From 6388eae7c62ccaffafff260a0c53bfd7fb3a6a35 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?St=C3=A9phane=20Caron?= Date: Mon, 11 Dec 2023 11:19:39 +0100 Subject: [PATCH] [minor] Move installation instructions up the readme --- README.md | 36 ++++++++++++++++++------------------ 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/README.md b/README.md index c617a8c..aa6c29e 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,22 @@ Benchmark for quadratic programming (QP) solvers available in Python. The objective is to compare and select the best QP solvers for given use cases. The benchmarking methodology is open to [discussions](https://github.com/qpsolvers/qpbenchmark/discussions). Standard and community [test sets](#test-sets) are available: all of them can be processed using the ``qpbenchmark`` command-line tool, resulting in standardized reports evaluating all [metrics](#metrics) across all QP solvers available on the test machine. -New test sets are welcome! The benchmark is designed so that each test set comes in a standalone directory. Check out the existing test sets below, and feel free to create a new one that better matches your particular use cases. +## Installation + +The recommended process is to install the benchmark and all solvers in an isolated environment using ``conda``: + +```console +conda env create -f environment.yaml +conda activate qpbenchmark +``` + +Alternatively, you can install the benchmarking tool individually using ``pip``: + +```console +pip install qpbenchmark +``` + +In that case, the benchmark will run all supported solvers it finds. (Quick way to install open source solvers from PyPI: ``pip install qpsolvers[open_source_solvers]``.) ## Test sets @@ -20,6 +35,8 @@ The benchmark comes with standard and community test sets to represent different | **Maros-Meszaros dense** | 62 | Subset of Maros-Meszaros restricted to smaller dense problems. | | **GitHub free-for-all** | 12 | Community-built, new problems [are welcome](https://github.com/qpsolvers/qpbenchmark/issues/new?template=new_problem.md)! | +New test sets are welcome! The benchmark is designed so that each test set comes in a standalone directory. Check out the existing test sets below, and feel free to create a new one that better matches your particular use cases. + ## Solvers | Solver | Keyword | Algorithm | Matrices | License | @@ -83,23 +100,6 @@ Here are some known areas of improvement for this benchmark: Check out the [issue tracker](https://github.com/qpsolvers/qpbenchmark/issues) for ongoing works and future improvements. -## Installation - -The recommended process is to install the benchmark and all solvers in an isolated environment using ``conda``: - -```console -conda env create -f environment.yaml -conda activate qpbenchmark -``` - -Alternatively, you can install the benchmarking tool individually using ``pip``: - -```console -pip install qpbenchmark -``` - -In that case, the benchmark will run all supported solvers it finds. (Quick way to install open source solvers from PyPI: ``pip install qpsolvers[open_source_solvers]``.) - ## Running the benchmark Once the benchmark is installed, you will be able to run the ``qpbenchmark`` command. Provide it with the script corresponding to the [test set](#test-sets) you want to run, followed by a benchmark command such as "run". For instance, let's run the "dense" subset of the Maros-Meszaros test set: