On the Global Linear Convergence of Frank-Wolfe Optimization Variants

Simon Lacoste-Julien; Martin Jaggi

NeurIPS 2015poster514 citations

On the Global Linear Convergence of Frank-Wolfe Optimization Variants

Abstract

The Frank-Wolfe (FW) optimization algorithm has lately re-gained popularity thanks in particular to its ability to nicely handle the structured constraints appearing in machine learning applications. However, its convergence rate is known to be slow (sublinear) when the solution lies at the boundary. A simple less-known fix is to add the possibility to take

BibTeX

@inproceedings{NIPS2015_c058f544,
 author = {Lacoste-Julien, Simon and Jaggi, Martin},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {C. Cortes and N. Lawrence and D. Lee and M. Sugiyama and R. Garnett},
 pages = {},
 publisher = {Curran Associates, Inc.},
 title = {On the Global Linear Convergence of Frank-Wolfe Optimization Variants},
 url = {https://proceedings.neurips.cc/paper_files/paper/2015/file/c058f544c737782deacefa532d9add4c-Paper.pdf},
 volume = {28},
 year = {2015}
}