Decomposition methods in stochastic programming
Ruszczynski, Andrzej

Data: 1997
Resum: 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. .
Llengua: Anglès.
Document: Article ; recerca ; article ; publishedVersion
Matèria: Stochastic programming ; Decomposition ; Primal methods ; Dual methods ; Stochastic methods
Publicat a: Mathematical Programming, vol. 79 n. 1-3 (1997) p. 333-353, ISSN 0025-5610

21 p, 1.1 MB
 Accés restringit a la UAB

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