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dc.contributor.authorEsteban-Pérez, Adrián
dc.contributor.authorMorales-González, Juan Miguel 
dc.date.accessioned2021-11-23T08:09:06Z
dc.date.available2021-11-23T08:09:06Z
dc.date.issued2021-11
dc.identifier.urihttps://hdl.handle.net/10630/23261
dc.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.es_ES
dc.description.sponsorshipOpen Access funding provided by Universidad de Málaga / CBUA thanks to the CRUE-CSIC agreement with Springer Nature. This research has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 755705). This work was also supported in part by the Spanish Ministry of Science and Innovation (AEI/10.13039/501100011033) through project PID2020-115460GB-I00 and in part by the Junta de Andalucía through the research project P20_00153. Finally, the authors thankfully acknowledge the computer resources, technical expertise, and assistance provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Málaga.es_ES
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.relation.ispartofseriesSeries A;
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectMatemáticas aplicadases_ES
dc.subject.otherOptimización de distribución robustaes_ES
dc.subject.otherRecorteses_ES
dc.subject.otherInformación complementariaes_ES
dc.subject.otherProblema de transporte masivo parciales_ES
dc.subject.otherOptimización de carteraes_ES
dc.titleDistributionally robust stochastic programs with side information based on trimmingses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroEscuela de Ingenierías Industrialeses_ES
dc.identifier.doihttps://doi.org/10.1007/s10107-021-01724-0
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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