2025/01/24 by Mathieu Besançon, Sébastien Designolle, Besançon, Mathieu +14 · 4 citations
Computer Science · #Advanced Data Storage Technologies #Algorithms and Data Compression #Distributed and Parallel Computing Systems #FOS: Computer and information sciences #FOS: Mathematics #Mathematical Software (cs.MS) #Optimization and Control (math.OC)
paper · pdf · doi:10.48550/arxiv.2501.14613
openalex publication_date 2025/01/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
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.