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Fast convergence of Frank-Wolfe algorithms on polytopes

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arxiv 2406.18789 v4 pith:7KPNDJ4Y submitted 2024-06-26 math.OC

classification math.OC
keywords frank-wolfeconvergenceratesalgorithmsboundderiveerrorpolytopes
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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.

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  1. Efficient Sparse Flow Decomposition Methods for RNA Multi-Assembly

    math.OC 2025-01 conditional novelty 5.0 of 10

    Sparse flow decomposition is reformulated as a convex fit over the flow polytope and solved with Frank-Wolfe, yielding fast, competitive reconstructions that do not require explicit path-count minimization.

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