On augmented Lagrangian decomposition methods for multistage stochastic programs

On augmented Lagrangian decomposition methods for multistage stochastic programs

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Article ID: iaor19971582
Country: Netherlands
Volume: 64
Issue: 1
Start Page Number: 289
End Page Number: 309
Publication Date: Jun 1996
Journal: Annals of Operations Research
Authors: ,
Keywords: decomposition
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.

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