This package is a toolbox for Frank-Wolfe and conditional gradient algorithms. Frank-Wolfe algorithms were designed to solve optimization problems where f is a differentiable convex function and C is a convex and compact set. They are especially useful when we know how to optimize a linear function over C in an efficient way.
Features
- Documentation available
- Examples available
- Julia implementation for various Frank-Wolfe and Conditional Gradient variants
- Licensed under the MIT License
- This package is a toolbox for Frank-Wolfe and conditional gradients algorithms
Categories
Data VisualizationLicense
MIT LicenseFollow FrankWolfe.jl
Other Useful Business Software
MongoDB Atlas runs apps anywhere
MongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
Rate This Project
Login To Rate This Project
User Reviews
Be the first to post a review of FrankWolfe.jl!