ISSN:
1572-9338
Keywords:
Stochastic programming
;
decomposition
;
augmented Lagrangian
;
Jacobi method
;
parallel computation
Source:
Springer Online Journal Archives 1860-2000
Topics:
Mathematics
,
Economics
Notes:
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.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF02187650
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