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Stochastic Extragradient with Random Reshuffling: Improved Convergence for Variational Inequalities
March 13, 2024, 4:42 a.m. | Konstantinos Emmanouilidis, Ren\'e Vidal, Nicolas Loizou
cs.LG updates on arXiv.org arxiv.org
Abstract: The Stochastic Extragradient (SEG) method is one of the most popular algorithms for solving finite-sum min-max optimization and variational inequality problems (VIPs) appearing in various machine learning tasks. However, existing convergence analyses of SEG focus on its with-replacement variants, while practical implementations of the method randomly reshuffle components and sequentially use them. Unlike the well-studied with-replacement variants, SEG with Random Reshuffling (SEG-RR) lacks established theoretical guarantees. In this work, we provide a convergence analysis of …
abstract algorithms arxiv convergence cs.gt cs.lg focus however inequality machine machine learning math.oc max optimization popular practical random replacement stat.ml stochastic tasks type variants
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