Show simple item record

2003-06-30Buch DOI: 10.18452/8294
Approximation in stochastic integer programming
dc.contributor.authorStougie, Leen
dc.contributor.authorVlerk, Maarten H. van der
dc.contributor.editorHigle, Julie L.
dc.contributor.editorRömisch, Werner
dc.contributor.editorSen, Surrajeet
dc.date.accessioned2017-06-16T19:52:47Z
dc.date.available2017-06-16T19:52:47Z
dc.date.created2006-03-01
dc.date.issued2003-06-30
dc.date.submitted2003-05-08
dc.identifier.urihttp://edoc.hu-berlin.de/18452/8946
dc.description.abstractApproximation algorithms are the prevalent solution methods in the field of stochastic programming. Problems in this field are very hard to solve. Indeed, most of the research in this field has concentrated on designing solution methods that approximate the optimal solutions. However, efficiency in the complexity theoretical sense is usually not taken into account. Quality statements mostly remain restricted to convergence to an optimal solution without accompanying implications on the running time of the algorithms for attaining more and more accurate solutions. However, over the last twenty years also some studies on performance analysis of approximation algorithms for stochastic programming have appeared. In this direction we find both probabilistic analysis and worst-case analysis.There have been studies on performance ratios and on absolute divergence from optimality. Only recently the complexity of stochastic programming problems has been addressed, indeed confirming that these problems are harder than most combinatorial optimization problems.Approximation in the traditional stochastic programming sense will not be discussed in this chapter. The reader interested in this issue is referred to surveys on stochastic programming, like the Handbook on Stochastic Programming [31 ]or the text books [2,16,29 ]. We concentrate on the studies of approximation algorithms which are more similar in nature to those for combinatorial optimization.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.subject.ddc510 Mathematik
dc.titleApproximation in stochastic integer programming
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-10059087
dc.identifier.doihttp://dx.doi.org/10.18452/8294
local.edoc.pages31
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
dc.identifier.zdb2936317-2
bua.series.nameStochastic Programming E-Print Series
bua.series.issuenumber2003,12

Show simple item record