On Augmented Lagrangian Decomposition Methods for Multistage Stochastic Programs

Rosa CH & Ruszczynski A (1994). On Augmented Lagrangian Decomposition Methods for Multistage Stochastic Programs. IIASA Working Paper. IIASA, Laxenburg, Austria: WP-94-125

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Abstract

A general decomposition framework for large convex optimization problems based on augmented Lagrangians is described. The approach is then applied to multistage stochastic programming problems in two different ways: by decomposing the problem into scenarios and by decomposing it into nodes corresponding to stages. Theoretical convergence properties of the two approaches are derived and a computational illustration is presented.

Item Type: Monograph (IIASA Working Paper)
Research Programs: Optimization under Uncertainty (OPT)
Depositing User: IIASA Import
Date Deposited: 15 Jan 2016 02:04
Last Modified: 08 Aug 2016 04:07
URI: http://pure.iiasa.ac.at/4089

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