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Analysis of Stochastic Dual Dynamic Programming Method

Alexander Shapiro (ashapiro***at***isye.gatech.edu)

Abstract: In this paper we discuss statistical properties and rates of convergence of the Stochastic Dual Dynamic Programming (SDDP) method applied to multistage linear stochastic programming problems. We assume that the underline data process is stagewise independent and consider the framework where at first a random sample from the original (true) distribution is generated and consequently the SDDP algorithm is applied to the constructed Sample Average Approximation (SAA) problem.

Keywords: Stochastic programming, Stochastic Dual Dynamic Programming algorithm, Sample Average Approximation method, Monte Carlo sampling, risk averse optimization.

Category 1: Stochastic Programming

Citation: Technical report

Download: [PDF]

Entry Submitted: 12/30/2009
Entry Accepted: 12/30/2009
Entry Last Modified: 05/29/2010

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