On Augmented Lagrangian Decomposition Methods for Multistage Stochastic Programs

C.H. Rosa and Andrzej Ruszczynski ()

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 and by decomposing it into nodes corresponding to stages. Theoretical convergence properties of the two approaches are derived and a computational illustration is presented.

Date: 1994-12
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Working Paper: On Augmented Lagrangian Decomposition Methods For Multistage Stochastic Programs (1994)
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