对称二次特征值问题中重特征值的特征向量导数

推导出了对称二次特征值问题灵敏度的一种新算法,给出了二次特征对的导数.算法只需要已知系统的部分模态,比较适合大型复杂动力系统.数值结果表明该算法是有效的.     

二次特征值问题特征对的一阶与二阶偏导数

本文研究了解析依赖于多参数的二次特征值问题特征对偏导数的计算.利用计算广义特征值问题特征向量偏导数的模态法.提出了一种计算二次特征值问题特征对一阶、二阶偏导数的方法.本文最后以弹簧质点阻尼系统为例验证了所给结论的正确性和方法的有效性.     

多重广义特征值的局部性态

本文给出了含参向量的矩阵多重广义特征值的方向导数，推广了文〔１〕的结果，所得结论对于结构优化和控制系统设计有一定意义。     

二次特征值问题中特征值和特征向量的可微性

二次特征值问题中特征值和特征向量的可微性,给出了其导数的计算公式及其算法.     

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．     

计算二次特征值问题单特征三元组二阶偏导数的单模态法

本文提出了计算二次特征值问题单特征三元组的二阶偏导数的单模态法.该方法只需要用到待求二阶偏导数的特征三元组的信息;在计算特征向量二阶偏导数时只需求解一个线性方程组,该线性方程组的系数矩阵的阶数为n-1(n为二次特征值问题的规模),且系数矩阵的条件数恰好为其最大与最小非零奇异值的比值.本文给出三个例子进行了数值试验,并与...     

边界条件都含有特征参数的四阶微分算子特征值的依赖性

本文考虑了区间[a,b]两端四个边界条件都含有特征函数的四阶微分算子特征值的依赖性问题,证明了特征值不仅连续依赖而且可微依赖于问题的各个参数(系数函数,权函数,区间端点,边界条件系数矩阵),并且给出了相应的微分表达式.特别地,给出特征值关于特征参数依赖的边界条件系数矩阵的Frechet导数.     

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．     

一类二次特征值反问题的中心对称解及其最佳逼近

1引言给定n阶实矩阵M,C和K,二次特征值问题:求数λ和非零向量x使得Q(λ)x=0, (1．1)其中Q(λ)=λ2M λC K称为二次束．数λ和相应的非零向量x分别称为二次束Q(λ)的特征值和特征向量．Tisseur和Meerbergen概述了二次特征值问题的各种应用、数学理论和数值方法．在工程技术,特别是结构动力模型修正技术领域经常遇到与二次特征值问题相反的问题(称之为二次特征值反问题)．对阻尼结构进行动力分析时,应用有限元方法可得到系统的质量矩阵M,阻尼矩阵C和刚度矩阵K,从而可求得二次特征值问题的特征值(频率)和特征向量(振型)．但是有限元模型毕竟是实际结构系统的离散化,并且     

非线性特征值问题的二次近似方法

本文研究求解非线性特征值问题的数值方法.基于矩阵值函数的二次近似,将非线性特征值问题转化为二次特征值问题,提出了求解非线性特征值问题的逐次二次近似方法,分析了该方法的收敛性.结合求解二次特征值问题的Arnoldi方法和Jacobi-Davidson方法,给出求解非线性特征值问题的一些二次近似方法.数值结果表明本文所给算法是有效的.     

DERIVATIVES OF EIGENPAIRS OF SYMMETRIC QUADRATIC EIGENVALUE PROBLEM

Derivatives of eigenvalues and eigenvectors with respect to parameters in symmetric quadratic eigenvalue problem are studied. The first and second order derivatives of eigenpairs are given. The derivatives are calculated in terms of the eigenvalues and eigenvectors of the quadratic eigenvalue problem, and the use of state space representation is avoided, hence the cost of computation is greatly reduced. The efficiency of the presented method is demonstrated by considering a spring-mass-damper system.     

非负矩阵最大特征值的平滑算法

１引　言 本文中Ａ＝（ａｉｊ）表示ｎ阶方阵,Ａ＞０表示Ａ为正矩阵,即ａｉｊ＞０（ｉ,ｊ＝１,２,…,ｎ）;Ａ≥０表示Ａ为非负矩阵,即ａｉｊ≥０（ｉ,ｊ＝１,２,…,ｎ）且至少有一个严格大于号成立,周知,当Ａ＞０时Ａ有一个正特征值λ满足λ＞｜λ｜,其中λ为Ａ的其它任一特征值;当Ａ≥０时Ａ有一个非负特征值λ满足λ≥｜λ｜,其中λ为Ａ的任一特征值．把这样的λ称为Ａ的最大特征值,为强调它属于Ａ,记作λ（Ａ）．同时,把与λ（Ａ）对应的Ａ的特征向量记作ｘ（Ａ）． 对Ａ≥０,记当Ｒｔ＞０（ｉ＝１,２,…,ｎ）时…     

The Laplacian spectra of graphs with a tree structure

Trees are very common in the theory and applications of combinatorics. In this article, we consider graphs whose underlying structure is a tree, except that its vertices are graphs in their own right and where adjacent graphs (vertices) are linked by taking their join. We study the spectral properties of the Laplacian matrices of such graphs. It turns out that in order to capture known spectral properties of the Laplacian matrices of trees, it is necessary to consider the Laplacians of vertex-weighted graphs. We focus on the second smallest eigenvalue of such Laplacians and on the properties of their corresponding eigenvector. We characterize the second smallest eigenvalue in terms of the Perron branches of a tree. Finally, we show that our results are applicable to advancing the solution to the problem of whether there exists a graph on n vertices whose Laplacian has the integer eigenvalues 0, 1, …, n ? 1.     

Eigenvalue perturbation bounds for Hermitian block tridiagonal matrices

We derive new perturbation bounds for eigenvalues of Hermitian matrices with block tridiagonal structure. The main message of this paper is that an eigenvalue is insensitive to blockwise perturbation, if it is well-separated from the spectrum of the diagonal blocks nearby the perturbed blocks. Our bound is particularly effective when the matrix is block-diagonally dominant and graded. Our approach is to obtain eigenvalue bounds via bounding eigenvector components, which is based on the observation that an eigenvalue is insensitive to componentwise perturbation if the corresponding eigenvector components are small. We use the same idea to explain two well-known phenomena, one concerning aggressive early deflation used in the symmetric tridiagonal QR algorithm and the other concerning the extremal eigenvalues of Wilkinson matrices.     

具有四个不同特征值的竞赛矩阵

设A是n阶竞赛矩阵，k是非负整数。文[3]刻划了恰好有三个不同特征值的n阶竞赛矩阵，文[4]刻划了恰好有四个不同特征值并且0作为一个一重特征值的n阶竞赛矩阵。在这篇文章中我们主要研究了两个问题：（1）讨论当k是A的特征值时A的性质。（2）刻划恰好有四个不同特征值并且k作为一个一重特征值的全部n阶竞赛矩阵。     

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.     

Computation of multiple eigenvalues and generalized eigenvectors for matrices dependent on parameters

The paper develops Newton's method of finding multiple eigenvalues with one Jordan block and corresponding generalized eigenvectors for matrices dependent on parameters. It computes the nearest value of a parameter vector with a matrix having a multiple eigenvalue of given multiplicity. The method also works in the whole matrix space (in the absence of parameters). The approach is based on the versal deformation theory for matrices. Numerical examples are given. Copyright © 2005 John Wiley & Sons, Ltd.     

带比例关系的实带状矩阵特征值反问题

研究了通过矩阵A的顺序主子矩阵A_((k))=(aij)_(i,j=1)^(n-k+1)的特征值{λ_i(n-k+1)的特征值{λ_i^((k)))}_(i=1)((k)))}_(i=1)^(n-k+1)k=1,2,…,r+1来构造一个带比例关系的实带状矩阵的特征值反问题.对当特征值{λ_i(n-k+1)k=1,2,…,r+1来构造一个带比例关系的实带状矩阵的特征值反问题.对当特征值{λ_i^((k))}_(i=1)((k))}_(i=1)^(n-k+1)中有多重特征值出现时,应当如何来构造这类矩阵进行了讨论,并给出了问题的具体算法及数值例子.     

Main eigenvalues of a graph

An eigenvalue of a graph is said to be a main eigenvalue if it has an eigenvector not orthogonal to the main vector j=(1,1,…,1). In this paper we shall study some properties of main eigenvalues of a graph.     

两个Hermitian矩阵的Hadamard乘积的特征值估计

Schur定理规定了半正定矩阵的Hadamard乘积的所有特征值的整体界限,Eric Iksoon lm在同样的条件下确定了每个特征值的特殊的界限,本文给出了Hermitian矩阵的Hadamard乘积的每个特征值的估计,改进和推广了I.Schur和Eric Iksoon Im的相应结果。