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2005-11-04Buch DOI: 10.18452/2618
Duality gaps in nonconvex stochastic optimization
dc.contributor.authorDentcheva, Darinka
dc.contributor.authorRömisch, Werner
dc.date.accessioned2017-06-15T17:44:38Z
dc.date.available2017-06-15T17:44:38Z
dc.date.created2005-11-04
dc.date.issued2005-11-04
dc.identifier.issn0863-0976
dc.identifier.urihttp://edoc.hu-berlin.de/18452/3270
dc.description.abstractWe consider multistage stochastic optimization models. Logical or integrality constraints, frequently present in optimization models, limit the application of powerful convex analysis tools. Different Lagrangian relaxation schemes and the resulting decomposition approaches provide estimates of the optimal value. We formulate convex optimization models equivalent to the dual problems of the Lagrangian relaxations. Our main results compare the resulting duality gap for these decomposition schemes. Attention is paid also to programs that model large systems with loosely coupled components.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, Institut für Mathematik
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectLagrangian relaxationeng
dc.subjectdecompositioneng
dc.subjectStochastic programmingeng
dc.subjectmixed-integereng
dc.subjectnonconvexeng
dc.subjectduality gapeng
dc.subject.ddc510 Mathematik
dc.titleDuality gaps in nonconvex stochastic optimization
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-10052526
dc.identifier.doihttp://dx.doi.org/10.18452/2618
local.edoc.pages23
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-year2002
dc.identifier.zdb2075199-0
bua.series.namePreprints aus dem Institut für Mathematik
bua.series.issuenumber2002,5

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