An Algorithmic Approach for Inner Max-Min Model Under Norm-2 Type Uncertainty Set in Data-Driven Distributionally Robust Optimization

To accelerate the computational speed of data-driven distributionally robust optimization (DDRO), this letter presents a novel algorithmic approach to solve the inner max-min model under norm-2 type uncertainty set in DDRO. The proposed method can solve the problem in an easily implemented way instead of a time-consuming optimization process using commercial solvers. Comparisons of computational time between the algorithmic approach and optimization solvers verify the effectiveness of the proposed method.