实对称矩阵对角化中正交矩阵的初等变换求法

对实对称矩阵正交对角化过程中正交矩阵的求解方法进行了研究,给出了利用初等变换求解正交矩阵的方法,该方法不需要通过特征方程求解特征值与特征向量,仅仅使用初等变换和Schmidt正交化方法.     

矩阵Frobenius标准形的初等变换解法及其应用

矩阵的初等变换在求矩阵的秩、求方阵的逆矩阵、化实对称阵合同于对角阵、求解线性方程组等中均有重要的应用,本文给出了初等变换使方阵相似于Frobenius标准形的方法;该方法运算简单,容易实现,并为求方阵的特征多项式,化方阵为Jordan标准形及求出相应的相似变换阵带来极大的方便。     

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.     

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.     

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.     

Schmidt正交化方法的改进

<正> 一个线性无关的向量组,总有一个正交化的向量组与之等价。为寻求这个等价的正交化向量组,一般都是应用Schmjdt 正交化方法。Schmidt 正交化方法:设α_1,α_2,…,α_n 是一组线性无关的向量,令     

关于正交矩阵的一种求法

我们知道,实对称阵A的属于不同特征根的特征向量彼此正交,所以,求正交矩阵T,使得T~(-1)AT具有对角形式的关键是对A的属于某一重根λ的特征向量正交化,所用到的是我们熟知的Schmidt正交化法。在此,笔者给出一     

方向导数的计算公式的注记

方向导数本质上也是函数的一种变化率.利用向量的Schmidt正交化方法进行坐标变换,将方向导数转换为对新变量的偏导数,再结合多元复合函数的求导法则,给出方向导数计算公式的一种新的证明.     

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     

用正交变换化实二次型为标准形的同步求解问题

作为《关于矩阵的特征值与特征向量同步求解问题》的续篇,利用其给出的方法,证明了新的定理.通过对实对称矩阵进行行列互逆变换,同步求出二次型的标准形及正交变换阵,简化了复杂的施密特正交化法,较好地解决了二次型标准形与正交变换阵同步求解问题.     

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 .     

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.     

矩阵的分块初等变换与分块初等阵及其应用

讨论了矩阵分块初等变换和分块初等阵的定义和性质,利用这一工具研究了行列式的分块运算,分块矩阵的求逆和对称阵的分块合同变换等问题.     

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.     

Reproducing kernel method to solve parabolic partial differential equations with nonlocal conditions

In this study, the parabolic partial differential equations with nonlocal conditions are solved. To this end, we use the reproducing kernel method (RKM) that is obtained from the combining fundamental concepts of the Galerkin method, and the complete system of reproducing kernel Hilbert space that was first introduced by Wang et al. who implemented RKM without Gram–Schmidt orthogonalization process. In this method, first the reproducing kernel spaces and their kernels such that satisfy the nonlocal conditions are constructed, and then the RKM without Gram–Schmidt orthogonalization process on the considered problem is implemented. Moreover, convergence theorem, error analysis theorems, and stability theorem are provided in detail. To show the high accuracy of the present method several numerical examples are solved.     

A NOVEL METHOD FOR NONLINEAR IMPULSIVE DIFFERENTIAL EQUATIONS IN BROKEN REPRODUCING KERNEL SPACE

In this article, a new algorithm is presented to solve the nonlinear impulsive differential equations. In the first time, this article combines the reproducing kernel method with the least squares method to solve the second-order nonlinear impulsive differential equations.Then, the uniform convergence of the numerical solution is proved, and the time consuming Schmidt orthogonalization process is avoided. The algorithm is employed successfully on some numerical examples.     

Simplified reproducing kernel method for impulsive delay differential equations

In this paper, a new algorithm is presented to solve the impulsive delay initial value problems. This is the first time to propose the simplified reproducing kernel method (SRKM for short) to solve the impulsive delay differential equations. Then the uniform convergence of the numerical solution is proved, and the time consuming Schmidt orthogonalization process is avoided. The proposed method is proved to be stable and is not less than second order convergence. The algorithm is employed successfully in some numerical examples.