On Augmented Lagrangian Decomposition Methods For Multistage Stochastic Programs

Ruszczynski A (1994). On Augmented Lagrangian Decomposition Methods For Multistage Stochastic Programs. IIASA Working Paper. IIASA, Laxenburg, Austria: WP-94-005

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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 or decomposing it into nodes corresponding to stages. In both cases the method has favorable convergence properties and a structure which makes it convenient for parallel computing environments.

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: 24 Jul 2016 04:29
URI: http://pure.iiasa.ac.at/4204

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