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Robust canonical duality theory for solving nonconvex programming problems under data uncertainty

Author

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  • Linsong Shen

    (Shanghai University of Finance and Economics)

  • Yanjun Wang

    (Shanghai University of Finance and Economics)

  • Xiaomei Zhang

    (Shanghai University of Finance and Economics)

Abstract

This paper presents a robust canonical duality–triality theory for solving nonconvex programming problems under data uncertainty. This theory includes a robust canonical saddle-point theorem and robust canonical optimality conditions, which can be used to identify both robust global and local extrema of the primal problem. Two numerical examples are presented to illustrate that the robust Triality theory is particularly powerful for solving nonconvex optimization problems with data uncertainty.

Suggested Citation

  • Linsong Shen & Yanjun Wang & Xiaomei Zhang, 2016. "Robust canonical duality theory for solving nonconvex programming problems under data uncertainty," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 84(1), pages 183-204, August.
    • Handle: RePEc:spr:mathme:v:84:y:2016:i:1:d:10.1007_s00186-016-0539-z
    DOI: 10.1007/s00186-016-0539-z
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