关于矩阵对角化的一种判别方法

阐述判别矩阵对角化的一种方法．如果复数域C上n阶矩阵A的对应于不同特征值的线性无关特征向量的个数都恰好等于该特征值的重数,则A相似于对角矩阵．     

对称正交反对称矩阵反问题解存在的条件

矩阵反问题和矩阵特征值反问题在科学和工程技术中具有广泛的应用,有关它们的研究已取得了许多进展[1,2].[3]和[4]分别研究了反对称矩阵反问题和双反对称矩阵特征值反问题等.本文研究一类更广泛的对称正交反对称矩阵反问题.用Rn×m（Cn×m）表示n×m实（复）矩阵的全体,ASRn×n表示n阶反对称矩阵的全体,ABSRn×n表示n阶双反对称矩阵的全体,ORn×n表示n阶正交矩阵的全体.A+表示矩阵A的Moore-Penrose广义逆.In表示n阶单位矩阵.ei表示n阶单位矩阵的第i列,Sn=[en,en-1,     

准对角占优矩阵的一些性质

本文讨论了准对角占优矩阵和准共轭对角占优矩阵特征值的分布以及一些其它性质．     

求解实对称三对角矩阵特征值的二分法

本文利用2×2阶实对称矩阵特征值的计算，并以秩—1修正为基础，通过建立一种二分模式，得到了计算n除实对称三对角矩阵所有特征值的新方法．结果表明，当要求所有特征值时，本文方法优于QR方法。由于算法过程中数据的不相关性，本文方法具有很好的并行性，尤其适合于MIMD并行实现。     

对称双随机矩阵逆特征值问题

主要研究随机矩阵逆特征值问题．特别是对称双随机矩阵和列随机矩阵逆特征值问题．对参考文献[1]与[2]的结论作了一些推广．并给出了—个数值例子．     

对称矩阵部分特征值之和的非光滑分析

本文研究n阶实对称矩阵A的前m项最大特征值之和fm（A）的非光滑分析问题．利用Ky－Fan的关于特征值之和的变分原理以及凸分析理论，得到了fm（A）的次梯度和方向导数的显式表达式．     

陀螺特征值问题的并行子空间迭代法

<正>1引言陀螺系统特征值问题是转子动力学中的基本问题,是一类特殊的二次特征值问题.假设M和K是n阶对称矩阵,C是n阶反对称矩阵,则二次特征值问题(λ~2M+λC+K)x=0(1)     

实对称矩阵的一种新表示

对于ｎ阶实对称矩阵Ａ，在不知道某个特征值（不管重数）所对应的特征向量时．我们得出了Ａ的表示式：其中λｒｉ是Ａ的ｒｉ重特征值ｐ１（λｒｉ），…，ｐｒｉ（λｒｉ）是λｒｉ的特征子空间的正交基底．     

共轭广义对角占优矩阵的特征值分布

文献[1]和[2]分别给出了复方阵A在准严格对角占优和共轭准严格对角占优(由定义知它包含了严格对角占优类和共轭严格占优类)条件下的特征值分布。[6]对此作了进一步的研究。这些结果对矩阵特征值理论和特殊矩阵理论有着重要的意义。 本文导出了复方阵A在广义对角占优和共轭广义对角占优条件下的特征值分布。由于广     

一类亚半正定矩阵的左右逆特征值问题

1．引言在工程技术中常常遇到这样一类逆特征值问题：要求在一个矩阵集合S中，找具有给定的部分右特征对（特征值及相应的特征向量）和给定的部分左特征对（特征值及相应的特征向量）的矩阵．文［2］，［3］讨论了S为。x。实矩阵集合的情形．文［4］－［7］对S为nxn实对称矩阵．对称正定矩阵，对称半正定矩阵集合的情形进行了讨论．文【川讨论了S为亚正定阵集合的情形．并提到了对于亚半正定矩阵的情形目下无人涉及，有待进一步研究．本文将对S为nxn亚半正定矩阵集合的情形进行讨论．给出了亚半正定矩阵的左右逆特征值问题有解的充要条件…     

The inverse eigenvalue problem for symmetric quasi anti-bidiagonal matrices

In this paper we construct the symmetric quasi anti-bidiagonal matrix that its eigenvalues are given, and show that the problem is also equivalent to the inverse eigenvalue problem for a certain symmetric tridiagonal matrix which has the same eigenvalues. Not only elements of the tridiagonal matrix come from quasi anti-bidiagonal matrix, but also the places of elements exchange based on some conditions.     

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．     

ARNOLDI TYPE ALGORITHMS FOR LARGE UNSYMMETRIC MULTIPLE EIGENVALUE PROBLEMS

1.IntroductionTheLanczosalgorithm[Zo]isaverypowerfultoolforextractingafewextremeeigenvaluesandassociatedeigenvectorsoflargesymmetricmatrices[4'5'22].Sincethe1980's,considerableattentionhasbeenpaidtogeneralizingittolargeunsymmetricproblems.Oneofitsgen...     

一种组合数矩阵及其性质

构造了一个对称正定矩阵,矩阵元素与组合数有关,讨论了该矩阵的一些性质.根据MATLAB数值计算的结果,提出了该矩阵有关特征值性质的一些猜想.     

On inverse eigenvalue problems for block Toeplitz matrices with Toeplitz blocks

We propose an algorithm for solving the inverse eigenvalue problem for real symmetric block Toeplitz matrices with symmetric Toeplitz blocks. It is based upon an algorithm which has been used before by others to solve the inverse eigenvalue problem for general real symmetric matrices and also for Toeplitz matrices. First we expose the structure of the eigenvectors of the so-called generalized centrosymmetric matrices. Then we explore the properties of the eigenvectors to derive an efficient algorithm that is able to deliver a matrix with the required structure and spectrum. We have implemented our ideas in a Matlab code. Numerical results produced with this code are included.     

Calculating the minimal/maximal eigenvalue of symmetric parameterized matrices using projection

In applications of linear algebra including nuclear physics and structural dynamics, there is a need to deal with uncertainty in the matrices. We focus on matrices that depend on a set of parameters ω and we are interested in the minimal eigenvalue of a matrix pencil ( A , B ) with A , B symmetric and B positive definite. If ω can be interpreted as the realization of random variables, one may be interested in statistical moments of the minimal eigenvalue. In order to obtain statistical moments, we need a fast evaluation of the eigenvalue as a function of ω . Because this is costly for large matrices, we are looking for a small parameterized eigenvalue problem whose minimal eigenvalue makes a small error with the minimal eigenvalue of the large eigenvalue problem. The advantage, in comparison with a global polynomial approximation (on which, e.g., the polynomial chaos approximation relies), is that we do not suffer from the possible nonsmoothness of the minimal eigenvalue. The small‐scale eigenvalue problem is obtained by projection of the large‐scale problem. Our main contribution is that, for constructing the subspace, we use multiple eigenvectors and derivatives of eigenvectors. We provide theoretical results and document numerical experiments regarding the beneficial effect of adding multiple eigenvectors and derivatives.     

关于正交矩阵特征值与行列式的两个定理

以两种证明思路给出了正交矩阵特征值与行列式关系的定理,并在此基础之上提出了对称正交矩阵行列式的迹定理.     

A property of eigenvalue bounds for a class of symmetric tridiagonal interval matrices

The eigenvalue bounds of interval matrices are often required in some mechanical and engineering fields. In this paper, we consider an interval eigenvalue problem with symmetric tridiagonal matrices. A theoretical result is obtained that under certain assumptions the upper and lower bounds of interval eigenvalues of the problem must be achieved just at some vertex matrices of the interval matrix. Then a sufficient condition is provided to guarantee the assumption to be satisfied. The conclusion is illustrated also by a numerical example. Copyright © 2010 John Wiley & Sons, Ltd.     

The Clarke and Michel-Penot Subdifferentials of the Eigenvalues of a Symmetric Matrix

We calculate the Clarke and Michel-Penot subdifferentials of the function which maps a symmetric matrix to its mth largest eigenvalue. We show these two subdifferentials coincide, and are identical for all choices of index m corresponding to equal eigenvalues. Our approach is via the generalized directional derivatives of the eigenvalue function, thereby completing earlier studies on the classical directional derivative.     

Computing a Nearest P-Symmetric Nonnegative Definite Matrix Under Linear Restriction

Let $P$ be an $n\times n$ symmetric orthogonal matrix. A real $n\times n$ matrix $A$ is called P-symmetric nonnegative definite if $A$ is symmetric nonnegative definite and $(PA)^T=PA$. This paper is concerned with a kind of inverse problem for P-symmetric nonnegative definite matrices: Given a real $n\times n$ matrix $\widetilde{A}$, real $n\times m$ matrices $X$ and $B$, find an $n\times n$ P-symmetric nonnegative definite matrix $A$ minimizing $||A-\widetilde{A}||_F$ subject to $AX =B$. Necessary and sufficient conditions are presented for the solvability of the problem. The expression of the solution to the problem is given. These results are applied to solve an inverse eigenvalue problem for P-symmetric nonnegative definite matrices.