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First-Order Algorithms for Robust Optimization Problems via Convex-Concave Saddle-Point Lagrangian Reformulation

Author

Listed:
  • Krzysztof Postek

    (Independent Researcher)

  • Shimrit Shtern

    (Faculty of Data and Decision Sciences, Technion—Israel Institute of Technology, Haifa 3200003, Israel)

Abstract

Robust optimization (RO) is one of the key paradigms for solving optimization problems affected by uncertainty. Two principal approaches for RO, the robust counterpart method and the adversarial approach, potentially lead to excessively large optimization problems. For that reason, first-order approaches, based on online convex optimization, have been proposed as alternatives for the case of large-scale problems. However, existing first-order methods are either stochastic in nature or involve a binary search for the optimal value. We show that this problem can also be solved with deterministic first-order algorithms based on a saddle-point Lagrangian reformulation that avoids both of these issues. Our approach recovers the other approaches’ O ( 1 / ϵ 2 ) convergence rate in the general case and offers an improved O ( 1 / ϵ ) rate for problems with constraints that are affine both in the decision and in the uncertainty. Experiment involving robust quadratic optimization demonstrates the numerical benefits of our approach.

Suggested Citation

  • Krzysztof Postek & Shimrit Shtern, 2025. "First-Order Algorithms for Robust Optimization Problems via Convex-Concave Saddle-Point Lagrangian Reformulation," INFORMS Journal on Computing, INFORMS, vol. 37(3), pages 557-581, May.
    • Handle: RePEc:inm:orijoc:v:37:y:2025:i:3:p:557-581
    DOI: 10.1287/ijoc.2022.0200
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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.
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