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Extragradient Method in Optimization: Convergence and Complexity

Author

Listed:
  • Trong Phong Nguyen

    (Université Toulouse I Capitole
    Centro de Modelamiento Matemático (UMI 2807, CNRS), Universidad de Chile)

  • Edouard Pauwels

    (IRIT-UPS)

  • Emile Richard

    (AdRoll)

  • Bruce W. Suter

    (Air Force Research Laboratory)

Abstract

We consider the extragradient method to minimize the sum of two functions, the first one being smooth and the second being convex. Under the Kurdyka–Łojasiewicz assumption, we prove that the sequence produced by the extragradient method converges to a critical point of the problem and has finite length. The analysis is extended to the case when both functions are convex. We provide, in this case, a sublinear convergence rate, as for gradient-based methods. Furthermore, we show that the recent small-prox complexity result can be applied to this method. Considering the extragradient method is an occasion to describe an exact line search scheme for proximal decomposition methods. We provide details for the implementation of this scheme for the one-norm regularized least squares problem and demonstrate numerical results which suggest that combining nonaccelerated methods with exact line search can be a competitive choice.

Suggested Citation

  • Trong Phong Nguyen & Edouard Pauwels & Emile Richard & Bruce W. Suter, 2018. "Extragradient Method in Optimization: Convergence and Complexity," Journal of Optimization Theory and Applications, Springer, vol. 176(1), pages 137-162, January.
    • Handle: RePEc:spr:joptap:v:176:y:2018:i:1:d:10.1007_s10957-017-1200-6
    DOI: 10.1007/s10957-017-1200-6
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    References listed on IDEAS

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    1. Y. Censor & A. Gibali & S. Reich, 2011. "The Subgradient Extragradient Method for Solving Variational Inequalities in Hilbert Space," Journal of Optimization Theory and Applications, Springer, vol. 148(2), pages 318-335, February.
    2. Patrick L. Combettes & Jean-Christophe Pesquet, 2011. "Proximal Splitting Methods in Signal Processing," Springer Optimization and Its Applications, in: Heinz H. Bauschke & Regina S. Burachik & Patrick L. Combettes & Veit Elser & D. Russell Luke & Henry (ed.), Fixed-Point Algorithms for Inverse Problems in Science and Engineering, chapter 0, pages 185-212, Springer.
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    Cited by:

    1. Xiantao Xiao, 2021. "A Unified Convergence Analysis of Stochastic Bregman Proximal Gradient and Extragradient Methods," Journal of Optimization Theory and Applications, Springer, vol. 188(3), pages 605-627, March.

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