We study the eigenvalues of large perturbed matrices. We consider a pattern matrix P, we blow it up to get a large block-matrix Bn. We can observe only a noisy version of matrix Bn. So we add a random noise Wn to obtain the perturbed matrix An = Bn + Wn. Our aim is to find the structural eigenvalues of An. We prove asymptotic theorems on this problem and also suggest a graphical method to distinguish the structural and the non-structural eigenvalues of An
We consider perturbations of a large Jordan matrix, either random and small in norm or of small rank...
An important topic in random matrix theory is the study of the statistical properties of the eigenva...
A reduced basis formulation is presented for efficient solution of large-scale random eigenvalue pro...
AbstractWe prove that block random matrices consisting of Wigner-type blocks have as many large (str...
Characterizing the exact asymptotic distributions of high-dimensional eigenvectors for large structu...
Abstract. Consider a deterministic self-adjoint matrix Xn with spectral measure con-verging to a com...
This paper demonstrates an introduction to the statistical distribution of eigenval-ues in Random Ma...
AbstractBehaviour of the eigenvalues of random matrices with an underlying linear structure is inves...
Abstract. Consider a deterministic self-adjoint matrix Xn with spectral measure con-verging to a com...
We consider matrices formed by a random $N\times N$ matrix drawn from the Gaussian Orthogonal Ensemb...
We prove large deviations principles for spectral measures of perturbed (or spiked) matrix models in...
11 pages, 2 figuresIn this text, based on elementary computations, we provide a perturbative expansi...
The goal of this article is to study how much the eigenvalues of large Hermitian random matrices dev...
AbstractWe consider perturbations of a large Jordan matrix, either random and small in norm or of sm...
It is known that in various random matrix models, large perturbations create outlier eigenvalues whi...
We consider perturbations of a large Jordan matrix, either random and small in norm or of small rank...
An important topic in random matrix theory is the study of the statistical properties of the eigenva...
A reduced basis formulation is presented for efficient solution of large-scale random eigenvalue pro...
AbstractWe prove that block random matrices consisting of Wigner-type blocks have as many large (str...
Characterizing the exact asymptotic distributions of high-dimensional eigenvectors for large structu...
Abstract. Consider a deterministic self-adjoint matrix Xn with spectral measure con-verging to a com...
This paper demonstrates an introduction to the statistical distribution of eigenval-ues in Random Ma...
AbstractBehaviour of the eigenvalues of random matrices with an underlying linear structure is inves...
Abstract. Consider a deterministic self-adjoint matrix Xn with spectral measure con-verging to a com...
We consider matrices formed by a random $N\times N$ matrix drawn from the Gaussian Orthogonal Ensemb...
We prove large deviations principles for spectral measures of perturbed (or spiked) matrix models in...
11 pages, 2 figuresIn this text, based on elementary computations, we provide a perturbative expansi...
The goal of this article is to study how much the eigenvalues of large Hermitian random matrices dev...
AbstractWe consider perturbations of a large Jordan matrix, either random and small in norm or of sm...
It is known that in various random matrix models, large perturbations create outlier eigenvalues whi...
We consider perturbations of a large Jordan matrix, either random and small in norm or of small rank...
An important topic in random matrix theory is the study of the statistical properties of the eigenva...
A reduced basis formulation is presented for efficient solution of large-scale random eigenvalue pro...