Distributionally Robust Stochastic Dual Dynamic Programming
Daniel Duque (danielduqueu.northwestern.edu)
Abstract: We consider a multi-stage 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.
Keywords: Distributionally robust optimization, multi-stage stochastic programming, stochastic dual dynamic programming
Category 1: Stochastic Programming
Category 2: Robust Optimization
Citation: Northwestern University, Evanston, IL. Dec 2019.
Entry Submitted: 12/26/2019
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