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

In: Data Analysis and Optimization

Author

Listed:
  • Aleksandr Beznosikov

    (Moscow Institute of Physics and Technology
    HSE University)

  • Alexander Gasnikov

    (Moscow Institute of Physics and Technology
    IITP RAS
    Adyghe State University)

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.

Suggested Citation

  • Aleksandr Beznosikov & Alexander Gasnikov, 2023. "SARAH-Based Variance-Reduced Algorithm for Stochastic Finite-Sum Cocoercive Variational Inequalities," Springer Optimization and Its Applications, in: Boris Goldengorin & Sergei Kuznetsov (ed.), Data Analysis and Optimization, pages 47-57, Springer.
    • Handle: RePEc:spr:spochp:978-3-031-31654-8_3
    DOI: 10.1007/978-3-031-31654-8_3
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