El0ps: An Exact L0-regularized Problems Solver
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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.
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References · 13
- 2014OpenML: networked science in machine learningcited by 526
- 2015Coordinate Descent Algorithmscited by 219
- 2020Sparse Regression at Scale: Branch-and-Bound rooted in First-Order Optimizationcited by 45
- 2020Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectivescited by 26
- 2017Sparse Classification: a scalable discrete optimization perspectivecited by 18
- 2019A unified approach to mixed-integer optimization problems with logical constraintscited by 18
- 2022Beyond L1: Faster and Better Sparse Models with skglmcited by 16
- 2021Cardinality Minimization, Constraints, and Regularization: A Surveycited by 12
- 2022L0Learn: A Scalable Package for Sparse Learning using L0 Regularizationcited by 12
- 2024FALCON: FLOP-Aware Combinatorial Optimization for Neural Network Pruningcited by 11
- 2020Screening Rules and its Complexity for Active Set Identificationcited by 8
- 2024A New Branch-and-Bound Pruning Framework for ℓ0-Regularized Problemscited by 5
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