基于Schmidt正交化的快速求逆法

基于Schmidt正交化过程获得了一种计算逆矩阵的新方法.对于可逆矩阵A,有Q=MA,其中Q是酉矩阵,M是下三角矩阵.本文直接从Schmidt规范正交化出发,获得下三角矩阵M的计算公式,从而求得逆矩阵A-1=QHM=AHMTM.     

LU分解与广义Schmidt正交化方法

利用对称内积的Schmidt正交化方法证明了各阶主子式不为零对称阵的LDLT分解.引入两个向量组关于弱内积广义正交的概念,并构造了将两组含相同个数向量的线性无关组化为广义正交组的广义Schmidt正交化方法.最后应用这一方法证明了各阶主子式不为零矩阵的LDU分解及一些相关的结果.     

合同变换阵的初等变换求法

引入一类特定的初等变换“H”,使当二次型化为标准形的同时得到了合同变换阵 ,计算量较小     

求标准正交基的初等变换法

用初等变换法可求Euclid空间的标准正交基,且只需要进行较少次数的第三种类型的初等变换就能实现这一结果.     

合同变换阵的初等变换求法

引入一类特定的初等变换“Ｈ”,使当二次型化为标准形的同时得到了合同变换阵,计算量较小。     

并行列扫描Gram—Schmidt正交化方法

Gram-Schmidt正交化方法在求解线性代数方程组、最小二乘问题、代数特征值问题等很多矩阵计算问题中有着广泛的应用。因而,设计一种能在并行计算机上高效运行的GS正交化方法,必将对其他若干实际计算问题带来莫大的益处。张丽君教授在文献[2]和[3]中就方阵的正交三角分解问题作了详细的讨论。但实际情况中常遇到长方阵的正交化问题(如最小二乘问题)。本文提出一种适于并行计算的GS正交化方法,该方法采用了类似于求解三角形方程组的“列扫描”处理技巧。本算法特别适用于最小二乘等问题中常见的向量序列短而向量维数高(即后文的m(?)n)的情形,程序实现也很简单,尤其在备有内积功能部件的向量机上运行效率可达O(1)。     

矩阵初等变换方法的几何原理

用向量的线性运算解释了矩阵初等行变换的本质,完善了用初等行变换求最大无关组的方法.     

关于矩阵合同变换的注记

1.设A=(α_■)是数域F上一个n阶对称矩阵,总存在F上的一个n阶可逆阵P,使得(?)。2.给定数域F上的一个n阶对称矩阵A,若对A施行一次初等行变换后,也对A施行同样的列初等变换。則称这样一对变换为矩阵的合同变换。[1] 中介绍了利用矩阵的合同变换化对称阵A为对角阵的方法:见[1]中348—349页。     

求演化矩阵的初等变换方法

已知矩阵A相似于矩阵B,借助初等变换的方法,可以构造性的获得演化矩阵P.即找到具体的可逆矩阵P,使B=P-1AP,而不是仅仅局限于存在性证明.应用实例显示这种方法具有一般性.     

基于矩阵初等变换的矩阵分解法

本根据矩阵的初等变换,提出一种简便的分解矩阵的方法。     

分块矩阵的初等变换

本文将矩阵的初等变换的概念推广到分块矩阵上并建立了计算分块矩阵的逆矩阵和分块方阵的行列式的若干简易方法．     

Jordan标准形定理的初等变换证明

本文对 Jordan标准形定理给出了一种使用初等变换的证明 ,直观意义明显、易于理解 ,可用于线性代数教学 .     

相似变换阵与合同变换阵的初等变换求法

引入初等相似变换与初等合同变换 ,使化方阵为 Jordan标准形的同时求得相似变换阵 ,化实对称阵为对角阵的同时求得合同变换阵 .算法易于理解 ,计算量较小 .     

初等变换的关系及可逆矩阵的分解

给出三种初等变换之间的关系 ,指出可逆矩阵可以分解为两种类型的初等矩阵的乘积 .对于行列式为 1的可逆阵 ,我们得出有趣的结果 ,所有这些 ,对于学习线性代数的同学们来说 ,都是很有益的     

Gram‐Schmidt orthogonalization: 100 years and more

In 1907, Erhard Schmidt published a paper in which he introduced an orthogonalization algorithm that has since become known as the classical Gram‐Schmidt process. Schmidt claimed that his procedure was essentially the same as an earlier one published by J. P. Gram in 1883. The Schmidt version was the first to become popular and widely used. An algorithm related to a modified version of the process appeared in an 1820 treatise by P. S. Laplace. Although related algorithms have been around for almost 200 years, it is the Schmidt paper that led to the popularization of orthogonalization techniques. The year 2007 marked the 100th anniversary of that paper. In celebration of that anniversary, we present a comprehensive survey of the research on Gram‐Schmidt orthogonalization and its related QR factorization. Its application for solving least squares problems and in Krylov subspace methods are also reviewed. Software and implementation aspects are also discussed. Copyright © 2012 John Wiley & Sons, Ltd.     

A modified Gram–Schmidt algorithm with iterative orthogonalization and column pivoting

Iterative orthogonalization is aimed to ensure small deviation from orthogonality in the Gram–Schmidt process. Former applications of this technique are restricted to classical Gram–Schmidt (CGS) and column-oriented modified Gram–Schmidt (MGS). The major aim of this paper is to explain how iterative orthogonalization is incorporated into row-oriented MGS. The interest that we have in a row-oriented iterative MGS comes from the observation that this method is capable of performing column pivoting. The use of column pivoting delays the deteriorating effects of rounding errors and helps to handle rank-deficient least-squares problems.

A second modification proposed in this paper considers the use of Gram–Schmidt QR factorization for solving linear least-squares problems. The standard solution method is based on one orthogonalization of the r.h.s. vector b against the columns of Q. The outcome of this process is the residual vector, r^*, and the solution vector, x^*. The modified scheme is a natural extension of the standard solution method that allows it to apply iterative orthogonalization. This feature ensures accurate computation of small residuals and helps in cases when Q has some deviation from orthogonality.