Adaptive Methods or Variational Inequalities with Relatively Smooth and Reletively Strongly Monotone Operators

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Abstract

The article is devoted to some adaptive methods for variational inequalities with relatively smooth and relatively strongly monotone operators. Based on the recently proposed proximal version of the extragradient method for this class of problems, we study in detail the method with adaptively selected parameter values. An estimate for the rate of convergence of this method is proved. The result is generalized to a class of variational inequalities with relatively strongly monotone δ-generalized smooth variational inequality operators. For the problem of ridge regression and variational inequality associated with box-simplex games, numerical experiments were performed demonstrating the effectiveness of the proposed method of adaptive selection of parameters during the running of the algorithm.

About the authors

S. S. Ablaev

Moscow Institute of Physics and Technology
; Vernadsky Crimean Federal University

Author for correspondence.
Email: seydamet.ablaev@yandex.ru
Russia, 141701, Moscow region, Dolgoprudny, Institutskiy per., 9; Russia, 295007, Simferopol, Academician Vernadsky Avenue, 4

F. S. Stonyakin

Moscow Institute of Physics and Technology
; Vernadsky Crimean Federal University

Author for correspondence.
Email: fedyor@mail.ru
Russia, 141701, Moscow region, Dolgoprudny, Institutskiy per., 9; Russia, 295007, Simferopol, Academician Vernadsky Avenue, 4

M. S. Alkousa

Moscow Institute of Physics and Technology
; National Research University “Higher School of Economics”

Author for correspondence.
Email: mohammad.alkousa@phystech.edu
Russia, 141701, Moscow region, Dolgoprudny, Institutskiy per., 9; Russia, 101000, Moscow, Myasnitskaya st., 20

D. A. Pasechnyk

Moscow Institute of Physics and Technology
; Trusted Artificial Intelligence Research Center of ISP RAS

Author for correspondence.
Email: dmivilensky1@gmail.com
Russia, 141701, Moscow region, Dolgoprudny, Institutskiy per., 9; Russia, 109004, Moscow, Alexander Solzhenitsyn st., 25

References

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  2. Cohen M.B., Sidford A., Tian K. Relative Lipschitzness in Extragradient Methods and a Direct Recipe for Acceleration. arXiv preprint https://arxiv.org/pdf/2011.06572.pdf (2020).
  3. Titov A.A., Ablaev S.S., Stonyakin F.S., Alkousa M.S., Gasnikov A. Some Adaptive First-Order Methods for Variational Inequalities with Relatively Strongly Monotone Operators and Generalized Smoothness. In: Olenev N., Evtushenko Y., Jaćimović M., Khachay M., Malkova V., Pospelov I. (eds) Optimization and Applications. OPTIMA 2022. Lecture Notes in Computer Science, vol 13781. Springer, Cham, 2022.
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  6. Hendrikx H., Xiao L., Bubeck S., Bach F., Massoulie L. Statistically preconditioned accelerated gradient method for distributed optimization. In International conference on machine learning, 4203–4227. PMLR, 2020.
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Copyright (c) 2023 С.С. Аблаев, Ф.С. Стонякин, М.С. Алкуса, Д.А. Пасечнюк

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