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Approximate Optimality and Approximate Duality for Quasi Approximate Solutions in Robust Convex Semidefinite Programs

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  • Liguo Jiao

    (Pukyong National University)

  • Jae Hyoung Lee

    (Pukyong National University)

Abstract

In this paper, we study quasi approximate solutions for a convex semidefinite programming problem in the face of data uncertainty. Using the robust optimization approach (worst-case approach), approximate optimality conditions and approximate duality theorems for quasi approximate solutions in robust convex semidefinite programming problems are explored under the robust characteristic cone constraint qualification. Moreover, some examples are given to illustrate the obtained results.

Suggested Citation

  • Liguo Jiao & Jae Hyoung Lee, 2018. "Approximate Optimality and Approximate Duality for Quasi Approximate Solutions in Robust Convex Semidefinite Programs," Journal of Optimization Theory and Applications, Springer, vol. 176(1), pages 74-93, January.
    • Handle: RePEc:spr:joptap:v:176:y:2018:i:1:d:10.1007_s10957-017-1199-8
    DOI: 10.1007/s10957-017-1199-8
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    References listed on IDEAS

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    Cited by:

    1. Thai Doan Chuong, 2022. "Approximate solutions in nonsmooth and nonconvex cone constrained vector optimization," Annals of Operations Research, Springer, vol. 311(2), pages 997-1015, April.

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