随机矩阵对数能量的单调性

IF 0.9 4区 数学 Q4 PHYSICS, MATHEMATICAL
Djalil Chafaï, Benjamin Dadoun, Pierre Youssef
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引用次数: 0

摘要

众所周知,半圆律是维格纳定理中的极限分布,是由第二矩惩罚的对数能量的最小化。吉尔科定理和马琴科-帕斯图尔定理也有非常相似的事实。在这项工作中,我们揭示了一个有趣的现象,即这个函数沿着矩阵维度的平均经验谱分布是单调的。这让人联想到波尔兹曼方程中的波尔兹曼熵单调性、遍历马尔可夫过程中的自由能单调性,以及经典或自由中心极限定理中的香农熵或自由熵单调性。虽然我们只在高斯单元集合、复数吉尼布雷集合和平方拉盖尔单元集合中验证了这种单调性现象,但数值模拟表明它实际上更具普遍性。同时,我们还获得了模型对数能量的明确公式,这可能会引起我们的兴趣。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Monotonicity of the logarithmic energy for random matrices

It is well known that the semi-circle law, which is the limiting distribution in the Wigner theorem, is the minimizer of the logarithmic energy penalized by the second moment. A very similar fact holds for the Girko and Marchenko–Pastur theorems. In this work, we shed the light on an intriguing phenomenon suggesting that this functional is monotonic along the mean empirical spectral distribution in terms of the matrix dimension. This is reminiscent of the monotonicity of the Boltzmann entropy along the Boltzmann equation, the monotonicity of the free energy along ergodic Markov processes, and the Shannon monotonicity of entropy or free entropy along the classical or free central limit theorem. While we only verify this monotonicity phenomenon for the Gaussian unitary ensemble, the complex Ginibre ensemble, and the square Laguerre unitary ensemble, numerical simulations suggest that it is actually more universal. We obtain along the way explicit formulas of the logarithmic energy of the models which can be of independent interest.

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来源期刊
Random Matrices-Theory and Applications
Random Matrices-Theory and Applications Decision Sciences-Statistics, Probability and Uncertainty
CiteScore
1.90
自引率
11.10%
发文量
29
期刊介绍: Random Matrix Theory (RMT) has a long and rich history and has, especially in recent years, shown to have important applications in many diverse areas of mathematics, science, and engineering. The scope of RMT and its applications include the areas of classical analysis, probability theory, statistical analysis of big data, as well as connections to graph theory, number theory, representation theory, and many areas of mathematical physics. Applications of Random Matrix Theory continue to present themselves and new applications are welcome in this journal. Some examples are orthogonal polynomial theory, free probability, integrable systems, growth models, wireless communications, signal processing, numerical computing, complex networks, economics, statistical mechanics, and quantum theory. Special issues devoted to single topic of current interest will also be considered and published in this journal.
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