Elastic Net Benchmark

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:

$$ \min_w \frac{1}{2n} \Vert y - Xw \Vert^2 + \lambda \ (\rho \Vert w \Vert_1 + \frac{1 - \rho}{2} \Vert w \Vert^2)$$

where $n$ (or n_samples) stands for the number of samples, $p$ (or n_features) stands for the number of features , $\rho \in (0, 1]$ is the l1_ratio and

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

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.