Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

Convergence Rate of Frank-Wolfe for Non-Convex Objectives

Simon Lacoste-Julien 1, 2, 3
1 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique - ENS Paris, CNRS - Centre National de la Recherche Scientifique, Inria de Paris
Abstract : We give a simple proof that the Frank-Wolfe algorithm obtains a stationary point at a rate of $O(1/\sqrt{t})$ 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).
Complete list of metadata
Contributor : Simon Lacoste-Julien Connect in order to contact the contributor
Submitted on : Tuesday, December 13, 2016 - 1:26:11 AM
Last modification on : Friday, January 21, 2022 - 3:19:56 AM

Links full text


  • HAL Id : hal-01415335, version 1
  • ARXIV : 1607.00345



Simon Lacoste-Julien. Convergence Rate of Frank-Wolfe for Non-Convex Objectives. 2016. ⟨hal-01415335⟩



Les métriques sont temporairement indisponibles