On relative perturbation theory for eigenvalues and eigenvectors of block operator matrices

We are concerned with singularly perturbed spectral problems which appear in engineering sciences. Typically under the influence of a singular perturbation the model can be approximated by a simpler, perturbation independent model. Such reduced model is usually better amenable to analytic or numeric analysis. However, the question of the quality of approximation has to be answered. Frequently, correctors which yield an improved solution–capturing important phenomena which the reduced model does not “see”–to the original problems are required. We tackle both question for self-adjoint eigenvalue/eigenvector problems posed in a general Hilbert space. Our technique is constructive and is based on methods (relative perturbation theory) of modern Numerical Linear Algebra. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

On the eigenvalues and eigenvectors of an overlapping Markov chain

Starting with a sequence of i.i.d. [uniform] random variables with m possible values, we consider the overlapping Markov chain formed by sliding a window of size k through the i.i.d. sequence. We study the limiting covariance matrix B_k of this Markov chain and give algorithms for constructing the eigenvectors of B_k. We also discuss the applicability of the results in strengthening Pearsons ² test as well as the relation to approximate entropy and the usefulness in the area of testing the hypothesis of uniformity of random number generators.Mathematics Subject Classification (2000):Primary: 60J10; Secondary: 11K45     

Asymptotic bounds for the eigenvalues and eigenvectors of a perturbed linear non-self-conjugate operator

We obtain asymptotic bounds for the perturbed eigenvalues and eigenvectors of a perturbed linear bounded operator A(), in a Hilbert space under the assumption that A() is holomorphic at the point =₀ and the eigenvalue ₀= gl(₀) of the operator A(₀) is isolated and of finite multiplicity. We study certain cases of high degeneracy in the limiting problem, i.e., the case when there are generalized associated vectors.Translated from Matematicheskie Zametki, Vol. 12, No. 4, pp. 403–412, October, 1972.The author wishes to express his sincere gratitude to his scientific director T. Sabirov for valuable observations and advice.     

On the eigenvalues of the fredholm operator

We prove that if ω(t, x, K ₂ ^(m) )?c(x)ω(t) for allxε[a, b] andx ε [0,b-a] wherec ∈L ¹(a, b) and ω is a modulus of continuity, then λ _n =O(n ^?m-1/2ω(1/n)) and this estimate is unimprovable.     

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.     

On the eigenvalues and eigenvectors of certain finite,vertex-weighted,bipartite graphs

The established, spectral characterisation of bipartite graphs with unweighted vertices (which are here termed homogeneous graphs) is extended to those bipartite graphs (called heterogeneous) in which all of the vertices in one set are weighted h₁ , and each of those in the other set of the bigraph is weighted h₂. All the eigenvalues of a homogeneous bipartite graph occur in pairs, around zero, while some of the eigenvalues of an arbitrary, heterogeneous graph are paired around $\text{1}\text{2}$(h₁ + h₂), the remainder having the value h₂ (or h_l). The well-documented, explicit relations between the eigenvectors belonging to “paired” eigenvalues of homogeneous graphs are extended to relate the components of the eigenvectors associated with each couple of “paired” eigenvalues of the corresponding heterogeneous graph. Details are also given of the relationships between the eigenvectors of an arbitrary, homogeneous, bipartite graph and those of its heterogeneous analogue.     

On least eigenvalues and least eigenvectors of real symmetric matrices and graphs

We give an upper bound for the least eigenvalue of a principal submatrix of a real symmetric matrix with zero diagonal, from which we establish an upper bound for the least eigenvalue of a graph when some vertices are removed using the components of the least eigenvector(s). We give lower and upper bounds for the least eigenvalue of a graph when some edges are removed. We also establish bounds for the components of the least eigenvector(s) of a real symmetric matrix and a graph.     

Differentiable roots,eigenvalues, and eigenvectors

We determine the conditions for the existence of C ^p -roots of curves of monic complex polynomials as well as for the existence of C ^p -eigenvalues and C ^p -eigenvectors of curves of normal complex matrices.     

On the number of eigenvalues of a matrix operator

We consider a matrix operator H in the Fock space. We prove the finiteness of the number of negative eigenvalues of H if the corresponding generalized Friedrichs model has the zero eigenvalue (0 = min σ _ess(H)). We also prove that H has infinitely many negative eigenvalues accumulating near zero (the Efimov effect) if the generalized Friedrichs model has zero energy resonance. We obtain asymptotics for the number of negative eigenvalues of H below z as z → −0.     

Error bounds for computed eigenvalues and eigenvectors

Summary On the basis of an existence theorem for solutions of nonlinear systems, a method is given for finding rigorous error bounds for computed eigenvalues and eigenvectors of real matrices. It does not require the usual assumption that the true eigenvectors span the whole space. Further, a priori error estimates for eigenpairs corrected by an iterative method are given. Finally the results are illustrated with numerical examples.Dedicated to Professor Yoshikazu Nakai on his sixtieth birthday     

Extremal eigenvalues and eigenvectors of deformed Wigner matrices

We consider random matrices of the form \(H = W + \lambda V, \lambda \in {\mathbb {R}}^+\), where \(W\) is a real symmetric or complex Hermitian Wigner matrix of size \(N\) and \(V\) is a real bounded diagonal random matrix of size \(N\) with i.i.d. entries that are independent of \(W\). We assume subexponential decay of the distribution of the matrix entries of \(W\) and we choose \(\lambda \sim 1\), so that the eigenvalues of \(W\) and \(\lambda V\) are typically of the same order. Further, we assume that the density of the entries of \(V\) is supported on a single interval and is convex near the edges of its support. In this paper we prove that there is \(\lambda _+\in {\mathbb {R}}^+\) such that the largest eigenvalues of \(H\) are in the limit of large \(N\) determined by the order statistics of \(V\) for \(\lambda >\lambda _+\). In particular, the largest eigenvalue of \(H\) has a Weibull distribution in the limit \(N\rightarrow \infty \) if \(\lambda >\lambda _+\). Moreover, for \(N\) sufficiently large, we show that the eigenvectors associated to the largest eigenvalues are partially localized for \(\lambda >\lambda _+\), while they are completely delocalized for \(\lambda <\lambda _+\). Similar results hold for the lowest eigenvalues.     

A computational method for eigenvalues and eigenvectors of a matrix with real eigenvalues

This paper describes a new computational procedure for calculating eigenvalues and eigenvectors of a square matrix. The method is based on a matrix function, the sign of a matrix. Eigenvalues and eigenvectors of matrices with distinct eigenvalues and nondefective matrices with repeated roots can be determined in a straightforward manner. Defective matrices require additional calculations.     

On stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrix

In applications of signal processing and pattern recognition, eigenvectors and eigenvalues of the statistical mean of a random matrix sequence are needed. Iterative methods are suggested and analyzed, in which no sample moments are used. Convergence is shown by stochastic approximation theory.