Decomposition Methods

Research output: Contribution to journalReview article

56 Citations (Scopus)

Abstract

Two- and multistage stochastic programming problems have very large dimension and characteristic structures which are tractable by decomposition. We present cutting plane methods, nested decomposition methods, regularized decomposition methods, trust region methods, augmented Lagrangian methods, and splitting methods for convex stochastic programming problems.

Original languageEnglish (US)
Pages (from-to)141-211
Number of pages71
JournalHandbooks in Operations Research and Management Science
Volume10
Issue numberC
DOIs
StatePublished - Dec 1 2003

Fingerprint

Stochastic programming
Decomposition

All Science Journal Classification (ASJC) codes

  • Finance
  • Economics and Econometrics
  • Computer Science Applications
  • Management Science and Operations Research

Keywords

  • Stochastic programming
  • decomposition
  • dual methods
  • operator splitting
  • primal methods

Cite this

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title = "Decomposition Methods",
abstract = "Two- and multistage stochastic programming problems have very large dimension and characteristic structures which are tractable by decomposition. We present cutting plane methods, nested decomposition methods, regularized decomposition methods, trust region methods, augmented Lagrangian methods, and splitting methods for convex stochastic programming problems.",
keywords = "Stochastic programming, decomposition, dual methods, operator splitting, primal methods",
author = "Andrzej Ruszczyński",
year = "2003",
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doi = "10.1016/S0927-0507(03)10003-5",
language = "English (US)",
volume = "10",
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journal = "Handbooks in Operations Research and Management Science",
issn = "0927-0507",
publisher = "North-Holland Publ Co",
number = "C",

}

Decomposition Methods. / Ruszczyński, Andrzej.

In: Handbooks in Operations Research and Management Science, Vol. 10, No. C, 01.12.2003, p. 141-211.

Research output: Contribution to journalReview article

TY - JOUR

T1 - Decomposition Methods

AU - Ruszczyński, Andrzej

PY - 2003/12/1

Y1 - 2003/12/1

N2 - Two- and multistage stochastic programming problems have very large dimension and characteristic structures which are tractable by decomposition. We present cutting plane methods, nested decomposition methods, regularized decomposition methods, trust region methods, augmented Lagrangian methods, and splitting methods for convex stochastic programming problems.

AB - Two- and multistage stochastic programming problems have very large dimension and characteristic structures which are tractable by decomposition. We present cutting plane methods, nested decomposition methods, regularized decomposition methods, trust region methods, augmented Lagrangian methods, and splitting methods for convex stochastic programming problems.

KW - Stochastic programming

KW - decomposition

KW - dual methods

KW - operator splitting

KW - primal methods

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U2 - 10.1016/S0927-0507(03)10003-5

DO - 10.1016/S0927-0507(03)10003-5

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JO - Handbooks in Operations Research and Management Science

JF - Handbooks in Operations Research and Management Science

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