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On safe tractable approximations of chance constraints

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  • Nemirovski, Arkadi

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.

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  • Nemirovski, Arkadi, 2012. "On safe tractable approximations of chance constraints," European Journal of Operational Research, Elsevier, vol. 219(3), pages 707-718.
    • Handle: RePEc:eee:ejores:v:219:y:2012:i:3:p:707-718
    DOI: 10.1016/j.ejor.2011.11.006
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