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Ruszczynski, A. (1994). On Augmented Lagrangian Decomposition Methods For Multistage Stochastic Programs. IIASA Working Paper. IIASA, Laxenburg, Austria: WP-94-005
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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.
| Item Type: | Monograph (IIASA Working Paper) |
|---|---|
| Research Programs: | Optimization under Uncertainty (OPT) |
| Depositing User: | IIASA Import |
| Date Deposited: | 15 Jan 2016 02:04 |
| Last Modified: | 27 Aug 2021 17:14 |
| URI: | https://pure.iiasa.ac.at/4204 |
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