On the Convergence of Decomposition Methods for Multistage Stochastic Convex Programs
Abstract
We prove the almost-sure convergence of a class of sampling-based nested decomposition algorithms for multistage stochastic convex programs in which the stage costs are general convex functions of the decisions , and uncertainty is modelled by a scenario tree. As special cases, our results imply the almost-sure convergence of SDDP, CUPPS and DOASA when applied to problems with general convex cost functions.
Origin | Files produced by the author(s) |
---|
Loading...