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Analysis of stochastic dual dynamic programming method
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DOI：10.1016/j.ejor.2010.08.007.png)
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Abstract
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In this paper we discuss statistical properties and 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. Then we proceed to analysis of the SDDP solutions of the SAA problem and their relations to solutions of the true problem. Finally we discuss an extension of the SDDP method to a risk averse formulation of multistage stochastic programs. We argue that the computational complexity of the corresponding SDDP algorithm is almost the same as in the risk neutral case. (C) 2010 Elsevier B.V. All rights reserved.
Keywords:
Stochastic programming
Stochastic Dual Dynamic Programming algorithm
Sample Average Approximation method
Monte Carlo sampling
Risk averse optimization
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