两类矩阵方程的极小范数解

设Rm×n表示所有m×n阶实矩阵的集合,SRn×n是所有n阶实对称矩阵的全体,ORn×n为n阶实正交矩阵的全体,In是n阶单位矩阵,AT、rankA分别表示矩阵A的转置与秩,||·||是矩阵的Frobenius范数.此外,对于A=（αij）s×s’,B=（βij）s×s’,A＊B表示A与B的Hadamard积,其定义为,现讨论如下两个问题:     

Orthogonal projection regularization operators

Tikhonov regularization often is applied with a finite difference regularization operator that approximates a low-order derivative. This paper proposes the use of orthogonal projections as regularization operators, e.g., with the same null space as commonly used finite difference operators. Applications to iterative and SVD-based methods for Tikhonov regularization are described. Truncated iterative and SVD methods are also considered. Research of L. Reichel was supported in part by an OBR Research Challenge Grant. Research of F. Sgallari was supported in part by PRIN 2004 grant 2004014411-005.     

监督降维算法的计算和理论分析

在小样本条件下,由于离散矩阵的奇异性,作为监督降维的传统线性鉴别分析（LDA）并不能直接计算．许多扩展算法被提出以克服此问题,一般可分为3类：基于类内离散矩阵零空间的方法、基于总体离散矩阵列空间的方法和基于其它子空间的方法．为了深入了解前2类算法的特性,作了计算和理论分析,并得出结论：在满足一定条件下（小样本高维数据一般都满足）,基于类内离散矩阵零空间和基于总体离散矩阵列空间的方法具有等价关系,仅最优矢量集的约束条件和实现途径有所区别．在人脸数据库ORL和YALE上的比较实验结果亦证实了上述结论．     

矩阵方程ATXA=C的双对称最小二乘解的最佳逼近

1引言根据矩阵分解理论求解线性矩阵方程的问题已经有多位作者研究([2],[3],[5]-[11]),比如文[6],[7],[9]基于GSVD、CCD方法给出了几个矩阵方程的最小二乘解以及方程(组)相     

矩阵方程A×B=D的(R,S)-斜对称解

矩阵方程A×B=D是教学、理论研究和工程实践中常见的一种矩阵方程.给出了A×B=D具有(R,S)-斜对称矩阵解的充分必要条件,及其解存在条件下全体解集合Sx的表达式.此外,还讨论了任意给定矩阵(X)在仿射子空间Sx中的最优近似解,并给出了最优解的显示表达式.     

中心对称与反中心对称矩阵的一类反问题

1 引言矩阵反问题和矩阵特征值反问题在科学和工程技术中具有广泛的应用,有关研究已取得了许多进展(见[1])．而中心对称矩阵与反中心对称矩阵在信息论、线性系统理论、线性估计理论及数值分析等领域中应用广泛(见[2])．本文研究矩阵方程XAY=B(X,Y,B 已知)的中心对称与反中心对称解及其最佳逼近．     

Perturbation Identities for Regularized Tikhonov Inverses and Weighted Pseudoinverses

We consider the perturbation analysis of two important problems for solving ill-conditioned or rank-deficient linear least squares problems. The Tikhonov regularized problem is a linear least squares problem with a regularization term balancing the size of the residual against the size of the weighted solution. The weight matrix can be a non-square matrix (usually with fewer rows than columns). The minimum-norm problem is the minimization of the size of the weighted solutions given by the set of solutions to the, possibly rank-deficient, linear least squares problem.It is well known that the solution of the Tikhonov problem tends to the minimum-norm solution as the regularization parameter of the Tikhonov problem tends to zero. Using this fact and the generalized singular value decomposition enable us to make a perturbation analysis of the minimum-norm problem with perturbation results for the Tikhonov problem. From the analysis we attain perturbation identities for Tikhonov inverses and weighted pseudoinverses.     

矩阵方程ATXB+BTXTA=D的极小范数最小二乘解

1引言本文用Rm×n表示所有m×n实矩阵全体,ORn×n,ASRn×n分别表示n×n实正交矩阵类与反对称矩阵类.‖·‖F表示矩阵的Frobenius范数,A+为矩阵A的Moore-Penrose广义逆,A*B与A(?)B分别表示矩阵4与B的Hadamard乘积及Kronecker乘积,即若A=(aij),B=(bij),则A*B=(ajibij),A(?)B=(aijB),vec4表示矩阵A的按行拉直,即若A=[aT1,aT2,…,aTm],其中ai为A的行向量,则vecA=(a1a2…am)T.设A∈Rn×m,B∈Rp×m,D∈Rm×m,我们考虑不相容线性矩阵方程ATXB+BTXTA=D(1.1)     

一类矩阵方程的公共解

By applying the GSVD of matrix pairs,we discuss common solutions of the matrix equations AXC = E, BXD = F, AXD = G, BXC = H, under consistent and nonconsistent case respectively. We also discuss common symmetric solutions of the matrix equations AXA^T = E, BXB^T = F, AXB^T = G, BXA^T = H under consistent and nonconsistent case respectively. The necessary and sufficient conditions for the existence and the expressions of solutions of these matrix equations are provided.