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hal.structure.identifierFormulations étendues et méthodes de décomposition pour des problèmes génériques d'optimisation [EDGE]
dc.contributor.authorARSLAN, Ayşe
hal.structure.identifierMethods, Algorithms for Operations REsearch [LIRMM | MAORE]
dc.contributor.authorPOSS, Michael
dc.date.accessioned2024-04-04T02:30:29Z
dc.date.available2024-04-04T02:30:29Z
dc.date.created2023
dc.date.issued2023-07-11
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/190248
dc.description.abstractEnUncertainty reduction has recently been introduced in the robust optimization literature as a relevant special case of decisiondependent uncertainty. Herein, we first show that when the uncertainty reduction decisions are constrained, the resulting optimizationproblem is NP-hard. We further show that relaxing these constraints leads to solving a linear number of deterministic problems in certain special cases and illustrate the numerical relevance of this result. We further provide insights into possible MILP reformulations and the strength of their continuous relaxations.
dc.description.sponsorshipBornes primales et duales pour optimisation robuste adjustable - ANR-22-CE48-0018
dc.language.isoen
dc.subject.enCombinatorial optimization
dc.subject.enRobust optimization
dc.subject.enNP-hardness
dc.subject.enReformulation
dc.title.enUncertainty reduction in robust optimization
dc.typeDocument de travail - Pré-publication
dc.subject.halInformatique [cs]/Recherche opérationnelle [cs.RO]
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
hal.identifierhal-04158877
hal.version1
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-04158877v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2023-07-11&rft.au=ARSLAN,%20Ay%C5%9Fe&POSS,%20Michael&rft.genre=preprint


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