On Complexity of Multistage Stochastic Programs

In this paper we derive estimates of the sample sizes required to solve a multistage stochastic programming problem with a given accuracy by the (conditional sampling) sample average approximation method. The presented analysis is self contained and is based on a, relatively elementary, one dimensional Cramer’s Large Deviations Theorem.

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Working paper, Georgia Institute of Technology, Atlanta, GA

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