BenchOpt benchmark for Convolutional Sparse Coding =====================

BenchOpt is a package to simplify and make more transparent and reproducible the comparisons of optimization algorithms. This benchmark is dedicated to solver of convolutional sparse coding:

$$\min{\theta_1, \ldots, \theta_K \in \mathbb{R}^d} \frac{1}{2} \^2_2 + \lambda \sum{k=1}^K \_1$$

where $K$ is the number of atoms in the dictionary, $d_1, \ldots, d_K \in \mathbb{R}^d$ are the atoms in the dictionary and $*$ denotes the convolution.

Install

This benchmark can be run using the following commands:

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

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_csc -s alphacsc -d simulated --max-runs 10 --n-repetitions 10

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