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On the Convergence of Sampling-Based Decomposition Algorithms for Multistage Stochastic Programs

Karsten Linowsky & Andrew B. Philpott

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abstract The paper presents a convergence proof for a broad class of sampling algorithms for multistage stochastic linear programs in which the uncertain parameters occur only in the constraint right-hand sides. This class includes SDDP, AND, ReSa, and CUPPS. We show that, under some independence assumptions on the sampling procedure, the algorithms converge with probability 1.
   
type journal paper
   
keywords
   
language English
kind of paper journal article
date of appearance 1-5-2005
journal Journal of Optimization Theory and Applications
publisher Springer Science + Business Media (Dordrecht)
ISSN 0022-3239
ISSN (online) 1573-2878
DOI 10.1007/s10957-004-1842-z
volume of journal 125
number of issue 2
page(s) 349-366
review double-blind review
   
citation Linowsky, K., & Philpott, A. B. (2005). On the Convergence of Sampling-Based Decomposition Algorithms for Multistage Stochastic Programs. Journal of Optimization Theory and Applications, 125(2), 349-366, DOI:10.1007/s10957-004-1842-z.