Show simple item record

2005-04-28Buch DOI: 10.18452/8341
Adaptive and nonadaptive samples in solving stochastic linear programs
dc.contributor.authorHigle, Julia L.
dc.contributor.authorZhao, Lei
dc.contributor.editorHigle, Julie L.
dc.contributor.editorRömisch, Werner
dc.contributor.editorSen, Surrajeet
dc.date.accessioned2017-06-16T20:04:43Z
dc.date.available2017-06-16T20:04:43Z
dc.date.created2006-03-08
dc.date.issued2005-04-28
dc.date.submitted2004-12-23
dc.identifier.urihttp://edoc.hu-berlin.de/18452/8993
dc.description.abstractLarge scale stochastic linear programs are typically solved using a combination of mathematical programming techniques and sample-based approximations. Some methods are designed to permit sample sizes to adapt to information obtained during the solution process, while others are not. In this paper, we experimentally examine the relative merits of approximations based on adaptive samples and those based on non-adaptive samples. We begin with an examination of two versions of an adaptive technique, Stochastic Decomposition (SD), and conclude with a comparison to a nonadaptive technique, the Sample Average Approximation method (SAA). Our results indicate that there is minimal di®erence in the quality of the solutions provided by SD and SAA, although SAA requires substantially more time to execute.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, Institut für Mathematik
dc.subject.ddc510 Mathematik
dc.titleAdaptive and nonadaptive samples in solving stochastic linear programs
dc.typebook
dc.subtitleA computational investigation
dc.identifier.urnurn:nbn:de:kobv:11-10059872
dc.identifier.doihttp://dx.doi.org/10.18452/8341
local.edoc.container-titleStochastic Programming E-Print Series
local.edoc.pages26
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2005
local.edoc.container-issue12
local.edoc.container-erstkatid2936317-2

Show simple item record