The limit empirical spectral distribution of complex matrix polynomials

IF 0.9 4区 数学 Q4 PHYSICS, MATHEMATICAL Random Matrices-Theory and Applications Pub Date : 2021-02-03 DOI:10.1142/S201032632250023X
Giovanni Barbarino, V. Noferini
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Abstract

We study the empirical spectral distribution (ESD) for complex [Formula: see text] matrix polynomials of degree [Formula: see text] under relatively mild assumptions on the underlying distributions, thus highlighting universality phenomena. In particular, we assume that the entries of each matrix coefficient of the matrix polynomial have mean zero and finite variance, potentially allowing for distinct distributions for entries of distinct coefficients. We derive the almost sure limit of the ESD in two distinct scenarios: (1) [Formula: see text] with [Formula: see text] constant and (2) [Formula: see text] with [Formula: see text] bounded by [Formula: see text] for some [Formula: see text]; the second result additionally requires that the underlying distributions are continuous and uniformly bounded. Our results are universal in the sense that they depend on the choice of the variances and possibly on [Formula: see text] (if it is kept constant), but not on the underlying distributions. The results can be specialized to specific models by fixing the variances, thus obtaining matrix polynomial analogues of results known for special classes of scalar polynomials, such as Kac, Weyl, elliptic and hyperbolic polynomials.
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复矩阵多项式的极限经验谱分布
我们在相对温和的底层分布假设下,研究了复杂的[公式:见文]次矩阵多项式的经验谱分布(ESD),从而突出了普遍性现象。特别是,我们假设矩阵多项式的每个矩阵系数的条目具有平均零和有限方差,可能允许不同系数的条目具有不同的分布。我们在两种不同的情况下推导出ESD的几乎确定极限:(1)[公式:见文]常数为[公式:见文],(2)[公式:见文]以[公式:见文]为界的[公式:见文];第二个结果还要求底层分布是连续且一致有界的。我们的结果是普遍的,因为它们取决于方差的选择,也可能取决于[公式:见文本](如果保持不变),但不取决于潜在的分布。通过固定方差,结果可以专门用于特定的模型,从而获得已知的特殊类别的标量多项式(如Kac, Weyl,椭圆和双曲多项式)结果的矩阵多项式类似物。
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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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