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

DOI10.1287/ijoc.2022.1191OpenAlexW3156920350MaRDI QIDQ5058004

Sebastian Pokutta, Mathieu Besançon, Alejandro Carderera

Publication date: 1 December 2022

Published in: INFORMS Journal on Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2104.06675

zbMATH Keywords

nonlinear programmingfirst-order methodsoptimization software

Mathematics Subject Classification ID

Mathematical programming (90Cxx)

Related Items (1)

FrankWolfe.jl

Uses Software

• ARPACK
• Julia
• JuMP
• GitHub
• Convex.jl
• Optim
• MovieLens
• MathOptInterface.jl
• JSOSolvers.jl

Cites Work

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• Complexity of linear minimization and projection on some sets
• Exact matrix completion via convex optimization
• Julia: A Fresh Approach to Numerical Computing
• Some comments on Wolfe's ‘away step’
• ARPACK Users' Guide
• Why Are Big Data Matrices Approximately Low Rank?
• The Power of Convex Relaxation: Near-Optimal Matrix Completion
• JuMP: A Modeling Language for Mathematical Optimization
• A Stochastic Approximation Method

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