Benchopt is a benchmarking suite for optimization algorithms. It is built for simplicity, transparency, and reproducibility. It is implemented in Python but can run algorithms written in many programming languages.

So far, benchopt has been tested with Python, R, Julia and C/C++ (compiled binaries with a command line interface). Programs available via conda should be compatible as well. See for instance an example of usage with R.

It is recommended to use benchopt within a conda environment to fully-benefit from benchopt Command Line Interface (CLI).

To install benchopt, start by creating a new conda environment and then activate it

conda create -n benchopt python
conda activate benchopt

Then run the following command to install the latest release of benchopt

pip install -U benchopt

It is also possible to use the latest development version. To do so, run instead

pip install -U -i https://test.pypi.org/simple/benchopt

After installing benchopt, you can

• replicate/modify an existing benchmark
• create your own benchmark

Replicating an existing benchmark is simple. Here is how to do so for the L2-logistic Regression benchmark.

1. Clone the benchmark repository and cd to it

git clone https://github.com/benchopt/benchmark_logreg_l2
cd benchmark_logreg_l2

2. Install the desired solvers automatically with benchopt

benchopt install . -s lightning -s sklearn

3. Run the benchmark to get the figure below

benchopt run . --config ./example_config.yml

These steps illustrate how to reproduce the L2-logistic Regression benchmark. Find the complete list of the Available benchmarks. Also, refer to the documentation to learn more about benchopt CLI and its features. You can also easily extend this benchmark by adding a dataset, solver or metric. Learn that and more in the Write a benchmark tutorial.

The section Write a benchmark of the documentation provides a tutorial for creating a benchmark. The benchopt community also maintains a template benchmark to quickly and easily start a new benchmark.

Join benchopt discord server and get in touch with the community! Feel free to drop us a message to get help with running/constructing benchmarks or (why not) discuss new features to be added and future development directions that benchopt should take.

Benchopt is a continuous effort to make reproducible and transparent optimization benchmarks. Join us in this endeavor! If you use benchopt in a scientific publication, please cite

@inproceedings{benchopt,
   author    = {Moreau, Thomas and Massias, Mathurin and Gramfort, Alexandre
                and Ablin, Pierre and Bannier, Pierre-Antoine
                and Charlier, Benjamin and Dagréou, Mathieu and Dupré la Tour, Tom
                and Durif, Ghislain and F. Dantas, Cassio and Klopfenstein, Quentin
                and Larsson, Johan and Lai, En and Lefort, Tanguy
                and Malézieux, Benoit and Moufad, Badr and T. Nguyen, Binh and Rakotomamonjy,
                Alain and Ramzi, Zaccharie and Salmon, Joseph and Vaiter, Samuel},
   title     = {Benchopt: Reproducible, efficient and collaborative optimization benchmarks},
   year      = {2022},
   booktitle = {NeurIPS},
   url       = {https://arxiv.org/abs/2206.13424}
}

Problem	Results	Build Status
Ordinary Least Squares (OLS)	Results
Non-Negative Least Squares (NNLS)	Results
LASSO: L1-Regularized Least Squares	Results
LASSO Path	Results
Elastic Net
MCP	Results
L2-Regularized Logistic Regression	Results
L1-Regularized Logistic Regression	Results
L2-regularized Huber regression
L1-Regularized Quantile Regression	Results
Linear SVM for Binary Classification
Linear ICA
Approximate Joint Diagonalization (AJD)
1D Total Variation Denoising
2D Total Variation Denoising
ResNet Classification	Results