The distributed Frank-Wolfe algorithm is a convex optimization algorithm to learn sparse combinations of elements distributed over a network. This software is C++/MPI implementation of the algorithm that can solve two problems: Kernel Support Vector Machines with distributed training examples, and LASSO regression with distributed attributes.
For more information / citation, refer to:
A. Bellet*, Y. Liang*, A. Bagheri Garakani*, M.-F. Balcan and F. Sha. (*: equal contribution)
A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning
SIAM International Conference on Data Mining (SDM), 2015, to appear
- Changes to previous version:
- BibTeX Entry: Download
- Corresponding Paper BibTeX Entry: Download
- URL: Project Homepage
- Supported Operating Systems: Linux
- Data Formats: Csv, Any Format Supported By Matlab
- Tags: Support Vector Machines, Kernel Methods, Convex Optimization, Mpi, Conditional Gradient, Distributed Learning, Distributed Optimization, Frank Wolfe Algorithm, Lasso Regression
- Archive: download here
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