# El0ps: An Exact L0-regularized Problems Solver

arXiv: https://arxiv.org/abs/2506.06373 · HTML: https://mlsanity.com/paper/2506.06373/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 4 Jun 2025.

## Builds on (most cited first, 13 of 13)

- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [Coordinate Descent Algorithms](https://mlsanity.com/paper/1502.04759/) (2015)
- [Sparse Regression at Scale: Branch-and-Bound rooted in First-Order Optimization](https://mlsanity.com/paper/2004.06152/) (2020)
- [Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives](https://mlsanity.com/paper/2001.06471/) (2020)
- [Sparse Classification: a scalable discrete optimization perspective](https://mlsanity.com/paper/1710.01352/) (2017)
- [A unified approach to mixed-integer optimization problems with logical constraints](https://mlsanity.com/paper/1907.02109/) (2019)
- [Beyond L1: Faster and Better Sparse Models with skglm](https://mlsanity.com/paper/2204.07826/) (2022)
- [Cardinality Minimization, Constraints, and Regularization: A Survey](https://mlsanity.com/paper/2106.09606/) (2021)
- [L0Learn: A Scalable Package for Sparse Learning using L0 Regularization](https://mlsanity.com/paper/2202.04820/) (2022)
- [FALCON: FLOP-Aware Combinatorial Optimization for Neural Network Pruning](https://mlsanity.com/paper/2403.07094/) (2024)
- [Screening Rules and its Complexity for Active Set Identification](https://mlsanity.com/paper/2009.02709/) (2020)
- [A New Branch-and-Bound Pruning Framework for ℓ0-Regularized Problems](https://mlsanity.com/paper/2406.03504/) (2024)
- [One to beat them all: "RYU" -- a unifying framework for the construction of safe balls](https://mlsanity.com/paper/2312.00640/) (2023)

## Abstract (arXiv)

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.