FrankWolfe.jl: A High-Performance and Flexible Toolbox for Frank–Wolfe Algorithms and Conditional Gradients

Mathieu Besançon (), Alejandro Carderera () and Sebastian Pokutta ()
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Mathieu Besançon: Zuse Institute Berlin, 14195 Berlin, Germany
Alejandro Carderera: Zuse Institute Berlin, 14195 Berlin, Germany; Georgia Institute of Technology, Atlanta, Georgia 30308
Sebastian Pokutta: Zuse Institute Berlin, 14195 Berlin, Germany; Technische Universität Berlin, 10623 Berlin, Germany

INFORMS Journal on Computing, 2022, vol. 34, issue 5, 2611-2620

Abstract: We present FrankWolfe.jl , an open-source implementation of several popular Frank–Wolfe and conditional gradients variants for first-order constrained optimization. The package is designed with flexibility and high performance in mind, allowing for easy extension and relying on few assumptions regarding the user-provided functions. It supports Julia’s unique multiple dispatch feature, and it interfaces smoothly with generic linear optimization formulations using MathOptInterface.jl .

Keywords: first-order methods; optimization software; nonlinear programming (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:34:y:2022:i:5:p:2611-2620

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