Abstract
In this paper, we examine stable exact relaxations for classes of parametric robust convex polynomial optimization problems under affinely parameterized data uncertainty in the constraints. We first show that a parametric robust convex polynomial problem with convex compact uncertainty sets enjoys stable exact conic relaxations under the validation of a characteristic cone constraint qualification. We then show that such stable exact conic relaxations become stable exact semidefinite programming relaxations for a parametric robust SOS-convex polynomial problem, where the uncertainty sets are assumed to be bounded spectrahedra. In addition, under the corresponding constraint qualification, we derive stable exact second-order cone programming relaxations for some classes of parametric robust convex quadratic programs under ellipsoidal uncertainty sets.
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Acknowledgements
The authors would like to thank a reviewer for valuable comments and suggestions which have contributed to a significant improvement in the paper. They are also grateful to Professor V. Jeyakumar for discussing the topic. Research of T.D. Chuong was supported by the National Foundation for Science and Technology Development of Vietnam (NAFOSTED) under grant number 101.01\(-\)2020.09. Research of J. Vicente-Pérez was partially supported by the Ministry of Science, Innovation and Universities of Spain and the European Regional Development Fund (ERDF) of the European Commission, Grant PGC2018-097960-B-C22, and by the Generalitat Valenciana, Grant AICO/2021/165.
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Chuong, T.D., Vicente-Pérez, J. Conic Relaxations with Stable Exactness Conditions for Parametric Robust Convex Polynomial Problems. J Optim Theory Appl 197, 387–410 (2023). https://doi.org/10.1007/s10957-023-02197-1
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DOI: https://doi.org/10.1007/s10957-023-02197-1
Keywords
- Robust optimization
- Convex polynomial
- Stable exact relaxation
- Spectrahedral uncertainty set
- Conic relaxation