This repository contains the code for the paper "Screening Rules for Lasso with Non-Convex Sparse Regularizers" published at ICML 2019, Long Beach.
The current version of the paper is available at https://arxiv.org/pdf/1902.06125.pdf For abstract, bibliography (.bib), you can look at http://proceedings.mlr.press/v97/rakotomamonjy19a.html
this repository is still in construction.
download the source code from the git repository
python (>= 3.6) , scipy (1.1.0), numpy (1.15.4).
structure of the repository
screening_lasso.py contains the code for solving lasso, weighted lasso and proximal weighted lasso using coordinatewise descent
noncvx_lasso.py contains codes for solving non-convex lasso using coordinate wise descent, with screening, and with screening and screening propagation
compare_algorithms.py allows to reproduce experiments using toy data. note that the case (n=500, d=5000) may take several hours especially for bcd
generate_figure.py allows to generate figures based on the saved results from "compare_algorithms".
for reproducing figure 1 (left) in the paper, run compare_algorithms.py as is and then run generate_figure.py