Method with batching for stochastic finite-sum variational inequalities in non-Euclidean setting

Alexander Pichugin, Maksim Pechin, Aleksandr Beznosikov, Vasilii Novitskii and Alexander Gasnikov

Chaos, Solitons & Fractals, 2024, vol. 187, issue C

Abstract: Variational inequalities are a universal optimization paradigm that incorporate classical minimization and saddle point problems. Nowadays more and more tasks require to consider stochastic formulations of optimization problems. In this paper, we present an analysis of a method that gives optimal convergence estimates for monotone stochastic finite-sum variational inequalities. In contrast to the previous works, our method supports batching, does not lose the oracle complexity optimality and uses an arbitrary Bregman distance to take into account geometry of the problem. Paper provides experimental confirmation to algorithm’s effectiveness.

Keywords: Stochastic optimization; Variational inequalities; Finite-sum problems; Batching; Bregman distance (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:187:y:2024:i:c:s0960077924009482

DOI: 10.1016/j.chaos.2024.115396

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