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Media type:
E-Article
Title:
Distributionally robust stochastic programs with side information based on trimmings
Contributor:
Esteban-Pérez, Adrián;
Morales, Juan M.
Published:
Springer Science and Business Media LLC, 2022
Published in:
Mathematical Programming, 195 (2022) 1-2, Seite 1069-1105
Language:
English
DOI:
10.1007/s10107-021-01724-0
ISSN:
0025-5610;
1436-4646
Origination:
Footnote:
Description:
AbstractWe consider stochastic programs conditional on some covariate information, where the only knowledge of the possible relationship between the uncertain parameters and the covariates is reduced to a finite data sample of their joint distribution. By exploiting the close link between the notion of trimmings of a probability measure and the partial mass transportation problem, we construct a data-driven Distributionally Robust Optimization (DRO) framework to hedge the decision against the intrinsic error in the process of inferring conditional information from limited joint data. We show that our approach is computationally as tractable as the standard (without side information) Wasserstein-metric-based DRO and enjoys performance guarantees. Furthermore, our DRO framework can be conveniently used to address data-driven decision-making problems under contaminated samples. Finally, the theoretical results are illustrated using a single-item newsvendor problem and a portfolio allocation problem with side information.