El0ps: An Exact L0-regularized Problems Solver

TL;DR

El0ps is a Python toolbox that enables flexible definition and efficient solving of L0-regularized problems, facilitating their application in machine learning, statistics, and signal processing.

Contribution

It introduces a customizable framework and a high-performance solver for L0-regularized problems, enhancing practical usability and integration.

Findings

Provides state-of-the-art solving performance.

Supports custom problem definitions.

Includes built-in machine learning pipelines.

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.