Scalable Distributional Robustness in a Class of Non-Convex Optimization with Guarantees

Avinandan Bose, Arunesh Sinha, Tien Anh Mai

Keywords: distributional robustness, variance regularization, non-convex optimization, global optimization, fractional program, mixed-integer second order cone

TL;DR: We propose distributionally robust optimization solutions for a class of sum of ratios, non-convex optimization which is used for decision-making in prominent areas such as facility location and security games

Abstract: Distributionally robust optimization (DRO) has shown a lot of promise in providing robustness in learning as well as sample-based optimization problems. We endeavor to provide DRO solutions for a class of sum of fractionals, non-convex optimization which is used for decision making in prominent areas such as facility location and security games. In contrast to previous work, we find it more tractable to optimize the equivalent variance regularized form of DRO rather than the minimax form. We transform the variance regularized form to a mixed-integer second-order cone program (MISOCP), which, while guaranteeing global optimality, does not scale enough to solve problems with real-world datasets. We further propose two abstraction approaches based on clustering and stratified sampling to increase scalability, which we then use for real-world datasets. Importantly, we provide global optimality guarantees for our approach and show experimentally that our solution quality is better than the locally optimal ones achieved by state-of-the-art gradient-based methods. We experimentally compare our different approaches and baselines and reveal nuanced properties of a DRO solution.