Stochastic programming by Monte Carlo simulation methods
dc.contributor.author | Shapiro, Alexander | |
dc.contributor.editor | Higle, Julie L. | |
dc.contributor.editor | Römisch, Werner | |
dc.contributor.editor | Sen, Surrajeet | |
dc.date.accessioned | 2017-06-16T19:36:44Z | |
dc.date.available | 2017-06-16T19:36:44Z | |
dc.date.created | 2005-09-07 | |
dc.date.issued | 2000-01-31 | |
dc.date.submitted | 1999-12-10 | |
dc.identifier.uri | http://edoc.hu-berlin.de/18452/8878 | |
dc.description.abstract | We consider in this paper stochastic programming problems which can be formulated as an optimization problem of an expected value function subject to deterministic constraints. We discuss a Monte Carlo simulation approach based on sample average approximations to a numerical solution of such problems. In particular, we give a survey of a statistical inference of the sample average estimators of the optimal value and optimal solutions of the true problem. We also discuss stopping rules and a validation analysis for such sample average approximation optimization procedures and give some illustration examples. | eng |
dc.language.iso | eng | |
dc.publisher | Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, Institut für Mathematik | |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | |
dc.subject.ddc | 510 Mathematik | |
dc.title | Stochastic programming by Monte Carlo simulation methods | |
dc.type | book | |
dc.identifier.urn | urn:nbn:de:kobv:11-10046191 | |
dc.identifier.doi | http://dx.doi.org/10.18452/8226 | |
local.edoc.pages | 30 | |
local.edoc.type-name | Buch | |
local.edoc.container-type | series | |
local.edoc.container-type-name | Schriftenreihe | |
local.edoc.container-year | 2000 | |
dc.identifier.zdb | 2936317-2 | |
bua.series.name | Stochastic Programming E-Print Series | |
bua.series.issuenumber | 2000,3 |