Sylvester方程在矩阵扰动分析中的应用

§1.引言 矩阵扰动分析的研究对于矩阵论的发展及数值分析问题计算结果的分析和处理都有重要意义.有关特征值、广义特征值及最小二乘问题的主要研究结果均含于[1]中,[5]运用二次方程根的判别法通过对代数Ricatti方程的解的估计给出了QR分解因子及Cholesky因子的扰动分析,但论证方法及所得结果都比较复杂且所求条件很强.[3]和     

矩阵广义逆新的扰动上界

利用矩阵的奇异值分解方法,研究了矩阵广义逆的扰动上界,得到了在F-范数下矩阵广义逆的扰动上界定理,所得定理推广并彻底改进了近期的相关结果.相应的数值算例验证了定理的有效性.     

大型对称不定箭形线性方程组的分解方法

1 引言 首先考虑2×2矩阵 显然当k>1/2时,矩阵K是对称正定的,且K可以分解成Cholesky因子:当k=1/2时,K为奇异矩阵;而当k<1/2时,K为对称不定矩阵,这时K有广义Cholesky分解式:并且这种分解是稳定的,一般地我们给出定义 定义1.1 设有矩阵K∈R~((m+n)×(m+n)),若总存在排列矩阵P∈R~((m+n)×(m+n))和对称正定矩阵H∈R~(m×n)、G∈R(m×m)使得则称矩阵K为对称拟定(Symmetric quasidefinite)矩阵。     

二种矩阵分解定理的证明和分解矩阵构造方法的探讨

本文利用上三角阵T的结果证明了正定矩阵的Cholesky分解定理和非奇异矩阵的QR分解定理,并由T得到分解矩阵L,构造出正交矩阵Q和上三角阵R。     

酉对称矩阵的极分解

为了简化大型行(列)酉对称矩阵的极分解,研究了酉对称矩阵的性质,获得了一些新的结果,给出了酉对称矩阵的极分解和广义逆的公式,它们可极大地减少行(列)酉对称矩阵的极分解的计算量与存储量,并且不会丧失数值精度.同时对酉对称矩阵的极分解作了扰动分析.     

次酉极因子在酉不变范数下的相对扰动界

设A是一个m×n阶复矩阵,分解A=QH称为广义极分解,如果Q是m×n次酉极因子且H为n×n半正定的Hermite矩阵．本文获得了次酉极因子在任意酉不变范数下的几个相对扰动界,在某种意义上,相对扰动界比R．C．Li等获得的绝对扰动界要好．     

图论在稀疏对称矩阵中的应用

本文简要叙述图论的基本概念及其与对称矩阵的关系,并建立对称正定矩阵的Cholesky分解及其相应的消去图、商图间的关系。利用所建立的关系为矩阵找到一个好的“排序”,使得它的Cholesky分解的下三角矩阵有较少的非零元素。同时应用可达集的概念确定新产生非零元素的位置和个数.并给出相应的实现方法和数值试验结果。     

纵向数据下均值协方差联合建模中的ARMA Cholesky因子模型

在广义估计方程框架下,发展了一类灵活的回归模型来参数化协方差结构.通过合并广泛使用的修正的Cholesky分解和滑动平均Cholesky分解,得到自回归滑动平均Cholesky分解.该分解能够参数化更一般的协方差结构,且其输入具有清晰的统计解释.对这些输入建立回归模型,并利用拟Fisher迭代算法估计回归系数.均值和协方差模型中的参数估计皆具有相合性和渐近正态性.最后通过模拟研究考察了所提方法的有限样本表现.     

广义延拓矩阵在乘法扰动下特征空间的相对扰动界

在矩阵A与其扰动矩阵A有相同分块的谱分解下,对于以A为母矩阵的广义延拓矩阵凡（A）及以A为母矩阵的广义延拓矩阵凡（A）,使用特征值双分离度方法,给出了广义延拓矩阵n（A）与其扰动矩阵n（A）的特征空间在乘法扰动下的相对扰动界．     

广义非负与正极因子的乘法扰动界

本文在乘法扰动下研究了加权极分解的广义非负极因子与广义正极因子的扰动界,同时,作为特殊情形,也获得了广义极分解与极分解的非负极因子与正极因子的乘法扰动界.     

A note on the perturbation analysis for the generalized Cholesky factorization

In this note, we consider the perturbation analysis for the generalized Cholesky factorization further. The conditions for the main theorems of the paper [W.-G. Wang, J.-X. Zhao, Perturbation analysis for the generalized Cholesky factorization, Appl. Math. Comput. 147 (2004) 601-606] are weakened by using an alternative method. Moreover, some new perturbation bounds are also derived.     

对称不定矩阵的广义Cholesky分解法

对称不定矩阵的广义Ｃｈｏｌｅｓｋｙ分解法赵金熙（南京大学）ＴＨＥＧＥＮＥＲＡＬＩＺＥＤＣＨＯＬＳＫＹＦＡＣＴＯＲＩＺＡＴＩＯＮＭＥＴＨＯＤＦＯＲＳＯＬＶＩＮＧＳＹＭＭＥＴＲＩＣＩＮＤＥＦＩＮＩＴＥＬＩＮＥＡＲＳＹＳＴＥＭＳ￥ＺｈａｏＪｉｎ－ｘｉ（Ｎａ...     

Generalized matrix inversion is not harder than matrix multiplication

Starting from the Strassen method for rapid matrix multiplication and inversion as well as from the recursive Cholesky factorization algorithm, we introduced a completely block recursive algorithm for generalized Cholesky factorization of a given symmetric, positive semi-definite matrix A∈R^n×n$A\in {\mathbb{R}}^{n\times n}$. We used the Strassen method for matrix inversion together with the recursive generalized Cholesky factorization method, and established an algorithm for computing generalized {2,3}$\{2,3\}$ and {2,4}$\{2,4\}$ inverses. Introduced algorithms are not harder than the matrix–matrix multiplication.     

A note on factoring 0-1 matrices

Nonnegative definite 0-1 matrices are shown to have a Cholesky factorization with the factors being 0-1 matrices. Conditions are derived for the existence of a "Cholesky" factorization of symmetric Boolean matrices. This condition is related to the structure of the graph associated with the matrix.     

Perturbation analysis for block downdating of a Cholesky decomposition

Summary. Let the Cholesky decomposition of be , where is upper triangular. The Cholesky block downdating problem is to determine such that , where is a block of rows from the data matrix . We analyze the sensitivity of this block downdating problem of the Cholesky factorization. A measure of conditioning for the Cholesky block downdating is presented and compared to the single row downdating case. Received September 15, 1993     

A fast algorithm for subspace state-space system identification via exploitation of the displacement structure

Two recent approaches (Van Overschee, De Moor, N4SID, Automatica 30 (1) (1994) 75; Verhaegen, Int. J. Control 58(3) (1993) 555) in subspace identification problems require the computation of the R factor of the QR factorization of a block-Hankel matrix H, which, in general has a huge number of rows. Since the data are perturbed by noise, the involved matrix H is, in general, full rank. It is well known that, from a theoretical point of view, the R factor of the QR factorization of H is equivalent to the Cholesky factor of the correlation matrix H^TH, apart from a multiplication by a sign matrix. In Sima (Proceedings Second NICONET Workshop, Paris-Versailles, December 3, 1999, p. 75), a fast Cholesky factorization of the correlation matrix, exploiting the block-Hankel structure of H, is described. In this paper we consider a fast algorithm to compute the R factor based on the generalized Schur algorithm. The proposed algorithm allows to handle the rank–deficient case.     

Unified approach to unconstrained minimization via basic matrix factorizations

Important classes of algorithms for unconstrained minimization, when applied to a quadratic with Hessian A, may be regarded as alternative ways to effect certain basic matrix factorizations of or with respect to A. This approach enables a unified presentation of many existing algorithms and suggests some new algorithms. Two basic underlying factorizations are of particular interest—tridiagonalization coupled with Cholesky factorization and the Gram-Schmidt or QR factorization.     

The Equality Constrained Indefinite Least Squares Problem: Theory and Algorithms

We present theory and algorithms for the equality constrained indefinite least squares problem, which requires minimization of an indefinite quadratic form subject to a linear equality constraint. A generalized hyperbolic QR factorization is introduced and used in the derivation of perturbation bounds and to construct a numerical method. An alternative method is obtained by employing a generalized QR factorization in combination with a Cholesky factorization. Rounding error analysis is given to show that both methods have satisfactory numerical stability properties and numerical experiments are given for illustration. This work builds on recent work on the unconstrained indefinite least squares problem by Chandrasekaran, Gu, and Sayed and by the present authors.