矩阵方程AXAT+BYBT=C的对称与反对称最小范数最小二乘解

对于任意给定的矩阵A∈Rk×m,B∈Rk×n和C∈Rk×k,利用奇异值分解和广义奇异值分解,我们给出了矩阵方程AXAT+BYBT=C的对称与反对称最小范数最小二乘解的表达式.     

矩阵方程AXAT=C的对称斜反对称解

设A∈Rm×n,C∈Rm×m给定,利用矩阵的广义奇异值分解和对称斜反对称矩阵的性质,得到了矩阵方程(1)AXAT=C存在对称斜反对称解的充要条件和通解表达式;证明了若方程(1)有解,则一定存在唯一极小范数解,并给出了极小范数解的具体表达式和求解步骤.     

矩阵方程AXA^T＋BYB^T=C的对称正定解

本研究矩阵方程AXA^T BYB^T=C的对称正定解。利用广义奇异值分解（GSVD）给出了该矩阵方程有解的充分条件及解的通式。     

矩阵方程AXAT=C的对称斜反对称解

设A∈R^m×n,C∈R^m×m给定,利用矩阵的广义奇异值分解和对称斜反对称矩阵的性质,得到了矩阵方程(1)AXA^T=C存在对称斜反对称解的充要条件和通解表达式;证明了若方程(1)有解,则一定存在唯一极小范数解,并给出了极小范数解的具体表达式和求解步骤.     

一类亚半正定矩阵的左右逆特征值问题(Ⅱ)

1.引言 令Rm×n表示所有m×n实矩阵集合;RN×nn表示所有非奇异的n阶实矩阵集合.令Rn×no={A∈Rn×n| X∈Rn×1:XTAX≥0},即亚半正定矩阵集合;Wr×t={A∈Rr×t|σ(A)≤1},即最大奇异值不超过1的r×t实矩阵集合,这里σ(A)表示矩阵A的最大奇异值.     

求矩阵奇异值分解的一种新方法

旨在给出求矩阵奇异值分解的一种新方法.改进和克服了以往方法的缺陷和不足.     

酉延拓矩阵的奇异值分解及其广义逆

从普通奇异值分解出发,导出了酉延拓矩阵的奇异值和奇异向量与母矩阵的奇异值和奇异向量间的定量关系,同时对酉延拓矩阵的满秩分解及g逆,反射g逆,最小二乘g逆,最小范数g逆作了定量分析,得到了酉延拓矩阵的满秩分解矩阵F*和G*与母矩阵A的分解矩阵F和G之间的关系.最后给出了相应的快速求解算法,并举例说明该算法大大降低了分解的计算量和存储量,提高了计算效率.     

O-对称矩阵的奇异值分解及其算法

本文研究了具有轴对称结构矩阵的奇异值分解,找出了这类矩阵奇异值分解与其子阵奇异值分解之间的定量关系.利用这些定量关系给出这类矩阵奇异值分解和Moore-Penrose逆的算法,据此可极大地节省求该类矩阵奇异值分解和Moore-Penrose逆时的计算量和存储量.     

Hermitian非自反矩阵的两类反问题

记J为一广义反射矩阵,HAJn×n为关于J的n阶Hermitian非自反矩阵的集合.本文考虑如下两个问题:问题Ⅰ给定X,B∈n×m,求A∈HAJn×n,使得‖AX-B‖=min.问题Ⅱ给定X∈n×m,B∈n×n,求A∈HAJn×n,使得XHAX=B.首先利用奇异值分解讨论问题Ⅰ的解的通式,然后利用广义奇异值分解得到了问题Ⅱ有解的充分必要条件和解的通式,最后给出问题Ⅰ和Ⅱ的逼近解的具体表达式.     

四元数正则矩阵束广义特征值反问题

对于任意给定的X∈Qn×m,∧=diag(λ1,…,λm)∈Rm×m,利用奇异值分解、谱分解及QR分解分别给出了满足AX=BX∧,及XHBX=Im,AX=BX∧,的正则矩阵束(A,B)的通解表达式.     

TLS和LS问题的比较

There are a number of articles discussing the total least squares(TLS) and the least squares(LS) problems.M.Wei(M.Wei, Mathematica Numerica Sinica 20(3)(1998),267-278) proposed a new orthogonal projection method to improve existing perturbation bounds of the TLS and LS problems.In this paper,wecontinue to improve existing bounds of differences between the squared residuals,the weighted squared residuals and the minimum norm correction matrices of the TLS and LS problems.     

ON THE ACCURACY OF THE LEAST SQUARES AND THE TOTAL LEAST SQUARES METHODS

Consider solving an overdetermined system of linear algebraic equations by both the least squares method (LS) and the total least squares method (TLS). Extensive published computational evidence shows that when the original system is consistent. one often obtains more accurate solutions by using the TLS method rather than the LS method. These numerical observations contrast with existing analytic perturbation theories for the LS and TLS methods which show that the upper bounds for the LS solution are always smaller than the corresponding upper bounds for the TLS solutions. In this paper we derive a new upper bound for the TLS solution and indicate when the TLS method can be more accurate than the LS method.Many applied problems in signal processing lead to overdetermined systems of linear equations where the matrix and right hand side are determined by the experimental observations (usually in the form of a lime series). It often happens that as the number of columns of the matrix becomes larger, the ra     

矩阵方程AXB+CYD=E的对称极小范数最小二乘解

对于任意给定的矩阵A∈Rm×n,B∈Rn×s,C∈Rm×k,D∈Rk×s,E∈Rm×s,本文利用矩阵的Kmnecker积和Moore-Penrose广义逆,研究矩阵方程AXB CYD=E的对称极小范数最小二乘解,得到了解的表达式．并由此给出了矩阵方程AXB=C的双对称极小范数最小二乘解的表达式．此外,我们还给出了求矩阵方程AXB=C的双对称极小范数最小二乘解的数值算法和数值例子．     

关于TLS问题

1.引 言考虑观测线性系统AX=B,（1.1a）其中A∈Cm×n,B∈Cm×d（本文通篇假设m≥n d）,分别是精确但不可观测的A0∈Cm×n,B0∈Cm×d的近似,即精确线性系统是A0X=B0.（1.1b）Golub和Van Loan于1980年提出的总体最小二乘问题（以下简称TLS问题）就是求解线性系统AX=B（1.2）     

ON THE SEPARABLE NONLINEAR LEAST SQUARES PROBLEMS

Separable nonlinear least squares problems are a special class of nonlinear least squares problems, where the objective functions are linear and nonlinear on different parts of variables. Such problems have broad applications in practice. Most existing algorithms for this kind of problems are derived from the variable projection method proposed by Golub and Pereyra, which utilizes the separability under a separate framework. However, the methods based on variable projection strategy would be invalid if there exist some constraints to the variables, as the real problems always do, even if the constraint is simply the ball constraint. We present a new algorithm which is based on a special approximation to the Hessian by noticing the fact that certain terms of the Hessian can be derived from the gradient. Our method maintains all the advantages of variable projection based methods, and moreover it can be combined with trust region methods easily and can be applied to general constrained separable nonlinear problems. Convergence analysis of our method is presented and numerical results are also reported.     

变量有界半定最小二乘问题

<正>1引言设A_i∈S~n,i=1,…,m,定义线性算子A:S~n→R~m,AX=(A_1·X,…,A_m·X)~T,其相应的伴随算子为A~*:R~m→S~n,且A~*y=sum from i=1 to my_iA_i.X∈S~n,b∈R~m.Malick.J在[6]中讨论了如下标准半定最小二乘问题(SDLS):     

加权总体最小二乘问题的分析

总体最小二乘问题由Golub和Van Loan首先进行数学的分析,随后人们对于总体最小二乘问题的算法、解的各种形式、总体最小二乘解和最小二乘解的关系、总体最小二乘解的扰动理论以及数值试验作了大量的研究工作。近来,[10]中给出了总体最小二乘问题(TLS)较一般地讨论。另一方面,Golub和Van Loan研究了总体最小二乘问题的特殊均加权形式。本文试图在[10,11]的基础上讨论最一般的总体最小二     

关于广义线性最小二乘问题的扰动分析

1问题 在应用统计中，常用的参数估计方法之一是广义线性最小二乘min(Cx-y)~TW~+(Cx-y).(1.1)其中C为m×n矩阵，W为m×m对称半正定矩阵，上标+代表Moore-Penrose广义逆Paige~[1]注意到：从统计观点看，W一般未必可逆，且通常具有对称满秩分解W=BB~T，因而，把问题改述为下述形式更合适     

Levenberg-Marquardt型非线性最小二乘算法的收敛性

在无约束最优化问题中,目标函数常常具有某些特殊的形式,最常见的一种是若干个函数的平方和形式,即