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Generalized Mirror Prox Algorithm for Monotone Variational Inequalities: Universality and Inexact Oracle

Author

Listed:
  • Fedor Stonyakin

    (V.I. Vernadsky Crimean Federal University
    Moscow Institute of Physics and Technology)

  • Alexander Gasnikov

    (Moscow Institute of Physics and Technology
    Institute for Information Transmission Problems
    HSE University)

  • Pavel Dvurechensky

    (Weierstrass Institute for Applied Analysis and Stochastics)

  • Alexander Titov

    (Moscow Institute of Physics and Technology
    HSE University)

  • Mohammad Alkousa

    (Moscow Institute of Physics and Technology
    HSE University)

Abstract

We introduce an inexact oracle model for variational inequalities with monotone operators, propose a numerical method that solves such variational inequalities, and analyze its convergence rate. As a particular case, we consider variational inequalities with Hölder-continuous operator and show that our algorithm is universal. This means that, without knowing the Hölder exponent and Hölder constant, the algorithm has the least possible in the worst-case sense complexity for this class of variational inequalities. We also consider the case of variational inequalities with a strongly monotone operator and generalize the algorithm for variational inequalities with inexact oracle and our universal method for this class of problems. Finally, we show how our method can be applied to convex–concave saddle point problems with Hölder-continuous partial subgradients.

Suggested Citation

  • Fedor Stonyakin & Alexander Gasnikov & Pavel Dvurechensky & Alexander Titov & Mohammad Alkousa, 2022. "Generalized Mirror Prox Algorithm for Monotone Variational Inequalities: Universality and Inexact Oracle," Journal of Optimization Theory and Applications, Springer, vol. 194(3), pages 988-1013, September.
    • Handle: RePEc:spr:joptap:v:194:y:2022:i:3:d:10.1007_s10957-022-02062-7
    DOI: 10.1007/s10957-022-02062-7
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    References listed on IDEAS

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    1. Pavel Dvurechensky & Yurii Nesterov & Vladimir Spokoiny, 2015. "Primal-Dual Methods for Solving Infinite-Dimensional Games," Journal of Optimization Theory and Applications, Springer, vol. 166(1), pages 23-51, July.
    2. DEVOLDER, Olivier & GLINEUR, François & NESTEROV, Yurii, 2011. "First-order methods of smooth convex optimization with inexact oracle," LIDAM Discussion Papers CORE 2011002, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. NESTEROV, Yurii, 2015. "Universal gradient methods for convex optimization problems," LIDAM Reprints CORE 2701, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Dvurechensky, Pavel & Gorbunov, Eduard & Gasnikov, Alexander, 2021. "An accelerated directional derivative method for smooth stochastic convex optimization," European Journal of Operational Research, Elsevier, vol. 290(2), pages 601-621.
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    6. NESTEROV, Yu, 2003. "Dual extrapolation and its applications for solving variational inequalities and related problems," LIDAM Discussion Papers CORE 2003068, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    7. DVURECHENSKY, Pavel & NESTEROV, Yurii & SPOKOINY, Vladimir, 2015. "Primal-dual methods for solving infinite-dimensional games," LIDAM Reprints CORE 2700, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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