On the jackknife statistics for eigenvalues and eigenvectors of a correlation matrix

This paper deals with some problems of eigenvalues and eigenvectors of a sample correlation matrix and derives the limiting distributions of their jackknife statistics with some numerical examples.     

Iterative computation of derivatives of repeated eigenvalues and the corresponding eigenvectors

Recently the authors proposed a simultaneous iteration algorithm for the computation of the partial derivatives of repeated eigenvalues and the corresponding eigenvectors of matrices depending on several real variables. This paper analyses the properties of that algorithm and extends it in several ways. The previous requirement that the repeated eigenvalue be dominant is relaxed, and the new generalized algorithm given here allows the simultaneous treatment of simple and repeated eigenvalues. Methods for accelerating convergence are examined. Numerical results support our theoretical analysis. Copyright © 2000 John Wiley & Sons, Ltd.     

On the eigenvalues of specially low-rank perturbed matrices

We study the eigenvalues of a matrix A perturbed by a few special low-rank matrices. The perturbation is constructed from certain basis vectors of an invariant subspace of A, such as eigenvectors, Jordan vectors, or Schur vectors. We show that most of the eigenvalues of the low-rank perturbed matrix stayed unchanged from the eigenvalues of A; the perturbation can only change the eigenvalues of A that are related to the invariant subspace. Existing results mostly studied using eigenvectors with full column rank for perturbations, we generalize the results to more general settings. Applications of our results to a few interesting problems including the Google’s second eigenvalue problem are presented.     

On the characteristic polynomial,eigenvectors and determinant of heptadiagonal matrices

In this paper, the characteristic polynomial of general heptadiagonal matrices is derived as well as eigenvectors associated to a prescribed eigenvalue. A symbolic algorithm to compute the determinant of heptadiagonal matrices is also presented allowing a suite implementation through computational software programs.     

基于系统空间结构的约当规范形广义特征向量的求解方法

基于系统空间结构的分析 ,给出了求解系统约当规范形广义特征向量的新方法 .与 [1 ]中给出的方法相比 ,本文的方法更简单直接 .最后举例说明了方法的应用 .     

Perron–Frobenius property of copositive matrices, and a block copositivity criterion

Haynsworth and Hoffman proved in 1969 that the spectral radius of a symmetric copositive matrix is an eigenvalue of this matrix. This note investigates conditions which guarantee that an eigenvector corresponding to this dominant eigenvalue has no negative coordinates, i.e., whether the Perron–Frobenius property holds. Also a block copositivity criterion using the Schur complement is specified which may be helpful to reduce dimension in copositivity checks and which generalizes results proposed by Andersson et al. in 1995, and Johnson and Reams in 2005. Apparently, the latter five researchers were unaware of the more general results by the author precedingly published in 1987 and 1996, respectively.     

A note on spectrum distribution of constraint preconditioned generalized saddle point matrices

In this note, results concerning the eigenvalue distribution and form of the eigenvectors of the constraint preconditioned generalized saddle point matrix and its minimal polynomial are given. These results extend previous ones that appeared in the literature. Copyright © 2009 John Wiley & Sons, Ltd.     

Upper bound for the non-maximal eigenvalues of irreducible nonnegative matrices

We present a lower and an upper bound for the second smallest eigenvalue of Laplacian matrices in terms of the averaged minimal cut of weighted graphs. This is used to obtain an upper bound for the real parts of the non-maximal eigenvalues of irreducible nonnegative matrices. The result can be applied to Markov chains.     

Linear functionals of eigenvalues of random matrices

Let be a random unitary matrix with distribution given by Haar measure on the unitary group. Using explicit moment calculations, a general criterion is given for linear combinations of traces of powers of to converge to a Gaussian limit as . By Fourier analysis, this result leads to central limit theorems for the measure on the circle that places a unit mass at each of the eigenvalues of . For example, the integral of this measure against a function with suitably decaying Fourier coefficients converges to a Gaussian limit without any normalisation. Known central limit theorems for the number of eigenvalues in a circular arc and the logarithm of the characteristic polynomial of are also derived from the criterion. Similar results are sketched for Haar distributed orthogonal and symplectic matrices.

     

Integration of some differential-difference nonlinear equations using the spectral theory of normal block Jacobi matrices

The following method for integrating the Cauchy problem for a Toda lattice on the half-line is well known: to a solution u(t), t ∈, [0, ∞), of the problem, one assigns a self-adjoint semi-infinite Jacobi matrix J(t) whose spectral measure dπ(λ; t) undergoes simple evolution in time t. The solution of the Cauchy problem goes as follows. One writes out the spectral measure dπ(λ; 0) for the initial value u(0) of the solution and the corresponding Jacobi matrix J(0) and then computes the time evolution dπ(λ; t) of this measure. Using the solution of the inverse spectral problem, one reconstructs the Jacobi matrix J(t) from dπ(λ; t) and hence finds the desired solution u(t). In the present paper, this approach is generalized to the case in which the role of J(t) is played by a block Jacobi matrix generating a normal operator in the orthogonal sum of finite-dimensional spaces with spectral measure dπ(ζ; t) defined on the complex plane. Some recent results on the spectral theory of these normal operators permit one to use the integration method described above for a rather wide class of differential-difference nonlinear equations replacing the Toda lattice.     

A scalable parallel factorization of finite element matrices with distributed Schur complements

We consider the parallel factorization of sparse finite element matrices on distributed memory machines. Our method is based on a nested dissection approach combined with a cyclic re‐distribution of the interface Schur complements. We present a detailed definition of the parallel method, and the well‐posedness and the complexity of the algorithm are analyzed. A lean and transparent functional interface to existing finite element software is defined, and the performance is demonstrated for several representative examples. Copyright © 2016 John Wiley & Sons, Ltd.     

Computation of bounds for eigenvalues of structures with interval parameters

In this paper, the computation of eigenvalue bounds for generalized interval eigenvalue problem is considered. Two algorithms based on the properties of continuous functions are developed for evaluating upper and lower eigenvalue bounds of structures with interval parameters. The method can provide the tightest bounds within a given precision. Numerical examples illustrate the effectiveness of the proposed method.     

一类块三对角阵特征值的求解及应用

提出了求一类块三对角矩阵A的特征值和特征向量的方法,求得了该类矩阵的特征值和特征向量的表达式,并写出了用迭代法解该类方程组Au=f时迭代矩阵的特征值.