广义中心对称矩阵反问题的最小二乘解

讨论了广义中心对称矩阵反问题的最小二乘解,得到了解的一般表达式,并就该问题的特殊情形:矩阵反问题,得到了可解的充分必要条件及解的通式.此外,证明了最佳逼近问题解的存在惟一性,并给出了其解的具体表达式.     

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     

反对称偏对称矩阵反问题的最小二乘解

该文研究了反对称偏对称矩阵反问题的最小二乘解,得到了该问题解的表达式以及该问题有解的充分必要条件.证明了其最佳逼近解的存在性和唯一性,建立了其最佳逼近解的表达式,并给出了求最佳逼近解的数值算法和算例.     

双反对称矩阵反问题的最小二乘解

1 引 言Rn×m表示所有n×m阶实矩阵集合,Rrn×m表示Rn×m中秩为r的子集;ORn×m表示所有n阶正交阵的集合;A+表示A的Moore-Penrose广义逆;Iκ表示κ阶单位阵;||·||表示Frobenius范数;ASRn×m表示n阶实反对称阵的全体;A＊B表示A与B的Hadamard乘     

线性流形上Hermite-广义反Hamilton矩阵反问题的最小二乘解

1.引言 令Rn×m表示所有n×m实矩阵集合,Cn×m表示所有n×m复矩阵集合,Cn=Cn×1,HCn×n表示所有n阶Hermite矩阵集合,UCn×n表示所有n阶酉矩阵集合,AHCn×n表示所有n阶反Hermite矩阵集合,R(A)表示A的列空间,N(A)表示A的零空间,A+表示A的Moore—Penrose广义逆,A*B表示A与B的Hadamard积,rank(A)表示矩阵A的秩.tr(A)表示矩阵A的迹.矩阵A,B的内积定义为(A,B)=tr(BHA),A,B∈Cn×m,由此内积诱导的范数为||A||=√(A,A)=[tr(AHA)]1/2,则此范数为Frobenius范数,并且Cn×m构成一个完备的内积空间,In表示n阶单位阵,i=√-1,记OASRn×n表示n×n阶正交反对称矩阵的全体,即     

Hermitian自反矩阵反问题的最小二乘解

本文研究了Hermitian自反矩阵反问题的最小二乘解及其最佳逼近.利用矩阵的奇异值分解理论,获得了最小二乘解的表达式.同时对于最小二乘解的解集合,得到了最佳逼近解.     

一类双对称矩阵反问题的最小二乘解

１．问题的提出近年来,对于矩阵反问题ＡＸ＝Ｂ的研究已取得了一系列的结果［１］,获得了解存在的条件,但由于实际问题中Ｘ,Ｂ由实验给出,很难保证满足解存在的条件,因此研究问题的最小二乘解是有实际意义的．本文就结构设计中用到的一类双对称矩阵的最小二乘问题进行探讨．令Ｒ~(ｎ×ｍ)表示所有ｎ×ｍ阶实矩阵集合,Ｒ~n=Ｒ~(n×1)　表示其中秩为ｒ的子集;ＯＲ~（ｎ×ｎ）　表示所有ｎ阶正交阵之集;Ａ~（ ）表示矩阵Ａ的Ｍｏｏｒｅ－Ｐｅｎｒｏｓｅ广义逆;Ｉ_ｋ表示ｋ阶单位阵;||·||表示Fｒｏｂｅｎｉｕｓ范数;表示ＳＲ~(ｎ…     

对称正交对称矩阵反问题的最小二乘解

Let P ∈ Rn×n be a symmetric orthogonal matrix. A∈Rn×n is called a symmetric orthogonal symmetric matrix if AT = A and (PA) T = PA. The set of all n × n symmetric orthogonal symmetric matrices is denoted by SRnxnp. This paper discusses the following problems: Problem I. Given X,B∈ Rn×m, find A ∈SRn×np such that||AX - B|| = min Problem II. Given A∈ Rn×n, find A∈SL such thatwhere ||·|| is the Frobenius norm, and SL is the solution set of Problem I.The general form of SL is given. The solvability conditions for the inverseproblem AX = B in SRn×nP are obtained. The expression of the solution toProblem II is presented.     

双变量矩阵方程组对称最小二乘解的迭代算法

本文研究了求双变量线性矩阵方程组的对称最小二乘解的问题.利用求解线性代数方程组的共轭梯度法的基本思想,通过对有关矩阵和系数的变形与近似处理,建立了一种迭代算法.拓宽了共轭梯度法的适用范围.算例表明,迭代算法是有效的.     

线性流形上D对称矩阵反问题的最小二乘解

本研究了线性流形上D对称矩阵反问的最小二乘解及其逼近问题，给出了最小二乘解的一般表达式，并就该问题的特殊情况－矩阵反问题，获得了有解的充分必要条件，并在有解的条件下得到了解的一段表达式。     

线性流形上两类矩阵反问题的最小二乘解

给定广义自反矩阵R,S,即R=R=R-1,S=S=S-1,若复矩阵X满足条件RXS=X（或RXS=X）,则称其为（R,S）-对称矩阵（或（R,S）-斜对称矩阵）.分别讨论了线性流形上（R,S）-对称矩阵和（R,S）-斜对称矩阵约束下矩阵方程MZN=E的最小二乘问题,得到了通解表达式.     

Preconditioned sor methods for generalized least-squares problems

1.IntroductionThegeneralizedleastsquaresproblem,definedasmin(Ax--b)"W--'(Ax--b),(1.1)xacwhereAERm",m>n,bERm,andWERm'misasymmetricandpositivedefinitematrix,isfrequentlyfoundwhensolvingproblemsinstatistics,engineeringandeconomics.Forexample,wegetgeneralizedleastsquaresproblemswhensolvingnonlinearregressionanalysisbyquasi-likelihoodestimation,imagereconstructionproblemsandeconomicmodelsobtainedbythemaximumlikelihoodmethod(of.[1,21).Paige[3,4]investigatestheproblemexplicitly.Hechangestheorig…     

The left and right inverse eigenvalue problems of generalized reflexive and anti-reflexive matrices

Let n×n complex matrices R and S be nontrivial generalized reflection matrices, i.e., R^∗=R=R⁻¹≠±I_n, S^∗=S=S⁻¹≠±I_n. A complex matrix A with order n is said to be a generalized reflexive (or anti-reflexive ) matrix, if RAS=A (or RAS=−A). In this paper, the solvability conditions of the left and right inverse eigenvalue problems for generalized reflexive and anti-reflexive matrices are derived, and the general solutions are also given. In addition, the associated approximation solutions in the solution sets of the above problems are provided. The results in present paper extend some recent conclusions.     

Least‐squares solutions of matrix inverse problem for bi‐symmetric matrices with a submatrix constraint

An n × n real matrix A = (a_ij)_{n × n} is called bi‐symmetric matrix if A is both symmetric and per‐symmetric, that is, a_ij = a_ji and a_ij = a_n+1?1,n+1?i (i, j = 1, 2,..., n). This paper is mainly concerned with finding the least‐squares bi‐symmetric solutions of matrix inverse problem AX = B with a submatrix constraint, where X and B are given matrices of suitable sizes. Moreover, in the corresponding solution set, the analytical expression of the optimal approximation solution to a given matrix A^* is derived. A direct method for finding the optimal approximation solution is described in detail, and three numerical examples are provided to show the validity of our algorithm. Copyright © 2007 John Wiley & Sons, Ltd.     

The inverse eigenvalue problem of generalized reflexive matrices and its approximation

This paper studies inverse eigenvalue problems of generalized reflexive matrices and their optimal approximations. Necessary and sufficient conditions for the solvability of the problems are derived, the solutions and their optimal approximations are provided.     

The inverse problem of bisymmetric matrices with a submatrix constraint

An n×n real matrix A is called a bisymmetric matrix if A=A^T and A=S_nAS_n, where S_n is an n×n reverse unit matrix. This paper is mainly concerned with solving the following two problems: Problem I Given n×m real matrices X and B, and an r×r real symmetric matrix A₀, find an n×n bisymmetric matrix A such that where A([1: r]) is a r×r leading principal submatrix of the matrix A. Problem II Given an n×n real matrix A^*, find an n×n matrix  in S_E such that where ∥·∥ is Frobenius norm, and S_E is the solution set of Problem I. The necessary and sufficient conditions for the existence of and the expressions for the general solutions of Problem I are given. The explicit solution, a numerical algorithm and a numerical example to Problem II are provided. Copyright © 2003 John Wiley & Sons, Ltd.     

The solvability conditions for the inverse eigenproblems of symmetric and generalized centro-symmetric matrices and their approximations

Let ∥ · ∥ be the Frobenius norm of matrices. We consider (I) the set S_E of symmetric and generalized centro-symmetric real n × n matrices R_n with some given eigenpairs (λ_j, q_j) (j = 1, 2, … , m) and (II) the element in S_E which minimizes for a given real matrix R^∗. Necessary and sufficient conditions for S_E to be nonempty are presented. A general form of elements in S_E is given and an explicit expression of the minimizer is derived. Finally, a numerical example is reported.