El0ps: An Exact L0-regularized Problems Solver
This is an incremental contribution providing a practical tool for researchers and practitioners working with L0-regularized problems.
The paper introduces El0ps, a Python toolbox for solving L0-regularized problems in machine learning and related fields, which provides a flexible framework for custom instances and achieves state-of-the-art performance with built-in pipelines.
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.