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We have calculated the joint probability distribution function for random reverse-cyclic matrices and shown that it is related to an N-body exactly solvable model. We refer to this well-known model potential as a screened harmonic…

Mathematical Physics · Physics 2013-02-13 Shashi C. L. Srivastava , Sudhir R. Jain

Statistical properties of non--symmetric real random matrices of size $M$, obtained as truncations of random orthogonal $N\times N$ matrices are investigated. We derive an exact formula for the density of eigenvalues which consists of two…

Statistical Mechanics · Physics 2010-10-21 Boris A. Khoruzhenko , Hans-Juergen Sommers , Karol Zyczkowski

We analyze gene co-expression network under the random matrix theory framework. The nearest neighbor spacing distribution of the adjacency matrix of this network follows Gaussian orthogonal statistics of random matrix theory (RMT). Spectral…

Molecular Networks · Quantitative Biology 2015-05-18 Sarika Jalan , Norbert Solymosi , Gabör Vattay , Baowen Li

The distribution function for the first eigenvalue spacing in the Laguerre unitary ensemble of finite rank random matrices is found in terms of a Painlev\'e V system, and the solution of its associated linear isomonodromic system. In…

Classical Analysis and ODEs · Mathematics 2007-07-12 Peter J. Forrester , Nicholas S. Witte

One of the main concepts in quantum physics is a density matrix, which is a symmetric positive definite matrix of trace one. Finite probability distributions can be seen as a special case when the density matrix is restricted to be…

Quantum Physics · Physics 2009-01-12 Manfred K Warmuth , Dima Kuzmin

In this article we study in detail a family of random matrix ensembles which are obtained from random permutations matrices (chosen at random according to the Ewens measure of parameter $\theta>0$) by replacing the entries equal to one by…

Probability · Mathematics 2010-05-05 Joseph Najnudel , Ashkan Nikeghbali

We present some new results on the joint distribution of an arbitrary subset of the ordered eigenvalues of complex Wishart, double Wishart, and Gaussian hermitian random matrices of finite dimensions, using a tensor pseudo-determinant…

Statistics Theory · Mathematics 2020-01-03 Marco Chiani , Alberto Zanella

The distribution of eigenvalues of N times N random matrices in the limit N to infinity is the solution to a variational principle that determines the ground state energy of a confined fluid of classical unit charges. This fact is a…

Mathematical Physics · Physics 2009-10-31 Michael K. -H. Kiessling , Herbert Spohn

We study the spectral properties of a class of random matrices where the matrix elements depend exponentially on the distance between uniformly and randomly distributed points. This model arises naturally in various physical contexts, such…

Disordered Systems and Neural Networks · Physics 2015-05-18 Ariel Amir , Yuval Oreg , Yoseph Imry

We compute exact asymptotic results for the probability of the occurrence of large deviations of the largest (smallest) eigenvalue of random matrices belonging to the Gaussian orthogonal, unitary and symplectic ensembles. In particular, we…

Statistical Mechanics · Physics 2009-11-13 David S. Dean , Satya N. Majumdar

We consider the empirical eigenvalue distribution of an $m\times m$ principle submatrix of an $n\times n$ random unitary matrix distributed according to Haar measure. Earlier work of Petz and R\'effy identified the limiting spectral measure…

Probability · Mathematics 2019-04-12 Elizabeth Meckes , Kathryn Stewart

We use the idea of a Wigner surmise to compute approximate distributions of the first eigenvalue in chiral Random Matrix Theory, for both real and complex eigenvalues. Testing against known results for zero and maximal non-Hermiticity in…

High Energy Physics - Theory · Physics 2010-02-16 G. Akemann , E. Bittner , M. J. Phillips , L. Shifrin

One of the main concepts in quantum physics is a density matrix, which is a symmetric positive definite matrix of trace one. Finite probability distributions are a special case where the density matrix is restricted to be diagonal. Density…

Quantum Physics · Physics 2014-08-14 Manfred K. Warmuth , Dima Kuzmin

Random matrix models consisting of normal matrices, defined by the sole constraint $[N^{\dag},N]=0$, will be explored. It is shown that cubic eigenvalue repulsion in the complex plane is universal with respect to the probability…

Statistical Mechanics · Physics 2009-10-28 Gary Oas

Based on the exact relationship to random matrix theory, we present an alternative method of evaluating the probability distribution of the k-th smallest Dirac eigenvalue in the epsilon-regime of QCD and QCD-like theories. By utilizing the…

High Energy Physics - Lattice · Physics 2016-07-13 Shinsuke M. Nishigaki

Statistical properties of ensembles of random density matrices are investigated. We compute traces and von Neumann entropies averaged over ensembles of random density matrices distributed according to the Bures measure. The eigenvalues of…

Quantum Physics · Physics 2009-11-10 Hans-Juergen Sommers , Karol Zyczkowski

We consider the reduced density matrix $\rho_{A}^{(m)}$ of a bipartite system $AB$ of dimensionality $mn$ in a Gaussian ensemble of random, complex pure states of the composite system. For a given dimensionality $m$ of the subsystem $A$,…

Quantum Physics · Physics 2022-09-28 B. Sharmila , V. Balakrishnan , S. Lakshmibala

We initiate a study of the following problem: Given a continuous domain $\Omega$ along with its convex hull $\mathcal{K}$, a point $A \in \mathcal{K}$ and a prior measure $\mu$ on $\Omega$, find the probability density over $\Omega$ whose…

Data Structures and Algorithms · Computer Science 2020-04-17 Jonathan Leake , Nisheeth K. Vishnoi

An important theorem in Gaussian quantum information tells us that we can diagonalise the covariance matrix of any Gaussian state via a symplectic transformation. Whilst the diagonal form is easy to find, the process for finding the…

Mathematical Physics · Physics 2021-11-16 Jason L. Pereira , Leonardo Banchi , Stefano Pirandola

Low rank inference on matrices is widely conducted by optimizing a cost function augmented with a penalty proportional to the nuclear norm $\Vert \cdot \Vert_*$. However, despite the assortment of computational methods for such problems,…

Machine Learning · Statistics 2025-10-08 Simon Segert , Nathan Wycoff