ISSN:
1572-9338
Schlagwort(e):
Stochastic programming
;
decomposition
;
augmented Lagrangian
;
Jacobi method
;
parallel computation
Quelle:
Springer Online Journal Archives 1860-2000
Thema:
Mathematik
,
Wirtschaftswissenschaften
Notizen:
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.
Materialart:
Digitale Medien
URL:
http://dx.doi.org/10.1007/BF02187650
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