Monotone Approximation of Decision Problems

Naveed Chehrazi, Thomas A. Weber
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引用次数: 24

Abstract

Many decision problems exhibit structural properties in the sense that the objective function is a composition of different component functions that can be identified using empirical data. We consider the approximation of such objective functions, subject to general monotonicity constraints on the component functions. Using a constrained B-spline approximation, we provide a data-driven robust optimization method for environments that can be sample-sparse. The method, which simultaneously identifies and solves the decision problem, is illustrated for the problem of optimal debt settlement in the credit-card industry.
决策问题的单调逼近
许多决策问题表现出结构特性,即目标函数是可以使用经验数据识别的不同组成函数的组合。我们考虑这类目标函数的近似,在分量函数的一般单调性约束下。使用约束b样条近似,我们为样本稀疏的环境提供了一种数据驱动的鲁棒优化方法。该方法能够同时识别和解决决策问题,并以信用卡行业的最优债务结算问题为例进行了说明。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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