A Homotopy Algorithm for Solving the Inverse Eigenvalue Problem for Complex Symmetric Matrices

A homotopy algorithm for solving the inverse eigenvalue problem for complex symmetric matrices is suggested. Some numerical examples are presented.     

对称矩阵与反对称矩阵广义特征值反问题的拓广

定义了上三角等次对角线矩阵和上三角交错次对角线矩阵;讨论了矩阵方程AX-XA=0的对称解与AX XA=0的反对称解.在此基础上考虑了以下问题的可解性:给定A∈Rn×m,D∈Rm×m,分别求X,Y∈SRn×n和X,Y∈ASRn×n,使得XA=YDA.     

对称自正交相似矩阵的左右逆特征值问题

讨论对称自正交相似矩阵的左右逆特征值及其最佳逼近问题,利用矩阵的奇异值分解（SVD）得到了其解集合SE的通式和逼近解的表达式.     

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．     

广义对称矩阵反问题有解的条件

本利用矩阵的奇异值分解讨论了一类广义对矩阵阵反问题，得到了此类矩阵反问题有解的充分必要条件及通解的表达式。     

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.     

实对称矩阵的一类逆特征值问题

利用矩阵的奇异值分解及广义逆，给出了矩阵约束下矩阵反问题AX=B有实对称解的充分必要条件及其通解的表达式．此外，给出了在矩阵方程的解集合中与给定矩阵的最佳逼近解的表达式．     

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．     

哈密顿矩阵的逆特征值问题

该文探讨了哈密顿矩阵的逆特征值问题, 得到了有解的充要条件、通解的表达式以及最小范数解.并给出了最佳逼近解的求法. 给出了相应的算法, 数值实例说明算法是可行的.     

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).     

对称正交反对称矩阵反问题

设P为一给定的对称正交矩阵, 记SAR\+n\-P={A∈R\+\{n×n\}|A\+T=A,(PA)\+T=-PA}. 该文考虑下列问题问题Ⅰ〓给定X∈R\+\{n×m, Λ=diag(λ\-1,λ\-2,…, λ\-m)∈R\+\{m×m\}, 求A∈SAR\+n\-P使AX=XΛ,问题Ⅱ〓给定X,B∈R\+\{n×m, 求A∈SAR\+n\-P使  ‖AX-B‖=min.问题Ⅲ设[AKA～]∈R\+\{n×n\},求A\+*∈S\-E使 ‖[AKA～]-A\+*‖=inf[DD(X]A∈S\-E[DD)]‖[AKA～]-A‖, 其中S\-E为问题Ⅱ的解集合, ‖·‖表示Frobenius范数.该文得到了问题Ⅰ有解的充要条件及解集合的表达式, 给出了解集合S\-E的通式和逼近解A\+*的具体表达式.     

The Cayley Method and the Inverse Eigenvalue Problem for Toeplitz Matrices

Despite the fact that symmetric Toeplitz matrices can have arbitrary eigenvalues, the numerical construction of such a matrix having prescribed eigenvalues remains to be a challenge. A two-step method using the continuation idea is proposed in this paper. The first step constructs a centro-symmetric Jacobi matrix with the prescribed eigenvalues in finitely many steps. The second step uses the Cayley transform to integrate flows in the linear subspace of skew-symmetric and centro-symmetric matrices. No special geometric integrators are needed. The convergence analysis is illustrated for the case of n = 3. Numerical examples are presented.     

非负矩阵最大特征值的新界值

对最大特征值的上下界进行估计是非负矩阵理论的重要部分,借助两个新的矩阵,从而得到一个判定非负矩阵最大特征值范围的界值定理,其结果比有关结论更加精确.     

子矩阵约束下的对称正交反对称矩阵反问题

利用矩阵对的广义奇异值分解,讨论矩阵方程AX=B在子矩阵约束下有对称正交反对称解的充要条件以及解的表达式,另外,给出了在矩阵方程的解集合中与给定矩阵的最佳逼近解的表达式.     

非负矩阵最大特征值的估计法

通过构造一个新的矩阵,从而得到一个非负矩阵最大特征值的估计法,该方法将适用范围推广到一般非负矩阵,并通过实例验证了这种新方法精确度更高.     

An Approach to Solving Inverse Eigenvalue Problems for Matrices

The paper considers different formulations of inverse eigenvalue problems for matrices whose entries either polynomially or rationally depend on unknown parameters. An approach to solving inverse problems together with numerical algorithms is suggested. The solution of inverse problems is reduced to the problem of finding the so-called discrete solutions of nonlinear algebraic systems. The corresponding systems are constructed using the method of traces, and their discrete roots are found by applying the algorithms for solving nonlinear algebraic systems in several variables previously suggested by the author. Bibliography: 30 titles.     

线性流形上广义次对称矩阵的左右逆特征值问题

研究线性流形上广义次对称矩阵的左右逆特征值问题及其最佳逼近问题.利用广义次对称矩阵的性质及矩阵的奇异值分解得到问题的通解表达式.同时,给出其有唯一的最佳逼近解以及求最佳逼近解的算法.     

一类对称正交对称矩阵反问题的最小二乘解

1 引言 本文记号R~(n×m),OR~(n×n),A~+,I_k,SR~(n×n),rank(A),||·||,A*B,BSR~(n×n)和ASR~(n×n)参见[1].若无特殊声明文中的P为一给定的矩阵且满足P∈OR~(n×n)和P=P~T. 定义1 设A=(α_(ij))∈R~(n×n).若A满足A=A~T,(PA)~T=PA则称A为n阶对称正交对称矩阵;所有n阶对称正交对称矩阵的全体记为SR_P~n.若A∈R~(n×n)满足A~T=A,(PA)~T=-PA,则称A为n阶对称正交反对称矩阵;所有n阶对称正交反对     

Direct and Inverse Eigenvalue Problems for Diagonal-Plus-Semiseparable Matrices

In this paper we study both direct and inverse eigenvalue problems for diagonal-plus-semiseparable (dpss) matrices. In particular, we show that the computation of the eigenvalues of a symmetric dpss matrix can be reduced by a congruence transformation to solving a generalized symmetric definite tridiagonal eigenproblem. Using this reduction, we devise a set of recurrence relations for evaluating the characteristic polynomial of a dpss matrix in a stable way at a linear time. This in turn allows us to apply divide-and-conquer eigenvalue solvers based on functional iterations directly to dpss matrices without performing any preliminary reduction into a tridiagonal form. In the second part of the paper, we exploit the structural properties of dpss matrices to solve the inverse eigenvalue problem of reconstructing a symmetric dpss matrix from its spectrum and some other informations. Finally, applications of our results to the computation of a QR factorization of a Cauchy matrix with real nodes are provided.     

Newton's Method for a Generalized Inverse Eigenvalue Problem

A kind of generalized inverse eigenvalue problem is proposed which includes the additive, multiplicative and classical inverse eigenvalue problems as special cases. Newton's method is applied, and a local convergence analysis is given for both the distinct and the multiple eigenvalue cases. When the multiple eigenvalues are present we show how to state the problem so that it is not over-determined, and discuss a Newton-method for the modified problem. We also prove that the modified method retains quadratic convergence, and present some numerical experiments to illustrate our results. © 1997 by John Wiley & Sons, Ltd.