On complexity of multistage stochastic programs

Authors:
Alexander Shapiro
Affiliations:
School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0205, USA
Venue:
Operations Research Letters
Year:
2006

Citing 3
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Abstract

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.