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

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

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.

Suggested Citation

  • Pichugin, Alexander & Pechin, Maksim & Beznosikov, Aleksandr & Novitskii, Vasilii & Gasnikov, Alexander, 2024. "Method with batching for stochastic finite-sum variational inequalities in non-Euclidean setting," Chaos, Solitons & Fractals, Elsevier, vol. 187(C).
    • Handle: RePEc:eee:chsofr:v:187:y:2024:i:c:s0960077924009482
    DOI: 10.1016/j.chaos.2024.115396
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    References listed on IDEAS

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    1. Ahmet Alacaoglu & Yura Malitsky & Volkan Cevher, 2021. "Forward-reflected-backward method with variance reduction," Computational Optimization and Applications, Springer, vol. 80(2), pages 321-346, November.
    2. Rieger, Janosch & Tam, Matthew K., 2020. "Backward-Forward-Reflected-Backward Splitting for Three Operator Monotone Inclusions," Applied Mathematics and Computation, Elsevier, vol. 381(C).
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