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FrankWolfe.jl: a high-performance and flexible toolbox for Frank-Wolfe algorithms and Conditional Gradients

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arxiv 2104.06675 v2 pith:QQY47XGF submitted 2021-04-14 math.OC

Mathieu Besançon , Alejandro Carderera , Sebastian Pokutta This is my paper · ORCID
classification math.OC
keywords conditionalfrank-wolfefrankwolfegradientshigh-performanceoptimizationalgorithmsallowing
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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 interfaces smoothly with generic linear optimization formulations using MathOptInterface.jl.

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