期刊
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
卷 219, 期 3, 页码 707-718出版社
ELSEVIER
DOI: 10.1016/j.ejor.2011.11.006
关键词
Uncertainty modeling; Convex programming; Optimization under uncertainty; Chance constraints; Robust Optimization
资金
- NSF [DMI-0619977, DMS-0914785]
- ONR [N000140811104]
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [0914785] Funding Source: National Science Foundation
A natural way to handle optimization problem with data affected by stochastic uncertainty is to pass to a chance constrained version of the problem, where candidate solutions should satisfy the randomly perturbed constraints with probability at least 1 - epsilon. While being attractive from modeling viewpoint, chance constrained problems as they are are, in general, computationally intractable. In this survey paper, we overview several simulation-based and simulation-free computationally tractable approximations of chance constrained convex programs, primarily, those of chance constrained linear, conic quadratic and semidefinite programming. (C) 2011 Elsevier B.V. All rights reserved.
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