On safe tractable approximations of chance constraints

Arkadi Nemirovski

European Journal of Operational Research, 2012, vol. 219, issue 3, 707-718

Abstract: 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−ϵ. 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.

Keywords: Uncertainty modeling; Convex programming; Optimization under uncertainty; Chance constraints; Robust Optimization (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:219:y:2012:i:3:p:707-718

DOI: 10.1016/j.ejor.2011.11.006

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