Learning in Inverse Optimization: Incenter Cost, Augmented Suboptimality Loss, and Algorithms

Enhancing the Efficiency and Accuracy of Inverse Optimization Inverse optimization (IO) is used to model the behavior of decision-making agents who solve optimization problems in response to external signals. Inspired by the geometry of IO problems, in “Learning in Inverse Optimization: Incenter Cost, Augmented Suboptimality Loss, and Algorithms,” Zattoni Scroccaro, Atasoy, and Mohajerin Esfahani propose the “incenter” concept to solve IO problems, which unlike previously proposed approaches, can be used to derive computationally tractable solutions to this modeling problem. Moreover, they also propose a novel loss function for IO problems and a tailored optimization algorithm to optimize it. Extensive numerical experiments showcase the improved efficiency and accuracy of the proposed IO formulations and algorithm.