SARAH-Based Variance-Reduced Algorithm for Stochastic Finite-Sum Cocoercive Variational Inequalities

Aleksandr Beznosikov and Alexander Gasnikov ()
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Aleksandr Beznosikov: Moscow Institute of Physics and Technology
Alexander Gasnikov: Moscow Institute of Physics and Technology

A chapter in Data Analysis and Optimization, 2023, pp 47-57 from Springer

Abstract: Abstract Variational inequalities are a broad formalism that encompasses a vast number of applications. Motivated by applications in machine learning and beyond, stochastic methods are of great importance. In this paper we consider the problem of stochastic finite-sum cocoercive variational inequalities. For this class of problems, we investigate the convergence of the method based on the SARAH variance reduction technique. We show that for strongly monotone problems it is possible to achieve linear convergence to a solution using this method. Experiments confirm the importance and practical applicability of our approach.

Keywords: Stochastic optimization; Variational inequalities; Finite-sum problems (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-031-31654-8_3

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DOI: 10.1007/978-3-031-31654-8_3

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