期刊
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
卷 125, 期 2, 页码 349-366出版社
SPRINGER/PLENUM PUBLISHERS
DOI: 10.1007/s10957-004-1842-z
关键词
multistage stochastic programming; sampling; almost sure convergence
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.
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