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.     

一类特殊矩阵的逆特征值问题

本文主要讨论如下形式矩阵的逆特征值问题:即对给定n个实数λ_1>λ_2>…>λ_2与n-1个实数μ_1>μ_2>…>μ_(n-1),满足λ_1>μ_1>λ_2>…>λ_(n-1)>μ_(n-1)>λ_n,在α_2>α_3>…>α_(n-1)的条件下,存在唯一的一个矩阵A_n是以λ_i为其特征值;且其截边矩阵的特征值为μ_1,μ_2,…,μ_(n-1).     

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。     

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     

关于图的规范拉普拉斯特征值和的若干结果（英文）

对任意一个连通图G,记L(G)和L(G)分别为G的拉普拉斯矩阵和规范拉普拉斯矩阵.令μ_1≥μ_2≥…≥μ_n=0和λ_1≥λ_2≥…≥λ_n=0分别为G的拉普拉斯特征值和规范拉普拉斯特征值.本文给出了λ_1的三个新的下界.前两个下界优于Das等在[Ars Cormbin.,2015,118:143-154]中给出的下界,第三个下界优于张晓东在[Ars Combin.,2004,72:191-198]中给出的下界.另一方面讨论了规范拉普拉斯特征值与G的度序列之间的关系.同时也讨论了图的拉普拉斯特征值和规范拉普拉斯特征值之间的关系.     

加法与乘法逆特征值问题的可解性

1.引言 本文讨论如下代数特征值反问题可解的充分条件: 问题A(加法逆特征值问题)。给定一Hermite矩阵A=(a_(ij))_(n×n)及n个实数λ_1,…,λ_n,求一实对角阵D=diag(c_1…,c_n),使得A+D的特征值为λ_1,…,λ_n。 问题M(乘法逆特征值问题)。给定一正定Hermite矩阵A=(a_(ij))_(n×n)和n个正实数     

关于两个正定矩阵之积的特征值估计的一个注记

令A>0及B>0记两个n×n(n≥2)厄尔米特正定矩阵;μ_1≥μ_2≥…μ_n及ν_1≥ν_2≥…≥ν_n记A和B的特征值;设λ为AB的任意特征值.ShaHu-yun证得2/nμ_n~2ν_n~2/μ_n~2 ν_n~2<λ相似文献   

单圈图的无符号拉普拉斯埃斯特拉达指数（英文）

设G是一个具有n个顶点的简单图.矩阵Q(G)=D(G)+A(G)表示图G的无符号拉普拉斯矩阵,其中D(G)和A(G)分别表示图G的顶点度对角矩阵和邻接矩阵.图G的无符号拉普拉斯埃斯特拉达指数定义为QEE(G)=∑_(i=1)~ne~(λ_i(G)),其中λ_1(G)≥λ_2(G)≥…λ_n(G)是指图G的无符号拉普拉斯特征值.本文确定了具有最大的无符号拉普拉斯埃斯特拉达指数的唯一的n个顶点的单圈图.     

Bargmann空间中无界Gribov-Intissar算子的谱逼近（英文）

在[Adv.Math.(China),2015,44(3):335-353]中,我们研究了经典Bargmann空间Bo中的非自伴算子H_μ:H_μ=S_μ+H_λ,其中S_μ=μz d/(dz),H_λ=iλ(z(d~2)/(dz~2)+z~2 d/(dz)),i~2=-1,参数μ,λ都是实数.我们给出了H_μ的谱分析和H_μ的广义特征向量的渐近分析.设ek(z)=(z~k)/((k!)~(1/2)),k=1,2,…是B0的正交基.算子H_μ可以被一列三对角矩阵逼近,此三对角矩阵的主对角线元素为β_k=μk,次对角线元素α_k=iλk(k+1)~(1/2),1≤k≤n,n∈N.对于μ∈C和λ∈C,本文主要研究上述矩阵的特征值z_(k,n)(μ,λ)的局部化,它是多项式P_(n+1)~(μ,λ)(z)的零点,P_(n+1)~(μ,λ)(z)满足三项递推关系:若"∈R和λ∈R,则上述矩阵是复对称的.在这种情况下,我们证明了R上有界变分复值函数∈(z)的存在性,它使得权重为∈(z)的多项式P_n~(μ,λ)(z)是正交的.我们也考虑了H_μ的扰动H_λ'=S_λ'+H_λ,其中S_λ'=λ'z~2(d~2)/(dz~2)+S_μ,λ'∈R,H_λ可以被矩阵(h_(jk)~λ)_(j,k=1)~∞表示.证明了可以通过S_λ'的特征值和有限矩阵(h_(jk)~λ)_(j,k=1)~n的特征值的组合来逼近H_λ'的特征值.     

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),     

Maximum and minimum weighted diagonal sums of certain non-negative matrices

This paper extends the notion of diagonal sums of a square matrix to “weighted diagonal sums”. Using simple probabilistic arguments, most of the results of Wang [5] concerning the maximum and minimum diagonal sums of doubly stochastic matrices are extended to maximum and minimum weighted diagonal sums of stochastic matrices (Sec. 3). Two stronger versions of one of Wang's conjectures are also proven (Theorems 4.1 and 5.1), of which the latter easily generalizes to the case of non-negative matrices (Theorem 5.2). The paper ends with a few open questions and counter-examples.     

On generalized inverses of Boolean matrices—II

Analogous to minimum norm g-inverses and least squares g-inverses for real matrices, we introduce the concepts of minimum weight g-inverses and least distance g-inverses for Boolean matrices. All those Boolean matrices which admit such g-inverses are characterized.This paper is a continuation of [2].     

Entropy of orthogonal matrices and minimum distance orthostochastic matrices from the uniform van der Waerden matrices

In this article we formulate an optimization problem of minimizing the distance from the uniform van der Waerden matrices to orthostochastic matrices of different orders. We find a lower bound for the number of stationary points of the minimization problem, which is connected to the number of possible partitions of a natural number. The existence of Hadamard matrices ensures the existence of global minimum orthostochastic matrices for such problems. The local minimum orthostochastic matrices have been obtained for all other orders except for 11 and 19. We explore the properties of Hadamard, conference and weighing matrices to obtain such minimizing orthostochastic matrices.     

Hermite-广义反Hamilton矩阵的一类反问题

给定矩阵X和B,利用矩阵的广义奇异值分解,得到了矩阵方程X~HAX=B有Hermite-广义反Hamiton解的充分必要条件及有解时解的—般表达式.用S_E表示此矩阵方程的解集合,证明了S_E中存在唯一的矩阵(?),使得(?)与给定矩阵A的差的Frobenius范数最小,并且给出了矩阵(?)的表达式;同时也证明了S_E中存在唯一的矩阵A_o,使得A_o是此矩阵方程的极小Frobenius范数Hermite-广义反Hamilton解,并且给出了矩阵A_o的表达式.     

The block conjugate gradient algorithm and related methods

The development of the Lanczos algorithm for finding eigenvalues of large sparse symmetric matrices was followed by that of block forms of the algorithm. In this paper, similar extensions are carried out for a relative of the Lanczos method, the conjugate gradient algorithm. The resulting block algorithms are useful for simultaneously solving multiple linear systems or for solving a single linear system in which the matrix has several separated eigenvalues or is not easily accessed on a computer. We develop a block biconjugate gradient algorithm for general matrices, and develop block conjugate gradient, minimum residual, and minimum error algorithms for symmetric semidefinite matrices. Bounds on the rate of convergence of the block conjugate gradient algorithm are presented, and issues related to computational implementation are discussed. Variants of the block conjugate gradient algorithm applicable to symmetric indefinite matrices are also developed.     

LSQR iterative method for generalized coupled Sylvester matrix equations

An iterative method is proposed to solve generalized coupled Sylvester matrix equations, based on a matrix form of the least-squares QR-factorization (LSQR) algorithm. By this iterative method on the selection of special initial matrices, we can obtain the minimum Frobenius norm solutions or the minimum Frobenius norm least-squares solutions over some constrained matrices, such as symmetric, generalized bisymmetric and (R, S)-symmetric matrices. Meanwhile, the optimal approximate solutions to the given matrices can be derived by solving the corresponding new generalized coupled Sylvester matrix equations. Finally, numerical examples are given to illustrate the effectiveness of the present method.     

PARTIAL FERRERS MATRICES

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Lower Bounds in Minimum Rank Problems

The minimum rank of a graph is the smallest possible rank among all real symmetric matrices with the given graph. The minimum semidefinite rank of a graph is the minimum rank among Hermitian positive semidefinite matrices with the given graph. We explore connections between OS-sets and a lower bound for minimum rank related to zero forcing sets as well as exhibit graphs for which the difference between the minimum semidefinite rank and these lower bounds can be arbitrarily large.     

Upper (lower) bounds of the eigenvalues,spread and the open problems for the real symmetric interval matrices

This work is concerned with exploring the upper bounds and lower bounds of the eigenvalues of real symmetric matrices of order n whose entries are in a given interval. It gives the maximum and minimum of the eigenvalues and the upper bounds of spread of real symmetric interval matrices in all cases. It also gives the answers of the open problems for the maximum and minimum of the eigenvalues of real symmetric interval matrices. Copyright © 2012 John Wiley & Sons, Ltd.     

Permanents of special classes of doubly stochastic matrices

We identify the doubly stochastic matrices with at least one zero entry which are closest in the Euclidean norm to J_n, the matrix with each entry equal to 1/n, and we show that at these matrices the permanent function has a relative minimum when restricted to doubly stochastic matrices having zero entries.