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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 of the convexity-based global contamination bounds for the optimal value; at least local results for probabilistic programs of a special structure will be derived. Several alternative approaches to output analysis are mentioned.
  • 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 of the convexity-based global contamination bounds for the optimal value; at least local results for probabilistic programs of a special structure will be derived. Several alternative approaches to output analysis are mentioned. (en)
Title
  • Robustness Analysis of Stochastic Programs with Joint Probabilistic Constraints
  • Robustness Analysis of Stochastic Programs with Joint Probabilistic Constraints (en)
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  • Robustness Analysis of Stochastic Programs with Joint Probabilistic Constraints
  • Robustness Analysis of Stochastic Programs with Joint Probabilistic Constraints (en)
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  • RIV/00216208:11320/13:10159022!RIV14-GA0-11320___
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  • P(GAP402/11/0150)
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  • 103179
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  • RIV/00216208:11320/13:10159022
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  • output analysis; contamination technique; Joint probabilistic constraints (en)
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  • [E39FA1E5C453]
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  • Berlin, Germany
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  • Heidelberg
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  • System Modeling and Optimization
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  • Dupačová, Jitka
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issn
  • 1868-4238
number of pages
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  • 10.1007/978-3-642-36062-6_16
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  • Springer-Verlag
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  • 978-3-642-36062-6
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  • 11320
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