广义逆矩阵A~-的计算方法及应用

根据广义逆矩阵(减号逆)的定义AA-A=A,给出了求任意矩阵A的一个或全部广义逆矩阵A-的计算方法.当A-为A的全部广义逆矩阵时,得出了矩阵方程(或线性方程组)AX=B的统一通解公式X=A-B.     

关于布尔矩阵的广义Moore-Penrose逆

讨论布尔矩阵的广义Moore-Penrose逆.给出了一些广义Moore-Penrose逆存在的充要条件以及广义Moore-Penrose逆的一些刻划.     

体上矩阵的广义逆

关于矩阵广义逆的研究,自 R.Penrose 的论文发表以来,目前巳进行得相当深入,域上,以至某些交换环上矩阵的广义逆也已相继进行.但是,由于一般体的非交换性限制,任意体上矩阵的广义逆尚未见过具体的论述。在这篇文章中,我们将对具有一个对合反自同构的体,阐述其上矩阵的广义逆,将域上矩阵的 Moore-Penrose 逆存在的一个充要条件作了相应的推广,并给出了体上矩阵的 Moore-Penrose 逆、(1,…,i)一逆、(2)-逆的显式;最后,我们利用[7]中的一个结果,得到了四元数矩阵的(3)-逆、(4)-逆的显式。     

爱因斯坦积下张量的加权Moore-Penrose逆的扰动理论

正1引言广义逆理论已成为现代数学重要的研究方向之一,是应用十分广泛的一个数学分支.它在数值分析、最优化、数理统计、微分方程及应用数学中都具有很大的作用.众所周知,广义逆有很多类型,如Moore-Penrose逆,Drazin逆,群逆,加权广义逆等等.其中矩阵的Moore-Penrose逆及其扰动理论和加权Moore-Penrose逆及其扰动理论已经发展相对完善,如[9][14]、及王国荣、魏益民和乔三正的著作[13]中都对矩阵的Moore-Penrose逆[12]及     

模糊矩阵三种广义逆的判定定理

给出模糊矩阵存在广义{1,3}逆,广义{1,4}逆和Moore-Penrose广义逆的判定定理.     

关于Fuzzy矩阵的广义逆

本文分别给出了Fuzzy矩阵存在广义{1,3}-逆、广义{1,4}-逆以及Moore-Penrose广义逆Fuzzy矩阵的一些充要条件。又得到求上述广义逆Fuzzy矩阵的一些公式。主要的结果有: 1.Fuzzy矩阵A的广义{1,3}-逆A~((1.3))(广义{1,4}-逆A~((1.4))存在的充要条件是Fuzzy关系方程有解。2.Fuzzy矩阵A的Moore-Penrose广义逆A~T存在的充要条件是Fuzzy关系方程均有解。3.如果B、C分别为Fuzzy关系方程的一个解,那么。     

环上矩阵广义Moore-Penrose逆的存在性

讨论带有对合反自同构*有单位元的结合环R上矩阵的广义Moore-Penrose 逆,给出了环R上矩阵的广义Moore-Penrose逆存在的几个充要条件．特别,得到了环 R上矩阵A的关于M和N的广义Moore-Penrose逆存在的充要条件是A有分解A= GDH,其中D2=D,(MD)*=MD,(GD)*MGD+M(I-D)和DHN-1(DH)*+ (I-D)M-1均可逆．     

基于线性算子的广义逆矩阵的几何表示

广义逆矩阵是矩阵理论的重要内容.由于广义逆矩阵的定义众多,计算较为繁杂,使得初学者很难理解和掌握其本质.基于线性方程组求解问题的等价表示,从线性算子的角度展示多种广义逆矩阵定义的背景及其几何直观意义.通过对一个特殊算例的分析与求解,实现了对多种广义逆矩阵的几何解释及其在线性方程组求解中的作用,淡化了广义逆矩阵计算的繁杂,加深初学者对广义逆矩阵的理解与掌握.     

基于线性算子的广义逆矩阵的几何表示北大核心

广义逆矩阵是矩阵理论的重要内容.由于广义逆矩阵的定义众多,计算较为繁杂,使得初学者很难理解和掌握其本质.基于线性方程组求解问题的等价表示,从线性算子的角度展示多种广义逆矩阵定义的背景及其几何直观意义.通过对一个特殊算例的分析与求解,实现了对多种广义逆矩阵的几何解释及其在线性方程组求解中的作用,淡化了广义逆矩阵计算的繁杂,加深初学者对广义逆矩阵的理解与掌握.     

具有满单泛分解态射的广义逆

态射的Moore-Penrose逆是矩阵的Moore-Penrose逆在有对合＊的范畴中的推广．本文着重给出具有满单泛分解态射f的（1，3．4）-逆和Moore-Penrose存在的充要条件，同时也推广了具有泛分解广义逆的相应结果．     

矩阵方程ATXA=D的条件数与向后扰动分析

讨论矩阵方程ATXA=D,该方程源于振动反问题和结构模型修正.本文利用Moore-Penrose广义逆的性质,给出该方程解的条件数的上、下界估计.同时,利用Schauder不动点理论给出该方程的向后扰动界,这些结果可用于该矩阵方程的数值计算.     

Perturbations of Moore–Penrose Metric Generalized Inverses of Linear Operators in Banach Spaces

In this paper,the perturbations of the Moore–Penrose metric generalized inverses of linear operators in Banach spaces are described.The Moore–Penrose metric generalized inverse is homogeneous and nonlinear in general,and the proofs of our results are different from linear generalized inverses.By using the quasi-additivity of Moore–Penrose metric generalized inverse and the theorem of generalized orthogonal decomposition,we show some error estimates of perturbations for the singlevalued Moore–Penrose metric generalized inverses of bounded linear operators.Furthermore,by means of the continuity of the metric projection operator and the quasi-additivity of Moore–Penrose metric generalized inverse,an expression for Moore–Penrose metric generalized inverse is given.     

An Imbedding Method for Computing the Generalized Inverses

This paper deals with a system of ordinary differential equations with known conditions associated with a given matrix. By using analytical and computational methods, the generalized inverses of the given matrix can be determined. Among these are the weighted Moore-Penrose inverse, the Moore-Penrose inverse, the Drazin inverse and the group inverse. In particular, a new insight is provided into the finite algorithms for computing the generalized inverse and the inverse.     

矩阵方程AXB+CY D=E的三对角中心对称极小范数最小二乘解

本文研究了矩阵方程AXB+CY D=E的三对角中心对称极小范数最小二乘解问题.利用矩阵的Kronecker积和Moore-Penrose广义逆方法,得到了矩阵方程AXB+CY D=E的三对角中心对称极小范数最小二乘解的表达式.     

METRIC GENERALIZED INVERSE OF LINEAR OPERATOR IN BANACH SPACE＊＊＊

The Moore-Penrose metric generalized inverse T of linear operator T in Banach space is systematically investigated in this paper. Unlike the case in Hilbert space, even T is a linear operator in Banach Space, the Moore-Penrose metric generalized inverse T is usually homogeneous and nonlinear in general. By means of the methods of geometry of Banach Space, the necessary and sufficient conditions for existence, continuitv, linearity and minimum property of the Moore-Penrose metric generalized inverse T will be given, and some properties of T will be investigated in this paper.     

Least Squares Properties of Generalized Inverses

The aim of this paper is to systematize solutions of some systems of linear equations in terms of generalized inverses.As a significant application of the Moore-Penrose inverse,the best approximation solution to linear matrix equations (i.e.both least squares and the minimal norm) is considered.Also,characterizations of least squares solution and solution of minimum norm are given.Basic properties of the Drazin-inverse solution and the outer-inverse so-lution are present.Motivated by recent research,important least square prop-erties of composite outer inverses are collected.     

Least Squares Properties of Generalized Inverses

The aim of this paper is to systematize solutions of some systems of linear equations in terms of generalized inverses.As a significant application of the Moore-Penrose inverse,the best approximation solution to linear matrix equations (i.e.both least squares and the minimal norm) is considered.Also,characterizations of least squares solution and solution of minimum norm are given.Basic properties of the Drazin-inverse solution and the outer-inverse so-lution are present.Motivated by recent research,important least square prop-erties of composite outer inverses are collected.     

Banach空间中基于Neumann引理的拟线性广义逆扰动定理

本文给出Banach空间中闭线性算子的Moore-Penrose有界拟线性投影广义逆的一种新的扰动分析方法.运用的核心工具是广义Neumann引理,这与以往其他结果中所运用的广义Banach引理的处理方法极为不同,得到了闭线性算子的MoorePenrose有界拟线性广义逆的又一个扰动定理及三个误差界不等式.     

The Inverse Problem of Centrosymmetric Matrices with a Submatrix Constraint

By using Moore-Penrose generalized inverse and the general singular value decomposition of matrices, this paper establishes the necessary and sufficient conditions for the existence of and the expressions for the centrosymmetric solutions with a submatrix constraint of matrix inverse problem AX = B. In addition, in the solution set of corresponding problem, the expression of the optimal approximation solution to a given matrix is derived.