Metric regularity and quantitative stability in stochastic programs with probabilistic constraints
نویسندگان
چکیده
منابع مشابه
Metric regularity and quantitative stability in stochastic programs with probabilistic constraints
Introducing probabilistic constraints leads in general to nonconvex, nonsmooth or even discontinuous optimization models. In this paper, necessary and sufficient conditions for metric regularity of (several joint) probabilistic constraints are derived using recent results from nonsmooth analysis. The conditions apply to fairly general constraints and extend earlier work in this direction. Furth...
متن کاملMetric regularity and quantitative stability in stochastic programs with probabilistic constraints
Necessary and su cient conditions for metric regularity of several joint probabilistic constraints are derived using recent results from nonsmooth analysis The conditions apply to fairly general nonconvex nonsmooth probabilistic constraints and extend earlier work in this direction Further a veri able su cient condition for quadratic growth of the objective function in a more speci c convex sto...
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Due to their frequently observed lack of convexity and/or smoothness, stochastic programs with joint probabilistic constraints have been considered as a hard type of constrained optimization problems, which are rather demanding both from the computational and robustness point of view. Dependence of the set of solutions on the probability distribution rules out the straightforward construction o...
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ژورنال
عنوان ژورنال: Mathematical Programming
سال: 1999
ISSN: 0025-5610,1436-4646
DOI: 10.1007/s10107980016a