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We give Hoeffding and Bernstein-type concentration inequalities for the largest eigenvalue of sums of random matrices arising from a Markov chain. We consider time-dependent matrix-valued functions on a general state space, generalizing…

Probability · Mathematics 2025-07-01 Joe Neeman , Bobby Shi , Rachel Ward

We establish the universality of the singular numbers in random matrix products over $\mathrm{GL}_n(\mathbb{Q}_p)$ as the number of products approaches infinity, with a fixed $n\ge 1$. We demonstrate that, under a broad class of…

Probability · Mathematics 2025-10-20 Jiahe Shen

We use the well-known isomorphism between operator algebras and function spaces equipped with a star product to study the asymptotic properties of certain matrix sequences in which the matrix dimension $D$ tends to infinity. Our approach is…

Mathematical Physics · Physics 2015-06-05 J. N. Kriel , F. G. Scholtz

For fixed $m > 1$, we study the product of $m$ independent $N \times N$ elliptic random matrices as $N$ tends to infinity. Our main result shows that the empirical spectral distribution of the product converges, with probability $1$, to the…

Probability · Mathematics 2015-06-26 Sean O'Rourke , David Renfrew , Alexander Soshnikov , Van Vu

The topic of this paper is the typical behavior of the spectral measures of large random matrices drawn from several ensembles of interest, including in particular matrices drawn from Haar measure on the classical Lie groups, random…

Probability · Mathematics 2013-09-16 Elizabeth S. Meckes , Mark W. Meckes

We investigate concentration properties of spectral measures of Hermitian random matrices with partially dependent entries. More precisely, let $X_n$ be a Hermitian random matrix of size $n\times n$ that can be split into independent blocks…

Probability · Mathematics 2020-07-31 Bartłomiej Polaczyk

We present some extensions of Bernstein's concentration inequality for random matrices. This inequality has become a useful and powerful tool for many problems in statistics, signal processing and theoretical computer science. The main…

Probability · Mathematics 2017-04-18 Stanislav Minsker

The concentration of measure phenomenon may be summarized as follows: a function of many weakly dependent random variables that is not too sensitive to any of its individual arguments will tend to take values very close to its expectation.…

Probability · Mathematics 2016-11-18 Aryeh Kontorovich , Maxim Raginsky

We prove a Chernoff-type bound for sums of matrix-valued random variables sampled via a random walk on an expander, confirming a conjecture due to Wigderson and Xiao. Our proof is based on a new multi-matrix extension of the Golden-Thompson…

Probability · Mathematics 2018-04-18 Ankit Garg , Yin Tat Lee , Zhao Song , Nikhil Srivastava

We study concentration inequalities for structured weighted sums of random data, including (i) tensor inner products and (ii) sequential matrix sums. We are interested in tail bounds and concentration inequalities for those structured…

Statistics Theory · Mathematics 2026-02-11 Chen Cheng , Rina Foygel Barber

We prove that a sum of random matrices generated by a $\psi$-mixing Markov chain has similar spectral properties to a Gaussian matrix with the same mean and covariance structure. This nonasymptotic universality principle enables sharp…

Probability · Mathematics 2026-04-29 Alexander Van Werde , Jaron Sanders

A generalization of the Bernstein matrix concentration inequality to random tensors of general order is proposed. This generalization is based on the use of Einstein products between tensors, from which a strong link can be established…

Statistics Theory · Mathematics 2021-05-31 Z. Luo , L. Qi , Ph. L. Toint

This paper derives exponential tail bounds and polynomial moment inequalities for the spectral norm deviation of a random matrix from its mean value. The argument depends on a matrix extension of Stein's method of exchangeable pairs for…

Probability · Mathematics 2013-05-06 Daniel Paulin , Lester Mackey , Joel A. Tropp

This paper presents new probability inequalities for sums of independent, random, self-adjoint matrices. These results place simple and easily verifiable hypotheses on the summands, and they deliver strong conclusions about the…

Probability · Mathematics 2014-04-29 Joel A. Tropp

For fixed $m>1$, we consider $m$ independent $n \times n$ non-Hermitian random matrices $X_1, ..., X_m$ with i.i.d. centered entries with a finite $(2+\eta)$-th moment, $ \eta>0.$ As $n$ tends to infinity, we show that the empirical…

Probability · Mathematics 2014-08-18 Sean O'Rourke , Alexander Soshnikov

Very recently we have shown that the spherical transform is a convenient tool for studying the relation between the joint density of the singular values and that of the eigenvalues for bi-unitarily invariant random matrices. In the present…

Classical Analysis and ODEs · Mathematics 2019-08-27 Mario Kieburg , Holger Kösters

We present a genus expansion-type expression for the expected values of products of traces of expressions involving Haar-distributed orthogonal matrices. As with other real genus expansions, nonorientable surfaces appear, in addition to the…

Probability · Mathematics 2015-11-05 C. E. I. Redelmeier

We consider inhomogeneous matrix products over max-plus algebra, where the matrices in the product satisfy certain assumptions under which the matrix products of sufficient length be rank-one, as it was shown in [6][L. Shue, B.D.O.…

Rings and Algebras · Mathematics 2019-04-15 Arthur Kennedy Cochran Patrick , Sergei Sergeev , Štefan Berežný

We prove Chernoff style exponential concentration bounds for classical quantum soft covering generalising previous works which gave bounds only in expectation. Our first result is an exponential concentration bound for fully smooth…

Quantum Physics · Physics 2025-04-08 Pranab Sen

In this short note, we revisit the work of T. Tao and V. Vu on large non-hermitian random matrices with independent and identically distributed entries with mean zero and unit variance. We prove under weaker assumptions that the limit…

Probability · Mathematics 2011-03-01 Charles Bordenave