Performance evaluation for distributionally robust optimization with binary entries

Shunichi Ohmori, Kazuho Yoshimoto


We consider the data-driven stochastic programming problem with binary entries where the probability of existence of each entry is not known, instead realization of data is provided. We applied the distributionally robust optimization technique to minimize the worst-case expected cost taken over the ambiguity set based on the Kullback-Leibler divergence. We investigate the out-of-sample performance of the resulting optimal decision and analyze its dependence on the sparsity of the problem.


Distributionally Robust Optimization, Robust Optimization; Stochastic Programming; Convex Optimization

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