Random matrices: Universality of ESDs and the circular law
TLDR
In this article, the authors considered the limiting distribution of the normalized ESD of a random matrix An, where the random variables aij−E(aij) are i.i.d. copies of a fixed random variable x with unit variance.Abstract:
Given an n×n complex matrix A, let $$\mu_{A}(x,y):=\frac{1}{n}|\{1\le i\le n,\operatorname{Re}\lambda_{i}\le x,\operatorname{Im}\lambda_{i}\le y\}|$$ be the empirical spectral distribution (ESD) of its eigenvalues λi∈ℂ, i=1, …, n. We consider the limiting distribution (both in probability and in the almost sure convergence sense) of the normalized ESD $\mu_{{1}/{\sqrt{n}}A_{n}}$ of a random matrix An=(aij)1≤i, j≤n, where the random variables aij−E(aij) are i.i.d. copies of a fixed random variable x with unit variance. We prove a universality principle for such ensembles, namely, that the limit distribution in question is independent of the actual choice of x. In particular, in order to compute this distribution, one can assume that x is real or complex Gaussian. As a related result, we show how laws for this ESD follow from laws for the singular value distribution of $\frac{1}{\sqrt{n}}A_{n}-zI$ for complex z. As a corollary, we establish the circular law conjecture (both almost surely and in probability), which asserts that $\mu_{{1}/{\sqrt{n}}A_{n}}$ converges to the uniform measure on the unit disc when the aij have zero mean.read more
Citations
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References
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Book
The concentration of measure phenomenon
TL;DR: Concentration functions and inequalities isoperimetric and functional examples Concentration and geometry Concentration in product spaces Entropy and concentration Transportation cost inequalities Sharp bounds of Gaussian and empirical processes Selected applications References Index
Book
Spectral Analysis of Large Dimensional Random Matrices
Zhidong Bai,Jack W. Silverstein +1 more
TL;DR: Wigner Matrices and Semicircular Law for Hadamard products have been used in this article for spectral separations and convergence rates of ESD for linear spectral statistics.
Journal ArticleDOI
On the Distribution of the Roots of Certain Symmetric Matrices
TL;DR: The distribution law obtained before' for a very special set of matrices is valid for much more general sets of real symmetric matrices of very high dimensionality.
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Eigenvalues and condition numbers of random matrices
TL;DR: For real or complex matrices with elements from a standard normal distribution, the condition number should be given given a random matrix, and as mentioned in this paper showed that condition number is not the right condition number for any real matrix.