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Parallel decomposition of large-scale stochastic nonlinear programs
dc.contributor.authorBirge, John R.en_US
dc.contributor.authorRosa, Charles H.en_US
dc.date.accessioned2006-09-11T14:32:28Z
dc.date.available2006-09-11T14:32:28Z
dc.date.issued1996-12en_US
dc.identifier.citationBirge, John R.; Rosa, Charles H.; (1996). "Parallel decomposition of large-scale stochastic nonlinear programs." Annals of Operations Research 64(1): 39-65. <http://hdl.handle.net/2027.42/44277>en_US
dc.identifier.issn0254-5330en_US
dc.identifier.issn1572-9338en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/44277
dc.description.abstractMany practical decision problems involve both nonlinear relationships and uncertainties. The resulting stochastic nonlinear programs become quite difficult to solve as the number of possible scenarios increases. In this paper, we provide a decomposition method for problems in which nonlinear constraints appear within periods. We also show how the method extends to lower bounding refinements of the set of scenarios when the random data are independent from period to period. We then apply the method to a stochastic model of the U.S. economy based on the Global 2100 method developed by Manne and Richels.en_US
dc.format.extent1088201 bytes
dc.format.extent3115 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.language.isoen_US
dc.publisherBaltzer Science Publishers, Baarn/Kluwer Academic Publishers; J.C. Baltzer AG, Science Publishers ; Springer Science+Business Mediaen_US
dc.subject.otherEconomics / Management Scienceen_US
dc.subject.otherTheory of Computationen_US
dc.subject.otherCombinatoricsen_US
dc.subject.otherOperations Research/Decision Theoryen_US
dc.subject.otherDecompositionen_US
dc.subject.otherEconomicsen_US
dc.subject.otherEnvironmenten_US
dc.subject.otherParallel Computationen_US
dc.subject.otherStochastic Programmingen_US
dc.titleParallel decomposition of large-scale stochastic nonlinear programsen_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelManagementen_US
dc.subject.hlbsecondlevelIndustrial and Operations Engineeringen_US
dc.subject.hlbsecondlevelEconomicsen_US
dc.subject.hlbtoplevelBusinessen_US
dc.subject.hlbtoplevelEngineeringen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Industrial and Operations Engineering, University of Michigan, 48109, Ann Arbor, MI, USAen_US
dc.contributor.affiliationotherInternational Institute for Applied Systems Analysis, A-2361, Laxenburg, Austriaen_US
dc.contributor.affiliationumcampusAnn Arboren_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/44277/1/10479_2005_Article_BF02187640.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1007/BF02187640en_US
dc.identifier.sourceAnnals of Operations Researchen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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