Improved algorithms and novel applications of the FrankWolfe.jl library
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908709875089408 |
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| author | Besançon, Mathieu Designolle, Sébastien Halbey, Jannis Hendrych, Deborah Kuzinowicz, Dominik Pokutta, Sebastian Troppens, Hannah Herrmannsdoerfer, Daniel Viladrich Wirth, Elias |
| author_facet | Besançon, Mathieu Designolle, Sébastien Halbey, Jannis Hendrych, Deborah Kuzinowicz, Dominik Pokutta, Sebastian Troppens, Hannah Herrmannsdoerfer, Daniel Viladrich Wirth, Elias |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_14613 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improved algorithms and novel applications of the FrankWolfe.jl library Besançon, Mathieu Designolle, Sébastien Halbey, Jannis Hendrych, Deborah Kuzinowicz, Dominik Pokutta, Sebastian Troppens, Hannah Herrmannsdoerfer, Daniel Viladrich Wirth, Elias Optimization and Control Mathematical Software 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. |
| title | Improved algorithms and novel applications of the FrankWolfe.jl library |
| topic | Optimization and Control Mathematical Software |
| url | https://arxiv.org/abs/2501.14613 |