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Surrogate duality for robust optimization
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DOI：10.1016/j.ejor.2013.02.050.png)
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Abstract
En 中文
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. (C) 2013 Elsevier B.V. All rights reserved.
Keywords:
Nonlinear programming
Quasiconvex programming
Robust optimization
Journal
IF:
6
Papers:
2.2W
Citations:
6.4W