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dc.contributor.authorLi, Bin
dc.contributor.authorQian, X.
dc.contributor.authorSun, Jie
dc.contributor.authorTeo, Kok Lay
dc.contributor.authorYu, C.
dc.identifier.citationLi, B. and Qian, X. and Sun, J. and Teo, K.L. and Yu, C. 2018. A model of distributionally robust two-stage stochastic convex programming with linear recourse. Applied Mathematical Modelling. 58: pp. 86-97.

We consider distributionally robust two-stage stochastic convex programming problems, in which the recourse problem is linear. Other than analyzing these new models case by case for different ambiguity sets, we adopt a unified form of ambiguity sets proposed by Wiesemann, Kuhn and Sim, and extend their analysis from a single stochastic constraint to the two-stage stochastic programming setting. It is shown that under a standard set of regularity conditions, this class of problems can be converted to a conic optimization problem. Numerical results are presented to show the efficiency of the distributionally robust approach.

dc.titleA model of distributionally robust two-stage stochastic convex programming with linear recourse
dc.typeJournal Article
dcterms.source.titleApplied Mathematical Modelling
curtin.departmentDepartment of Mathematics and Statistics
curtin.accessStatusOpen access via publisher

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