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A Projected Subgradient Method for Nondifferentiable Quasiconvex Multiobjective Optimization Problems

Author

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  • Xiaopeng Zhao

    (Tiangong University)

  • Markus A. Köbis

    (Norwegian University of Science and Technology)

  • Yonghong Yao

    (Tiangong University)

  • Jen-Chih Yao

    (China Medical University
    National Sun Yat-sen University)

Abstract

In this paper, we propose a projected subgradient method for solving constrained nondifferentiable quasiconvex multiobjective optimization problems. The algorithm is based on the Plastria subdifferential to overcome potential shortcomings known from algorithms based on the classical gradient. Under suitable, yet rather general assumptions, we establish the convergence of the full sequence generated by the algorithm to a Pareto efficient solution of the problem. Numerical results are presented to illustrate our findings.

Suggested Citation

  • Xiaopeng Zhao & Markus A. Köbis & Yonghong Yao & Jen-Chih Yao, 2021. "A Projected Subgradient Method for Nondifferentiable Quasiconvex Multiobjective Optimization Problems," Journal of Optimization Theory and Applications, Springer, vol. 190(1), pages 82-107, July.
    • Handle: RePEc:spr:joptap:v:190:y:2021:i:1:d:10.1007_s10957-021-01872-5
    DOI: 10.1007/s10957-021-01872-5
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    References listed on IDEAS

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    Cited by:

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    3. Mostafa Ghadampour & Ebrahim Soori & Ravi P. Agarwal & Donal O’Regan, 2022. "A Strong Convergence Theorem for Solving an Equilibrium Problem and a Fixed Point Problem Using the Bregman Distance," Journal of Optimization Theory and Applications, Springer, vol. 195(3), pages 854-877, December.

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