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Elastic Net Benchmark

Build Status Python 3.6+

Benchopt is a package to simplify and make more transparent and reproducible the comparisons of optimization algorithms. This benchmark is dedicated to elastic net regression:

\min_{w} 1/2n * ||y - Xw||^2 + \lambda * (l1ratio ||w||_1 + (1 - l1ratio) ||w||^2 / 2)

where n (or n_samples) stands for the number of samples, p (or n_features) stands for the number of features and

X = [x_1^\top, \dots, x_n^\top]^\top \in \mathbb{R}^{n \times p}

Install

This benchmark can be run using the following commands:

$ pip install -U benchopt
$ git clone https://github.com/benchopt/benchmark_elastic_net
$ benchopt run benchmark_elastic_net

Apart from the problem, options can be passed to benchopt run, to restrict the benchmarks to some solvers or datasets, e.g.:

$ benchopt run benchmark_elastic_net -s solver1 -d dataset2 --max-runs 10 --n-repetitions 10

Use benchopt run -h for more details about these options, or visit https://benchopt.github.io/api.html.

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