A Framework for Inherently Interpretable Optimization Models

M. Goerigk, Michael Hartisch
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引用次数: 4

Abstract

With dramatic improvements in optimization software, the solution of large-scale problems that seemed intractable decades ago are now a routine task. This puts even more real-world applications into the reach of optimizers. At the same time, solving optimization problems often turns out to be one of the smaller difficulties when putting solutions into practice. One major barrier is that the optimization software can be perceived as a black box, which may produce solutions of high quality, but can create completely different solutions when circumstances change leading to low acceptance of optimized solutions. Such issues of interpretability and explainability have seen significant attention in other areas, such as machine learning, but less so in optimization. In this paper we propose an optimization framework that inherently comes with an easily interpretable optimization rule, that explains under which circumstances certain solutions are chosen. Focusing on decision trees to represent interpretable optimization rules, we propose integer programming formulations as well as a heuristic method that ensure applicability of our approach even for large-scale problems. Computational experiments using random and real-world data indicate that the costs of inherent interpretability can be very small.
固有可解释优化模型的框架
随着优化软件的巨大进步,解决几十年前似乎难以解决的大规模问题现在是一项常规任务。这将使更多的实际应用程序进入优化器的范围。与此同时,解决优化问题往往成为将解决方案付诸实践时较小的困难之一。一个主要的障碍是,优化软件可能被视为一个黑盒,它可能产生高质量的解决方案,但当环境变化导致优化解决方案的接受度降低时,可能会创建完全不同的解决方案。这些可解释性和可解释性的问题在其他领域(如机器学习)已经引起了极大的关注,但在优化方面却很少受到关注。在本文中,我们提出了一个优化框架,该框架固有地带有一个易于解释的优化规则,该规则解释了在何种情况下选择某些解。我们着重于决策树来表示可解释的优化规则,提出了整数规划公式以及一种启发式方法,以确保我们的方法即使对于大规模问题也是适用的。使用随机和真实数据的计算实验表明,固有可解释性的代价可能非常小。
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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