On Augmented Lagrangian Decomposition Methods for Multistage Stochastic Programs

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/3858 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/3858 http://eprints.org/relation/isCoversheetVersionOf https://pure.iiasa.ac.at/id/document/3858 archive 351 disk0/00/00/40/89 2016-01-15 02:04:03 2021-08-27 17:14:45 2016-01-15 02:04:03 monograph show 4 Rosa C.H. 1545 Ruszczynski A. 1475 On Augmented Lagrangian Decomposition Methods for Multistage Stochastic Programs pub iis_cmp iis_met iis_sys prog_opt 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. 1994-12 published WP-94-125 WP-94-125 10 1541 1544 public working_paper IIASA, Laxenburg, Austria 23 FALSE info:eu-repo/semantics/book