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Conic reformulations for Kullback-Leibler divergence constrained distributionally robust optimization and applications
2021
An International Journal of Optimization and Control: Theories & Applications
In this paper, we consider a Kullback-Leibler divergence constrained distributionally robust optimization model. This model considers an ambiguity set that consists of all distributions whose Kullback-Leibler divergence to an empirical distribution is bounded. Utilizing the fact that this divergence measure has an exponential cone representation, we obtain the robust counterpart of the Kullback-Leibler divergence constrained distributionally robust optimization problem as a dual exponential
doi:10.11121/ijocta.01.2021.001001
fatcat:apxhcak4lbftfkpthby76d4l7a