Fast convergence of Frank-Wolfe algorithms on polytopes

Fuente: arXiv
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Main Authors: Wirth, Elias, Pena, Javier, Pokutta, Sebastian
Format: Preprint
Published: 2024
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author Wirth, Elias
Pena, Javier
Pokutta, Sebastian
author_facet Wirth, Elias
Pena, Javier
Pokutta, Sebastian
contents We provide a template to derive convergence rates for the following popular versions of the Frank-Wolfe algorithm on polytopes: vanilla Frank-Wolfe, Frank-Wolfe with away steps, Frank-Wolfe with blended pairwise steps, and Frank-Wolfe with in-face directions. Our template shows how the convergence rates follow from two affine-invariant properties of the problem, namely, error bound and extended curvature. These properties depend solely on the polytope and objective function but not on any affine-dependent object like norms. For each one of the above algorithms, we derive rates of convergence ranging from sublinear to linear depending on the degree of the error bound.
format Preprint
id arxiv_https___arxiv_org_abs_2406_18789
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Fast convergence of Frank-Wolfe algorithms on polytopes
Wirth, Elias
Pena, Javier
Pokutta, Sebastian
Optimization and Control
90C25, 90C52
We provide a template to derive convergence rates for the following popular versions of the Frank-Wolfe algorithm on polytopes: vanilla Frank-Wolfe, Frank-Wolfe with away steps, Frank-Wolfe with blended pairwise steps, and Frank-Wolfe with in-face directions. Our template shows how the convergence rates follow from two affine-invariant properties of the problem, namely, error bound and extended curvature. These properties depend solely on the polytope and objective function but not on any affine-dependent object like norms. For each one of the above algorithms, we derive rates of convergence ranging from sublinear to linear depending on the degree of the error bound.
title Fast convergence of Frank-Wolfe algorithms on polytopes
topic Optimization and Control
90C25, 90C52
url https://arxiv.org/abs/2406.18789