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An Alternating Iteration Algorithm for a Parameter-Dependent Distributionally Robust Optimization Model

Author

Listed:
  • Shuang Lin

    (Department of Basic Courses Teaching, Dalian Polytechnic University, Dalian 116034, China)

  • Jie Zhang

    (School of Mathematics, Liaoning Normal University, Dalian 116029, China)

  • Nan Shi

    (School of Mathematics, Liaoning Normal University, Dalian 116029, China)

Abstract

Based on a successive convex programming method, an alternating iteration algorithm is proposed for solving a parameter-dependent distributionally robust optimization. Under the Slater-type condition, the convergence analysis of the algorithm is obtained. When the objective function is convex, a modified algorithm is proposed and a less-conservative solution is obtained. Lastly, some numerical tests results are illustrated to show the efficiency of the algorithm.

Suggested Citation

  • Shuang Lin & Jie Zhang & Nan Shi, 2022. "An Alternating Iteration Algorithm for a Parameter-Dependent Distributionally Robust Optimization Model," Mathematics, MDPI, vol. 10(7), pages 1-12, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:7:p:1175-:d:786775
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    References listed on IDEAS

    as
    1. Joel Goh & Melvyn Sim, 2010. "Distributionally Robust Optimization and Its Tractable Approximations," Operations Research, INFORMS, vol. 58(4-part-1), pages 902-917, August.
    2. Erick Delage & Yinyu Ye, 2010. "Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems," Operations Research, INFORMS, vol. 58(3), pages 595-612, June.
    3. Wolfram Wiesemann & Daniel Kuhn & Melvyn Sim, 2014. "Distributionally Robust Convex Optimization," Operations Research, INFORMS, vol. 62(6), pages 1358-1376, December.
    4. Georg Pflug & David Wozabal, 2007. "Ambiguity in portfolio selection," Quantitative Finance, Taylor & Francis Journals, vol. 7(4), pages 435-442.
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