #> Presolve time: 0.00s -#> Presolved: 37 rows, 2547 columns, 2589 nonzeros -#> Variable types: 2512 continuous, 35 integer (35 binary) -#> Root relaxation presolve removed 3 rows and 3 columns -#> Root relaxation presolved: 34 rows, 2544 columns, 2583 nonzeros +#> Presolved: 37 rows, 2530 columns, 2572 nonzeros +#> Variable types: 2495 continuous, 35 integer (35 binary) +#> Root relaxation presolved: 34 rows, 2527 columns, 2566 nonzeros #> #> -#> Root relaxation: objective 4.074411e+00, 25 iterations, 0.01 seconds (0.00 work units) +#> Root relaxation: objective 4.074411e+00, 24 iterations, 0.00 seconds (0.01 work units) #> #> Nodes | Current Node | Objective Bounds | Work #> Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time #> #> 0 0 4.07441 0 6 2.38851 4.07441 70.6% - 0s -#> H 0 0 4.0567449 4.07441 0.44% - 0s +#> H 0 0 4.0560715 4.05682 0.02% - 0s +#> H 0 0 4.0567449 4.05682 0.00% - 0s +#> 0 0 cutoff 0 4.05674 4.05674 0.00% - 0s #> -#> Explored 1 nodes (25 simplex iterations) in 0.07 seconds (0.03 work units) +#> Explored 1 nodes (25 simplex iterations) in 0.01 seconds (0.03 work units) #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 4.05674 +#> No other solutions better than 4.05674 #> #> Optimal solution found (tolerance 0.00e+00) -#> Best objective 4.056744940702e+00, best bound 4.056744940718e+00, gap 0.0000% +#> Best objective 4.056744940702e+00, best bound 4.056744940702e+00, gap 0.0000% # print solution based on feature weights print(s2) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 4.06 199. 0 0 0 1 1 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 4.06 199. 0 0 0 1 1 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # plot solution based on feature weights plot(p2, s2) @@ -374,7 +397,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

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z&Yd^$-j*z0yg1M>cXiHv2Dciijk-x@}Kfu`>q@ zp$axJX;>3cL9h0C#b!?@

    Examples

    # \dontrun{ # solve problem s1 <- solve(p1) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0x193cb636 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -227,6 +239,7 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 2.19038 +#> No other solutions better than 2.19038 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 2.190380737245e+00, best bound 2.190380737245e+00, gap 0.0000% @@ -234,14 +247,12 @@

    Examples

    # print solution print(s1) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 2.19 195. 1 1 0 0 0 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 2.19 195. 1 1 0 0 0 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # plot solution plot(p1, s1) @@ -255,8 +266,20 @@

    Examples

    # \dontrun{ # solve problem with feature weights s2 <- solve(p2) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0x40fa7344 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -285,6 +308,7 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 1.51123 +#> No other solutions better than 1.51123 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 1.511229665304e+00, best bound 1.511229665304e+00, gap 0.0000% @@ -292,14 +316,12 @@

    Examples

    # print solution based on feature weights print(s2) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 1.51 199. 0 0 0 1 1 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 1.51 199. 0 0 0 1 1 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # plot solution based on feature weights plot(p2, s2) @@ -319,7 +341,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

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    Examples

    # \dontrun{ # solve problem s1 <- solve(p1) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0x00731be5 #> Variable types: 0 continuous, 47 integer (47 binary) @@ -257,13 +269,14 @@

    Examples

    #> Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time #> #> 0 0 4.96141 0 4 3.00000 4.96141 65.4% - 0s -#> H 0 0 4.0000000 4.96141 24.0% - 0s -#> 0 0 4.96141 0 4 4.00000 4.96141 24.0% - 0s +#> H 0 0 4.0000000 4.96013 24.0% - 0s +#> 0 0 4.96013 0 4 4.00000 4.96013 24.0% - 0s #> #> Explored 1 nodes (7 simplex iterations) in 0.00 seconds (0.00 work units) #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 4 +#> No other solutions better than 4 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 4.000000000000e+00, best bound 4.000000000000e+00, gap 0.0000% @@ -271,14 +284,12 @@

    Examples

    # print solution print(s1) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 4 99.9 0 0 0 0 1 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 4 99.9 0 0 0 0 1 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # plot solution, and add a dashed line to indicate the feature targets # we can see the three features meet the targets under the baseline @@ -296,8 +307,20 @@

    Examples

    # \dontrun{ # solve problem s2 <- solve(p2) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0x2cfdc744 #> Variable types: 0 continuous, 47 integer (47 binary) @@ -311,7 +334,7 @@

    Examples

    #> Presolve time: 0.00s #> Presolved: 33 rows, 40 columns, 69 nonzeros #> Variable types: 0 continuous, 40 integer (40 binary) -#> Root relaxation presolved: 33 rows, 40 columns, 69 nonzeros +#> Root relaxation presolved: 33 rows, 39 columns, 69 nonzeros #> #> #> Root relaxation: objective 1.996013e+01, 7 iterations, 0.00 seconds (0.00 work units) @@ -320,13 +343,15 @@

    Examples

    #> Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time #> #> 0 0 19.96013 0 4 13.00000 19.96013 53.5% - 0s -#> H 0 0 19.0000000 19.96013 5.05% - 0s -#> 0 0 19.96013 0 4 19.00000 19.96013 5.05% - 0s +#> H 0 0 14.0000000 19.76848 41.2% - 0s +#> H 0 0 19.0000000 19.76848 4.04% - 0s +#> 0 0 19.76848 0 4 19.00000 19.76848 4.04% - 0s #> -#> Explored 1 nodes (7 simplex iterations) in 0.01 seconds (0.00 work units) +#> Explored 1 nodes (7 simplex iterations) in 0.00 seconds (0.00 work units) #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 19 +#> No other solutions better than 19 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 1.900000000000e+01, best bound 1.900000000000e+01, gap 0.0000% @@ -334,14 +359,12 @@

    Examples

    # print solution print(s2) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 19 103. 0 0 1 0 0 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 19 103. 0 0 1 0 0 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # plot solution, and add a dashed line to indicate the feature targets # we can see that adding weights to the problem has changed the solution @@ -365,7 +388,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

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zm}Pev;-y7kV=}xpejXJ8-2+k5+OJ}PLY2RqK@P4*F7>sH$E0{zgF|FEvaskpVqZ+d0;>C+HO+-E# zj7%-3q#uMK7h|``+LRfqwP~Owh88z^;o?O(NJRF0P z65j%Y*$Az$B1aDMJaV6Uan*@%{1@jjV<7H>8py+Jw6VMUQ3hRrO8DP@Z-+BBCy8C= zQVokX%`6fWfC>1MOYwU?hf#pYC!R-M|OLQpFfwB zmp8yO7{Zxh?4gjDnvWi-rWxgv$!HnaZ*|}+k|t`}8UP+cGO_X}ejFngIpsnf=ad14 zk`k##54*@@R+F?2ic75+l#60BXs=mDhXGOUU(QFf3t9_4cpndCD^ z4_Qk+phj2>7b*4v2xC=_4L`)O5D+7J9}ws{u;nd+zpKrZ1eM=ylYd%y{_|`m`FP6z dSyA#+(EjM%U3QU<_sEZ-?a oppr - 1.0.4 + 1.0.4.1 @@ -192,8 +192,20 @@

    Examples

    # \dontrun{ # solve problem s <- solve(p) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 46 rows, 42 columns and 92 nonzeros #> Model fingerprint: 0xde05e947 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -212,6 +224,7 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 497.767 +#> No other solutions better than 497.767 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 4.977671458279e+02, best bound 4.977671458279e+02, gap 0.0000% @@ -219,14 +232,12 @@

    Examples

    # print solution print(s) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 498. 498. 1 1 1 1 1 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 498. 498. 1 1 1 1 1 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # plot solution, and add a dashed line to indicate the feature targets plot(p, s) + @@ -247,7 +258,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

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b/docs/reference/add_random_solver.html index e788f62..ecf98c4 100644 --- a/docs/reference/add_random_solver.html +++ b/docs/reference/add_random_solver.html @@ -20,7 +20,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -157,23 +157,22 @@

    Examples

    # print solutions print(s1) #> # A tibble: 100 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 NA 1.91 199. 0 0 0 1 1 1 -#> 2 2 NA 1.96 198. 1 0 1 0 0 1 -#> 3 3 NA 2.01 194. 1 0 0 1 0 1 -#> 4 4 NA 2.19 195. 1 1 0 0 0 1 -#> 5 5 NA 1.91 199. 0 0 0 1 1 1 -#> 6 6 NA 2.01 194. 1 0 0 1 0 1 -#> 7 7 NA 2.19 195. 1 1 0 0 0 1 -#> 8 8 NA 2.19 195. 1 1 0 0 0 1 -#> 9 9 NA 1.99 194. 1 0 0 0 1 1 -#> 10 10 NA 1.96 198. 1 0 1 0 0 1 -#> # … with 90 more rows, 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 NA 1.99 194. 1 0 0 0 1 +#> 2 2 NA 2.01 194. 1 0 0 1 0 +#> 3 3 NA 1.96 198. 1 0 1 0 0 +#> 4 4 NA 1.96 198. 1 0 1 0 0 +#> 5 5 NA 1.91 199. 0 0 0 1 1 +#> 6 6 NA 1.99 194. 1 0 0 0 1 +#> 7 7 NA 1.96 198. 1 0 1 0 0 +#> 8 8 NA 1.96 198. 1 0 1 0 0 +#> 9 9 NA 1.96 198. 1 0 1 0 0 +#> 10 10 NA 2.19 195. 1 1 0 0 0 +#> # ℹ 90 more rows +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # plot first random solution plot(p1, s1) @@ -194,8 +193,20 @@

    Examples

    add_default_solver() %>% solve() #> Warning: overwriting previously defined solver -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0x193cb636 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -224,6 +235,7 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 2.19038 +#> No other solutions better than 2.19038 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 2.190380737245e+00, best bound 2.190380737245e+00, gap 0.0000% @@ -251,7 +263,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

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    Examples

    # solve problems s1 <- solve(p1) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 46 rows, 42 columns and 92 nonzeros #> Model fingerprint: 0xe339eeda #> Variable types: 0 continuous, 42 integer (42 binary) @@ -221,12 +233,25 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 497.767 +#> No other solutions better than 497.767 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 4.977671458279e+02, best bound 4.977671458279e+02, gap 0.0000% s2 <- solve(p2) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 46 rows, 42 columns and 92 nonzeros #> Model fingerprint: 0xe4091247 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -245,12 +270,25 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 403.368 +#> No other solutions better than 403.368 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 4.033678533759e+02, best bound 4.033678533759e+02, gap 0.0000% s3 <- solve(p3) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 46 rows, 42 columns and 92 nonzeros #> Model fingerprint: 0xe4091247 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -269,6 +307,7 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 403.368 +#> No other solutions better than 403.368 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 4.033678533759e+02, best bound 4.033678533759e+02, gap 0.0000% @@ -276,34 +315,28 @@

    Examples

    # print solutions print(s1) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 498. 498. 1 1 1 1 1 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 498. 498. 1 1 1 1 1 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> print(s2) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 403. 403. 0 1 1 1 1 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 403. 403. 0 1 1 1 1 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> print(s3) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 403. 403. 0 1 1 1 1 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 403. 403. 0 1 1 1 1 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # plot solutions plot(p1, s1) @@ -327,7 +360,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

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+164,12 @@

    Examples

    # print solution print(s) #> # A tibble: 1 × 21 -#> solution status obj cost F1_ac…¹ F2_ac…² F3_ac…³ F4_ac…⁴ F5_ac…⁵ basel…⁶ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 TM_OPTIM… 2.19 195. 1 1 0 0 0 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F1_action, ²​F2_action, ³​F3_action, -#> # ⁴​F4_action, ⁵​F5_action, ⁶​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 TM_OPT… 2.19 195. 1 1 0 0 0 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # plot solution plot(p, s) @@ -191,7 +189,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/array_parameters.html b/docs/reference/array_parameters.html index b528145..cd06429 100644 --- a/docs/reference/array_parameters.html +++ b/docs/reference/array_parameters.html @@ -18,7 +18,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -150,7 +150,7 @@

    Examples

    #> b 0.2 #> c 0.3 p1$id # get id -#> id: 94c06762-698e-4c26-a201-e37189f99391 +#> id: e48fad35-beb0-4218-893c-ce4876d7dba4 invalid <- data.frame(value = 1:3, row.names=letters[1:3]) # invalid values p1$validate(invalid) # check invalid input is invalid valid <- data.frame(value = c(0.4, 0.5, 0.6), row.names=letters[1:3]) # valid @@ -169,7 +169,7 @@

    Examples

    #> b 1 #> c 0 p2$id # get id -#> id: e9387d8e-0fe0-4709-a990-e569f2988a52 +#> id: 3bd34428-603f-4bf8-b0b0-a69e5455ec6a invalid <- data.frame(value = 1:3, row.names=letters[1:3]) # invalid values p2$validate(invalid) # check invalid input is invalid valid <- data.frame(value = c(0L, 0L, 0L), row.names=letters[1:3]) # valid @@ -188,7 +188,7 @@

    Examples

    #> b 2 #> c 3 p3$id # get id -#> id: 0bef8045-0690-442d-b6db-71b622cc69a9 +#> id: a0ea679a-c433-4030-83a4-7bb98fc1aa3c invalid <- data.frame(value = rnorm(3), row.names=letters[1:3]) # invalid p3$validate(invalid) # check invalid input is invalid valid <- data.frame(value = 5:7, row.names=letters[1:3]) # valid @@ -207,7 +207,7 @@

    Examples

    #> b 4.0 #> c -5.0 p4$id # get id -#> id: 92da33ac-6abd-42b9-8669-d2cfb5c5da1a +#> id: 418d0e76-eda5-4fab-ae42-a24e8f6abb87 invalid <- data.frame(value = c(NA, 1, 2), row.names=letters[1:3]) # invalid p4$validate(invalid) # check invalid input is invalid valid <- data.frame(value = c(1, 2, 3), row.names=letters[1:3]) # valid @@ -227,7 +227,7 @@

    Examples

    #> b 4.0 #> c -5.0 p5$id# get id -#> id: 245965ee-0bf5-41a4-8fd5-4519cb8bfe61 +#> id: 37d2ca83-6605-4645-b81e-a41f3a6a3704 invalid <- data.frame(value = c(-1, 5, 5), row.names=letters[1:3]) # invalid p5$validate(invalid) # check invalid input is invalid valid <- data.frame(value = c(0, 1, 2), row.names=letters[1:3]) # valid @@ -250,7 +250,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/as.html b/docs/reference/as.html index 9db5862..1721281 100644 --- a/docs/reference/as.html +++ b/docs/reference/as.html @@ -17,7 +17,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -94,7 +94,7 @@

    Value

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/as.list.html b/docs/reference/as.list.html index e222d56..5435d5a 100644 --- a/docs/reference/as.list.html +++ b/docs/reference/as.list.html @@ -17,7 +17,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -89,7 +89,7 @@

    Value

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/branch_matrix.html b/docs/reference/branch_matrix.html index 3ab244b..3738620 100644 --- a/docs/reference/branch_matrix.html +++ b/docs/reference/branch_matrix.html @@ -24,7 +24,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -107,7 +107,7 @@

    Value

    Examples

    # load Matrix package to plot matrices
    -library(Matrix)
    +library(Matrix)
     
     # load data
     data(sim_tree)
    @@ -135,7 +135,7 @@ 

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/compile.html b/docs/reference/compile.html index 14606ec..9b1d94e 100644 --- a/docs/reference/compile.html +++ b/docs/reference/compile.html @@ -18,7 +18,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -139,7 +139,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/constraints.html b/docs/reference/constraints.html index 1f26973..3333420 100644 --- a/docs/reference/constraints.html +++ b/docs/reference/constraints.html @@ -18,7 +18,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -148,8 +148,20 @@

    Examples

    # \dontrun{ # solve problems s1 <- solve(p1) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0xf97d9094 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -178,12 +190,25 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 1.68015 +#> No other solutions better than 1.68015 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 1.680145013696e+00, best bound 1.680145013696e+00, gap 0.0000% s2 <- solve(p2) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0xaace6374 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -203,12 +228,25 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 1.44561 +#> No other solutions better than 1.44561 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 1.445609277954e+00, best bound 1.445609277954e+00, gap 0.0000% s3 <- solve(p3) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0x80197d20 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -237,6 +275,7 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 1.57544 +#> No other solutions better than 1.57544 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 1.575440809243e+00, best bound 1.575440809243e+00, gap 0.0000% @@ -272,7 +311,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

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    Examples

    # \dontrun{ # solve problem s <- solve(p) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0x193cb636 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -135,6 +147,7 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 2.19038 +#> No other solutions better than 2.19038 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 2.190380737245e+00, best bound 2.190380737245e+00, gap 0.0000% @@ -142,14 +155,12 @@

    Examples

    # print solution print(s) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 2.19 195. 1 1 0 0 0 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 2.19 195. 1 1 0 0 0 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # plot solution plot(p, s) @@ -169,7 +180,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/feature_names.html b/docs/reference/feature_names.html index c16b7e0..dd40981 100644 --- a/docs/reference/feature_names.html +++ b/docs/reference/feature_names.html @@ -17,7 +17,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -119,7 +119,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

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zAZ4F?`pz;O$glfq+r%W{!!vVLs@Ei2(mzats1${}#Z{5ol>;WpeQHtec*BlNx65pP z{i3~P8NCi06`nPAxH?^Plsh%fVsaB2gjyOb!`+gpd zFmZWNRv-KabA-b3CA;MWtPFd%wQJmIxq&<5@if^u+J+EH{Z^~ zMr;^O04zpli{YD}*EDs)M3Ir?jkbgR085za=B@_Gy^U)wbkF)trUjd|uYtFA#GH%0kg|jw zwx2O6!ZJs(J1IkXMq*Go?_k}CVYaFzlZWnBhdshF52O#XZC}ziLyu!lOA?!#H_S+J zHPcx5w@446!%eWG-ZM%3em-j?P*d^dnsRMc#_#3vfgwX@{#on8PmyNkY|?{=FX1)2 zCdeS`=5b3if@BfOfeQo;FQFuR(zU~)1W7y9*Zr}?xm04<_JEd)^vkNk?w%gqGB8SY zbEud%$^t>ODB6qDN`LstZCW9i46rv4XJqcKrjkyUkvI&f7xoR2Xi_L>PlE5fL_fU% zA-zLw&1dwT7+a41af!?FRcVbX@v_Z@coabC zc<9OvKE%Ci4op|bI#2N_ oppr - 1.0.4 + 1.0.4.1 @@ -54,7 +54,7 @@

    Summary

    oppr

    +

    oppr oppr-package

    oppr: Optimal Project Prioritization

    @@ -430,7 +430,7 @@

    Parameter definitions -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/is.html b/docs/reference/is.html index a5e5a45..192ec5d 100644 --- a/docs/reference/is.html +++ b/docs/reference/is.html @@ -17,7 +17,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -86,7 +86,7 @@

    Value

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/matrix_parameters.html b/docs/reference/matrix_parameters.html index 0e4ad5a..303e23f 100644 --- a/docs/reference/matrix_parameters.html +++ b/docs/reference/matrix_parameters.html @@ -17,7 +17,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -111,17 +111,17 @@

    Examples

    # create a numeric matrix parameter p1 <- numeric_matrix_parameter("m", m) print(p1) # print it -#> <environment: 0x556a1b7c63e0> +#> <environment: 0x55774e6c3d58> #> attr(,"class") #> [1] "MiscParameter" "MiscParameter" "Parameter" "pproto" #> [5] "proto" "environment" p1$get() # get value -#> a b c -#> a 0.3714649 0.21764370 0.3577770 -#> b 0.5189460 0.05271852 0.3794181 -#> c 0.8149471 0.97241605 0.1247372 +#> a b c +#> a 0.21314931 0.9447748 0.2882372 +#> b 0.21031074 0.2449280 0.8753579 +#> c 0.03952069 0.7811226 0.2957501 p1$id # get id -#> id: f91439b7-76b8-4492-9766-f0e19d6e1451 +#> id: 93842016-7783-4d49-b430-17a7aba38a59 p1$validate(m[, -1]) # check if parameter can be updated #> [1] FALSE #> attr(,"msg") @@ -138,17 +138,17 @@

    Examples

    # create a binary matrix parameter p2 <- binary_matrix_parameter("m", m) print(p2) # print it -#> <environment: 0x556a1aa979b0> +#> <environment: 0x55774ccb1dc8> #> attr(,"class") #> [1] "MiscParameter" "MiscParameter" "Parameter" "pproto" #> [5] "proto" "environment" p2$get() # get value #> a b c -#> a 1 0 1 -#> b 0 0 1 -#> c 0 0 0 +#> a 1 1 0 +#> b 1 1 0 +#> c 1 0 1 p2$id # get id -#> id: 254cfd68-73d1-4ccd-8fc3-0d323205ee46 +#> id: ebed9e50-ce26-45af-8f91-960295411e1a p2$validate(m[, -1]) # check if parameter can be updated #> [1] FALSE #> attr(,"msg") @@ -170,7 +170,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/misc_parameter.html b/docs/reference/misc_parameter.html index 28316ba..80544f0 100644 --- a/docs/reference/misc_parameter.html +++ b/docs/reference/misc_parameter.html @@ -17,7 +17,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -99,7 +99,7 @@

    Examples

    function(x) TRUE, function(id, x) structure(id, .Class = "shiny.tag")) print(p1) # print it -#> <environment: 0x556a1f2b1d20> +#> <environment: 0x55774d75d028> #> attr(,"class") #> [1] "MiscParameter" "MiscParameter" "Parameter" "pproto" #> [5] "proto" "environment" @@ -256,7 +256,7 @@

    Examples

    #> 149 6.2 3.4 5.4 2.3 virginica #> 150 5.9 3.0 5.1 1.8 virginica p1$id # get id -#> id: 186a5ebe-11b6-4645-a682-399bcf7f8cf4 +#> id: 4529cbeb-9568-48cb-a8cc-368df5fbe3d4 p1$validate(mtcars) # check if parameter can be updated #> [1] TRUE p1$set(mtcars) # set parameter to mtcars @@ -271,7 +271,7 @@

    Examples

    all(x[[1]] < 200), function(id, x) structure(id, .Class = "shiny.tag")) print(p2) # print it -#> <environment: 0x556a227c5068> +#> <environment: 0x55774f164488> #> attr(,"class") #> [1] "MiscParameter" "MiscParameter" "Parameter" "pproto" #> [5] "proto" "environment" @@ -428,7 +428,7 @@

    Examples

    #> 149 6.2 3.4 5.4 2.3 virginica #> 150 5.9 3.0 5.1 1.8 virginica p2$id # get id -#> id: 1f26bfc8-ed3c-4480-83e7-2af9b8900836 +#> id: d76d4e6c-8b83-4833-98e7-f7fbc6ba951f p2$validate(mtcars) # check if parameter can be updated #> [1] FALSE iris2 <- iris; iris2[1,1] <- 300 # create updated iris data set @@ -453,7 +453,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/new_id.html b/docs/reference/new_id.html index b033351..a689de0 100644 --- a/docs/reference/new_id.html +++ b/docs/reference/new_id.html @@ -17,7 +17,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -81,11 +81,11 @@

    Examples

    # print id print(i) -#> id: 4fd55b80-f1e8-48ab-8aa1-bd6c3264288d +#> id: d1b3329e-c03c-4f53-81b0-a72758765e41 # convert to character as.character(i) -#> [1] "4fd55b80-f1e8-48ab-8aa1-bd6c3264288d" +#> [1] "d1b3329e-c03c-4f53-81b0-a72758765e41" # check if it is an Id object is.Id(i) @@ -105,7 +105,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/new_optimization_problem.html b/docs/reference/new_optimization_problem.html index ab93b23..488d0c2 100644 --- a/docs/reference/new_optimization_problem.html +++ b/docs/reference/new_optimization_problem.html @@ -17,7 +17,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -92,7 +92,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/new_waiver.html b/docs/reference/new_waiver.html index d81a0c6..842ee1d 100644 --- a/docs/reference/new_waiver.html +++ b/docs/reference/new_waiver.html @@ -17,7 +17,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -103,7 +103,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/number_of_actions.html b/docs/reference/number_of_actions.html index caa9bd9..9856e9c 100644 --- a/docs/reference/number_of_actions.html +++ b/docs/reference/number_of_actions.html @@ -17,7 +17,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -123,7 +123,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/number_of_features.html b/docs/reference/number_of_features.html index f983dcf..974adb3 100644 --- a/docs/reference/number_of_features.html +++ b/docs/reference/number_of_features.html @@ -17,7 +17,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -123,7 +123,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/number_of_projects.html b/docs/reference/number_of_projects.html index ae1554a..25abfaa 100644 --- a/docs/reference/number_of_projects.html +++ b/docs/reference/number_of_projects.html @@ -17,7 +17,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -123,7 +123,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

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    Examples

    # \dontrun{ # solve problem s1 <- solve(p1) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0x193cb636 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -182,6 +194,7 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 2.19038 +#> No other solutions better than 2.19038 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 2.190380737245e+00, best bound 2.190380737245e+00, gap 0.0000% @@ -189,14 +202,12 @@

    Examples

    # print solution print(s1) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 2.19 195. 1 1 0 0 0 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 2.19 195. 1 1 0 0 0 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # plot solution plot(p1, s1) @@ -212,10 +223,22 @@

    Examples

    # \dontrun{ # solve problem s2 <- solve(p2) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 50 rows, 50 columns and 123 nonzeros -#> Model fingerprint: 0x3d5512d7 +#> Model fingerprint: 0x8b6e070d #> Model has 3 piecewise-linear objective terms #> Variable types: 3 continuous, 42 integer (42 binary) #> Semi-Variable types: 5 continuous, 0 integer @@ -229,27 +252,28 @@

    Examples

    #> Found heuristic solution: objective 1.7230501 #> Presolve removed 16 rows and 12 columns #> Presolve time: 0.00s -#> Presolved: 37 rows, 2547 columns, 2589 nonzeros -#> Variable types: 2512 continuous, 35 integer (35 binary) -#> Root relaxation presolve removed 3 rows and 3 columns -#> Root relaxation presolved: 34 rows, 2544 columns, 2583 nonzeros +#> Presolved: 37 rows, 2530 columns, 2572 nonzeros +#> Variable types: 2495 continuous, 35 integer (35 binary) +#> Root relaxation presolved: 34 rows, 2527 columns, 2566 nonzeros #> #> -#> Root relaxation: objective 2.638346e+00, 24 iterations, 0.01 seconds (0.00 work units) +#> Root relaxation: objective 2.638346e+00, 23 iterations, 0.00 seconds (0.01 work units) #> #> Nodes | Current Node | Objective Bounds | Work #> Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time #> #> 0 0 2.63835 0 6 1.72305 2.63835 53.1% - 0s -#> H 0 0 1.9924877 2.63835 32.4% - 0s -#> H 0 0 2.1724148 2.63835 21.4% - 0s -#> H 0 0 2.5726722 2.63835 2.55% - 0s -#> 0 2 2.63696 0 6 2.57267 2.63696 2.50% - 0s +#> H 0 0 1.9924876 2.62002 31.5% - 0s +#> H 0 0 2.0656903 2.62002 26.8% - 0s +#> H 0 0 2.1724148 2.62002 20.6% - 0s +#> H 0 0 2.5726722 2.62002 1.84% - 0s +#> 0 0 infeasible 0 2.57267 2.57267 0.00% - 0s #> -#> Explored 3 nodes (28 simplex iterations) in 0.07 seconds (0.02 work units) +#> Explored 1 nodes (28 simplex iterations) in 0.02 seconds (0.04 work units) #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 2.57267 +#> No other solutions better than 2.57267 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 2.572672188340e+00, best bound 2.572672188340e+00, gap 0.0000% @@ -257,14 +281,12 @@

    Examples

    # print solution print(s2) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 2.57 194. 1 0 0 0 1 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 2.57 194. 1 0 0 0 1 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # plot solution plot(p2, s2) @@ -281,8 +303,20 @@

    Examples

    # \dontrun{ # solve problem s3 <- solve(p3) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0xec4d8189 #> Variable types: 0 continuous, 47 integer (47 binary) @@ -310,6 +344,7 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 2 +#> No other solutions better than 2 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 2.000000000000e+00, best bound 2.000000000000e+00, gap 0.0000% @@ -317,14 +352,12 @@

    Examples

    # print solution print(s3) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 2 195. 1 1 0 0 0 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 2 195. 1 1 0 0 0 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # plot solution plot(p3, s3) @@ -342,8 +375,20 @@

    Examples

    # \dontrun{ # solve problem s4 <- solve(p4) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 46 rows, 42 columns and 92 nonzeros #> Model fingerprint: 0x9cba05f4 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -362,6 +407,7 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 497.767 +#> No other solutions better than 497.767 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 4.977671458279e+02, best bound 4.977671458279e+02, gap 0.0000% @@ -369,14 +415,12 @@

    Examples

    # print solution print(s4) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 498. 498. 1 1 1 1 1 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 498. 498. 1 1 1 1 1 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # plot solution plot(p4, s4) @@ -396,7 +440,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

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    Installation

    If the installation process fails, please consult the package's online documentation. To install the gurobi package, the Gurobi optimization suite will first need to -be installed (see instructions for Linux, -Mac OSX, and -Windows operating systems). Although +be installed (see https://support.gurobi.com/hc/en-us/articles/4534161999889-How-do-I-install-Gurobi-Optimizer for instructions). Although Gurobi is a commercial software, academics can obtain a special license for no cost. After installing the Gurobi optimization suite, the gurobi -package can then be installed (see instructions for Linux, -Mac OSX, and -Windows operating systems).

    +package can then be installed (see https://www.gurobi.com/documentation/current/refman/r_ins_the_r_package.html for instructions).

    See also

    @@ -115,6 +111,13 @@

    See also

    examples. This can be accessed using the code vignette("oppr").

    +
    +

    Author

    +

    Maintainer: Jeffrey O Hanson jeffrey.hanson@uqconnect.edu.au (ORCID)

    +

    Authors:

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zx73V-D0a|Cx(d=*8DEZp{lS@#b5|X@YJzLTXqMaQB>TLTN-`ax)gwfsd7flq?(P}H zZGg=pbur2hy=fkpDb$bLK;F1ylb%CjtXm1jfjptkBCIdKu0lK$q+Ta)*zTiDMk(jb za9JpBqya%a;Bbb3kKX{K!8#@cugLozPplCSah%sir^J`n53l$AHG+ws{_8uiVt>89 v`sr`fCSjSrysioz{!3FDKmGr|d0D#d;ThGmBOjlVi=iJjK9v3A>3{zRPQhKl diff --git a/docs/reference/objectives.html b/docs/reference/objectives.html index afc17e2..a297a45 100644 --- a/docs/reference/objectives.html +++ b/docs/reference/objectives.html @@ -28,7 +28,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -152,8 +152,20 @@

    Examples

    @@ -124,17 +127,16 @@

    Examples

    # print project data print(sim_projects) #> # A tibble: 6 × 13 -#> name success F1 F2 F3 F4 F5 F1_ac…¹ F2_ac…² F3_ac…³ -#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <lgl> -#> 1 F1_project 0.919 0.791 NA NA NA NA TRUE FALSE FALSE -#> 2 F2_project 0.923 NA 0.888 NA NA NA FALSE TRUE FALSE -#> 3 F3_project 0.829 NA NA 0.502 NA NA FALSE FALSE TRUE -#> 4 F4_project 0.848 NA NA NA 0.690 NA FALSE FALSE FALSE -#> 5 F5_project 0.814 NA NA NA NA 0.617 FALSE FALSE FALSE -#> 6 baseline_… 1 0.298 0.250 0.0865 0.249 0.182 FALSE FALSE FALSE -#> # … with 3 more variables: F4_action <lgl>, F5_action <lgl>, -#> # baseline_action <lgl>, and abbreviated variable names ¹​F1_action, -#> # ²​F2_action, ³​F3_action +#> name success F1 F2 F3 F4 F5 F1_action F2_action +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> +#> 1 F1_project 0.919 0.791 NA NA NA NA TRUE FALSE +#> 2 F2_project 0.923 NA 0.888 NA NA NA FALSE TRUE +#> 3 F3_project 0.829 NA NA 0.502 NA NA FALSE FALSE +#> 4 F4_project 0.848 NA NA NA 0.690 NA FALSE FALSE +#> 5 F5_project 0.814 NA NA NA NA 0.617 FALSE FALSE +#> 6 baseline_proj… 1 0.298 0.250 0.0865 0.249 0.182 FALSE FALSE +#> # ℹ 4 more variables: F3_action <lgl>, F4_action <lgl>, F5_action <lgl>, +#> # baseline_action <lgl> # print action data print(sim_features) @@ -184,8 +186,20 @@

    Examples

    # \dontrun{ # solve problem s <- solve(p) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0xa33f6587 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -214,6 +228,7 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 1.74904 +#> No other solutions better than 1.74904 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 1.749044775334e+00, best bound 1.749044775334e+00, gap 0.0000% @@ -221,14 +236,12 @@

    Examples

    # print output print(s) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 1.75 395. 1 1 0 1 1 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 1.75 395. 1 1 0 1 1 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # print which actions are funded in the solution s[, sim_actions$name, drop = FALSE] @@ -249,6 +262,7 @@

    Examples

    plot(p, s) # } +
    @@ -263,7 +277,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/parameters.html b/docs/reference/parameters.html index ed16f20..9b6753b 100644 --- a/docs/reference/parameters.html +++ b/docs/reference/parameters.html @@ -17,7 +17,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -90,7 +90,7 @@

    Examples

    # store Parameter objects in a Parameters object p <- parameters(p1, p2) print(p) -#> <environment: 0x556a22161588> +#> <environment: 0x557740dec0f8> #> attr(,"class") #> [1] "Parameters" "pproto" "proto" "environment" @@ -108,7 +108,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/pipe.html b/docs/reference/pipe.html index 3966dcf..5699acf 100644 --- a/docs/reference/pipe.html +++ b/docs/reference/pipe.html @@ -18,7 +18,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -100,7 +100,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

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    Examples

    # \dontrun{ # solve problem without phylogenetic data s1 <- solve(p1) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0xa33f6587 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -179,6 +191,7 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 1.74904 +#> No other solutions better than 1.74904 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 1.749044775334e+00, best bound 1.749044775334e+00, gap 0.0000% @@ -197,10 +210,22 @@

    Examples

    # \dontrun{ # solve problem with phylogenetic data s2 <- solve(p2) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 50 rows, 50 columns and 123 nonzeros -#> Model fingerprint: 0xb7356220 +#> Model fingerprint: 0x932219c1 #> Model has 3 piecewise-linear objective terms #> Variable types: 3 continuous, 42 integer (42 binary) #> Semi-Variable types: 5 continuous, 0 integer @@ -214,23 +239,23 @@

    Examples

    #> Found heuristic solution: objective 1.7230501 #> Presolve removed 16 rows and 12 columns #> Presolve time: 0.00s -#> Presolved: 37 rows, 3035 columns, 3076 nonzeros -#> Variable types: 3000 continuous, 35 integer (35 binary) -#> Root relaxation presolve removed 3 rows and 3 columns -#> Root relaxation presolved: 34 rows, 3032 columns, 3070 nonzeros +#> Presolved: 37 rows, 2995 columns, 3036 nonzeros +#> Variable types: 2960 continuous, 35 integer (35 binary) +#> Root relaxation presolved: 34 rows, 2992 columns, 3030 nonzeros #> #> -#> Root relaxation: objective 3.112324e+00, 17 iterations, 0.01 seconds (0.01 work units) +#> Root relaxation: objective 3.112324e+00, 13 iterations, 0.00 seconds (0.01 work units) #> #> Nodes | Current Node | Objective Bounds | Work #> Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time #> #> * 0 0 0 3.1123243 3.11232 0.00% - 0s #> -#> Explored 1 nodes (17 simplex iterations) in 0.02 seconds (0.01 work units) +#> Explored 1 nodes (13 simplex iterations) in 0.00 seconds (0.01 work units) #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 3.11232 +#> No other solutions better than 3.11232 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 3.112324258740e+00, best bound 3.112324258740e+00, gap 0.0000% @@ -253,7 +278,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

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    Examples

    print(plot_data) #> # A tibble: 5 × 4 #> name prob weight status -#> <fct> <dbl> <dbl> <chr> +#> <fct> <dbl> <dbl> <fct> #> 1 F5 0.592 1.59 Funded #> 2 F4 0.688 0.630 Funded #> 3 F3 0.0865 0.221 NA @@ -189,7 +189,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

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    Examples

    end = 0.9, direction = -1)) + ggtitle("solution") -#> Scale for 'colour' is already present. Adding another scale for 'colour', -#> which will replace the existing scale. +#> Scale for colour is already present. +#> Adding another scale for colour, which will replace the existing scale. # we could also change the color ramp @@ -199,8 +199,8 @@

    Examples

    scale_color_gradient(name = "Probability of\npersistence", low = "red", high = "black") + ggtitle("solution") -#> Scale for 'colour' is already present. Adding another scale for 'colour', -#> which will replace the existing scale. +#> Scale for colour is already present. +#> Adding another scale for colour, which will replace the existing scale. # we could even hide the legend if desired @@ -209,8 +209,8 @@

    Examples

    low = "red", high = "black") + theme(legend.position = "hide") + ggtitle("solution") -#> Scale for 'colour' is already present. Adding another scale for 'colour', -#> which will replace the existing scale. +#> Scale for colour is already present. +#> Adding another scale for colour, which will replace the existing scale. # we can also obtain the raw plotting data using return_data=TRUE @@ -260,7 +260,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/pproto.html b/docs/reference/pproto.html index ee22e48..8b56095 100644 --- a/docs/reference/pproto.html +++ b/docs/reference/pproto.html @@ -18,7 +18,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -119,7 +119,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/print.html b/docs/reference/print.html index c04e5f8..5485f23 100644 --- a/docs/reference/print.html +++ b/docs/reference/print.html @@ -17,7 +17,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -121,7 +121,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

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zgFq6hArKgs1pW~-Ee2f=<#|=L1q1{*0}I_5cXey+qAKB$4y+kSydnh=Y*iJ&?nu=G|g~LA}Fgr)#07gJ-XCOHR zn>|7rb_3qs1^y%<{m1IG;;^j0P+>z^C{ryVJ1JPWLkz6s1?3_r1XK#cqL(mliH9dht~g! zkOlft;$iVIc`2!4dc^@c0J6jqPCyq5kRTzl9+1f|j3ch}DOd=!7cZb{JrEOIy71B- zSk>Yo_a2YsXbq%iC(^Tc*FaQ oppr - 1.0.4 + 1.0.4.1 @@ -247,17 +247,16 @@

    Examples

    # print project data print(sim_projects) #> # A tibble: 6 × 13 -#> name success F1 F2 F3 F4 F5 F1_ac…¹ F2_ac…² F3_ac…³ -#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <lgl> -#> 1 F1_project 0.919 0.791 NA NA NA NA TRUE FALSE FALSE -#> 2 F2_project 0.923 NA 0.888 NA NA NA FALSE TRUE FALSE -#> 3 F3_project 0.829 NA NA 0.502 NA NA FALSE FALSE TRUE -#> 4 F4_project 0.848 NA NA NA 0.690 NA FALSE FALSE FALSE -#> 5 F5_project 0.814 NA NA NA NA 0.617 FALSE FALSE FALSE -#> 6 baseline_… 1 0.298 0.250 0.0865 0.249 0.182 FALSE FALSE FALSE -#> # … with 3 more variables: F4_action <lgl>, F5_action <lgl>, -#> # baseline_action <lgl>, and abbreviated variable names ¹​F1_action, -#> # ²​F2_action, ³​F3_action +#> name success F1 F2 F3 F4 F5 F1_action F2_action +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> +#> 1 F1_project 0.919 0.791 NA NA NA NA TRUE FALSE +#> 2 F2_project 0.923 NA 0.888 NA NA NA FALSE TRUE +#> 3 F3_project 0.829 NA NA 0.502 NA NA FALSE FALSE +#> 4 F4_project 0.848 NA NA NA 0.690 NA FALSE FALSE +#> 5 F5_project 0.814 NA NA NA NA 0.617 FALSE FALSE +#> 6 baseline_proj… 1 0.298 0.250 0.0865 0.249 0.182 FALSE FALSE +#> # ℹ 4 more variables: F3_action <lgl>, F4_action <lgl>, F5_action <lgl>, +#> # baseline_action <lgl> # print action data print(sim_features) @@ -307,8 +306,20 @@

    Examples

    # \dontrun{ # solve problem s <- solve(p) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0xa33f6587 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -337,6 +348,7 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 1.74904 +#> No other solutions better than 1.74904 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 1.749044775334e+00, best bound 1.749044775334e+00, gap 0.0000% @@ -344,14 +356,12 @@

    Examples

    # print output print(s) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 1.75 395. 1 1 0 1 1 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 1.75 395. 1 1 0 1 1 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # print which actions are funded in the solution s[, sim_actions$name, drop = FALSE] @@ -386,7 +396,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/project_cost_effectiveness.html b/docs/reference/project_cost_effectiveness.html index c1958c8..bf67440 100644 --- a/docs/reference/project_cost_effectiveness.html +++ b/docs/reference/project_cost_effectiveness.html @@ -19,7 +19,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -135,17 +135,16 @@

    Examples

    # print project data print(sim_projects) #> # A tibble: 6 × 13 -#> name success F1 F2 F3 F4 F5 F1_ac…¹ F2_ac…² F3_ac…³ -#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <lgl> -#> 1 F1_project 0.919 0.791 NA NA NA NA TRUE FALSE FALSE -#> 2 F2_project 0.923 NA 0.888 NA NA NA FALSE TRUE FALSE -#> 3 F3_project 0.829 NA NA 0.502 NA NA FALSE FALSE TRUE -#> 4 F4_project 0.848 NA NA NA 0.690 NA FALSE FALSE FALSE -#> 5 F5_project 0.814 NA NA NA NA 0.617 FALSE FALSE FALSE -#> 6 baseline_… 1 0.298 0.250 0.0865 0.249 0.182 FALSE FALSE FALSE -#> # … with 3 more variables: F4_action <lgl>, F5_action <lgl>, -#> # baseline_action <lgl>, and abbreviated variable names ¹​F1_action, -#> # ²​F2_action, ³​F3_action +#> name success F1 F2 F3 F4 F5 F1_action F2_action +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> +#> 1 F1_project 0.919 0.791 NA NA NA NA TRUE FALSE +#> 2 F2_project 0.923 NA 0.888 NA NA NA FALSE TRUE +#> 3 F3_project 0.829 NA NA 0.502 NA NA FALSE FALSE +#> 4 F4_project 0.848 NA NA NA 0.690 NA FALSE FALSE +#> 5 F5_project 0.814 NA NA NA NA 0.617 FALSE FALSE +#> 6 baseline_proj… 1 0.298 0.250 0.0865 0.249 0.182 FALSE FALSE +#> # ℹ 4 more variables: F3_action <lgl>, F4_action <lgl>, F5_action <lgl>, +#> # baseline_action <lgl> # print action data print(sim_features) @@ -224,7 +223,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/project_names.html b/docs/reference/project_names.html index dc58ebf..bc90de7 100644 --- a/docs/reference/project_names.html +++ b/docs/reference/project_names.html @@ -17,7 +17,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -120,7 +120,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/replacement_costs.html b/docs/reference/replacement_costs.html index ae3f7ed..70178b9 100644 --- a/docs/reference/replacement_costs.html +++ b/docs/reference/replacement_costs.html @@ -21,7 +21,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -171,8 +171,20 @@

    Examples

    # solve problem s <- solve(p) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0xa33f6587 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -201,6 +213,7 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 1.74904 +#> No other solutions better than 1.74904 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 1.749044775334e+00, best bound 1.749044775334e+00, gap 0.0000% @@ -208,14 +221,12 @@

    Examples

    # print solution print(s) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 1.75 395. 1 1 0 1 1 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 1.75 395. 1 1 0 1 1 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # calculate replacement cost values r <- replacement_costs(p, s) @@ -251,7 +262,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/scalar_parameters.html b/docs/reference/scalar_parameters.html index ab4f870..61e100d 100644 --- a/docs/reference/scalar_parameters.html +++ b/docs/reference/scalar_parameters.html @@ -21,7 +21,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -146,7 +146,7 @@

    Examples

    p1$get() # get value #> [1] 0.5 p1$id # get id -#> id: 83111d5a-0878-4acf-85a4-4e89aced7154 +#> id: 95b43321-6a31-41fd-9088-ccc24225c158 p1$validate(5) # check if 5 is a validate input p1$validate(0.1) # check if 0.1 is a validate input p1$set(0.1) # change value to 0.1 @@ -160,7 +160,7 @@

    Examples

    p2$get() # get value #> [1] 0 p2$id # get id -#> id: 53bca62a-02ef-433d-8d5e-9b37f0556279 +#> id: c94eac1a-4c76-4536-8068-276bf01dd495 p2$validate(5) # check if 5 is a validate input p2$validate(1L) # check if 1L is a validate input p2$set(1L) # change value to 1L @@ -174,7 +174,7 @@

    Examples

    p3$get() # get value #> [1] 5 p3$id # get id -#> id: d3bb68f4-5d23-4d81-b17f-19a4ae6aa49a +#> id: f5289e17-887f-4d99-bd23-77874c9e921e p3$validate(5.6) # check if 5.6 is a validate input p3$validate(2L) # check if 2L is a validate input p3$set(2L) # change value to 2L @@ -188,7 +188,7 @@

    Examples

    p4$get() # get value #> [1] -7.6 p4$id # get id -#> id: dc000f44-9f47-4c72-bd24-bbc7ff2f7429 +#> id: c569f332-9a07-4128-ba56-7b1c4a60887a p4$validate(NA) # check if NA is a validate input p4$validate(8.9) # check if 8.9 is a validate input p4$set(8.9) # change value to 8.9 @@ -202,7 +202,7 @@

    Examples

    p5$get() # get value #> [1] 6 p5$id # get id -#> id: 38e23d65-e3ab-4c73-852b-9288c04fcb6a +#> id: 45d32466-871a-4ea4-af0d-11a357f25027 p5$validate(-10) # check if -10 is a validate input p5$validate(90) # check if 90 is a validate input p5$set(90) # change value to 8.9 @@ -223,7 +223,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/show.html b/docs/reference/show.html index 9243948..820d2fb 100644 --- a/docs/reference/show.html +++ b/docs/reference/show.html @@ -17,7 +17,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -104,7 +104,7 @@

    See also

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/sim_data.html b/docs/reference/sim_data.html index c39e778..93e3922 100644 --- a/docs/reference/sim_data.html +++ b/docs/reference/sim_data.html @@ -17,7 +17,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -168,17 +168,16 @@

    Examples

    # print project data print(sim_projects) #> # A tibble: 6 × 13 -#> name success F1 F2 F3 F4 F5 F1_ac…¹ F2_ac…² F3_ac…³ -#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <lgl> -#> 1 F1_project 0.919 0.791 NA NA NA NA TRUE FALSE FALSE -#> 2 F2_project 0.923 NA 0.888 NA NA NA FALSE TRUE FALSE -#> 3 F3_project 0.829 NA NA 0.502 NA NA FALSE FALSE TRUE -#> 4 F4_project 0.848 NA NA NA 0.690 NA FALSE FALSE FALSE -#> 5 F5_project 0.814 NA NA NA NA 0.617 FALSE FALSE FALSE -#> 6 baseline_… 1 0.298 0.250 0.0865 0.249 0.182 FALSE FALSE FALSE -#> # … with 3 more variables: F4_action <lgl>, F5_action <lgl>, -#> # baseline_action <lgl>, and abbreviated variable names ¹​F1_action, -#> # ²​F2_action, ³​F3_action +#> name success F1 F2 F3 F4 F5 F1_action F2_action +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> +#> 1 F1_project 0.919 0.791 NA NA NA NA TRUE FALSE +#> 2 F2_project 0.923 NA 0.888 NA NA NA FALSE TRUE +#> 3 F3_project 0.829 NA NA 0.502 NA NA FALSE FALSE +#> 4 F4_project 0.848 NA NA NA 0.690 NA FALSE FALSE +#> 5 F5_project 0.814 NA NA NA NA 0.617 FALSE FALSE +#> 6 baseline_proj… 1 0.298 0.250 0.0865 0.249 0.182 FALSE FALSE +#> # ℹ 4 more variables: F3_action <lgl>, F4_action <lgl>, F5_action <lgl>, +#> # baseline_action <lgl> # print action data print(sim_actions) #> # A tibble: 6 × 4 @@ -219,7 +218,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/simulate_ppp_data.html b/docs/reference/simulate_ppp_data.html index c632b41..ef7bf29 100644 --- a/docs/reference/simulate_ppp_data.html +++ b/docs/reference/simulate_ppp_data.html @@ -19,7 +19,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -287,17 +287,15 @@

    Examples

    print(s) #> $projects #> # A tibble: 6 × 13 -#> name success F1 F2 F3 F4 F5 F1_ac…¹ F2_ac…² F3_ac…³ -#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <lgl> -#> 1 F1_project 0.711 0.830 NA NA NA NA TRUE FALSE FALSE -#> 2 F2_project 0.879 NA 0.516 NA NA NA FALSE TRUE FALSE -#> 3 F3_project 0.728 NA NA 0.872 NA NA FALSE FALSE TRUE -#> 4 F4_project 0.809 NA NA NA 0.518 NA FALSE FALSE FALSE -#> 5 F5_project 0.944 NA NA NA NA 0.578 FALSE FALSE FALSE -#> 6 baseline_p… 1 0.174 0.335 0.199 0.343 0.240 FALSE FALSE FALSE -#> # … with 3 more variables: F4_action <lgl>, F5_action <lgl>, -#> # baseline_action <lgl>, and abbreviated variable names ¹​F1_action, -#> # ²​F2_action, ³​F3_action +#> name success F1 F2 F3 F4 F5 F1_action F2_action F3_action +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <lgl> +#> 1 F1_p… 0.711 0.830 NA NA NA NA TRUE FALSE FALSE +#> 2 F2_p… 0.879 NA 0.516 NA NA NA FALSE TRUE FALSE +#> 3 F3_p… 0.728 NA NA 0.872 NA NA FALSE FALSE TRUE +#> 4 F4_p… 0.809 NA NA NA 0.518 NA FALSE FALSE FALSE +#> 5 F5_p… 0.944 NA NA NA NA 0.578 FALSE FALSE FALSE +#> 6 base… 1 0.174 0.335 0.199 0.343 0.240 FALSE FALSE FALSE +#> # ℹ 3 more variables: F4_action <lgl>, F5_action <lgl>, baseline_action <lgl> #> #> $actions #> # A tibble: 6 × 4 @@ -344,7 +342,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/simulate_ptm_data.html b/docs/reference/simulate_ptm_data.html index fcbeddc..4d8f363 100644 --- a/docs/reference/simulate_ptm_data.html +++ b/docs/reference/simulate_ptm_data.html @@ -21,7 +21,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -304,18 +304,17 @@

    Examples

    print(s) #> $projects #> # A tibble: 7 × 16 -#> name success F1 F2 F3 F4 F5 actio…¹ actio…² actio…³ -#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <lgl> -#> 1 project_1 0.931 NA 0.533 0.606 NA 0.878 TRUE TRUE TRUE -#> 2 project_2 0.899 0.691 NA 0.668 0.509 0.889 TRUE TRUE TRUE -#> 3 project_3 0.867 0.644 0.544 0.845 NA 0.547 TRUE TRUE TRUE -#> 4 project_4 0.892 NA 0.791 NA NA 0.666 TRUE FALSE TRUE -#> 5 project_5 0.876 0.556 NA 0.755 0.837 0.800 TRUE FALSE FALSE -#> 6 project_6 0.932 0.517 0.894 NA 0.520 0.663 TRUE FALSE TRUE -#> 7 baseline_… 1 0.0868 0.0692 0.321 0.376 0.303 FALSE FALSE FALSE -#> # … with 6 more variables: action_4 <lgl>, action_5 <lgl>, action_6 <lgl>, -#> # action_7 <lgl>, action_8 <lgl>, baseline_action <lgl>, and abbreviated -#> # variable names ¹​action_1, ²​action_2, ³​action_3 +#> name success F1 F2 F3 F4 F5 action_1 action_2 action_3 +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <lgl> +#> 1 projec… 0.931 NA 0.533 0.606 NA 0.878 TRUE TRUE TRUE +#> 2 projec… 0.899 0.691 NA 0.668 0.509 0.889 TRUE TRUE TRUE +#> 3 projec… 0.867 0.644 0.544 0.845 NA 0.547 TRUE TRUE TRUE +#> 4 projec… 0.892 NA 0.791 NA NA 0.666 TRUE FALSE TRUE +#> 5 projec… 0.876 0.556 NA 0.755 0.837 0.800 TRUE FALSE FALSE +#> 6 projec… 0.932 0.517 0.894 NA 0.520 0.663 TRUE FALSE TRUE +#> 7 baseli… 1 0.0868 0.0692 0.321 0.376 0.303 FALSE FALSE FALSE +#> # ℹ 6 more variables: action_4 <lgl>, action_5 <lgl>, action_6 <lgl>, +#> # action_7 <lgl>, action_8 <lgl>, baseline_action <lgl> #> #> $actions #> # A tibble: 9 × 4 @@ -365,7 +364,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/solution_statistics.html b/docs/reference/solution_statistics.html index 67d1336..043a627 100644 --- a/docs/reference/solution_statistics.html +++ b/docs/reference/solution_statistics.html @@ -18,7 +18,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -124,17 +124,16 @@

    Examples

    # print project data print(sim_projects) #> # A tibble: 6 × 13 -#> name success F1 F2 F3 F4 F5 F1_ac…¹ F2_ac…² F3_ac…³ -#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <lgl> -#> 1 F1_project 0.919 0.791 NA NA NA NA TRUE FALSE FALSE -#> 2 F2_project 0.923 NA 0.888 NA NA NA FALSE TRUE FALSE -#> 3 F3_project 0.829 NA NA 0.502 NA NA FALSE FALSE TRUE -#> 4 F4_project 0.848 NA NA NA 0.690 NA FALSE FALSE FALSE -#> 5 F5_project 0.814 NA NA NA NA 0.617 FALSE FALSE FALSE -#> 6 baseline_… 1 0.298 0.250 0.0865 0.249 0.182 FALSE FALSE FALSE -#> # … with 3 more variables: F4_action <lgl>, F5_action <lgl>, -#> # baseline_action <lgl>, and abbreviated variable names ¹​F1_action, -#> # ²​F2_action, ³​F3_action +#> name success F1 F2 F3 F4 F5 F1_action F2_action +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> +#> 1 F1_project 0.919 0.791 NA NA NA NA TRUE FALSE +#> 2 F2_project 0.923 NA 0.888 NA NA NA FALSE TRUE +#> 3 F3_project 0.829 NA NA 0.502 NA NA FALSE FALSE +#> 4 F4_project 0.848 NA NA NA 0.690 NA FALSE FALSE +#> 5 F5_project 0.814 NA NA NA NA 0.617 FALSE FALSE +#> 6 baseline_proj… 1 0.298 0.250 0.0865 0.249 0.182 FALSE FALSE +#> # ℹ 4 more variables: F3_action <lgl>, F4_action <lgl>, F5_action <lgl>, +#> # baseline_action <lgl> # print action data print(sim_features) @@ -202,14 +201,13 @@

    Examples

    # calculate statistics solution_statistics(p, solutions) #> # A tibble: 3 × 13 -#> cost obj F1_pr…¹ F2_pr…² F3_pr…³ F4_pr…⁴ F5_pr…⁵ basel…⁶ F1 F2 F3 -#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 0 0.581 0 0 0 0 0 1 0.298 0.250 0.0865 -#> 2 498. 1.83 1 1 1 1 1 1 0.808 0.865 0.467 -#> 3 298. 1.43 1 0 1 0 1 1 0.808 0.250 0.467 -#> # … with 2 more variables: F4 <dbl>, F5 <dbl>, and abbreviated variable names -#> # ¹​F1_project, ²​F2_project, ³​F3_project, ⁴​F4_project, ⁵​F5_project, -#> # ⁶​baseline_project +#> cost obj F1_project F2_project F3_project F4_project F5_project +#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 0 0.581 0 0 0 0 0 +#> 2 498. 1.83 1 1 1 1 1 +#> 3 298. 1.43 1 0 1 0 1 +#> # ℹ 6 more variables: baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, +#> # F4 <dbl>, F5 <dbl> @@ -224,7 +222,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/solve.html b/docs/reference/solve.html index 6534da9..924a7bb 100644 --- a/docs/reference/solve.html +++ b/docs/reference/solve.html @@ -17,7 +17,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -149,17 +149,16 @@

    Examples

    # print project data print(sim_projects) #> # A tibble: 6 × 13 -#> name success F1 F2 F3 F4 F5 F1_ac…¹ F2_ac…² F3_ac…³ -#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <lgl> -#> 1 F1_project 0.919 0.791 NA NA NA NA TRUE FALSE FALSE -#> 2 F2_project 0.923 NA 0.888 NA NA NA FALSE TRUE FALSE -#> 3 F3_project 0.829 NA NA 0.502 NA NA FALSE FALSE TRUE -#> 4 F4_project 0.848 NA NA NA 0.690 NA FALSE FALSE FALSE -#> 5 F5_project 0.814 NA NA NA NA 0.617 FALSE FALSE FALSE -#> 6 baseline_… 1 0.298 0.250 0.0865 0.249 0.182 FALSE FALSE FALSE -#> # … with 3 more variables: F4_action <lgl>, F5_action <lgl>, -#> # baseline_action <lgl>, and abbreviated variable names ¹​F1_action, -#> # ²​F2_action, ³​F3_action +#> name success F1 F2 F3 F4 F5 F1_action F2_action +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> +#> 1 F1_project 0.919 0.791 NA NA NA NA TRUE FALSE +#> 2 F2_project 0.923 NA 0.888 NA NA NA FALSE TRUE +#> 3 F3_project 0.829 NA NA 0.502 NA NA FALSE FALSE +#> 4 F4_project 0.848 NA NA NA 0.690 NA FALSE FALSE +#> 5 F5_project 0.814 NA NA NA NA 0.617 FALSE FALSE +#> 6 baseline_proj… 1 0.298 0.250 0.0865 0.249 0.182 FALSE FALSE +#> # ℹ 4 more variables: F3_action <lgl>, F4_action <lgl>, F5_action <lgl>, +#> # baseline_action <lgl> # print action data print(sim_features) @@ -209,8 +208,20 @@

    Examples

    # \dontrun{ # solve problem s <- solve(p) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0xa33f6587 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -239,6 +250,7 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 1.74904 +#> No other solutions better than 1.74904 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 1.749044775334e+00, best bound 1.749044775334e+00, gap 0.0000% @@ -246,14 +258,12 @@

    Examples

    # print output print(s) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 1.75 395. 1 1 0 1 1 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 1.75 395. 1 1 0 1 1 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # print the solver status print(s$obj) @@ -296,7 +306,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/solvers.html b/docs/reference/solvers.html index 699f776..aade9b8 100644 --- a/docs/reference/solvers.html +++ b/docs/reference/solvers.html @@ -19,7 +19,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -100,7 +100,7 @@

    Details

    add_lpsolveapi_solver()
    -

    lp_solve is an +

    lp_solve is an open-source integer programming solver. The lpSolveAPI package provides an interface to this solver and is available on CRAN. Although this solver is the slowest currently supported solver, @@ -182,8 +182,20 @@

    Examples

    # generate solutions using each of the solvers s <- rbind(solve(p2), solve(p3), solve(p4), solve(p5), solve(p6), solve(p7), solve(p8)) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0x193cb636 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -212,11 +224,24 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 2.19038 +#> No other solutions better than 2.19038 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 2.190380737245e+00, best bound 2.190380737245e+00, gap 0.0000% -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0x193cb636 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -245,6 +270,7 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 2.19038 +#> No other solutions better than 2.19038 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 2.190380737245e+00, best bound 2.190380737245e+00, gap 0.0000% @@ -331,7 +357,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

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    Examples

    # \dontrun{ # solve problems s1 <- solve(p1) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 46 rows, 42 columns and 92 nonzeros #> Model fingerprint: 0xde05e947 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -172,12 +184,25 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 497.767 +#> No other solutions better than 497.767 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 4.977671458279e+02, best bound 4.977671458279e+02, gap 0.0000% s2 <- solve(p2) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 46 rows, 42 columns and 92 nonzeros #> Model fingerprint: 0xb55f4224 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -191,20 +216,21 @@

    Examples

    #> Presolve time: 0.00s #> Presolved: 22 rows, 31 columns, 44 nonzeros #> Variable types: 0 continuous, 31 integer (31 binary) -#> Root relaxation presolved: 22 rows, 31 columns, 44 nonzeros +#> Root relaxation presolved: 12 rows, 22 columns, 24 nonzeros #> #> -#> Root relaxation: objective 2.042172e+02, 5 iterations, 0.00 seconds (0.00 work units) +#> Root relaxation: objective 2.042172e+02, 0 iterations, 0.00 seconds (0.00 work units) #> #> Nodes | Current Node | Objective Bounds | Work #> Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time #> #> * 0 0 0 204.2171997 204.21720 0.00% - 0s #> -#> Explored 1 nodes (5 simplex iterations) in 0.00 seconds (0.00 work units) +#> Explored 1 nodes (0 simplex iterations) in 0.00 seconds (0.00 work units) #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 204.217 +#> No other solutions better than 204.217 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 2.042171996644e+02, best bound 2.042171996644e+02, gap 0.0000% @@ -212,24 +238,20 @@

    Examples

    # print solutions print(s1) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 498. 498. 1 1 1 1 1 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 498. 498. 1 1 1 1 1 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> print(s2) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 204. 204. 0 1 1 0 0 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 204. 204. 0 1 1 0 0 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # plot solutions plot(p1, s1) @@ -251,7 +273,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/tee.html b/docs/reference/tee.html index 777b63f..2513266 100644 --- a/docs/reference/tee.html +++ b/docs/reference/tee.html @@ -17,7 +17,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -89,7 +89,7 @@

    Examples

    # but if we use the tee operator then the result contains a copy of "e" e3 <- e %T>% f() print(e3) -#> <environment: 0x556a21f6e2d0> +#> <environment: 0x557750df1f70> @@ -105,7 +105,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/tibble-methods.html b/docs/reference/tibble-methods.html index fafa12b..a970b0f 100644 --- a/docs/reference/tibble-methods.html +++ b/docs/reference/tibble-methods.html @@ -17,7 +17,7 @@ oppr - 1.0.4 + 1.0.4.1 @@ -131,7 +131,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

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z&Yd^$-j*z0yg1M>cXiHv2Dciijk-x@}Kfu`>q@ zp$axJX;>3cL9h0C#b!?@

    Examples

    # \dontrun{ # solve problem s <- solve(p) -#> Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64) +#> Set parameter Username +#> Set parameter TimeLimit to value 2147483647 +#> Set parameter MIPGap to value 0 +#> Set parameter NumericFocus to value 3 +#> Set parameter Presolve to value 2 +#> Set parameter Threads to value 1 +#> Set parameter PoolSolutions to value 1 +#> Set parameter PoolSearchMode to value 2 +#> Academic license - for non-commercial use only - expires 2025-04-21 +#> Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS") +#> +#> CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512] #> Thread count: 4 physical cores, 8 logical processors, using up to 1 threads +#> #> Optimize a model with 47 rows, 47 columns and 102 nonzeros #> Model fingerprint: 0x40fa7344 #> Variable types: 0 continuous, 42 integer (42 binary) @@ -124,6 +136,7 @@

    Examples

    #> Thread count was 1 (of 8 available processors) #> #> Solution count 1: 1.51123 +#> No other solutions better than 1.51123 #> #> Optimal solution found (tolerance 0.00e+00) #> Best objective 1.511229665304e+00, best bound 1.511229665304e+00, gap 0.0000% @@ -131,14 +144,12 @@

    Examples

    # print solution print(s) #> # A tibble: 1 × 21 -#> solution status obj cost F1_action F2_ac…¹ F3_ac…² F4_ac…³ F5_ac…⁴ basel…⁵ -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 OPTIMAL 1.51 199. 0 0 0 1 1 1 -#> # … with 11 more variables: F1_project <dbl>, F2_project <dbl>, -#> # F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, -#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl>, -#> # and abbreviated variable names ¹​F2_action, ²​F3_action, ³​F4_action, -#> # ⁴​F5_action, ⁵​baseline_action +#> solution status obj cost F1_action F2_action F3_action F4_action F5_action +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1 OPTIMAL 1.51 199. 0 0 0 1 1 +#> # ℹ 12 more variables: baseline_action <dbl>, F1_project <dbl>, +#> # F2_project <dbl>, F3_project <dbl>, F4_project <dbl>, F5_project <dbl>, +#> # baseline_project <dbl>, F1 <dbl>, F2 <dbl>, F3 <dbl>, F4 <dbl>, F5 <dbl> # plot solution plot(p, s) @@ -158,7 +169,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/inst/CITATION b/inst/CITATION index 90abf79..4b4945b 100644 --- a/inst/CITATION +++ b/inst/CITATION @@ -11,9 +11,9 @@ if (ndots == 3) { citHeader("To cite the oppr package in publications, please use:") -citEntry( - entry = "Article", - author = personList( +bibentry( + bibtype = "Article", + author = c( person(c("Jeffrey", "O"), "Hanson"), person(c("Richard"), "Schuster"), person(c("Matthew"), "Strimas-Mackey"), @@ -28,9 +28,9 @@ citEntry( textVersion = paste0("Hanson JO, Schuster R, Strimas-Mackey M & Bennett JR (2019) Optimality in prioritizing conservation projects. Methods in Ecology & Evolution, 10: 1655--1663.") ) -citEntry( - entry = "Manual", - author = personList( +bibentry( + bibtype = "Manual", + author = c( person(c("Jeffrey", "O"), "Hanson"), person(c("Richard"), "Schuster"), person(c("Matthew"), "Strimas-Mackey"), diff --git a/inst/doc/oppr.Rmd b/inst/doc/oppr.Rmd index eb6d780..68d2557 100644 --- a/inst/doc/oppr.Rmd +++ b/inst/doc/oppr.Rmd @@ -140,7 +140,7 @@ p1 <- problem(projects = projects, actions = actions, features = features, print(p1) ``` -After building the problem, we can solve it. Although backwards heuristic algorithms have conventionally been used to solve project prioritization problems [e.g. @r3; @r4; @r11], here we will use exact algorithms to develop a prioritization. Exact algorithms are superior to heuristic algorithms because they can provide guarantees on solution quality [@r12; @r13]. In other words, if you specify that you want an optimal solution when using exact algorithms, then you are guaranteed to get an optimal solution. Heuristic algorithms---and even more advanced meta-heuristic algorithms such as simulated annealing that are commonly to guide conservation decision making [@r22]---provide no such guarantees and can actually deliver remarkably poor solutions [e.g. @r21]. Later on we will experiment with heuristic algorithms, but for now, we will use exact algorithms. Since we haven't explicitly stated which solver we would like to use, the _oppr R_ package will identify the best exact algorithm solver currently installed on your system. This is typically the _lpSolveAPI R_ package unless you have manually installed the [_gurobi R_ package](https://www.gurobi.com/documentation/8.1/quickstart_mac/r_installing_the_r_package.html) and the [Gurobi optimization suite](https://www.gurobi.com/). Although not shown here, when you try solving the problem you will see information printed to your screen as the solver tries to find an optimal solution. Please do not be alarmed, this is normal behavior and some of the information can actually be useful (e.g. to estimate how long it might take to solve the problem). +After building the problem, we can solve it. Although backwards heuristic algorithms have conventionally been used to solve project prioritization problems [e.g. @r3; @r4; @r11], here we will use exact algorithms to develop a prioritization. Exact algorithms are superior to heuristic algorithms because they can provide guarantees on solution quality [@r12; @r13]. In other words, if you specify that you want an optimal solution when using exact algorithms, then you are guaranteed to get an optimal solution. Heuristic algorithms---and even more advanced meta-heuristic algorithms such as simulated annealing that are commonly to guide conservation decision making [@r22]---provide no such guarantees and can actually deliver remarkably poor solutions [e.g. @r21]. Later on we will experiment with heuristic algorithms, but for now, we will use exact algorithms. Since we haven't explicitly stated which solver we would like to use, the _oppr R_ package will identify the best exact algorithm solver currently installed on your system. This is typically the _lpSolveAPI R_ package unless you have manually installed the [_gurobi R_ package](https://www.gurobi.com/documentation/current/refman/r_ins_the_r_package.html) and the [Gurobi optimization suite](https://www.gurobi.com/). Although not shown here, when you try solving the problem you will see information printed to your screen as the solver tries to find an optimal solution. Please do not be alarmed, this is normal behavior and some of the information can actually be useful (e.g. to estimate how long it might take to solve the problem). ```{r, results = "hide", message = FALSE, warn = FALSE} # solve problem @@ -276,7 +276,7 @@ a2 <- actions$name[which(as.logical(as.matrix(s2[, action_names(p2)])))] assertthat::assert_that(!setequal(a1, a2)) ``` -Earlier, we talked about the _oppr R_ package using the default exact algorithm solver. Although you should have the _lpSolveAPI_ solver automatically installed, we strongly recommend installing the [_gurobi R_ package](https://www.gurobi.com/documentation/8.1/quickstart_mac/r_installing_the_r_package.html) and the [Gurobi optimization suite](https://www.gurobi.com/). This is because Gurobi can solve optimization problems much faster than any other software currently supported and it can also easily generate multiple solutions. Unfortunately, you will have to install Gurobi manually, but please see the documentation for the solver for instructions (i.e. enter the command `?add_gurobi_solver` into the console). If you have Gurobi installed, let's try using the Gurobi solver to generate multiple solutions. Here, we will manually specify the Gurobi solver and request 1,000 solutions (though we will probably obtain less than 1,000 solutions, so if we actually wanted 1,000 solutions then we would need to specify a much larger number). +Earlier, we talked about the _oppr R_ package using the default exact algorithm solver. Although you should have the _lpSolveAPI_ solver automatically installed, we strongly recommend installing the [_gurobi R_ package](https://www.gurobi.com/documentation/current/refman/r_ins_the_r_package.html) and the [Gurobi optimization suite](https://www.gurobi.com/). This is because Gurobi can solve optimization problems much faster than any other software currently supported and it can also easily generate multiple solutions. Unfortunately, you will have to install Gurobi manually, but please see the documentation for the solver for instructions (i.e. enter the command `?add_gurobi_solver` into the console). If you have Gurobi installed, let's try using the Gurobi solver to generate multiple solutions. Here, we will manually specify the Gurobi solver and request 1,000 solutions (though we will probably obtain less than 1,000 solutions, so if we actually wanted 1,000 solutions then we would need to specify a much larger number). ```{r} # create new problem, with Gurobi solver added and request multiple solutions diff --git a/inst/doc/oppr.html b/inst/doc/oppr.html index fd56025..3903bdf 100644 --- a/inst/doc/oppr.html +++ b/inst/doc/oppr.html @@ -252,13 +252,13 @@

    Data simulation

    # 8 project… 0.932 0.593 NA NA NA NA 0.803 0.580 0.666 NA # 9 project… 0.900 NA NA NA 0.572 0.547 NA NA NA NA # 10 project… 0.952 NA NA NA NA NA NA NA NA NA -# # … with 61 more rows, and 62 more variables: F10 <dbl>, F11 <dbl>, F12 <dbl>, -# # F13 <dbl>, F14 <dbl>, F15 <dbl>, F16 <dbl>, F17 <dbl>, F18 <dbl>, -# # F19 <dbl>, F20 <dbl>, F21 <dbl>, F22 <dbl>, F23 <dbl>, F24 <dbl>, -# # F25 <dbl>, F26 <dbl>, F27 <dbl>, F28 <dbl>, F29 <dbl>, F30 <dbl>, -# # F31 <dbl>, F32 <dbl>, F33 <dbl>, F34 <dbl>, F35 <dbl>, F36 <dbl>, -# # F37 <dbl>, F38 <dbl>, F39 <dbl>, F40 <dbl>, action_1 <lgl>, action_2 <lgl>, -# # action_3 <lgl>, action_4 <lgl>, action_5 <lgl>, action_6 <lgl>, … +# # ℹ 61 more rows +# # ℹ 62 more variables: F10 <dbl>, F11 <dbl>, F12 <dbl>, F13 <dbl>, F14 <dbl>, +# # F15 <dbl>, F16 <dbl>, F17 <dbl>, F18 <dbl>, F19 <dbl>, F20 <dbl>, +# # F21 <dbl>, F22 <dbl>, F23 <dbl>, F24 <dbl>, F25 <dbl>, F26 <dbl>, +# # F27 <dbl>, F28 <dbl>, F29 <dbl>, F30 <dbl>, F31 <dbl>, F32 <dbl>, +# # F33 <dbl>, F34 <dbl>, F35 <dbl>, F36 <dbl>, F37 <dbl>, F38 <dbl>, +# # F39 <dbl>, F40 <dbl>, action_1 <lgl>, action_2 <lgl>, action_3 <lgl>, …
    print(actions)  # data for management actions
    @@ -276,7 +276,7 @@

    Data simulation

    # 8 action_8 104. FALSE FALSE # 9 action_9 99.9 FALSE FALSE # 10 action_10 93.1 FALSE FALSE -# # … with 21 more rows +# # ℹ 21 more rows
    print(features) # data for biodiversity features
    @@ -294,7 +294,7 @@

    Data simulation

    # 8 F8 108301. # 9 F9 3.62 # 10 F10 6956. -# # … with 30 more rows +# # ℹ 30 more rows @@ -324,23 +324,23 @@

    Prioritizations with exact algorithms

    # solver: default -

    After building the problem, we can solve it. Although backwards heuristic algorithms have conventionally been used to solve project prioritization problems (e.g. Joseph et al. 2009; Bennett et al. 2014; Gerber et al. 2018), here we will use exact algorithms to develop a prioritization. Exact algorithms are superior to heuristic algorithms because they can provide guarantees on solution quality (Underhill 1994; Rodrigues & Gaston 2002). In other words, if you specify that you want an optimal solution when using exact algorithms, then you are guaranteed to get an optimal solution. Heuristic algorithms—and even more advanced meta-heuristic algorithms such as simulated annealing that are commonly to guide conservation decision making (Kirkpatrick et al. 1983)—provide no such guarantees and can actually deliver remarkably poor solutions (e.g. Beyer et al. 2016). Later on we will experiment with heuristic algorithms, but for now, we will use exact algorithms. Since we haven’t explicitly stated which solver we would like to use, the oppr R package will identify the best exact algorithm solver currently installed on your system. This is typically the lpSolveAPI R package unless you have manually installed the gurobi R package and the Gurobi optimization suite. Although not shown here, when you try solving the problem you will see information printed to your screen as the solver tries to find an optimal solution. Please do not be alarmed, this is normal behavior and some of the information can actually be useful (e.g. to estimate how long it might take to solve the problem).

    +

    After building the problem, we can solve it. Although backwards heuristic algorithms have conventionally been used to solve project prioritization problems (e.g. Joseph et al. 2009; Bennett et al. 2014; Gerber et al. 2018), here we will use exact algorithms to develop a prioritization. Exact algorithms are superior to heuristic algorithms because they can provide guarantees on solution quality (Underhill 1994; Rodrigues & Gaston 2002). In other words, if you specify that you want an optimal solution when using exact algorithms, then you are guaranteed to get an optimal solution. Heuristic algorithms—and even more advanced meta-heuristic algorithms such as simulated annealing that are commonly to guide conservation decision making (Kirkpatrick et al. 1983)—provide no such guarantees and can actually deliver remarkably poor solutions (e.g. Beyer et al. 2016). Later on we will experiment with heuristic algorithms, but for now, we will use exact algorithms. Since we haven’t explicitly stated which solver we would like to use, the oppr R package will identify the best exact algorithm solver currently installed on your system. This is typically the lpSolveAPI R package unless you have manually installed the gurobi R package and the Gurobi optimization suite. Although not shown here, when you try solving the problem you will see information printed to your screen as the solver tries to find an optimal solution. Please do not be alarmed, this is normal behavior and some of the information can actually be useful (e.g. to estimate how long it might take to solve the problem).

    # solve problem
     s1 <- solve(p1)
    # print solution
     print(s1)
    # # A tibble: 1 × 146
    -#   solution status    obj  cost action_1 action_2 actio…¹ actio…² actio…³ actio…⁴
    -#      <int> <chr>   <dbl> <dbl>    <dbl>    <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
    -# 1        1 OPTIMAL  31.0  993.        1        1       0       0       0       0
    -# # … with 136 more variables: action_7 <dbl>, action_8 <dbl>, action_9 <dbl>,
    -# #   action_10 <dbl>, action_11 <dbl>, action_12 <dbl>, action_13 <dbl>,
    -# #   action_14 <dbl>, action_15 <dbl>, action_16 <dbl>, action_17 <dbl>,
    -# #   action_18 <dbl>, action_19 <dbl>, action_20 <dbl>, action_21 <dbl>,
    -# #   action_22 <dbl>, action_23 <dbl>, action_24 <dbl>, action_25 <dbl>,
    -# #   action_26 <dbl>, action_27 <dbl>, action_28 <dbl>, action_29 <dbl>,
    -# #   action_30 <dbl>, baseline_action <dbl>, project_1 <dbl>, project_2 <dbl>, …
    +#   solution status    obj  cost action_1 action_2 action_3 action_4 action_5
    +#      <int> <chr>   <dbl> <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
    +# 1        1 OPTIMAL  31.0  993.        1        1        0        0        0
    +# # ℹ 137 more variables: action_6 <dbl>, action_7 <dbl>, action_8 <dbl>,
    +# #   action_9 <dbl>, action_10 <dbl>, action_11 <dbl>, action_12 <dbl>,
    +# #   action_13 <dbl>, action_14 <dbl>, action_15 <dbl>, action_16 <dbl>,
    +# #   action_17 <dbl>, action_18 <dbl>, action_19 <dbl>, action_20 <dbl>,
    +# #   action_21 <dbl>, action_22 <dbl>, action_23 <dbl>, action_24 <dbl>,
    +# #   action_25 <dbl>, action_26 <dbl>, action_27 <dbl>, action_28 <dbl>,
    +# #   action_29 <dbl>, action_30 <dbl>, baseline_action <dbl>, project_1 <dbl>, …
     

    The s1 table contains the solution (i.e. a prioritization) and also various statistics associated with the solution. Here, each row corresponds to a different solution. By default only one solution will be returned, and so the table has one row. The "solution" column contains an integer identifier for the solution (this is when multiple solutions are output), the "obj" column contains the objective value (i.e. the expected feature richness in this case), the "cost" column stores the cost of the solution, and the "status" column contains information from the solver about the solution (i.e. it found an optimal solution). Additionally, it contains columns for each action ("action_1", "action_2", "action_3", …, "baseline_action") which indicate their status in the solution. In other words, these columns tell us if each action was prioritized for funding in the solution (i.e. a value of one) or not (i.e. a value of zero). Additionally, it contains columns for each project ("project_1", "project_2", "project_3", …, "baseline_project") that indicate if the project was completely funded or not. Finally, this table contains columns for each feature ("F1, "F2", "F3, …) which indicate the probability that each feature is expected to persist into the future if the solution was implemented (for information on how this is calculated see ?add_max_richness_objective).

    @@ -365,7 +365,7 @@

    Prioritizations with exact algorithms

    # 8 1 project_8 0 # 9 1 project_9 0 # 10 1 project_10 0 -# # … with 61 more rows +# # ℹ 61 more rows
    
    @@ -395,7 +395,7 @@ 

    Prioritizations with exact algorithms

    # 8 project_8 2761. 33.1 22.3 0.00809 44 # 9 project_9 1786. 15.7 4.95 0.00277 65 # 10 project_10 2977. 34.7 24.0 0.00806 46.5 -# # … with 61 more rows +# # ℹ 61 more rows

    The p1_pce table contains the cost-effectiveness of each project. Specifically, the "project" column contains the name of each project. The "cost" column contains the total cost of all the management actions associated with the project. The "obj" column contains the objective value associated with a solution that contains all of the management actions for each project, and also the actions associated with a zero cost. The "benefit" column contains the difference between (i) the objective value associated with each project and also any zero cost actions (as per the "obj" column), and (ii) the objective value associated with the baseline project. The "ce" column contains the cost-effectiveness of each project. For a given project, this is calculated by dividing its benefit by its cost (as per the "benefit" and "cost" columns). The "rank" column contains the rank for each project, with the most cost-effective project having a rank of one.

    @@ -417,7 +417,7 @@

    Prioritizations with exact algorithms

    Since visualizations are an effective way to understand data, let’s create a bar plot to visualize how well this solution would conserve the features. In this plot, each bar corresponds to a different feature, the width of each bar corresponds to its expected probability of persistence, the color of each bar corresponds to the feature’s weight (since we didn’t specify any weights, all bars are the same color). Asterisks denote features that benefit from fully funded projects, and open circles (if any) denote features that do not benefit from fully funded projects but may indirectly benefit from partially funded projects.

    # plot solution
     plot(p1, s1)
    -

    +

    Overall, we can see that most species have a fairly decent chance at persisting into the future (approx. 77%). But when making this prioritization, we assumed that we valued the persistence of each feature equally. It is often important to account for the fact that certain features are valued more highly than other features when making prioritizations (e.g. for cultural or taxonomic reasons). The features table that we created earlier contains a "weight" column, and features with larger values mean that they are more important. Let’s quickly visualize the feature weight values.

    # print features table
     print(features)
    @@ -435,7 +435,7 @@

    Prioritizations with exact algorithms

    # 8 F8 108301. # 9 F9 3.62 # 10 F10 6956. -# # … with 30 more rows +# # ℹ 30 more rows
    
    @@ -474,21 +474,21 @@ 

    Prioritizations with exact algorithms

    print(s2)
    # # A tibble: 1 × 146
    -#   solution status      obj  cost actio…¹ actio…² actio…³ actio…⁴ actio…⁵ actio…⁶
    -#      <int> <chr>     <dbl> <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
    -# 1        1 OPTIMAL  6.29e6  987.       0       1       1       0       0       0
    -# # … with 136 more variables: action_7 <dbl>, action_8 <dbl>, action_9 <dbl>,
    -# #   action_10 <dbl>, action_11 <dbl>, action_12 <dbl>, action_13 <dbl>,
    -# #   action_14 <dbl>, action_15 <dbl>, action_16 <dbl>, action_17 <dbl>,
    -# #   action_18 <dbl>, action_19 <dbl>, action_20 <dbl>, action_21 <dbl>,
    -# #   action_22 <dbl>, action_23 <dbl>, action_24 <dbl>, action_25 <dbl>,
    -# #   action_26 <dbl>, action_27 <dbl>, action_28 <dbl>, action_29 <dbl>,
    -# #   action_30 <dbl>, baseline_action <dbl>, project_1 <dbl>, project_2 <dbl>, …
    +#   solution status       obj  cost action_1 action_2 action_3 action_4 action_5
    +#      <int> <chr>      <dbl> <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
    +# 1        1 OPTIMAL 6290606.  987.        0        1        1        0        0
    +# # ℹ 137 more variables: action_6 <dbl>, action_7 <dbl>, action_8 <dbl>,
    +# #   action_9 <dbl>, action_10 <dbl>, action_11 <dbl>, action_12 <dbl>,
    +# #   action_13 <dbl>, action_14 <dbl>, action_15 <dbl>, action_16 <dbl>,
    +# #   action_17 <dbl>, action_18 <dbl>, action_19 <dbl>, action_20 <dbl>,
    +# #   action_21 <dbl>, action_22 <dbl>, action_23 <dbl>, action_24 <dbl>,
    +# #   action_25 <dbl>, action_26 <dbl>, action_27 <dbl>, action_28 <dbl>,
    +# #   action_29 <dbl>, action_30 <dbl>, baseline_action <dbl>, project_1 <dbl>, …
     
    # plot solution
     plot(p2, s2)
    -

    +

    We can also examine how adding feature weights changed our solution.

    # print actions prioritized for funding in first solution
     actions$name[which(as.logical(as.matrix(s1[, action_names(p1)])))]
    @@ -514,7 +514,7 @@

    Prioritizations with exact algorithms

    # [1] 6
     
    -

    Earlier, we talked about the oppr R package using the default exact algorithm solver. Although you should have the lpSolveAPI solver automatically installed, we strongly recommend installing the gurobi R package and the Gurobi optimization suite. This is because Gurobi can solve optimization problems much faster than any other software currently supported and it can also easily generate multiple solutions. Unfortunately, you will have to install Gurobi manually, but please see the documentation for the solver for instructions (i.e. enter the command ?add_gurobi_solver into the console). If you have Gurobi installed, let’s try using the Gurobi solver to generate multiple solutions. Here, we will manually specify the Gurobi solver and request 1,000 solutions (though we will probably obtain less than 1,000 solutions, so if we actually wanted 1,000 solutions then we would need to specify a much larger number).

    +

    Earlier, we talked about the oppr R package using the default exact algorithm solver. Although you should have the lpSolveAPI solver automatically installed, we strongly recommend installing the gurobi R package and the Gurobi optimization suite. This is because Gurobi can solve optimization problems much faster than any other software currently supported and it can also easily generate multiple solutions. Unfortunately, you will have to install Gurobi manually, but please see the documentation for the solver for instructions (i.e. enter the command ?add_gurobi_solver into the console). If you have Gurobi installed, let’s try using the Gurobi solver to generate multiple solutions. Here, we will manually specify the Gurobi solver and request 1,000 solutions (though we will probably obtain less than 1,000 solutions, so if we actually wanted 1,000 solutions then we would need to specify a much larger number).

    # create new problem, with Gurobi solver added and request multiple solutions
     # note that we set the gap to 0.5 because we are not necessarily interested
     # in the top 1,000 solutions (for more information on why read the Gurobi
    @@ -526,29 +526,29 @@ 

    Prioritizations with exact algorithms

    # print solution
     print(s3)
    -
    # # A tibble: 85 × 146
    -#    solution status     obj  cost actio…¹ actio…² actio…³ actio…⁴ actio…⁵ actio…⁶
    -#       <int> <chr>    <dbl> <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
    -#  1        1 OPTIMAL 6.29e6  974.       0       1       1       0       0       0
    -#  2        2 OPTIMAL 6.29e6  969.       0       1       1       0       0       0
    -#  3        3 OPTIMAL 6.29e6  967.       0       1       1       0       0       0
    -#  4        4 OPTIMAL 6.29e6  974.       0       1       1       0       0       0
    -#  5        5 OPTIMAL 6.29e6  977.       0       1       1       0       0       0
    -#  6        6 OPTIMAL 6.29e6  874.       0       1       1       0       0       0
    -#  7        7 OPTIMAL 6.29e6  980.       0       1       1       0       0       0
    -#  8        8 OPTIMAL 6.29e6  977.       0       1       1       1       0       0
    -#  9        9 OPTIMAL 6.29e6  987.       0       1       1       0       0       0
    -# 10       10 OPTIMAL 6.29e6  970.       0       1       1       0       1       0
    -# # … with 75 more rows, 136 more variables: action_7 <dbl>, action_8 <dbl>,
    +
    # # A tibble: 39 × 146
    +#    solution status        obj  cost action_1 action_2 action_3 action_4 action_5
    +#       <int> <chr>       <dbl> <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
    +#  1        1 OPTIMAL    6.29e6  971.        0        1        1        0        0
    +#  2        2 OPTIMAL    6.29e6  977.        0        1        1        0        0
    +#  3        3 OPTIMAL    6.29e6  987.        0        1        1        0        0
    +#  4        4 OPTIMAL    6.29e6  874.        0        1        1        0        0
    +#  5        5 OPTIMAL    6.29e6  974.        0        1        1        0        0
    +#  6        6 OPTIMAL    6.29e6  980.        0        1        1        0        0
    +#  7        7 OPTIMAL    6.29e6  977.        0        1        1        1        0
    +#  8        8 OPTIMAL    6.29e6  971.        0        1        1        0        0
    +#  9        9 SUBOPTIMAL 6.29e6  974.        0        1        1        0        0
    +# 10       10 SUBOPTIMAL 6.10e6  976.        0        0        1        0        0
    +# # ℹ 29 more rows
    +# # ℹ 137 more variables: action_6 <dbl>, action_7 <dbl>, action_8 <dbl>,
     # #   action_9 <dbl>, action_10 <dbl>, action_11 <dbl>, action_12 <dbl>,
     # #   action_13 <dbl>, action_14 <dbl>, action_15 <dbl>, action_16 <dbl>,
     # #   action_17 <dbl>, action_18 <dbl>, action_19 <dbl>, action_20 <dbl>,
     # #   action_21 <dbl>, action_22 <dbl>, action_23 <dbl>, action_24 <dbl>,
    -# #   action_25 <dbl>, action_26 <dbl>, action_27 <dbl>, action_28 <dbl>,
    -# #   action_29 <dbl>, action_30 <dbl>, baseline_action <dbl>, project_1 <dbl>, …
    +# #   action_25 <dbl>, action_26 <dbl>, action_27 <dbl>, action_28 <dbl>, …
     
    -

    We obtained 85 solutions. Let’s briefly explore them.

    +

    We obtained 39 solutions. Let’s briefly explore them.

    # plot histogram of objective values, and add red dashed line to indicate the
     # objective value for the optimal solution
     ggplot(data = s3, aes(obj)) +
    @@ -558,7 +558,7 @@ 

    Prioritizations with exact algorithms

    xlab("Expected richness (objective function)") + ylab("Frequency") + theme(plot.margin = unit(c(5.5, 10.5, 5.5, 5.5), "pt"))
    -

    +

    We can see that some of our solutions performed much better than other solutions. And we can also see that there are several solutions that perform nearly as well as the optimal solution.

    @@ -567,26 +567,26 @@

    Assessing action importance

    # print the solution object to remind ourselves what it looks like
     print(s3)
    -
    # # A tibble: 85 × 146
    -#    solution status     obj  cost actio…¹ actio…² actio…³ actio…⁴ actio…⁵ actio…⁶
    -#       <int> <chr>    <dbl> <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
    -#  1        1 OPTIMAL 6.29e6  974.       0       1       1       0       0       0
    -#  2        2 OPTIMAL 6.29e6  969.       0       1       1       0       0       0
    -#  3        3 OPTIMAL 6.29e6  967.       0       1       1       0       0       0
    -#  4        4 OPTIMAL 6.29e6  974.       0       1       1       0       0       0
    -#  5        5 OPTIMAL 6.29e6  977.       0       1       1       0       0       0
    -#  6        6 OPTIMAL 6.29e6  874.       0       1       1       0       0       0
    -#  7        7 OPTIMAL 6.29e6  980.       0       1       1       0       0       0
    -#  8        8 OPTIMAL 6.29e6  977.       0       1       1       1       0       0
    -#  9        9 OPTIMAL 6.29e6  987.       0       1       1       0       0       0
    -# 10       10 OPTIMAL 6.29e6  970.       0       1       1       0       1       0
    -# # … with 75 more rows, 136 more variables: action_7 <dbl>, action_8 <dbl>,
    +
    # # A tibble: 39 × 146
    +#    solution status        obj  cost action_1 action_2 action_3 action_4 action_5
    +#       <int> <chr>       <dbl> <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
    +#  1        1 OPTIMAL    6.29e6  971.        0        1        1        0        0
    +#  2        2 OPTIMAL    6.29e6  977.        0        1        1        0        0
    +#  3        3 OPTIMAL    6.29e6  987.        0        1        1        0        0
    +#  4        4 OPTIMAL    6.29e6  874.        0        1        1        0        0
    +#  5        5 OPTIMAL    6.29e6  974.        0        1        1        0        0
    +#  6        6 OPTIMAL    6.29e6  980.        0        1        1        0        0
    +#  7        7 OPTIMAL    6.29e6  977.        0        1        1        1        0
    +#  8        8 OPTIMAL    6.29e6  971.        0        1        1        0        0
    +#  9        9 SUBOPTIMAL 6.29e6  974.        0        1        1        0        0
    +# 10       10 SUBOPTIMAL 6.10e6  976.        0        0        1        0        0
    +# # ℹ 29 more rows
    +# # ℹ 137 more variables: action_6 <dbl>, action_7 <dbl>, action_8 <dbl>,
     # #   action_9 <dbl>, action_10 <dbl>, action_11 <dbl>, action_12 <dbl>,
     # #   action_13 <dbl>, action_14 <dbl>, action_15 <dbl>, action_16 <dbl>,
     # #   action_17 <dbl>, action_18 <dbl>, action_19 <dbl>, action_20 <dbl>,
     # #   action_21 <dbl>, action_22 <dbl>, action_23 <dbl>, action_24 <dbl>,
    -# #   action_25 <dbl>, action_26 <dbl>, action_27 <dbl>, action_28 <dbl>,
    -# #   action_29 <dbl>, action_30 <dbl>, baseline_action <dbl>, project_1 <dbl>, …
    +# #   action_25 <dbl>, action_26 <dbl>, action_27 <dbl>, action_28 <dbl>, …
     
    
    @@ -600,17 +600,17 @@ 

    Assessing action importance

    # # A tibble: 31 × 5
     #    name       cost locked_in locked_out sel_freq
     #    <chr>     <dbl> <lgl>     <lgl>         <dbl>
    -#  1 action_1   97.8 FALSE     FALSE         11.8 
    -#  2 action_2   94.0 FALSE     FALSE         71.8 
    -#  3 action_3  100.  FALSE     FALSE         27.1 
    -#  4 action_4  103.  FALSE     FALSE         21.2 
    -#  5 action_5   96.1 FALSE     FALSE         17.6 
    +#  1 action_1   97.8 FALSE     FALSE          5.13
    +#  2 action_2   94.0 FALSE     FALSE         69.2 
    +#  3 action_3  100.  FALSE     FALSE         61.5 
    +#  4 action_4  103.  FALSE     FALSE         12.8 
    +#  5 action_5   96.1 FALSE     FALSE          2.56
     #  6 action_6   98.1 FALSE     FALSE          0   
    -#  7 action_7   97.6 FALSE     FALSE         11.8 
    -#  8 action_8  104.  FALSE     FALSE         11.8 
    -#  9 action_9   99.9 FALSE     FALSE          3.53
    -# 10 action_10  93.1 FALSE     FALSE         32.9 
    -# # … with 21 more rows
    +#  7 action_7   97.6 FALSE     FALSE         10.3 
    +#  8 action_8  104.  FALSE     FALSE          7.69
    +#  9 action_9   99.9 FALSE     FALSE          7.69
    +# 10 action_10  93.1 FALSE     FALSE         28.2 
    +# # ℹ 21 more rows
     
    
    @@ -620,16 +620,16 @@ 

    Assessing action importance

    # # A tibble: 10 × 5
     #    name             cost locked_in locked_out sel_freq
     #    <chr>           <dbl> <lgl>     <lgl>         <dbl>
    -#  1 action_14        99.4 FALSE     FALSE          97.6
    -#  2 baseline_action   0   FALSE     FALSE          97.6
    -#  3 action_19        89.8 FALSE     FALSE          74.1
    -#  4 action_2         94.0 FALSE     FALSE          71.8
    -#  5 action_22        93.9 FALSE     FALSE          68.2
    -#  6 action_28       102.  FALSE     FALSE          56.5
    -#  7 action_27        91.1 FALSE     FALSE          48.2
    -#  8 action_29       103.  FALSE     FALSE          41.2
    -#  9 action_10        93.1 FALSE     FALSE          32.9
    -# 10 action_17       101.  FALSE     FALSE          32.9
    +#  1 action_14        99.4 FALSE     FALSE          97.4
    +#  2 baseline_action   0   FALSE     FALSE          94.9
    +#  3 action_28       102.  FALSE     FALSE          89.7
    +#  4 action_27        91.1 FALSE     FALSE          82.1
    +#  5 action_19        89.8 FALSE     FALSE          76.9
    +#  6 action_2         94.0 FALSE     FALSE          69.2
    +#  7 action_22        93.9 FALSE     FALSE          69.2
    +#  8 action_17       101.  FALSE     FALSE          66.7
    +#  9 action_29       103.  FALSE     FALSE          66.7
    +# 10 action_3        100.  FALSE     FALSE          61.5
     
    
    @@ -639,7 +639,7 @@ 

    Assessing action importance

    coord_cartesian(xlim = c(0, 100)) + xlab("Selection frequency (%)") + ylab("Frequency")
    -

    +

    Although selection frequency might seem like an appealing metric, it suffers from an assumption that is unrealistic for most large-scale problems. Specifically, it assumes that our portfolio of solutions is a representative sample of the near-optimal solutions to the problem. And we do not currently have any method to verify this, except by enumerating all possible solutions—which is not feasible for reasonably sized project prioritization exercises. So now we shall examine a superior metric termed the “replacement cost” (Moilanen et al. 2009). Given a set of priority actions and a project prioritization problem, the replacement cost can be calculated for a given priority action by re-solving the problem with the priority action locked out, and calculating the difference between the objective value based on the priority actions and the new objective value with the priority action locked out. So let’s calculate the replacement cost for the priority actions in object s2 using the problem p2.

    # print p2 to remind ourselves about the problem
     print(p2)
    @@ -663,16 +663,16 @@

    Assessing action importance

    print(s2)
    # # A tibble: 1 × 146
    -#   solution status      obj  cost actio…¹ actio…² actio…³ actio…⁴ actio…⁵ actio…⁶
    -#      <int> <chr>     <dbl> <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
    -# 1        1 OPTIMAL  6.29e6  987.       0       1       1       0       0       0
    -# # … with 136 more variables: action_7 <dbl>, action_8 <dbl>, action_9 <dbl>,
    -# #   action_10 <dbl>, action_11 <dbl>, action_12 <dbl>, action_13 <dbl>,
    -# #   action_14 <dbl>, action_15 <dbl>, action_16 <dbl>, action_17 <dbl>,
    -# #   action_18 <dbl>, action_19 <dbl>, action_20 <dbl>, action_21 <dbl>,
    -# #   action_22 <dbl>, action_23 <dbl>, action_24 <dbl>, action_25 <dbl>,
    -# #   action_26 <dbl>, action_27 <dbl>, action_28 <dbl>, action_29 <dbl>,
    -# #   action_30 <dbl>, baseline_action <dbl>, project_1 <dbl>, project_2 <dbl>, …
    +#   solution status       obj  cost action_1 action_2 action_3 action_4 action_5
    +#      <int> <chr>      <dbl> <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
    +# 1        1 OPTIMAL 6290606.  987.        0        1        1        0        0
    +# # ℹ 137 more variables: action_6 <dbl>, action_7 <dbl>, action_8 <dbl>,
    +# #   action_9 <dbl>, action_10 <dbl>, action_11 <dbl>, action_12 <dbl>,
    +# #   action_13 <dbl>, action_14 <dbl>, action_15 <dbl>, action_16 <dbl>,
    +# #   action_17 <dbl>, action_18 <dbl>, action_19 <dbl>, action_20 <dbl>,
    +# #   action_21 <dbl>, action_22 <dbl>, action_23 <dbl>, action_24 <dbl>,
    +# #   action_25 <dbl>, action_26 <dbl>, action_27 <dbl>, action_28 <dbl>,
    +# #   action_29 <dbl>, action_30 <dbl>, baseline_action <dbl>, project_1 <dbl>, …
     
    
    @@ -695,7 +695,7 @@ 

    Assessing action importance

    # 8 action_8 NA NA NA # 9 action_9 NA NA NA # 10 action_10 NA NA NA -# # … with 21 more rows +# # ℹ 21 more rows

    The r1 table contains the replacement costs for each action and also various statistics associated with the solutions obtained when locking out each action. Here, each row corresponds to a different action. The "action" column contains the name of the actions (as per the actions table used when building the problem), the "cost" column contains the cost of the solutions obtained when each action was locked out, the "obj" column contains the objective value of the solutions when each action was locked out (i.e. the expected feature richness in this case), and the "rep_cost" column contains the replacement costs for the actions. Actions associated with larger replacement cost values are more irreplaceable, actions associated with missing (NA) replacement cost values were not selected for funding in the input solution (i.e. s2), and actions associated with infinite (Inf) replacement cost values are absolutely critical for meeting the constraints (though infinite values should only problems with minimum set objectives and potentially the baseline project).

    @@ -708,17 +708,17 @@

    Assessing action importance

    # # A tibble: 31 × 6
     #    name             cost locked_in locked_out sel_freq rep_cost
     #    <chr>           <dbl> <lgl>     <lgl>         <dbl>    <dbl>
    -#  1 action_14        99.4 FALSE     FALSE          97.6  345636.
    -#  2 action_2         94.0 FALSE     FALSE          71.8  187698.
    -#  3 action_22        93.9 FALSE     FALSE          68.2  187698.
    -#  4 action_3        100.  FALSE     FALSE          27.1  167620.
    -#  5 action_17       101.  FALSE     FALSE          32.9  167620.
    -#  6 action_19        89.8 FALSE     FALSE          74.1  167620.
    -#  7 action_27        91.1 FALSE     FALSE          48.2  167620.
    -#  8 action_28       102.  FALSE     FALSE          56.5  167620.
    -#  9 action_29       103.  FALSE     FALSE          41.2  167620.
    -# 10 baseline_action   0   FALSE     FALSE          97.6  167620.
    -# # … with 21 more rows
    +#  1 action_14        99.4 FALSE     FALSE          97.4  345636.
    +#  2 action_2         94.0 FALSE     FALSE          69.2  187698.
    +#  3 action_22        93.9 FALSE     FALSE          69.2  187698.
    +#  4 action_3        100.  FALSE     FALSE          61.5  167620.
    +#  5 action_17       101.  FALSE     FALSE          66.7  167620.
    +#  6 action_19        89.8 FALSE     FALSE          76.9  167620.
    +#  7 action_27        91.1 FALSE     FALSE          82.1  167620.
    +#  8 action_28       102.  FALSE     FALSE          89.7  167620.
    +#  9 action_29       103.  FALSE     FALSE          66.7  167620.
    +# 10 baseline_action   0   FALSE     FALSE          94.9  167620.
    +# # ℹ 21 more rows
     
    
    @@ -729,13 +729,13 @@ 

    Assessing action importance

    # Pearson's product-moment correlation # # data: actions$sel_freq and actions$rep_cost -# t = 2.8052, df = 9, p-value = 0.02054 +# t = 4.0716, df = 9, p-value = 0.002793 # alternative hypothesis: true correlation is not equal to 0 # 95 percent confidence interval: -# 0.1408131 0.9100231 +# 0.3968373 0.9474027 # sample estimates: # cor -# 0.6829953 +# 0.8050645
    
    @@ -780,22 +780,22 @@ 

    Complementarity in project prioritizations

    print(s4)
    # # A tibble: 1 × 146
    -#   solution status    obj  cost action_1 action_2 actio…¹ actio…² actio…³ actio…⁴
    -#      <int> <chr>   <dbl> <dbl>    <dbl>    <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
    -# 1        1 OPTIMAL    36  993.        1        1       0       0       0       0
    -# # … with 136 more variables: action_7 <dbl>, action_8 <dbl>, action_9 <dbl>,
    -# #   action_10 <dbl>, action_11 <dbl>, action_12 <dbl>, action_13 <dbl>,
    -# #   action_14 <dbl>, action_15 <dbl>, action_16 <dbl>, action_17 <dbl>,
    -# #   action_18 <dbl>, action_19 <dbl>, action_20 <dbl>, action_21 <dbl>,
    -# #   action_22 <dbl>, action_23 <dbl>, action_24 <dbl>, action_25 <dbl>,
    -# #   action_26 <dbl>, action_27 <dbl>, action_28 <dbl>, action_29 <dbl>,
    -# #   action_30 <dbl>, baseline_action <dbl>, project_1 <dbl>, project_2 <dbl>, …
    +#   solution status    obj  cost action_1 action_2 action_3 action_4 action_5
    +#      <int> <chr>   <dbl> <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
    +# 1        1 OPTIMAL    36  993.        1        1        0        0        0
    +# # ℹ 137 more variables: action_6 <dbl>, action_7 <dbl>, action_8 <dbl>,
    +# #   action_9 <dbl>, action_10 <dbl>, action_11 <dbl>, action_12 <dbl>,
    +# #   action_13 <dbl>, action_14 <dbl>, action_15 <dbl>, action_16 <dbl>,
    +# #   action_17 <dbl>, action_18 <dbl>, action_19 <dbl>, action_20 <dbl>,
    +# #   action_21 <dbl>, action_22 <dbl>, action_23 <dbl>, action_24 <dbl>,
    +# #   action_25 <dbl>, action_26 <dbl>, action_27 <dbl>, action_28 <dbl>,
    +# #   action_29 <dbl>, action_30 <dbl>, baseline_action <dbl>, project_1 <dbl>, …
     
    
     # plot solution
     plot(p4, s4)
    -

    +

    But how would the number of features which meet the target change if we increased the budget? Or how would the number of features which meet the target change if we increased the target to 85%? These are common questions in project prioritization exercises (e.g. Chadés et al. 2015; Di Fonzo et al. 2016; Martin et al. 2018). So, let’s try solving this problem with 70% and 85% targets under a range of different budgets and plot the relationships.

    # specify budgets, ranging between zero and the total cost of all the budgets,
     # with the total number of different budgets equaling 50
    @@ -830,7 +830,7 @@ 

    Complementarity in project prioritizations

    xlab("Solution cost ($)") + ylab("Number of features with targets") + labs(color = "Target")
    -

    +

    We might also be interested in understanding how exactly how much it would cost to implement a set of management actions that would result in all of the features meeting a specific target. Let’s see if we can find out how much it would cost to ensure that every feature has a 99% probability of persistence.

    # build problem
     p5 <- problem(projects = projects, actions = actions, features = features,
    @@ -859,8 +859,20 @@ 

    Complementarity in project prioritizations

    # attempt to solve problem, but this will throw an error
     s5 <- solve(p5)
    -
    # Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64)
    +
    # Set parameter Username
    +# Set parameter TimeLimit to value 2147483647
    +# Set parameter MIPGap to value 0
    +# Set parameter NumericFocus to value 3
    +# Set parameter Presolve to value 2
    +# Set parameter Threads to value 1
    +# Set parameter PoolSolutions to value 1
    +# Set parameter PoolSearchMode to value 2
    +# Academic license - for non-commercial use only - expires 2025-04-21
    +# Gurobi Optimizer version 11.0.2 build v11.0.2rc0 (linux64 - "Ubuntu 22.04.4 LTS")
    +# 
    +# CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512]
     # Thread count: 4 physical cores, 8 logical processors, using up to 1 threads
    +# 
     # Optimize a model with 4068 rows, 2942 columns and 10690 nonzeros
     # Model fingerprint: 0xa60a2b7f
     # Variable types: 0 continuous, 2942 integer (2942 binary)
    @@ -871,7 +883,7 @@ 

    Complementarity in project prioritizations

    # RHS range [1e+00, 1e+00] # Presolve time: 0.00s # -# Explored 0 nodes (0 simplex iterations) in 0.01 seconds (0.00 work units) +# Explored 0 nodes (0 simplex iterations) in 0.00 seconds (0.00 work units) # Thread count was 1 (of 8 available processors) # # Solution count 0 @@ -915,21 +927,21 @@

    Complementarity in project prioritizations

    print(s6)
    # # A tibble: 1 × 146
    -#   solution status    obj  cost action_1 action_2 actio…¹ actio…² actio…³ actio…⁴
    -#      <int> <chr>   <dbl> <dbl>    <dbl>    <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
    -# 1        1 OPTIMAL  972.  972.        0        1       0       0       0       0
    -# # … with 136 more variables: action_7 <dbl>, action_8 <dbl>, action_9 <dbl>,
    -# #   action_10 <dbl>, action_11 <dbl>, action_12 <dbl>, action_13 <dbl>,
    -# #   action_14 <dbl>, action_15 <dbl>, action_16 <dbl>, action_17 <dbl>,
    -# #   action_18 <dbl>, action_19 <dbl>, action_20 <dbl>, action_21 <dbl>,
    -# #   action_22 <dbl>, action_23 <dbl>, action_24 <dbl>, action_25 <dbl>,
    -# #   action_26 <dbl>, action_27 <dbl>, action_28 <dbl>, action_29 <dbl>,
    -# #   action_30 <dbl>, baseline_action <dbl>, project_1 <dbl>, project_2 <dbl>, …
    +#   solution status    obj  cost action_1 action_2 action_3 action_4 action_5
    +#      <int> <chr>   <dbl> <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
    +# 1        1 OPTIMAL  972.  972.        0        1        0        0        0
    +# # ℹ 137 more variables: action_6 <dbl>, action_7 <dbl>, action_8 <dbl>,
    +# #   action_9 <dbl>, action_10 <dbl>, action_11 <dbl>, action_12 <dbl>,
    +# #   action_13 <dbl>, action_14 <dbl>, action_15 <dbl>, action_16 <dbl>,
    +# #   action_17 <dbl>, action_18 <dbl>, action_19 <dbl>, action_20 <dbl>,
    +# #   action_21 <dbl>, action_22 <dbl>, action_23 <dbl>, action_24 <dbl>,
    +# #   action_25 <dbl>, action_26 <dbl>, action_27 <dbl>, action_28 <dbl>,
    +# #   action_29 <dbl>, action_30 <dbl>, baseline_action <dbl>, project_1 <dbl>, …
     
    # plot solution
     plot(p6, s6)
    -

    +

    Benchmarking conventional algorithms

    @@ -954,25 +966,25 @@

    Benchmarking conventional algorithms

    print(s7)
    # # A tibble: 100 × 147
    -#    solution status     obj  cost actio…¹ actio…² actio…³ actio…⁴ actio…⁵ actio…⁶
    -#       <int> <chr>    <dbl> <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
    -#  1        1 NA      2.28e6   0         0       0       0       0       0       0
    -#  2        1 NA      2.28e6   0         0       0       0       0       0       0
    -#  3        1 NA      2.28e6   0         0       0       0       0       0       0
    -#  4        1 NA      2.28e6   0         0       0       0       0       0       0
    -#  5        1 NA      4.51e6  99.4       0       0       0       0       0       0
    -#  6        1 NA      4.51e6  99.4       0       0       0       0       0       0
    -#  7        1 NA      4.51e6  99.4       0       0       0       0       0       0
    -#  8        1 NA      4.51e6  99.4       0       0       0       0       0       0
    -#  9        1 NA      4.51e6  99.4       0       0       0       0       0       0
    -# 10        1 NA      4.51e6  99.4       0       0       0       0       0       0
    -# # … with 90 more rows, 137 more variables: action_7 <dbl>, action_8 <dbl>,
    +#    solution status      obj  cost action_1 action_2 action_3 action_4 action_5
    +#       <int> <chr>     <dbl> <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
    +#  1        1 NA     2284593.   0          0        0        0        0        0
    +#  2        1 NA     2284593.   0          0        0        0        0        0
    +#  3        1 NA     2284593.   0          0        0        0        0        0
    +#  4        1 NA     2284593.   0          0        0        0        0        0
    +#  5        1 NA     4513353.  99.4        0        0        0        0        0
    +#  6        1 NA     4513353.  99.4        0        0        0        0        0
    +#  7        1 NA     4513353.  99.4        0        0        0        0        0
    +#  8        1 NA     4513353.  99.4        0        0        0        0        0
    +#  9        1 NA     4513353.  99.4        0        0        0        0        0
    +# 10        1 NA     4513353.  99.4        0        0        0        0        0
    +# # ℹ 90 more rows
    +# # ℹ 138 more variables: action_6 <dbl>, action_7 <dbl>, action_8 <dbl>,
     # #   action_9 <dbl>, action_10 <dbl>, action_11 <dbl>, action_12 <dbl>,
     # #   action_13 <dbl>, action_14 <dbl>, action_15 <dbl>, action_16 <dbl>,
     # #   action_17 <dbl>, action_18 <dbl>, action_19 <dbl>, action_20 <dbl>,
     # #   action_21 <dbl>, action_22 <dbl>, action_23 <dbl>, action_24 <dbl>,
    -# #   action_25 <dbl>, action_26 <dbl>, action_27 <dbl>, action_28 <dbl>,
    -# #   action_29 <dbl>, action_30 <dbl>, baseline_action <dbl>, project_1 <dbl>, …
    +# #   action_25 <dbl>, action_26 <dbl>, action_27 <dbl>, action_28 <dbl>, …
     

    Now let’s generate a portfolio of solutions using random processes.

    @@ -1006,7 +1018,7 @@

    Benchmarking conventional algorithms

    # 8 211. 2284651. 4513353. # 9 241. 2284651. 4513353. # 10 271. 2284651. 4513353. -# # … with 90 more rows +# # ℹ 90 more rows

    Now we can visualize how well the solutions identified using the heuristic algorithm compare to solutions generated using random processes. In the plot below, the orange line shows the performance of solutions generated using the heuristic algorithm, and the blue ribbon shows the performance of the best and worst solutions generated using random processes.

    @@ -1020,7 +1032,7 @@

    Benchmarking conventional algorithms

    xlab("Budget available ($)") + ylab("Expected richness (objective function)") + theme(axis.text.y = element_text(angle = 90, vjust = 1))
    -

    +

    Here, we can see the that heuristic algorithm generally performs better than funding conservation projects using random processes. We can also see that for some budgets, the heuristic algorithm returns a worse solution (i.e. has a lower objective value) then solutions it found for lower budgets (i.e. where there is a step down in the orange line). Unfortunately, this behavior is normal because heuristic algorithms often deliver suboptimal solutions and the degree of suboptimality often varies depending on the budget. Despite these occasional drops in solution quality, you might be tempted to think that these results show that heuristic algorithms can perform pretty well on balance. But we can do better. Let’s generate a series of solutions using exact algorithms.

    # generate solutions
     s9 <- lapply(budgets, function(b) {
    @@ -1039,25 +1051,25 @@ 

    Benchmarking conventional algorithms

    print(s9)
    # # A tibble: 100 × 147
    -#    solution status     obj  cost actio…¹ actio…² actio…³ actio…⁴ actio…⁵ actio…⁶
    -#       <int> <chr>    <dbl> <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
    -#  1        1 OPTIMAL 2.28e6   0         0       0       0       0       0       0
    -#  2        1 OPTIMAL 2.28e6   0         0       0       0       0       0       0
    -#  3        1 OPTIMAL 2.28e6   0         0       0       0       0       0       0
    -#  4        1 OPTIMAL 2.28e6   0         0       0       0       0       0       0
    -#  5        1 OPTIMAL 4.51e6  99.4       0       0       0       0       0       0
    -#  6        1 OPTIMAL 4.51e6  99.4       0       0       0       0       0       0
    -#  7        1 OPTIMAL 4.51e6  99.4       0       0       0       0       0       0
    -#  8        1 OPTIMAL 4.51e6  99.4       0       0       0       0       0       0
    -#  9        1 OPTIMAL 4.51e6  99.4       0       0       0       0       0       0
    -# 10        1 OPTIMAL 4.51e6  99.4       0       0       0       0       0       0
    -# # … with 90 more rows, 137 more variables: action_7 <dbl>, action_8 <dbl>,
    +#    solution status       obj  cost action_1 action_2 action_3 action_4 action_5
    +#       <int> <chr>      <dbl> <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
    +#  1        1 OPTIMAL 2284593.   0          0        0        0        0        0
    +#  2        1 OPTIMAL 2284593.   0          0        0        0        0        0
    +#  3        1 OPTIMAL 2284593.   0          0        0        0        0        0
    +#  4        1 OPTIMAL 2284593.   0          0        0        0        0        0
    +#  5        1 OPTIMAL 4513353.  99.4        0        0        0        0        0
    +#  6        1 OPTIMAL 4513353.  99.4        0        0        0        0        0
    +#  7        1 OPTIMAL 4513353.  99.4        0        0        0        0        0
    +#  8        1 OPTIMAL 4513353.  99.4        0        0        0        0        0
    +#  9        1 OPTIMAL 4513353.  99.4        0        0        0        0        0
    +# 10        1 OPTIMAL 4513353.  99.4        0        0        0        0        0
    +# # ℹ 90 more rows
    +# # ℹ 138 more variables: action_6 <dbl>, action_7 <dbl>, action_8 <dbl>,
     # #   action_9 <dbl>, action_10 <dbl>, action_11 <dbl>, action_12 <dbl>,
     # #   action_13 <dbl>, action_14 <dbl>, action_15 <dbl>, action_16 <dbl>,
     # #   action_17 <dbl>, action_18 <dbl>, action_19 <dbl>, action_20 <dbl>,
     # #   action_21 <dbl>, action_22 <dbl>, action_23 <dbl>, action_24 <dbl>,
    -# #   action_25 <dbl>, action_26 <dbl>, action_27 <dbl>, action_28 <dbl>,
    -# #   action_29 <dbl>, action_30 <dbl>, baseline_action <dbl>, project_1 <dbl>, …
    +# #   action_25 <dbl>, action_26 <dbl>, action_27 <dbl>, action_28 <dbl>, …
     

    Now let’s redraw the previous graph and add a red line to the plot to represent the solutions generated using the exact algorithm solver.

    @@ -1072,7 +1084,7 @@

    Benchmarking conventional algorithms

    xlab("Budget available ($)") + ylab("Expected richness (objective function)") + theme(axis.text.y = element_text(angle = 90, hjust = 0.5, vjust = 1))
    -

    +

    So, we can see that the exact algorithm solver performs much better than heuristic algorithms—even if heuristic algorithms perform better than random on average.

    @@ -1084,16 +1096,15 @@

    Conclusion

    Citation

    -
    
    -To cite the oppr package in publications, please use:
    +
    To cite the oppr package in publications, please use:
     
       Hanson JO, Schuster R, Strimas-Mackey M & Bennett JR (2019)
       Optimality in prioritizing conservation projects. Methods in Ecology
       & Evolution, 10: 1655--1663.
     
    -  Hanson JO, Schuster R, Strimas-Mackey M, Bennett J (2022) oppr:
    -  Optimal Project Prioritization R package version 1.0.4.
    -  https://CRAN.R-project.org/package=oppr
    +  Hanson JO, Schuster R, Strimas-Mackey M, Bennett J (2024) oppr:
    +  Optimal Project Prioritization R package version 1.0.4.1.
    +  https://github.com/prioritizr/oppr
     
     To see these entries in BibTeX format, use 'print(<citation>,
     bibtex=TRUE)', 'toBibtex(.)', or set
    diff --git a/man/add_gurobi_solver.Rd b/man/add_gurobi_solver.Rd
    index 8206ac7..b8cbdf3 100644
    --- a/man/add_gurobi_solver.Rd
    +++ b/man/add_gurobi_solver.Rd
    @@ -91,16 +91,12 @@ This solver uses the \pkg{gurobi} package to solve problems.
     
     To install the \pkg{gurobi} package, the
     \href{https://www.gurobi.com}{Gurobi} optimization suite will first need to
    -be installed (see instructions for \href{https://www.gurobi.com/documentation/8.1/quickstart_linux/software_installation_guid.html}{Linux},
    -\href{https://www.gurobi.com/documentation/8.1/quickstart_mac/software_installation_guid.html}{Mac OSX}, and
    -\href{https://www.gurobi.com/documentation/8.1/quickstart_windows/software_installation_guid.html}{Windows} operating systems). Although
    +be installed (see \url{https://support.gurobi.com/hc/en-us/articles/4534161999889-How-do-I-install-Gurobi-Optimizer} for instructions). Although
     \href{https://www.gurobi.com}{Gurobi} is a commercial software, academics
     can obtain a
     \href{https://www.gurobi.com/downloads/end-user-license-agreement-academic/}{special license for no cost}. After installing the
     \href{https://www.gurobi.com}{Gurobi} optimization suite, the \pkg{gurobi}
    -package can then be installed (see instructions for \href{https://www.gurobi.com/documentation/8.1/quickstart_linux/r_installing_the_r_package.html}{Linux},
    -\href{https://www.gurobi.com/documentation/8.1/quickstart_mac/r_installing_the_r_package.html}{Mac OSX}, and
    -\href{https://www.gurobi.com/documentation/8.1/quickstart_windows/r_installing_the_r_package.html}{Windows} operating systems).
    +package can then be installed (see \url{https://www.gurobi.com/documentation/current/refman/r_ins_the_r_package.html} for instructions).
     }
     \examples{
     \dontrun{
    diff --git a/man/add_lpsolveapi_solver.Rd b/man/add_lpsolveapi_solver.Rd
    index 020d33b..3358e7b 100644
    --- a/man/add_lpsolveapi_solver.Rd
    +++ b/man/add_lpsolveapi_solver.Rd
    @@ -35,7 +35,7 @@ package. This function can also be used to customize the behavior of the
     solver. It requires the \pkg{lpSolveAPI} package.
     }
     \details{
    -\href{http://lpsolve.sourceforge.net/5.5/}{\emph{lp_solve}} is an
    +\href{https://lpsolve.sourceforge.net/5.5/}{\emph{lp_solve}} is an
     open-source integer programming solver.
     Although this solver is the slowest currently supported solver,
     it is also the only exact algorithm solver that can be installed on all
    diff --git a/man/figures/README-readme-plot-1.png b/man/figures/README-readme-plot-1.png
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