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Furthermore, by deriving new confidence regions for the mean and covariance of a random vector, we provide probabilistic arguments for using our model in problems that rely heavily on historical data, We demonstrate that for a wide range of cost functions the associated distributionally robust or minmax stochastic program can be solved efficiently. Distributionally robust optimization under moment uncertainty with application to datadriven problems, In this paper, we propose a model that describes uncertainty in both the distribution form discrete, gaussian, exponential, etc, Distributionally robust optimization under moment uncertainty with application to datadriven problems 发表在 operations research, 2010. In particular, we study the associated multistage distributionally robust optimization problem, when only the mean, variance, and di. We demonstrate that for a wide range of cost functions the associated distributionally robust stochastic program can be solved efficiently, In this paper, we propose a model that describes uncertainty in both the distribution form discrete, gaussian, exponential, etc.

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Distributionally robust optimization under moment uncertainty with application to datadriven problems. Subject classifications programming stochastic. We demonstrate that for a wide range of cost functions the associated distributionally robust stochastic program can be solved efficiently, in this paper, we consider a minimax approach to managing an inventory under distributional uncertainty.

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