Backward errors for eigenvalues and eigenvectors of Hermitian,skew-Hermitian,H-even and H-odd matrix polynomials

We discuss the perturbation analysis for eigenvalues and eigenvectors of structured homogeneous matrix polynomials with Hermitian, skew-Hermitian, H-even and H-odd structure. We construct minimal structured perturbations (structured backward errors) such that an approximate eigenvalue and eigenvector pair (finite or infinite eigenvalues) is an exact eigenvalue eigenvector pair of an appropriately perturbed structured matrix polynomial. We present various comparisons with unstructured backward errors and previous backward errors constructed for the non-homogeneous case and show that our results generalize previous results.     

Vector space of linearizations for the quadratic two-parameter matrix polynomial

Given a quadratic two-parameter matrix polynomial Q(λ,?μ), we develop a systematic approach to generating a vector space of linear two-parameter matrix polynomials. The purpose for constructing this vector space is that potential linearizations of Q(λ,?μ) lie in it. Then, we identify a set of linearizations and describe their constructions. Finally, we determine a class of linearizations for a quadratic two-parameter eigenvalue problem.     

Inverses and eigenpairs of tridiagonal Toeplitz matrix with opposite-bordered rows

In this paper, tridiagonal Toeplitz matrix (type I, type II) with opposite-bordered rows are introduced. Main attention is paid to calculate the determinants, the inverses and the eigenpairs of these matrices. Specifically, the determinants of an $n\times n$ tridiagonal Toeplitz matrix with opposite-bordered rows can be explicitly expressed by using the $(n-1)$th Fibonacci number, the inversion of the tridiagonal Toeplitz matrix with opposite-bordered rows can also be explicitly expressed by using the Fibonacci numbers and unknown entries from the new matrix. Besides, we give the expression of eigenvalues and eigenvectors of the tridiagonal Toeplitz matrix with opposite-bordered rows. In addition, some algorithms are presented based on these theoretical results. Numerical results show that the new algorithms have much better computing efficiency than some existing algorithms studied recently.     

A stability analysis of the jacobi matrix inverse eigenvalue problem

In this paper, we give a perturbation bound for the solution of the Jacobi matrix inverse eigenvalue problem.China State Major Key Project for Basic Researches.     

Computing top eigenpairs of Hermitizable matrix

The top eigenpairs at the title mean the maximal, the submaximal, or a few of the subsequent eigenpairs of an Hermitizable matrix. Restricting on top ones is to handle with the matrices having large scale, for which only little is known up to now. This is different from some mature algorithms, that are clearly limited only to medium-sized matrix for calculating full spectrum. It is hoped that a combination of this paper with the earlier works, to be seen soon, may provide some effective algorithms for computing the spectrum in practice, especially for matrix mechanics.     

Backward error bounds for approximate Krylov subspaces

Let A be a matrix of order n and let be a subspace of dimension k. In this note, we determine a matrix E of minimal norm such that is a Krylov subspace of A+E.     

实对称带状矩阵逆特征值问题

研究了一类实对称带状矩阵逆特征值问题：给定三个互异实数λ,μ和v及三个非零实向量x,y和z,分别构造实对称五对角矩阵T和实对称九对角矩阵A,使其都具有特征对(λ,x),(μ,y)和(v,z)．给出了此类问题的两种提法,研究了问题的可解性以及存在惟一解的充分必要条件,最后给出了数值算法和数值例子．     

关于矩阵特征值问题向后误差分析的注记

本文研究实矩阵关于复近似特征对的范数型向后误差．在复扰动情形,这个问题已被Higham 等学者解决．本文研究实扰动情形．结果表明,通常情况下,两种情形差别不大,但在某些情形,二者可以相差很大．作为推广,我们还讨论了矩阵多项式的相应问题．文中的一个结果部分地解决了D．J．Higham和N．J．Higham 1999年提出的一个待解决的问题．     

关于Jacobi矩阵的特征值反问题可解的充分必要条件的一个注记 (英)

本文讨论一类具有特殊结构的Jacobi矩阵的特征值反问题,该问题由描述变截面杆的微分方程离散化得到．我们得到了这个问题有解的一些必要条件,并且通过一些数值例子,说明了L．Lu和K．Michael给出的充分条件和算法在矩阵的阶数高于3的时候是错误的。     

Structured backward error for palindromic polynomial eigenvalue problems,II: Approximate eigentriplets

A detailed structured backward error analysis for four kinds of palindromic polynomial eigenvalue problems (PPEPs)P(\lambda )\equiv \left({\displaystyle \sum _{l=\mathrm{0}}^d{{\displaystyle A}}_l{\lambda }^l}\right)x=\mathrm{0},{{\displaystyle A}}_{d-l}=\epsilon {{\displaystyle A}}_l^{★},L=\mathrm{0},\mathrm{1},\dots ,\left[{\displaystyle \frac{d}{\mathrm{2}}}\right], for an approximate eigentriplet is performed, where ★ is one of the two actions: transpose and conjugate transpose, and \epsilon \in \{\pm \mathrm{1}\}. The analysis is concerned with estimating the smallest perturbation to P(\lambda); while preserving the respective palindromic structure, such that the given approximate eigentriplet is an exact eigentriplet of the perturbed PPEP. Previously, R. Li, W. Lin, and C. Wang [Numer. Math., 2010, 116(1): 95–122] had only considered the case of an approximate eigenpair for PPEP but commented that attempt for an approximate eigentriplet was unsuccessful. Indeed, the latter case is much more complicated. We provide computable upper bounds for the structured backward errors. Our main results in this paper are several informative and very sharp upper bounds that are capable of revealing distinctive features of PPEP from general polynomial eigenvalue problems (PEPs). In particular, they reveal the critical cases in which there is no structured backward perturbation such that the given approximate eigentriplet becomes an exact one of any perturbed PPEP, unless further additional conditions are imposed. These critical cases turn out to the same as those from the earlier studies on an approximate eigenpair.     

An inexact inverse iteration for large sparse eigenvalue problems

In this paper, we propose an inverse inexact iteration method for the computation of the eigenvalue with the smallest modulus and its associated eigenvector for a large sparse matrix. The linear systems of the traditional inverse iteration are solved with accuracy that depends on the eigenvalue with the second smallest modulus and iteration numbers. We prove that this approach preserves the linear convergence of inverse iteration. We also propose two practical formulas for the accuracy bound which are used in actual implementation. © 1997 John Wiley & Sons, Ltd.     

Computation of the Newton step for the even and odd characteristic polynomials of a symmetric positive definite Toeplitz matrix

We compute the Newton step for the characteristic polynomial and for the even and odd characteristic polynomials of a symmetric positive definite Toeplitz matrix as the reciprocal of the trace of an appropriate matrix. We show that, after the Yule-Walker equations are solved, this trace can be computed in additional arithmetic operations, which is in contrast to existing methods, which rely on a recursion, requiring additional arithmetic operations.

     

斜对称双对角矩阵特征值反问题

讨论了关于斜对称双对角矩阵的特征值反问题．即：已知一个n阶斜对称双对角矩阵的特征值和两个n-1阶子矩阵的部分特征值，则可求得该矩阵．最后给出了数值例子．     

Polynomial numerical hulls of matrix polynomials,II

In this article, we study some algebraic and geometrical properties of polynomial numerical hulls of matrix polynomials and joint polynomial numerical hulls of a finite family of matrices (possibly the coefficients of a matrix polynomial). Also, we study polynomial numerical hulls of basic A-factor block circulant matrices. These are block companion matrices of particular simple monic matrix polynomials. By studying the polynomial numerical hulls of the Kronecker product of two matrices, we characterize the polynomial numerical hulls of unitary basic A-factor block circulant matrices.     

非首一矩阵多项式的解

发展了一种关于非首一矩阵多项式解的计算理论.综述了基于特征对概念的相伴lambda矩阵的谱理论.研究了伴随型的线性化,引进了广义Vandermonde矩阵.给出了解存在的条件,并获得解的个数的估计.数值例子阐明了所提出的理论.     

A note on the residual type a posteriori error estimates for finite element eigenpairs of nonsymmetric elliptic eigenvalue problems

In this paper we study the residual type a posteriori error estimates for general elliptic (not necessarily symmetric) eigenvalue problems. We present estimates for approximations of semisimple eigenvalues and associated eigenvectors. In particular, we obtain the following new results: 1) An error representation formula which we use to reduce the analysis of the eigenvalue problem to the analysis of the associated source problem; 2) A local lower bound for the error of an approximate finite element eigenfunction in a neighborhood of a given mesh element T.     

一类二次矩阵方程的条件数和后向误差

主要讨论一类二次矩阵方程X^2-EX-F=0的条件数和后向误差,其中E是一个对角矩阵,F是一个M矩阵.这类二次矩阵方程来源于Markov链的噪声Wiener-Hopf问题.实际问题中人们感兴趣的是它的M矩阵的解.应用Rice创立的基于Frobenius范数下的条件数理论,导出此类二次矩阵方程的M矩阵解的条件数的显式表达式.同时,也给出近似解的后向误差的定义以及一个可计算的表达式.最后,通过数值例子验证理论结果是有效的.     

Backward error,sensitivity, and refinement of computed solutions of algebraic Riccati equations

In this paper, a new backward error criterion, together with a sensitivity measure, is presented for assessing solution accuracy of nonsymmetric and symmetric algebraic Riccati equations (AREs). The usual approach to assessing reliability of computed solutions is to employ standard perturbation and sensitivity results for linear systems and to extend them further to AREs. However, such methods are not altogether appropriate since they do not take account of the underlying structure of these matrix equations. The approach considered here is to first compute the backward error of a computed solution X? that measures the amount by which data must be perturbed so that X? is the exact solution of the perturbed original system. Conventional perturbation theory is used to define structured condition numbers that fully respect the special structure of these matrix equations. The new condition number, together with the backward error of computed solutions, provides accurate estimates for the sensitivity of solutions. Optimal perturbations are then used in an iterative refinement procedure to give further more accurate approximations of actual solutions. The results are derived in their most general setting for nonsymmetric and symmetric AREs. This in turn offers a unifying framework through which it is possible to establish similar results for Sylvester equations, Lyapunov equations, linear systems, and matrix inversions.     

A smoothing Newton's method for the construction of a damped vibrating system from noisy test eigendata

In this paper we consider an inverse problem for a damped vibration system from the noisy measured eigendata, where the mass, damping, and stiffness matrices are all symmetric positive‐definite matrices with the mass matrix being diagonal and the damping and stiffness matrices being tridiagonal. To take into consideration the noise in the data, the problem is formulated as a convex optimization problem involving quadratic constraints on the unknown mass, damping, and stiffness parameters. Then we propose a smoothing Newton‐type algorithm for the optimization problem, which improves a pre‐existing estimate of a solution to the inverse problem. We show that the proposed method converges both globally and quadratically. Numerical examples are also given to demonstrate the efficiency of our method. Copyright © 2008 John Wiley & Sons, Ltd.