Robust Chance Constrained Optimization with Pearson Divergence

Abstract

In this paper, we mainly discuss robust chance constrained optimization with Pearson divergence. We discuss the proprieties of PE divergence and develop solvable models. We also give a reformulation of distributionally robust chance constrained SVM model with PE divergence. Finally, the above results are applied to binary classification problem with uncertainty. Numerical experiments show that our model is feasible and efficient.