A. M.-C. So
Distributionally robust optimization (DRO) has received much attention lately due to its ability to incorporate data uncertainty in and provide robustness interpretation of optimization models. Many of the DRO problems that arise in practice admit exact convex reformulations and can be solved by off-the-shelf solvers. Nevertheless, the use of such solvers severely limits the applicability of DRO in large-scale problems, as they often rely on general purpose interior-point algorithms. Our goal in this project is to develop practically efficient algorithmic frameworks for tackling various DRO problems.