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- W2062843008 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 − ∊. 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." @default.
- W2062843008 created "2016-06-24" @default.
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- W2062843008 date "2012-06-01" @default.
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- W2062843008 title "On safe tractable approximations of chance constraints" @default.
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- W2062843008 doi "https://doi.org/10.1016/j.ejor.2011.11.006" @default.
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