FrankWolfe.jl: a high-performance and flexible toolbox for Frank-Wolfe algorithms and Conditional Gradients
Optimization and Control
2021-10-06 v2
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 interfaces smoothly with generic linear optimization formulations using MathOptInterface.jl.
Cite
@article{arxiv.2104.06675,
title = {FrankWolfe.jl: a high-performance and flexible toolbox for Frank-Wolfe algorithms and Conditional Gradients},
author = {Mathieu Besançon and Alejandro Carderera and Sebastian Pokutta},
journal= {arXiv preprint arXiv:2104.06675},
year = {2021}
}