Surrogate duality for robust optimization

Satoshi Suzuki, Daishi Kuroiwa and Gue Myung Lee

European Journal of Operational Research, 2013, vol. 231, issue 2, 257-262

Abstract: Robust optimization problems, which have uncertain data, are considered. We prove surrogate duality theorems for robust quasiconvex optimization problems and surrogate min–max duality theorems for robust convex optimization problems. We give necessary and sufficient constraint qualifications for surrogate duality and surrogate min–max duality, and show some examples at which such duality results are used effectively. Moreover, we obtain a surrogate duality theorem and a surrogate min–max duality theorem for semi-definite optimization problems in the face of data uncertainty.

Keywords: Nonlinear programming; Quasiconvex programming; Robust optimization (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:231:y:2013:i:2:p:257-262

DOI: 10.1016/j.ejor.2013.02.050

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