Decomposition methods in stochastic programming

Andrzej Ruszczyński

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.
Author Andrzej Ruszczyński (FEIT / AK)
Andrzej Ruszczyński,,
- The Institute of Control and Computation Engineering
Journal seriesMathematical Programming, ISSN 0025-5610, 1436-4646
Issue year1997
Vol79
No1-3
Pages333-353
Keywords in EnglishCalculus of Variations and Optimal Control, Combinatorics, Decomposition, Dual methods, Mathematical and Computational Physics, Mathematical Methods in Physics, Mathematics of Computing, numerical analysis, optimization, Primal methods, Stochastic methods, Stochastic programming
DOIDOI:10.1007/BF02614323
URL http://link.springer.com/article/10.1007/BF02614323
Score (nominal)0
Citation count*170 (2015-04-30)
Cite
Share Share

Get link to the record


* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
Back
Confirmation
Are you sure?