Data-driven distributionally robust chance-constrained optimization with Wasserstein metric

We study distributionally robust chance-constrained programming ( DRCCP ) optimization problems with data-driven Wasserstein ambiguity sets. The proposed algorithmic and reformulation framework applies to all types of distributionally robust chance-constrained optimization problems subjected to indi...

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Bibliographic Details
Published in:	Journal of global optimization Vol. 79; no. 4; pp. 779 - 811
Main Authors:	Ji, Ran, Lejeune, Miguel A.
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.04.2021 Springer Springer Nature B.V
Subjects:	Computer Science Constraints Integer programming Investment analysis Knapsack problem Linear programming Mathematics Mathematics and Statistics Mixed integer Operations Research/Decision Theory Optimization Real Functions Robustness Series (mathematics) Tightness Chance-constrained programming Mixed-integer programming Distributionally robust optimization Wasserstein metric
ISSN:	0925-5001, 1573-2916
Online Access:	Get full text
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