论文标题
逆混合整数优化：多面体见解和信任区域方法
Inverse Mixed Integer Optimization: Polyhedral Insights and Trust Region Methods
论文作者
论文摘要
反优化，确定使给定解决方案最佳的优化问题的参数近年来受到了越来越多的关注。尽管存在凸优化问题的显着反相反优化文献，但尽管存在根本依赖离散决策的应用程序无处不在，但离散问题的进展很少。在本文中，我们提供了一组新的理论见解和算法，用于一般混合整数线性优化问题的一般类别。具体而言，建立并利用了最佳条件的一般表征来设计新的切割平面解决方案算法。通过大量的计算实验，我们表明我们的方法对迄今为止最大，最困难的实例进行了实质性改进。
Inverse optimization, determining parameters of an optimization problem that render a given solution optimal, has received increasing attention in recent years. While significant inverse optimization literature exists for convex optimization problems, there have been few advances for discrete problems, despite the ubiquity of applications that fundamentally rely on discrete decision-making. In this paper, we present a new set of theoretical insights and algorithms for the general class of inverse mixed integer linear optimization problems. Specifically, a general characterization of optimality conditions is established and leveraged to design new cutting plane solution algorithms. Through an extensive set of computational experiments, we show that our methods provide substantial improvements over existing methods in solving the largest and most difficult instances to date.
