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Distributionally Robust Stochastic Dual Dynamic Programming

Daniel Duque (danielduque***at***u.northwestern.edu)
David P. Morton (david.morton***at***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.

Download: [PDF]

Entry Submitted: 12/26/2019
Entry Accepted: 12/26/2019
Entry Last Modified: 12/28/2019

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