On Augmented Lagrangian Decomposition Methods For Multistage Stochastic Programs

Andrzej Ruszczynski (rusz@business.rutgers.edu)

Working Papers from International Institute for Applied Systems Analysis

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.

Date: 1994-02
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Working Paper: On Augmented Lagrangian Decomposition Methods for Multistage Stochastic Programs (1994)
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