Efficient data-driven optimization with noisy data

IF 0.8 4区管理学 Q4 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Letters Pub Date : 2024-02-06 DOI:10.1016/j.orl.2024.107089

Bart P.G. Van Parys

引用次数: 0

Abstract

The classical Kullback-Leibler distance is known to enjoy desirable statistical properties in the context of decision-making with noiseless data. However, in most practical situations data is subject to a certain amount of measurement noise. We hence study here data-driven prescription problems in which the data is corrupted by a known noise source. We derive efficient data-driven formulations in this noisy regime and indicate that they enjoy an entropic optimal transport interpretation.

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利用噪声数据进行高效的数据驱动优化

众所周知，经典的库尔巴克-莱伯勒距离（Kullback-Leibler distance）在使用无噪声数据进行决策时具有理想的统计特性。然而，在大多数实际情况下，数据会受到一定量的测量噪声的影响。因此，我们在此研究数据驱动的处方问题，在这种情况下，数据会受到已知噪声源的干扰。我们推导出了这种噪声环境下的高效数据驱动公式，并指出这些公式具有熵最优传输解释。

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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来源期刊

Operations Research Letters 管理科学-运筹学与管理科学

CiteScore

2.10

自引率

9.10%

发文量

111

审稿时长

83 days

期刊介绍： Operations Research Letters is committed to the rapid review and fast publication of short articles on all aspects of operations research and analytics. Apart from a limitation to eight journal pages, quality, originality, relevance and clarity are the only criteria for selecting the papers to be published. ORL covers the broad field of optimization, stochastic models and game theory. Specific areas of interest include networks, routing, location, queueing, scheduling, inventory, reliability, and financial engineering. We wish to explore interfaces with other fields such as life sciences and health care, artificial intelligence and machine learning, energy distribution, and computational social sciences and humanities. Our traditional strength is in methodology, including theory, modelling, algorithms and computational studies. We also welcome novel applications and concise literature reviews.