一类线性方程组的结构向后误差分析

考虑如下结构线性方程组(A B C 0)(x y)=(a b),其中A∈R~(m×m),B∈R~(m×n),C∈R~(n×m).本文给出该类结构方程组的结构向后扰动误差的显式表达式.数值例子表明求解该类问题稳定的算法得到的解不必是强稳定的.     

一类二次矩阵方程的条件数和后向误差

主要讨论一类二次矩阵方程X^2-EX-F=0的条件数和后向误差,其中E是一个对角矩阵,F是一个M矩阵.这类二次矩阵方程来源于Markov链的噪声Wiener-Hopf问题.实际问题中人们感兴趣的是它的M矩阵的解.应用Rice创立的基于Frobenius范数下的条件数理论,导出此类二次矩阵方程的M矩阵解的条件数的显式表达式.同时,也给出近似解的后向误差的定义以及一个可计算的表达式.最后,通过数值例子验证理论结果是有效的.     

基于Taylor级数的矩阵双曲余弦函数的数值算法

提出了一种基于Taylor级数的矩阵双曲余弦函数的数值逼近算法,为减少计算量使用了Paterson-Stockmeyer方法来计算矩阵Taylor多项式,对逼近误差进行了绝对后向误差分析以减少误差,并设计了算法可以较为快速且准确地求解矩阵双曲余弦函数,最后进行了数值实验,验证了算法的有效性.     

关于合同变换矩阵的一种结构形式

对于任意非零的对称矩阵A,B∈P~(n×n),介绍A,B在P中合同的一个充分必要条件,并基于该条件构造满足P~TAP=B的所有可逆矩阵P∈P~(n×n)是目标.针对■和■这两种常见情形,获得了P的明确结构.     

共轭斜量法和Lanczos算法解线性方程组时一系列右端项的处理

给定方程组Ax=b,其中A是n×n非奇实对称矩阵,b是一个或几个给定的n维向量。当A是高阶正定稀疏矩阵时,对一个右端项,共轭斜量法是一个非常有效的方法。当A不定时,共轭斜量法可能失败,有效的方法是利用Lanczos算法产生的SYMMLQ算法,我们考虑怎样利用第一个右端项计算的结果来对第二个右端项求     

关于矩阵特征值问题向后误差分析的注记

本文研究实矩阵关于复近似特征对的范数型向后误差．在复扰动情形,这个问题已被Higham 等学者解决．本文研究实扰动情形．结果表明,通常情况下,两种情形差别不大,但在某些情形,二者可以相差很大．作为推广,我们还讨论了矩阵多项式的相应问题．文中的一个结果部分地解决了D．J．Higham和N．J．Higham 1999年提出的一个待解决的问题．     

一类广义半正定线性方程组的直接解法

1 引言 在具有等式约束的二次规划或椭圆型边值问题离散化分析中经常会遇到解线性方程组 (1)其中A∈R~(m×m)为对称正定矩阵,B∈R~(n×m)为行满秩矩阵,f∈R~m,g∈R~n为右端向量. 为了讨论的方便,首先引进, 定义1 若G∈R~(N×N),且对任何非零向量x∈R~N都有x~TGx>0(≥0),则称矩阵G     

求解大规模Hamilton矩阵特征问题的辛Lanczos算法的误差分析

对求解大规模稀疏Hamilton矩阵特征问题的辛Lanczos算法给出了舍入误差分析.分析表明辛Lanczos算法在无中断时,保Hamilton结构的限制没有破坏非对称Lanczos算法的本质特性.本文还讨论了辛Lanczos算法计算出的辛Lanczos向量的J一正交性的损失与Ritz值收敛的关系.结论正如所料,当某些Ritz值开始收敛时.计算出的辛Lanczos向量的J-正交性损失是必然的.以上结果对辛Lanczos算法的改进具有理论指导意义.     

共轭对角占优矩阵的特征值分布

<正> 设 A=(a_(rs)_(n×n)为 n 阶复矩阵.记μ_r=sum from s≠r |a_(rs)|,N={1,2,…,n},J(A)={r∈N||a_(rr)>μ_r}.我们引入下述定义:定义1 若对r=1,2,…,n 皆有|a_(rr)|>μ_r,则称 A 为按行严格对角占优矩阵,记为 A∈D.若对 r=1,2,…,n 皆有|a_(rr)|≥μ_r,J(A)非空集,且对任一 k(?)J(A),有a_(ks_1)a_(s_1s_2)…a_(s_m)l≠0,l∈J(A),则称 A 为按行准严格对角占优矩阵,记为 A∈SC.若 A为此二类矩阵之一,则记为 A∈D∪SC.     

一个Lie代数的子代数及其相关的两类Loop代数

本文构造了Lie代数A2的一个子代数A2,通过选取恰当的基元阶数得到相应的一个loop代数A2,由此设计一个等谱问题,利用屠格式得到了一个新的Liouville可积的Hamilton方程族.作为其约化情形,得到了一个非线性有理分式型演化方程.再由一个矩阵变换,得到了换位运算与A2等价的Lie代数A1的一个子代数A1,将A1再扩展成一个新的高维loop代数G,利用G获得了所得方程族的一类扩展可积系统.     

A note on backward errors for Toeplitz systems

This paper investigates the relations between the structured backward error (SBE) and the partial structured backward error (P‐SBE), and the partial backward error (P‐BE) and the P‐SBE for a computed solution to general Toeplitz system. In particular, we show in some cases the P‐SBE for general Toeplitz system is usually a good estimate of the SBE. Copyright © 2007 John Wiley & Sons, Ltd.     

A note on backward errors for structured linear systems

Let x? be a computed solution to a linear system Ax=b with , where is a proper subclass of matrices in . A structured backward error (SBE) of x? is defined by a measure of the minimal perturbations and such that (1) and that the SBE can be used to distinguish the structured backward stability of the computed solution x?. For simplicity, we may define a partial SBE of x? by a measure of the minimal perturbation such that (2) Can one use the partial SBE to distinguish the structured backward stability of x?? In this note we show that the partial SBE may be much larger than the SBE for certain structured linear systems such as symmetric Toeplitz systems, KKT systems, and dual Vandermonde systems. Besides, certain backward errors for linear least squares are discussed. Copyright © 2004 John Wiley & Sons, Ltd.     

Backward errors for eigenvalues and eigenvectors of Hermitian,skew-Hermitian,H-even and H-odd matrix polynomials

We discuss the perturbation analysis for eigenvalues and eigenvectors of structured homogeneous matrix polynomials with Hermitian, skew-Hermitian, H-even and H-odd structure. We construct minimal structured perturbations (structured backward errors) such that an approximate eigenvalue and eigenvector pair (finite or infinite eigenvalues) is an exact eigenvalue eigenvector pair of an appropriately perturbed structured matrix polynomial. We present various comparisons with unstructured backward errors and previous backward errors constructed for the non-homogeneous case and show that our results generalize previous results.     

THE STRUCTURE SENSITIVITY ANALYSIS FOR TRIANGULAR SYSTEMS

Triangular systems play a fundamental role in matrix computations. It has become commonplace that triangular systems are solved to be more accurate even if they are ill-conditioned. In this paper, we define structured condition number and give structured (forward) perturbation bound. In addition, we derive the representation of optimal structured backward perturbation bound.     

J-子空间格代数上中心化子和广义导子的刻画

设L是Banach空间X上的J-子空间格,AlgL是相应的(J-子空间格代数.设φ:AlgL→AlgL是可加映射,对每个K∈(J)(L),dimK≥2.该文证明了下列表述等价:(1)φ是中心化子;(2)φ满足AB=0■φ(A)B=Aφ(B)=0;(3)φ满足AB+BA=0■φ(A)B+φ(B)A=Aφ(B)+Bφ(A)=0;(4)φ满足ABC+CBA=0■φ(A)BC+φ(C)BA=ABφ(C)+CBφ(A)=0.作为应用,得到AlgL上在零点广义可导的可加映射的完全刻画.     

The structured sensitivity of Vandermonde-like systems

Summary We consider a general class of structured matrices that includes (possibly confluent) Vandermonde and Vandermonde-like matrices. Here the entries in the matrix depend nonlinearly upon a vector of parameters. We define, condition numbers that measure the componentwise sensitivity of the associated primal and dual solutions to small componentwise perturbations in the parameters and in the right-hand side. Convenient expressions are derived for the infinity norm based condition numbers, and order-of-magnitude estimates are given for condition numbers defined in terms of a general vector norm. We then discuss the computation of the corresponding backward errors. After linearising the constraints, we derive an exact expression for the infinity norm dual backward error and show that the corresponding primal backward error is given by the minimum infinity-norm solution of an underdetermined linear system. Exact componentwise condition numbers are also derived for matrix inversion and the least squares problem, and the linearised least squares backward error is characterised.     

A NOTE ON THE BACKWARD ERRORS FOR INVERSE EIGENVALUE PROBLEMS

In this note,we consider the backward errors for more general inverse eigenvalus prob-lems by extending Sun‘‘‘‘s approach.The optimal backward errors defined for diagonal-ization matrix inverse eigenvalue problem with respect to an approximate solution,and the upper and lower bounds are derived for the optimal backward errors.The results may be useful for testing the stability of practical algorithms.     

The equivalence between the convergence of Ishikawa and Mann iterations with errors for strongly successively pseudocontractive mappings without Lipschitzian assumption

In this paper we prove some new equivalences between convergence of the Ishikawa and Mann iteration sequences with errors in two schemes by Xu [Y.G. Xu, Ishikawa and Mann iteration process with errors for nonlinear strongly accretive operator equations, J. Math. Anal. Appl. 224 (1998) 91-101] and Liu [L.S. Liu, Ishikawa and Mann iterative process with errors for nonlinear strongly accretive mappings in Banach spaces, J. Math. Anal. Appl. 194 (1995) 114-125], respectively, for strongly successively pseudocontractive mappings. Our main results improve and extend the corresponding results of the all references listed in this article.     

Stopping criteria, forward and backward errors for perturbed asynchronous linear fixed point methods in finite precision

^** Email: pierre.spiteri{at}enseeiht.fr This paper deals with perturbed linear fixed point methods inthe presence of round-off errors. Successive approximationsas well as the more general asynchronous iterations are treated.Forward and backward error estimates are presented, and theseare used to propose theoretical stopping criteria for thesemethods. In the case of asynchronous iterations, macro-iterationsare used as a tool in order to obtain estimates.     

Backward–forward algorithms for structured monotone inclusions in Hilbert spaces

In this paper, we study the backward–forward algorithm as a splitting method to solve structured monotone inclusions, and convex minimization problems in Hilbert spaces. It has a natural link with the forward–backward algorithm and has the same computational complexity, since it involves the same basic blocks, but organized differently. Surprisingly enough, this kind of iteration arises when studying the time discretization of the regularized Newton method for maximally monotone operators. First, we show that these two methods enjoy remarkable involutive relations, which go far beyond the evident inversion of the order in which the forward and backward steps are applied. Next, we establish several convergence properties for both methods, some of which were unknown even for the forward–backward algorithm. This brings further insight into this well-known scheme. Finally, we specialize our results to structured convex minimization problems, the gradient-projection algorithms, and give a numerical illustration of theoretical interest.