El0ps: An Exact L0-regularized Problems Solver

Théo Guyard, Cédric Herzet, Clément Elvira
6/4/2025
cs.MScs.LGmath.OC

No official code found. Best implementation: TheoGuyard/El0ps (13 stars, GPL-3.0, last commit 2026-01-06). 1 more below.

Abstract

This paper presents El0ps, a Python toolbox providing several utilities to handle L0-regularized problems related to applications in machine learning, statistics, and signal processing, among other fields. In contrast to existing toolboxes, El0ps allows users to define custom instances of these problems through a flexible framework, provides a dedicated solver achieving state-of-the-art performance, and offers several built-in machine learning pipelines. Our aim with El0ps is to provide a comprehensive tool which opens new perspectives for the integration of L0-regularized problems in practical applications.

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Code Implementations(2)

TheoGuyard/El0psCommunity100%
GPL-3.0

El0ps: An Exact L0-Problem Solver

131PythonJul 10, 20239 months agoGPL-3.0
TheoGuyard/l0expCommunity100%
GPL-3.0
00Python, Jupyter NotebookJan 7, 20258 months agoGPL-3.0

Cite this paper

@article{guyard2025elps,
  title  = {El0ps: An Exact L0-regularized Problems Solver},
  author = {Théo Guyard and Cédric Herzet and Clément Elvira},
  year   = {2025},
  eprint = {2506.06373},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2506.06373v1}
}

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