基于矩阵幂运算的重特征值存在性定理

对于判断矩阵重特征值的存在性问题,运用“若λ是矩阵A的特征值,则入“是Ak的特征值”这一性质,通过矩阵的迹与特征值的关系,得到了实数域上矩阵重特征值的存在性定理并给出了证明．定理实现了“由矩阵幂运算来判断矩阵重特征值的存在性”这样一个计算过程,对讨论矩阵特征值问题具有一定的启示意义．     

二阶矩阵的特征值和特征向量常考题型例析

矩阵的特征值和特征向量是矩阵与变换的一个非常重要的内容,利用矩阵的特征值和特征向量,可以方便地计算多次矩阵变换的结果,而且在实际工程计算和工程控制中也发挥着重要作用.二阶矩阵的特征值和特征向量有两个基本内容.一是二阶矩阵的特征值和特征向量的概念：设A是一个二阶矩阵,如果对于实数λ,存在一个非零向量α,使得Aα=λα,那么λ称为A的一个特征值,而α称为A的属于特征值λ的一个特征向量.     

特征值反问题的唯一性

证明了由特征值及特征向量反求矩阵时,特征值在对角矩阵中的排序可以是任意的,只须将对应特征向量作相应排序,所得矩阵唯一。对于重特征值的线性无关的特征向量可任意选取,所得矩阵唯一。     

实规范矩阵的特征值问题

实对称矩阵的特征值问题,无论是低阶稠密矩阵的全部特征值问题,或高阶稀疏矩阵的部分特征值问题,都已有许多有效的计算方法,迄今最重要的一些成果已总结在[5]中。本文利用规范矩阵的一些重要性质将对于Hermite矩阵(特别是对弥矩阵)特征值问题的一些有效算法推广到规范矩阵的特征值问题,由于对复规范阵的推广是简单的,而且实际上常遇到的是实矩阵(这时常要求只用实运算),因此我们着重讨论实规范矩阵的特征值问题。     

Hermite矩阵与可对称化矩阵特征值之间的扰动上界

正1引言矩阵特征值的扰动问题,就是研究矩阵元素的改变对矩阵特征值的影响.设矩阵A,B为n阶复矩阵,矩阵B为矩阵A经过扰动之后的矩阵,且λ(A)={λ_i},λ(B))={μ_i},研究矩阵特征值的扰动就是研究λ(A)与λ(B)之间的差距,一般用2范数和Frobenius范数来描述它们之间的差距.矩阵特征值问题是由于处理数据时存在误差而引起的,使得到的特征值往往是经过     

用改进遗传算法求解矩阵实特征值

依据矩阵特征值的分布理论,通过确定矩阵实特征值的分布区域,用实数编码和具有自适应交叉概率和变异概率的遗传算法来求解矩阵实特征值的近似值.仿真结果表明,此算法可以达到一定的精度,具有一定的通用性.并给求矩阵特征值提供了一种快速的方法.     

一类特殊矩阵的极值特征值反问题

针对矩阵特征值反问题,如何构造矩阵显得尤为重要,鉴于此,引入一种新的带比例关系矩阵.结果表明,只需利用其顺序主子阵的最小和最大特征值即可反构原矩阵,同时亦总结了矩阵元素与顺序主子阵特征值的关系.     

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.     

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．     

矩阵特征值的一类新的包含域

用盖尔圆盘定理来估计矩阵的特征值是一个经典的方法,这种方法仅利用矩阵的元素来确定特征值的分布区域.本文利用相似矩阵有相同的特征值这一理论,得到了矩阵特征值的一类新的包含域,它们与盖尔圆盘等方法结合起来能提高估计的精确度.     

Numerical enclosure for multiple eigenvalues of an Hermitian matrix whose graph is a tree

In this paper we consider a numerical enclosure method for multiple eigenvalues of an Hermitian matrix whose graph is a tree. If an Hermitian matrix A whose graph is a tree has multiple eigenvalues, it has the property that matrices which are associated with some branches in the undirected graph of A have the same eigenvalues. By using this property and interlacing inequalities for Hermitian matrices, we show an enclosure method for multiple eigenvalues of an Hermitian matrix whose graph is a tree. Since we do not generally know whether a given matrix has exactly a multiple eigenvalue from approximate computations, we use the property of interlacing inequalities to enclose some eigenvalues including multiplicities.In this process, we only use the enclosure of simple eigenvalues to enclose a multiple eigenvalue by using a computer and interval arithmetic.     

求非亏损矩阵特征值的一种数值计算方法

借助相似变换将非亏损矩阵转为Hessenberg矩阵,通过获得确定Hessenberg矩阵特征多项式系数的方法,利用特征值与特征多项式系数间的关系,给出求非亏损矩阵特征值的一种数值算法。     

A note on quaternion matrices and split quaternion matrix pencils

In this paper, localization theorems for left and right eigenvalues of a quaternion matrix are presented. Some differences between quaternion matrices and split quaternion matrices are summarized. A counter example for Gerschgorin theorems for left and right eigenvalues of a split quaternion matrix is given. Finally, a method for finding right eigenvalues of a split quaternion matrix pencil is presented.     

由混合谱数据构造矩阵

The eigenvalues and singular values are two of the most distinguished characteristics in a square matrix. Weyl has proved the majorization between them. Horn has proved its inverse, i.e. there exists a matrix with prescribed eigenvalues and singular values. This paper presents a direct transform method which shows the matrix can be upper triangular with its diagonal elements in any order. There exists a real-valued matrix with prescribed complex-conjugate eigenvalues and singular values. Construction of matrices with mixed data is also considered.     

The Moore-Penrose inverse of a retrocirculant

In a recent paper Chao [2] has determined the eigenvalues of a matrix of the form A=PC where P is a permutation matrix which commutes with a certain unitary matrix and C is a circulant. Here we determine the Moore-Penrose inverse of such a “retrocirculant” and show that the nonzero eigenvalues of the Moore-Penrose inverse are the reciprocals of the nonzero eigenvalues of the retrocirculant.     

Computing the matrix sign and absolute value functions

We present two algorithms for the computation of the matrix sign and absolute value functions. Both algorithms avoid a complete diagonalisation of the matrix, but they however require some informations regarding the eigenvalues location. The first algorithm consists in a sequence of polynomial iterations based on appropriate estimates of the eigenvalues, and converging to the matrix sign if all the eigenvalues are real. Convergence is obtained within a finite number of steps when the eigenvalues are exactly known. Nevertheless, we present a second approach for the computation of the matrix sign and absolute value functions, when the eigenvalues are exactly known. This approach is based on the resolution of an interpolation problem, can handle the case of complex eigenvalues and appears to be faster than the iterative approach. To cite this article: M. Ndjinga, C. R. Acad. Sci. Paris, Ser. I 346 (2008).