Learning in Inverse Optimization: Incenter Cost, Augmented Suboptimality Loss, and Algorithms
针对逆优化问题，提出“内心”概念以得到计算可行的解，并设计新损失函数与优化算法，数值实验显示效率和精度提升。
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.