Distributionally Robust Discrete Optimization

Z. Y. Long

We study the discrete optimization problem under the distributionally robust framework. We optimize the
Entropic Value-at-Risk, which is a coherent risk measure and is also known as Bernstein approximation for the chance constraint. We propose an efficient approximation algorithm to resolve the problem via solving a sequence of nominal problems. The computational results show that the number of nominal problems required to be solved is small under various distributional information sets.