期望值函数的维塔利变异误差边界

IF 0.8 4区管理学 Q4 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Operations Research Letters Pub Date : 2024-08-06 DOI:10.1016/j.orl.2024.107157

Alban Kryeziu, Ward Romeijnders, Evrim Ursavas

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引用次数: 0

摘要

我们推导出二维期望值函数的误差边界，它取决于相应随机向量的联合概率密度函数的维塔利变化。与文献中的界限相反，我们的界限并不局限于一维和周期性的基础函数。在证明过程中，我们首先推导出离散环境下的边界，这要求我们描述所有矩阵集合的极值点，这些矩阵的行和列均为零，且其 L1 值以 1 为界。这一结果可能具有独立的意义。

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Vitali variation error bounds for expected value functions

We derive error bounds for two-dimensional expected value functions that depend on the Vitali variation of the joint probability density function of the corresponding random vector. Contrary to bounds from the literature, our bounds are not restricted to underlying functions that are one-dimensional and periodic. In our proof, we first derive the bounds in a discrete setting, which requires us to characterize the extreme points of the set of all matrices that have zero-sum rows and columns and have an $L_{1}$ -norm bounded by one. This result may be of independent interest.

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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.