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arXiv · 2504.04213

A New Convergence Analysis of Two Stochastic Frank-Wolfe Algorithms

Natthawut Boonsiriphatthanajaroen·Shane G. Henderson
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Abstract

We study the convergence properties of the original and away-step Frank-Wolfe algorithms for linearly constrained stochastic optimization assuming the availability of unbiased objective function gradient estimates. The objective function is not restricted to a finite summation form, like in previous analyses tailored to machine-learning applications. To enable the use of concentration inequalities we assume either a uniform bound on the variance of gradient estimates or uniformly sub-Gaussian tails on gradient estimates. With one of these regularity assumptions along with sufficient sampling, we can ensure sufficiently accurate gradient estimates. We then use a Lyapunov argument to obtain the desired complexity bounds, relying on existing geometrical results for polytopes.

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Natthawut Boonsiriphatthanajaroen, Shane G. Henderson. 2025-04-05. A New Convergence Analysis of Two Stochastic Frank-Wolfe Algorithms. https://arxiv.org/abs/2504.04213

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