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We prove that with high probability, every eigenvector of a random matrix is delocalized in the sense that any subset of its coordinates carries a non-negligible portion of its $\ell_2$ norm. Our results pertain to a wide class of random…

Probability · Mathematics 2016-12-23 Mark Rudelson , Roman Vershynin

We prove that an n by n random matrix G with independent entries is completely delocalized. Suppose the entries of G have zero means, variances uniformly bounded below, and a uniform tail decay of exponential type. Then with high…

Probability · Mathematics 2015-11-04 Mark Rudelson , Roman Vershynin

Consider a random block matrix model consisting of $D$ random systems arranged along a circle, where each system is modeled by an independent $N\times N$ complex Hermitian Wigner matrix. Neighboring systems interact via an arbitrary…

Probability · Mathematics 2025-07-14 Jiaqi Fan , Bertrand Stone , Fan Yang , Jun Yin

Let $x \in S^{n-1}$ be a unit eigenvector of an $n \times n$ random matrix. This vector is delocalized if it is distributed roughly uniformly over the real or complex sphere. This intuitive notion can be quantified in various ways. In these…

Probability · Mathematics 2017-07-27 Mark Rudelson

Improving upon results of Rudelson and Vershynin, we establish delocalization bounds for eigenvectors of independent-entry random matrices. In particular, we show that with high probability every eigenvector is delocalized, meaning any…

Probability · Mathematics 2019-02-01 Kyle Luh , Sean O'Rourke

We consider random walk on a mildly random environment on finite transitive d- regular graphs of increasing girth. After scaling and centering, the analytic spectrum of the transition matrix converges in distribution to a Gaussian noise. An…

Probability · Mathematics 2011-11-10 Dimitrios Cheliotis , Balint Virag

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

We consider a general class of $n\times n$ random band matrices with bandwidth $W$. When $W^2\ll n$, we prove that with high probability the eigenvectors of such matrices are localized and decay exponentially at the sharp scale $W^2$.…

Probability · Mathematics 2025-08-29 Reuben Drogin

We study delocalization of null vectors and eigenvectors of random matrices with i.i.d entries. Let $A$ be an $n\times n$ random matrix with i.i.d real subgaussian entries of zero mean and unit variance. We show that with probability at…

Probability · Mathematics 2019-09-19 Anna Lytova , Konstantin Tikhomirov

We propose a technique for calculating and understanding the eigenvalue distribution of sums of random matrices from the known distribution of the summands. The exact problem is formidably hard. One extreme approximation to the true density…

Quantum Physics · Physics 2017-10-27 Ramis Movassagh , Alan Edelman

We consider $N\times N$ symmetric random matrices where the probability distribution for each matrix element is given by a measure $\nu$ with a subexponential decay. We prove that the eigenvalue spacing statistics in the bulk of the…

Mathematical Physics · Physics 2010-11-25 Laszlo Erdos , Benjamin Schlein , Horng-Tzer Yau

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

We consider some random band matrices with band-width $N^\mu$ whose entries are independent random variables with distribution tail in $x^{-\alpha}$. We consider the largest eigenvalues and the associated eigenvectors and prove the…

Probability · Mathematics 2015-06-25 Florent Benaych-Georges , Sandrine Péché

We study the eigenvectors of generalized Wigner matrices with subexponential entries and prove that they delocalize at the optimal rate with overwhelming probability. We also prove high probability delocalization bounds with sharp…

Probability · Mathematics 2021-09-10 Lucas Benigni , Patrick Lopatto

We study the hierarchical analogue of power-law random band matrices, a symmetric ensemble of random matrices with independent entries whose variances decay exponentially in the metric induced by the tree topology on $\mathbb{N}$. We map…

Mathematical Physics · Physics 2018-01-19 Per von Soosten , Simone Warzel

In this text, we consider an N by N random matrix X such that all but o(N) rows of X have W non identically zero entries, the other rows having lass than $W$ entries (such as, for example, standard or cyclic band matrices). We always…

Probability · Mathematics 2014-01-21 Florent Benaych-Georges , Sandrine Péché

We consider $N\times N$ Hermitian random band matrices $H=(H_{xy})$, whose entries are centered complex Gaussian random variables. The indices $x,y$ range over the $d$-dimensional discrete torus $(\mathbb Z/L\mathbb Z)^d$ with $d\in…

Probability · Mathematics 2025-06-25 Fan Yang , Jun Yin

We investigate the noise sensitivity of the top eigenvector of a sparse random symmetric matrix. Let $v$ be the top eigenvector of an $N\times N$ sparse random symmetric matrix with an average of $d$ non-zero centered entries per row. We…

Probability · Mathematics 2022-04-07 Charles Bordenave , Jaehun Lee

We prove that the eigenvectors associated to small enough eigenvalues of an heavy-tailed symmetric random matrix are delocalized with probability tending to one as the size of the matrix grows to infinity. The delocalization is measured…

Probability · Mathematics 2017-08-23 Charles Bordenave , Alice Guionnet

Analyzing the spectral behavior of random matrices with dependency among entries is a challenging problem. The adjacency matrix of the random $d$-regular graph is a prominent example that has attracted immense interest. A crucial spectral…

Probability · Mathematics 2023-06-07 Jaehun Lee , Kyeongsik Nam
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