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The spectral graph theory provides an algebraical approach to investigate the characteristics of weighted networks using the eigenvalues and eigenvectors of a matrix (e.g., normalized Laplacian matrix) that represents the structure of the…

Physics and Society · Physics 2021-04-19 Yusuke Sakumoto , Masaki Aida

These notes provide an introduction to the local semicircle law from random matrix theory, as well as some of its applications. We focus on Wigner matrices, Hermitian random matrices with independent upper-triangular entries with zero…

Probability · Mathematics 2018-09-11 Florent Benaych-Georges , Antti Knowles

We study a class of Hermitian random matrices which includes and generalizes Wigner matrices, heavy-tailed random matrices, and sparse random matrices such as the adjacency matrices of Erdos-Renyi random graphs with p ~ 1/N. Our NxN random…

Probability · Mathematics 2016-02-16 Paul Jung

We consider the empirical eigenvalue distribution of random real symmetric matrices with stochastically independent skew-diagonals and study its limit if the matrix size tends to infinity. We allow correlations between entries on the same…

Probability · Mathematics 2015-10-23 Kristina Schubert

We investigate the spectral norms of symmetric $N \times N$ matrices from two pseudo-random ensembles. The first is the pseudo-Wigner ensemble introduced in "Pseudo-Wigner Matrices" by Soloveychik, Xiang and Tarokh and the second is its…

Probability · Mathematics 2017-08-16 Ilya Soloveychik , Vahid Tarokh

We present a simple and versatile method for deriving (an)isotropic local laws for general random matrices constructed from independent random variables. Our method is applicable to mean-field random matrices, where all independent…

Probability · Mathematics 2017-04-13 Yukun He , Antti Knowles , Ron Rosenthal

We prove a local law in the bulk of the spectrum for random Gram matrices $XX^*$, a generalization of sample covariance matrices, where $X$ is a large matrix with independent, centered entries with arbitrary variances. The limiting…

Probability · Mathematics 2017-03-13 Johannes Alt , László Erdős , Torben Krüger

For a sufficiently nice 2 dimensional shape, we define its approximating matrix (or patterned matrix) as a random matrix with iid entries arranged according to a given pattern. For large approximating matrices, we observe that the…

Probability · Mathematics 2022-01-04 Tapesh Yadav

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…

Probability · Mathematics 2024-07-22 Djalil Chafaï , Benjamin Dadoun , Pierre Youssef

An equation is obtained for the Stieltjes transform of the normalized distribution of singular values of non-symmetric band random matrices in the limit when the band width and rank of the matrix simultaneously tend to infinity. Conditions…

Mathematical Physics · Physics 2015-03-17 Anna Lytova , Leonid Pastur

Let $X_N$ be an $N\ts N$ random symmetric matrix with independent equidistributed entries. If the law $P$ of the entries has a finite second moment, it was shown by Wigner \cite{wigner} that the empirical distribution of the eigenvalues of…

Probability · Mathematics 2007-07-17 Gerard Ben Arous , Alice Guionnet

In this note, we show that the limiting spectral distribution of symmetric random matrices with stationary entries is absolutely continuous under some sufficient conditions. This result is applied to obtain sufficient conditions on a…

Probability · Mathematics 2015-02-10 Arijit Chakrabarty , Rajat Subhra Hazra

The properties of eigenvalues of large dimensional random matrices have received considerable attention. One important achievement is the existence and identification of the limiting spectral distribution of the empirical spectral…

Combinatorics · Mathematics 2009-06-12 Wenxue Du , Xueliang Li , Yiyang Li

We analyze the spectral distribution of symmetric random matrices with correlated entries. While we assume that the diagonals of these random matrices are stochastically independent, the elements of the diagonals are taken to be correlated.…

Probability · Mathematics 2012-05-31 Olga Friesen , Matthias Löwe

We consider random $d$-regular graphs on $N$ vertices, with degree $d$ at least $(\log N)^4$. We prove that the Green's function of the adjacency matrix and the Stieltjes transform of its empirical spectral measure are well approximated by…

Probability · Mathematics 2019-08-21 Roland Bauerschmidt , Antti Knowles , Horng-Tzer Yau

Let $X$ be a matrix sampled uniformly from the set of doubly stochastic matrices of size $n\times n$. We show that the empirical spectral distribution of the normalized matrix $\sqrt{n}(X-{\mathbf {E}}X)$ converges almost surely to the…

Combinatorics · Mathematics 2014-03-28 Hoi H. Nguyen

We consider the nearest-neighbor spacing distributions of mixed random matrix ensembles interpolating between different symmetry classes, or between integrable and non-integrable systems. We derive analytical formulas for the spacing…

Mathematical Physics · Physics 2012-08-22 Sebastian Schierenberg , Falk Bruckmann , Tilo Wettig

The spectral density of various ensembles of sparse symmetric random matrices is analyzed using the cavity method. We consider two cases: matrices whose associated graphs are locally tree-like, and sparse covariance matrices. We derive a…

Disordered Systems and Neural Networks · Physics 2009-11-13 Tim Rogers , Koujin Takeda , Isaac Pérez Castillo , Reimer Kühn

We consider sample covariance matrices of the form $X^*X$, where $X$ is an $M \times N$ matrix with independent random entries. We prove the isotropic local Marchenko-Pastur law, i.e. we prove that the resolvent $(X^* X - z)^{-1}$ converges…

Probability · Mathematics 2015-07-17 Alex Bloemendal , Laszlo Erdos , Antti Knowles , Horng-Tzer Yau , Jun Yin

In this paper, we consider the empirical spectral distribution of the sample correlation matrix and investigate its asymptotic behavior under mild assumptions on the data's distribution, when dimension and sample size increase at the same…

Probability · Mathematics 2022-09-01 Nina Dörnemann , Johannes Heiny