Exact Penalization in Stochastic Programming - Calmness and Constraint Qualification

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F14%3A10281183" target="_blank" >RIV/00216208:11320/14:10281183 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1155/2014/569458" target="_blank" >http://dx.doi.org/10.1155/2014/569458</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1155/2014/569458" target="_blank" >10.1155/2014/569458</a>

Result language
angličtina
Original language name
Exact Penalization in Stochastic Programming - Calmness and Constraint Qualification
Original language description
We deal with the conditions which ensure exact penalization in stochastic programming problems under finite discrete distributions. We give several sufficient conditions for problem calmness including graph calmness, existence of an error bound, and generalized Mangasarian-Fromowitz constraint qualification. We propose a new version of the theorem on asymptotic equivalence of local minimizers of chance constrained problems and problems with exact penalty objective. We apply the theory to a problem witha stochastic vanishing constraint.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
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Project
<a href="/en/project/GP13-03749P" target="_blank" >GP13-03749P: Efficiency tests for investment opportunities</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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