Emulating the Expert: Inverse Optimization through Online Learning
Emulating the Expert: Inverse Optimization through Online Learning
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模仿专家：通过在线学习进行逆向优化
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Oskar Schneider
中科院分区：
文献类型：
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作者：
Andreas Bärmann;S. Pokutta;Oskar Schneider
在本文中，我们演示了如何学习决策者的目标函数，同时只观察问题的输入数据和决策者的相应决策多轮。我们的方法是基于在线学习技术和工程的线性目标在任意的集合，我们有一个线性优化预言，因此概括了以前的工作的基础上KKT系统分解和对偶方法。我们的框架学习线性约束的适用性也进行了简要讨论。我们的算法收敛速度为O（1 <$T），我们证明了它的有效性和应用程序的初步计算结果。
In this paper, we demonstrate how to learn the objective function of a decision maker while only observing the problem input data and the decision maker’s corresponding decisions over multiple rounds. Our approach is based on online learning techniques and works for linear objectives over arbitrary sets for which we have a linear optimization oracle and as such generalizes previous work based on KKT-system decomposition and dualization approaches. The applicability of our framework for learning linear constraints is also discussed briefly. Our algorithm converges at a rate of O( 1 √ T ), and we demonstrate its effectiveness and applications in preliminary computational results.