Distributionally robust joint chance constraints with second-order moment information
type: Item
instance of: scholarly article
title: Distributionally robust joint chance constraints with second-order moment information
zbMATH Open document ID: 1286.90103
author: Steve Zymler
author: Daniel Kuhn
author: Berç Rustem
published in: Mathematical Programming. Series A. Series B
publication date: 2013-03-18T00:00:00Z
full work available at URL: http://hdl.handle.net/10044/1/13955
review text: The chance constraint stochastic programming is considered assuming that only the first- and second-order moments of the random parameters and their supports are given. The authors show that the worst-case conditional value at risk approximation of robust individual constraints with quadratic constraint functions is exact. Moreover, such an approximation can be efficiently computed. For the case of joint chance constraints, the tightness of the proposed approximation is evaluated. An algorithm based on this approximation is implemented and tested on an applied problem related to the dynamic water reservoir control for the hydro-power generation.
Mathematics Subject Classification ID: 90C15
Mathematics Subject Classification ID: 90C22
zbMATH DE Number: 6145984
zbMATH Keywords: stochastic programming
zbMATH Keywords: chance constraints
zbMATH Keywords: robustness
zbMATH Keywords: conditional value at risk
reviewed by: Antanas Žilinskas
describes a project that uses: YALMIP
describes a project that uses: ROME
MaRDI profile type: Publication
OpenAlex ID: W2092660864
Wikidata QID: Q92188264
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DOI: 10.1007/S10107-011-0494-7
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description: scientific article; zbMATH DE number 6145984