Publication Type

Conference Proceeding Article

Version

submittedVersion

Publication Date

12-2022

Abstract

Distributionally robust optimization (DRO) has shown 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 near global optimality, does not scale enough to solve problems with real world data-sets. We further propose two abstraction approaches based on clustering and stratified sampling to increase scalability, which we then use for real world data-sets. Importantly, we provide near 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 andbaselines, and reveal nuanced properties of a DRO solution.

Keywords

Fractional, distributionally robustness, mixed-integer second order cone

Discipline

Artificial Intelligence and Robotics | Systems Architecture

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 36th Conference on Neural Information Processing Systems, New Orleans, United States, 2022 November 28 - December 9

First Page

1

Last Page

26

Publisher

Curran Associates

City or Country

New Orleans, United States

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