Linear Convergence of Frank-Wolfe for Rank-One Matrix Recovery Without Strong Convexity

TL;DR

This paper proves that the Frank-Wolfe algorithm converges linearly for certain low-rank matrix recovery problems under a natural condition, even without strong convexity, improving previous convergence bounds.

Contribution

It establishes linear convergence of Frank-Wolfe for rank-one matrix recovery under a new sufficient condition, with no parameter tuning and fewer SVD computations per iteration.

Findings

Frank-Wolfe achieves $O( ext{log}(1/\epsilon))$ convergence rate.

The method requires only one rank-one SVD per iteration.

Extensions include variants, robust PCA, and nonsmooth problems.

Abstract

We consider convex optimization problems which are widely used as convex relaxations for low-rank matrix recovery problems. In particular, in several important problems, such as phase retrieval and robust PCA, the underlying assumption in many cases is that the optimal solution is rank-one. In this paper we consider a simple and natural sufficient condition on the objective so that the optimal solution to these relaxations is indeed unique and rank-one. Mainly, we show that under this condition, the standard Frank-Wolfe method with line-search (i.e., without any tuning of parameters whatsoever), which only requires a single rank-one SVD computation per iteration, finds an $\epsilon$-approximated solution in only $O(\log{1/\epsilon})$ iterations (as opposed to the previous best known bound of $O(1/\epsilon)$), despite the fact that the objective is not strongly convex. We consider several variants of the basic method with improved complexities, as well as an extension motivated by robust PCA, and finally, an extension to nonsmooth problems.