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

被引：3
作者：
Besancon, Mathieu [1 ]
Carderera, Alejandro [1 ,2 ]
Pokutta, Sebastian [1 ,3 ]
机构：
[1] Zuse Inst Berlin, D-14195 Berlin, Germany
[2] Georgia Inst Technol, Atlanta, GA 30308 USA
[3] Tech Univ Berlin, D-10623 Berlin, Germany
来源：
INFORMS JOURNAL ON COMPUTING | 2022年 / 34卷 / 05期
关键词：
first-order methods; optimization software; nonlinear programming;
D O I：
10.1287/ijoc.2022.1191
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
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.
引用
收藏
页码：2611 / 2620
页数：10
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