关于Kahan定理的一个注记

设A∈C~(n×n),B∈C~(k×k)均为Hermite矩阵,它们的特征值分别为{λ_j}_(j=1)~n和{μ_j}_(j=1)~k(k≤n);Q∈~(n×k)为列满秩矩阵.令 (1) 则存在A的k个特征值λ_(j_2),λ_(j_2),…,λ_(j_k),使得 (2) 其中σ_k为Q的最小奇异值,||·||_2表示矩阵的谱范数.这是著名的Kahan定理·1996年曹志浩等在[2]中将(2)加强为 (3) 这是Kahan的猜想.在本文中,我们讨论将Kahan定理中“B为k阶Hermite矩阵”改为B为k阶(任意)方阵后,特征值的扰动估计,有以下结果. 定理 设A∈C~(n×n)为Hermite矩阵,其特征值为{λ_j}_(j=1)~n,B∈C~(k×k)的特征值为{μ_j}_(j=1)~k,而Q∈C~(n×k)为列满秩矩阵.则存在A的k个特征值λ_(j_1),λ_(j_2),…,λ_(j_k),使得     

求解特征值互补问题的ABS算法

正1引言对给定的矩阵A∈R~(n×n)和正定阵B∈R~(n×n),特征值互补问题(EiCP)~([1-3])是指:求实数λ和向量x∈R~n\{0}使得{y=(A-λB)x y≥0,x≥0 y~Tx=0 (1)它源于工程和物理问题,如对力学接触问题和结构力学系统的稳定性的研究[3-6].EiCP也可表示为如下形式的锥约束特征值问题[7,8]:对给定的矩阵A∈R~(n×n)和正定阵B∈R~(n×n),求实数λ和向量量x∈R~n\{0}使得     

代数特征值反问题之解的稳定性分析

考虑如下代数特征值反问题: 问题 G(A;{A_k}_1~n;λ).设 A=(a_(ij)),A_k=(a_(ij)~((k))),k=1,…,n是n+1个n×n的实对称矩阵,λ=(λ_1,…,λ_n)是n维实向量且λ_i≠λ_j,i≠j.求n维实向量c=(c_1,…,c_n)~T,使矩阵A(c)=A+sum from k=1 to n (c_kA_k)的特征值是λ_1,…,λ_n. 这一问题是经典加法问题的推广.当A_k-e_ke_k~~T(e_k是n阶单位阵的第k列)时,     

解广义特征值反问题的同伦方法

1.引言 我们讨论下列广义特征值反问题: (G)已知B是n×n阶对称半正定矩阵,λ=(λ_1,…,λ_(2n-1))~T∈R~(2n-1),且{λ_i}~(n_3),和{λ_i}_(n+1)~(2n-1)严格交错。问题是欲求一个实对称三对角n×n阶矩阵A,使得λ_1…,λ_n是Ax=λBx的特征值,λ_(n+1),…,λ_(2n-1)是A_(n-1)x=λB_(n-1)x的特征值,其中A_(n-1),B_(n-1)分别是矩阵A,B的前n-1阶主子阵。     

正规矩阵的任意扰动

设A为n×n矩阵,其特征值为λ1,λ2,…,λn;矩阵B=A+X之特征值为μ1,μ2,…,μn.若A,B均为正规矩阵,由Wielandt-Hoffman定理[1],存在1,2,…,n的一个排列k1,k2,…,kn,使得nj=1|λj-μkj|2≤‖X‖2F,(1)其中‖·‖F表示Frobenius范数.又,在同样条件下,存在1,2,…,n的一个排列l1,l2,…,ln,使得对1≤j≤n均有|λj-μlj|≤2.91‖X‖2,(2)其中‖·‖2表示谱范数,这是R.Bhatia等人的结果[2].本文旨在讨论A为正规矩阵,B为任意矩阵时特征值的扰动估计,得到了几个扰动定理,分别推广了上述两个结果.本文用CH表示矩阵C的共轭转置,trC表示C的迹;…     

重特征值敏度的数值计算

一个结构系统的设计,往往归结为下述代数特征值问题:其中A(p)与B(p)为n×n实解析的对称矩阵,B(p)正定,λ(p)是特征值,x(p)是相应的特征向量. 设λ_1是问题(1.1)在点p=p~*的r重特征值,即存在矩阵X=(X_1,X_2)∈R~(n×n),     

一类具有转向点问题的n阶近似解

用Langer变换和Olver变换求得一类具有转向点问题的n阶近似解:y(x)=v(x)ψ(x),其中ψ=λ12-14×(x2-1)14,2332=-λx∫11-τ2dτ,v(z)=A(z,λ)ξ(λ23z)+B(z,λ)'ζ(λ23z).并探讨了其特征值问题,得到λn=4n+1112,n=0,1,2….由此给出了该类问题的解的一般性结论.     

2006年高考数学安徽卷

一、选择题:共12小题,每小题5分,共60分.1.复数1+3i3-i等于A.i B.-i C.3+i D.3-i2.设集合A={x||x-2|≤2,x∈R},B={y|y=-x2,-1≤x≤2},则R(A∩B)等于A.RB.{x|x∈R,x≠0}C.{0}D.3.若抛物线y2=2px的焦点与椭圆x62+y22=1的右焦点重合,则p的值为A.-2B.2C.-4D.44.设a,b∈R,已知命题p∶a=b;命题q∶(a2+b)2≤a22+b2,则p是q成立的A.必要不充分条件B.充分不必要条件C.充分必要条件D.既不充分也不必要条件5.函数y=2x,x≥0,-x2,x<0的反函数是A.y=x2,x≥0-x,x<0B.2x,x≥0-x,x<0C.y=x2,x≥0--x,x<0D.2x,x≥0--x,x<0第(6)题图6.将函数y=sinωx(…     

点到空间直线距离公式的两种简洁证明

对空间中任意一点P(x0,y0,z0)到直线l:π1∶A1x B1y C1z D1=0π2∶A2x B2y C2z D2=0的距离公式:d=n1→×n→2,(A1x0 B1y0 C1z0 D1)n→2-(A2x0 B2y0 C2z0 D2)n→1介绍另两种过程简洁并且几何意义明显的证明     

2007年普通高等学校招生全国统一考试 (天津卷)数学(理工类)

一、选择题:共10个小题,满分50分.1.i是虚数单位,12-i3i=()A.1 iB.-1 iC.1-iD.-1-i2.设变量x,y满足约束条件x-y≥-1,x y≥1,3x-y≤3,则目标函数z=4x y的最大值为()A.4B.11C.12D.143.“θ=23π”是“tanθ=2cos2π θ”的()A.充分而不必要条件B.必要而不充分条件C.充分必要条件D.既不充分也不必要条件4.设双曲线xa22-by22=1(a>0,b>0)的离心率为3,且它的一条准线与抛物线y2=4x的准线重合,则此双曲线的方程为()A.1x22-2y42=1B.4x82-9y62=1C.x32-23y2=1D.x32-y62=15.函数y=log2x 4 2(x>0)的反函数是()A.y=4x-2x 1(x>2)B.y=4x-2x 1(x>1)C…     

AN INVERSE EIGENVALUE PROBLEM FOR JACOBI MATRICES

Let T1,n be an n x n unreduced symmetric tridiagonal matrix with eigenvaluesand is an (n - 1) x (n - 1) submatrix by deleting the kth row and kth column, k = 1, 2,be the eigenvalues of T1,k andbe the eigenvalues of Tk+1,nA new inverse eigenvalues problem has put forward as follows: How do we construct anunreduced symmetric tridiagonal matrix T1,n, if we only know the spectral data: theeigenvalues of T1,n, the eigenvalues of Ti,k-1 and the eigenvalues of Tk+1,n?Namely if we only know the data: A1, A2, An,how do we find the matrix T1,n? A necessary and sufficient condition and an algorithm ofsolving such problem, are given in this paper.     

Estimation of the Separation of Two Matrices

The estimation of the solution to the matrix equation AX-XB=C is primarily dependent on the quantity sep(A,B) introduced by Stewart. Varah has given some examples to show that $sep_F(A,B)$ can be very small even though the eigenvalues of A and B are well separated. In this paper we give some lower bounds of $sep_F(A,B)$.     

给定顶点数和边数的连通图的Q-谱半径的界

图的无符号拉普拉斯矩阵是图的邻接矩阵和度对角矩阵的和,其特征值记为q1≥q2≥…≥qn.设C(n,m)是由n个顶点m条边的连通图构成的集合,这里1≤n-1≤m≤(n2).如果对于任意的G∈C(n,m)都有q1(G*)≥q1(G)成立,图G*∈C(n,m)叫做最大图.这篇文章证明了对任意给定的正整数a=m-n+1,如果n...     

SOME FURTHER NOTES ON THE MATRIX EQUATIONS A~TXB+B~TX~TA=C AND A~TXB+B~TXA=C

Dehghan and Hajarian, [4], investigated the matrix equations ATXB +BTXTA =C and ATXB + BTXA = C providing inequalities for the determinant of the solutions of these equations. In the same paper, the authors presented a lower bound for the product of the eigenvalues of the solutions to these matrix equations. Inspired by their work, we give some generalizations of Dehghan and Hajarian results. Using the theory of the numerical ranges, we present an inequality involving the trace of C when A, B, X are normal matrices satisfying ATB = BAT.     

An atlas for tridiagonal isospectral manifolds

Let be the compact manifold of real symmetric tridiagonal matrices conjugate to a given diagonal matrix Λ with simple spectrum. We introduce bidiagonal coordinates, charts defined on open dense domains forming an explicit atlas for . In contrast to the standard inverse variables, consisting of eigenvalues and norming constants, every matrix in now lies in the interior of some chart domain. We provide examples of the convenience of these new coordinates for the study of asymptotics of isospectral dynamics, both for continuous and discrete time.     

Solution of the partial eigenvalue problem for a regular matrix pencilD(λ) =A − λB

One presents the ALGOL procedures which implement the algorithm for the determination of the group of smallest (greatest) eigenvalues and their corresponding eigenvectors for a matrix pencil where A and B are real square matrices of simple structure. From the initial pencil one constructs a matrix C, whose eigenvalues are taken as the initial approximations to the eigenvalues from the group of the smallest (greatest) eigenvalues of the pencil. The refinement of the eigen-values is performed on the basis of the theory of perturbations. Then one determines the eigen-vectors and one computes the infinite norm of the residual. One gives ALGOL programs and test examples.     

Construction of Real Band Anti-Symmetric Matrices from Spectral Data

In this paper,we describe how to construct a real anti-symmetric(2p-1)-band matrix with prescribed eigenvalues in its ρ leading principal submatrices.This is done in two steps.First,an anti-symmetric matrix B is constructed with the specified spectral data but not necessary a band matrix.Then B is transformed by Householder transformations to a (2ρ-1)-band matrix with the prescribed eigenvalues.An algorithm is presented.Numerical results are presented to demonstrate that the proposed method is effective.     

Mn(C)中的不可约性

对于Mn(C)(所有n×n矩阵的全体)中的不可约矩阵得到以下结果:对于任意A∈Mn(C),设λ1,λ2,…,λm为A的所有特征值,这里m≤n而且当i≠j时,λi≠λj.则A是不可约的当且仅当任意P∈A'(A),P*=P=P2,有σ(P|ker(A-λ1))=σ(P|ker(A-λ2))=…=σ(P|ker(A-λm))为单点集.     

On computing of arbitrary positive integer powers for one type of even order tridiagonal matrices with eigenvalues on imaginary axis – II

This paper is an extension of the work [J. Rimas, On computing of arbitrary positive integer powers for one type of even order tridiagonal matrices with eigenvalues on imaginary axis – I, Appl. Math. Comput., in press] in which the general expression of the lth power (lN) for one type of even order tridiagonal matrices is given. In this new paper we present the complete derivation of this general expression. Expressions of eigenvectors of the matrix and of the transforming matrix and its inverse are given, too.     

Asymptotic Behavior of Solutions of Some Linear Difference Equations with Oscillatory Decreasing Coefficients

We investigate the asymptotic behavior of solutions of the system x ( n +1)=[ A + B ( n ) V ( n )+ R ( n )] x ( n ), n S n 0 , where A is an invertible m 2 m matrix with real eigenvalues, B ( n )= ~ j =1 r B j e i u j n , u j are real and u j p ~ (1+2 M ) for any M ] Z , B j are constant m 2 m matrices, the matrix V ( n ) satisfies V ( n ) M 0 as n M X , ~ n =0 X Á V ( n +1) m V ( n ) Á < X , ~ n =0 X Á V ( n ) Á 2 < X , and ~ n =0 X Á R ( n ) Á < X . If AV ( n )= V ( n ) A , then we show that the original system is asymptotically equivalent to a system x ( n +1)=[ A + B 0 V ( n )+ R 1 ( n )] x ( n ), where B 0 is a constant matrix and ~ n =0 X Á R 1 ( n ) Á < X . From this, it is possible to deduce the asymptotic behavior of solutions as n M X . We illustrate our method by investigating the asymptotic behavior of solutions of x 1 ( n +2) m 2(cos f 1 ) x 1 ( n +1)+ x 1 ( n )+ a sin n f n g x 2 ( n )=0 x 2 ( n +2) m 2(cos f 2 ) x 2 ( n +1)+ x 2 ( n )+ b sin n f n g x 1 ( n )=0 , where 0< f 1 , f 2 < ~ , 1/2< g h 1, f 1 p f 2 , and 0< f <2 ~ .