雷尼-索博列夫不等式及其与谱图论的联系

IF 2.2 3区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Transactions on Information Theory Pub Date : 2024-07-29 DOI:10.1109/TIT.2024.3435414
Lei Yu;Hao Wu
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引用次数: 0

摘要

在本文中,我们将 log-Sobolev 不等式推广为 Rényi-Sobolev 不等式,用双参数熵代替熵,双参数熵是熵的广义版本,与 Rényi 分歧密切相关。我们推导出了这类不等式的尖锐非线性无维版本。有趣的是,由此得出的不等式显示出一种取决于参数的过渡现象。然后,我们将 Rényi-Sobolev 不等式与收缩不等式、数据处理不等式、集中不等式和谱图理论联系起来。本文的证明基于 Rényi-Sobolev 不等式的信息论特征以及类型法。
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Rényi–Sobolev Inequalities and Connections to Spectral Graph Theory
In this paper, we generalize the log-Sobolev inequalities to Rényi–Sobolev inequalities by replacing the entropy with the two-parameter entropy, which is a generalized version of entropy and closely related to Rényi divergences. We derive the sharp nonlinear dimension-free version of this kind of inequalities. Interestingly, the resultant inequalities show a transition phenomenon depending on the parameters. We then connect Rényi–Sobolev inequalities to contractive and data-processing inequalities, concentration inequalities, and spectral graph theory. Our proofs in this paper are based on the information-theoretic characterization of the Rényi–Sobolev inequalities, as well as the method of types.
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来源期刊
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory 工程技术-工程:电子与电气
CiteScore
5.70
自引率
20.00%
发文量
514
审稿时长
12 months
期刊介绍: The IEEE Transactions on Information Theory is a journal that publishes theoretical and experimental papers concerned with the transmission, processing, and utilization of information. The boundaries of acceptable subject matter are intentionally not sharply delimited. Rather, it is hoped that as the focus of research activity changes, a flexible policy will permit this Transactions to follow suit. Current appropriate topics are best reflected by recent Tables of Contents; they are summarized in the titles of editorial areas that appear on the inside front cover.
期刊最新文献
Table of Contents IEEE Transactions on Information Theory Publication Information IEEE Transactions on Information Theory Information for Authors Large and Small Deviations for Statistical Sequence Matching Derivatives of Entropy and the MMSE Conjecture
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