On Augmented Lagrangian Decomposition Methods for Multistage Stochastic Programs
AbstractA 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.
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Bibliographic InfoPaper provided by International Institute for Applied Systems Analysis in its series Working Papers with number wp94125.
Date of creation: Dec 1994
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- A. Ruszczynski, 1994. "On Augmented Lagrangian Decomposition Methods For Multistage Stochastic Programs," Working Papers wp94005, International Institute for Applied Systems Analysis.
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