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Random Descent Steps in a Probability Maximization Scheme

Author

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  • Edit Csizmás

    (John von Neumann University)

  • Rajmund Drenyovszki

    (John von Neumann University)

  • Tamás Szántai

    (Budapest University of Technology and Economics)

  • Csaba I. Fábián

    (John von Neumann University)

Abstract

Gradient computation of multivariate distribution functions calls for considerable effort. Hence coordinate descent and derivative-free approaches are attractive. This paper deals with constrained convex problems. We perform random descent steps in an approximation scheme that is an inexact cutting-plane method from a dual viewpoint. We prove that the scheme converges and present a computational study comparing different descent methods applied in the approximation scheme.

Suggested Citation

  • Edit Csizmás & Rajmund Drenyovszki & Tamás Szántai & Csaba I. Fábián, 2025. "Random Descent Steps in a Probability Maximization Scheme," Journal of Optimization Theory and Applications, Springer, vol. 205(1), pages 1-26, April.
    • Handle: RePEc:spr:joptap:v:205:y:2025:i:1:d:10.1007_s10957-025-02619-2
    DOI: 10.1007/s10957-025-02619-2
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    References listed on IDEAS

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    1. Butyn, Emerson & Karas, Elizabeth W. & de Oliveira, Welington, 2022. "A derivative-free trust-region algorithm with copula-based models for probability maximization problems," European Journal of Operational Research, Elsevier, vol. 298(1), pages 59-75.
    2. Darinka Dentcheva & Bogumila Lai & Andrzej Ruszczyński, 2004. "Dual methods for probabilistic optimization problems ," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 60(2), pages 331-346, October.
    3. NESTEROV, Yurii, 2012. "Efficiency of coordinate descent methods on huge-scale optimization problems," LIDAM Reprints CORE 2511, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. C. van de Panne & W. Popp, 1963. "Minimum-Cost Cattle Feed Under Probabilistic Protein Constraints," Management Science, INFORMS, vol. 9(3), pages 405-430, April.
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