Decomposition methods in stochastic programming
Ruszczynski, Andrzej

Date: 1997
Abstract: Stochastic programming problems have very large dimension and characteristic structures which are tractable by decomposition. We review basic ideas of cutting plane methods, augmented Lagrangian and splitting methods, and stochastic decomposition methods for convex polyhedral multi-stage stochastic programming problems. .
Rights: Tots els drets reservats.
Language: Anglès
Document: Article ; recerca ; Versió publicada
Subject: Stochastic programming ; Decomposition ; Primal methods ; Dual methods ; Stochastic methods
Published in: Mathematical Programming, vol. 79 n. 1-3 (1997) p. 333-353, ISSN 0025-5610



21 p, 1.1 MB
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Articles > Research articles
Articles > Published articles

 Record created 2006-03-13, last modified 2023-06-03



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