El0ps是求解L0正则化问题的高效工具箱，支持自定义建模与前沿求解。
El0ps: An Exact L0-regularized Problems Solver
- 通过灵活框架支持用户自定义L0正则化问题
- 内置高性能求解器，性能达当前最优水平
- 提供机器学习流水线，便于实际应用部署
本文介绍El0ps，一个Python工具箱，用于处理机器学习、统计学、信号处理等领域中的L0正则化问题。与现有工具箱不同，El0ps允许用户通过灵活框架定义自定义问题实例，提供专用求解器实现当前最佳性能，并配备多个内置机器学习流水线。其目标是构建一个全面的工具，推动L0正则化问题在实际应用中的集成与应用。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。