DISTRIBUTIONALLY ROBUST STOCHASTIC DUAL DYNAMIC PROGRAMMING.

We consider a multistage stochastic linear program that lends itself to solution by stochastic dual dynamic programming (SDDP). In this context, we consider a distributionally robust variant of the model with a finite number of realizations at each stage. Distributional robustness is with respect to the probability mass function governing these realizations. We describe a computationally tractable variant of SDDP to handle this model using the Wasserstein distance to characterize distributional uncertainty. [ABSTRACT FROM AUTHOR]