Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations

We consider stochastic programs where the distribution of the uncertain parameters is only observable through a finite training dataset. Using the Wasserstein metric, we construct a ball in the space of (multivariate and non-discrete) probability distributions centered at the uniform distribution on...

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Bibliographic Details
Published in:	Mathematical programming Vol. 171; no. 1-2; pp. 115 - 166
Main Authors:	Mohajerin Esfahani, Peyman, Kuhn, Daniel
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2018 Springer Nature B.V
Subjects:	Calculus of Variations and Optimal Control; Optimization Combinatorics Economic models Full Length Paper Global optimization Mathematical and Computational Physics Mathematical Methods in Physics Mathematics Mathematics and Statistics Mathematics of Computing Monte Carlo simulation Nonlinear programming Numerical Analysis Optimization techniques Parameter uncertainty Theoretical Training 90C25 Convex programming 90C47 Minimax problems 90C15 Stochastic programming
ISSN:	0025-5610, 1436-4646
Online Access:	Get full text
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