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The universality for the eigenvalue spacing statistics of generalized Wigner matrices was established in our previous work \cite{EYY} under certain conditions on the probability distributions of the matrix elements. A major class of…

Mathematical Physics · Physics 2011-09-27 László Erdos , Horng-Tzer Yau , Jun Yin

Random Matrix Theory is a powerful tool in applied mathematics. Three canonical models of random matrix distributions are the Gaussian Orthogonal, Unitary and Symplectic Ensembles. For matrix ensembles defined on k-fold tensor products of…

Mathematical Physics · Physics 2024-05-06 Michael Brodskiy , Owen L. Howell

It is shown that certain ensembles of random matrices with entries that vanish outside a band around the diagonal satisfy a localization condition on the resolvent which guarantees that eigenvectors have strong overlap with a vanishing…

Mathematical Physics · Physics 2010-06-29 Jeffrey Schenker

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

The model of heavy Wigner matrices generalizes the classical ensemble of Wigner matrices: the sub-diagonal entries are independent, identically distributed along to and out of the diagonal, and the moments its entries are of order 1/N,…

Probability · Mathematics 2012-09-12 Camille Male

We consider the local eigenvalue distribution of large self-adjoint $N\times N$ random matrices $\mathbf{H}=\mathbf{H}^*$ with centered independent entries. In contrast to previous works the matrix of variances $s_{ij} = \mathbb{E}\,…

Probability · Mathematics 2017-08-09 Oskari Ajanki , Laszlo Erdos , Torben Krüger

Tensor-valued and matrix-valued measurements of different physical properties are increasingly available in material sciences and medical imaging applications. The eigenvalues and eigenvectors of such multivariate data provide novel and…

Methodology · Statistics 2017-07-24 Dario Gasbarra , Sinisa Pajevic , Peter J. Basser

We study random, symmetric $N \times N$ band matrices with a band of size $W$ and Bernoulli random variables as entries. This interpolates between nearest neighbour interaction $W = 1$ and Wigner matrices $W = N$. Eigenvectors are known to…

Probability · Mathematics 2020-05-07 Stefan Steinerberger

We numerically study the extreme-value statistics of the Schmidt eigenvalues of reduced density matrices obtained from the ergodic eigenstates. We start by exploring the extreme value statistics of the ultrametric random matrices and then…

Quantum Physics · Physics 2025-02-03 Tanay Pathak

We present numerical results for the Rosenzweig Porter model for all symmetry classes of the Dyson threefold way. We analyzed the fluctuation properties in the eigenvalue spectra, and compared them with existing and new analytical results.…

Statistical Mechanics · Physics 2024-09-11 Tilen Cadez , Dillip Nandy , Dario Rosa , Alexei Andreanov , Barbara Dietz

We show that a Wigner induced random orthonormal basis of spherical harmonics is almost surely quantum ergodic. Here, a random basis is identified with an element of the product probability space of unitary groups, each endowed with the…

Probability · Mathematics 2021-07-13 Robert Chang

Characterizing the asymptotic distributions of eigenvectors for large random matrices poses important challenges yet can provide useful insights into a range of statistical applications. To this end, in this paper we introduce a general…

Statistics Theory · Mathematics 2020-10-14 Jianqing Fan , Yingying Fan , Xiao Han , Jinchi Lv

In this work, we study some statistical properties of the extreme eigenstates of the randomly-weighted adjacency matrices of random graphs. We focus on two random graph models: Erd\H{o}s-R\'{e}nyi (ER) graphs and random geometric graphs…

Disordered Systems and Neural Networks · Physics 2025-06-17 C. T Martínez Martínez , J. A. Méndez Bermúdez

In an attempt to characterize the structure of eigenvectors of random regular graphs, we investigate the correlations between the components of the eigenvectors associated to different vertices. In addition, we provide numerical…

Mathematical Physics · Physics 2009-11-13 Yehonatan Elon

We prove a general local law for Wigner matrices which optimally handles observables of arbitrary rank and thus it unifies the well-known averaged and isotropic local laws. As an application, we prove that the quadratic forms of a general…

Probability · Mathematics 2023-09-08 Giorgio Cipolloni , László Erdős , Dominik Schröder

McKay proved that the limiting spectral measures of the ensembles of $d$-regular graphs with $N$ vertices converge to Kesten's measure as $N\to\infty$. In this paper we explore the case of weighted graphs. More precisely, given a large…

Probability · Mathematics 2013-07-01 Leo Goldmakher , Cap Khoury , Steven J. Miller , Kesinee Ninsuwan

We revisit the classic Wigner semi-circle from two different angles. One consists in studying the Stieltjes transform directly on the real axis, which does not converge to a fixed value but follows a Cauchy distribution that depends on the…

Mathematical Physics · Physics 2018-12-26 J. P. Bouchaud , M. Potters

We study the statistics of the local resolvent and non-ergodic properties of eigenvectors for a generalised Rosenzweig-Porter $N\times N$ random matrix model, undergoing two transitions separated by a delocalised non-ergodic phase.…

Disordered Systems and Neural Networks · Physics 2016-09-29 Davide Facoetti , Pierpaolo Vivo , Giulio Biroli

Recent work has proposed fading ergodicity as a mechanism for many-body ergodicity breaking. Here, we show that two paradigmatic random matrix ensembles -- the Rosenzweig-Porter model and the ultrametric model -- fall within the same…

Statistical Mechanics · Physics 2026-03-26 Rafał Świętek , Maksymilian Kliczkowski , Miroslav Hopjan , Lev Vidmar

Understanding the distributions of spectral estimators in low-rank random matrix models, also known as signal-plus-noise matrix models, is fundamentally important in various statistical learning problems, including network analysis, matrix…

Statistics Theory · Mathematics 2024-03-15 Fangzheng Xie , Yichi Zhang