Analysis of stochastic dual dynamic programming method

作者：Shapiro Alexander^*

来源：European Journal of Operational Research, 2011, 209(1): 63-72.

DOI：10.1016/j.ejor.2010.08.007

• Stochastic programming
• Stochastic Dual Dynamic Programming algorithm
• Sample Average Approximation method
• Monte Carlo sampling
• Risk averse optimization

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.

• 出版日期2011-2-16