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[1] arXiv:2501.14613 (替换) [中文pdf, pdf, 其他]

标题： FrankWolfe.jl库的改进算法和新应用 显示英文标题

标题： Improved algorithms and novel applications of the FrankWolfe.jl library

Mathieu Besançon, Sébastien Designolle, Jannis Halbey, Deborah Hendrych, Dominik Kuzinowicz, Sebastian Pokutta, Hannah Troppens, Daniel Viladrich Herrmannsdoerfer, Elias Wirth

主题： 优化与控制 (math.OC) ; 数学软件 (cs.MS)

弗兰克-沃尔夫（FW）算法已成为约束优化中一个重要方法类，特别是在大规模问题上。 在本文中，我们总结了FrankWolfe.jl的发展中近年来的算法设计选择和进展，FrankWolfe.jl是一个汇集最先进的FW变体高性能实现的Julia包。 我们回顾了该库在最近文献中的关键使用案例，这些案例符合其原始双重目的：首先，成为从业者将FW方法应用于他们问题的事实上的工具箱，其次，为算法设计者提供一个模块化的生态系统，让他们尝试自己的算法块变体和实现。 最后，我们在几个实验中展示了几种FW变体在重要问题类别上的性能，这些实验我们整理在一个单独的仓库中以进行持续基准测试。 显示英文摘要

Frank-Wolfe (FW) algorithms have emerged as an essential class of methods for constrained optimization, especially on large-scale problems. In this paper, we summarize the algorithmic design choices and progress made in the last years of the development of FrankWolfe.jl, a Julia package gathering high-performance implementations of state-of-the-art FW variants. We review key use cases of the library in the recent literature, which match its original dual purpose: first, becoming the de-facto toolbox for practitioners applying FW methods to their problem, and second, offering a modular ecosystem to algorithm designers who experiment with their own variants and implementations of algorithmic blocks. Finally, we demonstrate the performance of several FW variants on important problem classes in several experiments, which we curated in a separate repository for continuous benchmarking.