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 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.
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Bibliographic InfoPaper provided by International Institute for Applied Systems Analysis in its series Working Papers with number wp94005.
Date of creation: Feb 1994
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- C.H. Rosa & A. Ruszczynski, 1994. "On Augmented Lagrangian Decomposition Methods for Multistage Stochastic Programs," Working Papers wp94125, International Institute for Applied Systems Analysis.
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- Diana Barro & Elio Canestrelli, 2005. "Time and nodal decomposition with implicit non-anticipativity constraints in dynamic portfolio optimization," GE, Growth, Math methods 0510011, EconWPA.
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