Convergence Rate of Frank-Wolfe for Non-Convex Objectives
Optimization and Control
2016-07-07 v1 Machine Learning
Numerical Analysis
Machine Learning
Abstract
We give a simple proof that the Frank-Wolfe algorithm obtains a stationary point at a rate of on non-convex objectives with a Lipschitz continuous gradient. Our analysis is affine invariant and is the first, to the best of our knowledge, giving a similar rate to what was already proven for projected gradient methods (though on slightly different measures of stationarity).
Keywords
Cite
@article{arxiv.1607.00345,
title = {Convergence Rate of Frank-Wolfe for Non-Convex Objectives},
author = {Simon Lacoste-Julien},
journal= {arXiv preprint arXiv:1607.00345},
year = {2016}
}
Comments
6 pages