解大规模矩阵特征问题的复合正交投影方法 *

对于求解大规模矩阵特征问题的经典正交投影类方法 ,当矩阵非Hermite时 ,Ritz向量收敛比Ritz值收敛要困难得多 .已有一类新的精化正交投影类方法 ,它们用精化的近似特征向量取代标准的Ritz向量来逼近所求的特征向量 .证明了在某种意义下 ,每个精化方法是两个经典方法的复合 ,精化近似特征向量满足某个Her mite半正定矩阵在同一个子空间上的经典正交投影 ,进而 ,用特征向量到子空间的距离建立了精化近似特征向量的先验误差界 .结果表明 ,精化的近似特征向量和对应的Ritz值收敛的充分条件相同 .     

计算最小奇异组的一个精化调和 Lanczos双对角化方法

在很多实际应用中需要计算大规模矩阵的若干个最小奇异组.调和投影方法是计算内部特征对的常用方法,其原理可用于求解大规模奇异值分解问题.本文证明了,当投影空间足够好时,该方法得到的近似奇异值收敛,但近似奇异向量可能收敛很慢甚至不收敛.根据第二作者近年来提出的精化投影方法的原理,本文提出一种精化的调和Lanczos双对角化方法,证明了它的收敛性.然后将该方法与Sorensen提出的隐式重新启动技术相结合,开发出隐式重新启动的调和Lanczos双对角化算法(IRHLB)和隐式重新启动的精化调和Lanczos双对角化算法(IRRHLB).位移的合理选取是算法成功的关键之一,本文对精化算法提出了一种新的位移策略,称之为"精化调和位移".理论分析表明,精化调和位移比IRHLB中所用的调和位移要好,且可以廉价可靠地计算出来.数值实验表明,IRRHLB比IRHLB要显著优越,而且比目前常用的隐式重新启动的Lanczos双对角化方法(IRLB)和精化算法IRRLB更有效.     

(0,1)-矩阵类(?)(R,S)

我们把元素全部是1或0的矩阵称为(0,1)-矩阵。设A是一个m×n阶(0,1)-矩阵,其第ⅰ行全部元素之和为r_i(1≤i≤m),第j列全部元素之和为s_j(1≤j≤n)。那么称向量R=(r_1,r_2,…,r_m)为A的行和向量;S=(s_1,s_2,…,s_n)为A的列和向量。所谓具有行和向量R,列和向量S的(0,1)-矩阵类(R,S)是指:     

Householder矩阵的又一特性

给出了Householder矩阵的其它若干性质,利用本文中得到的正交向量组所对应的Householder矩阵的重要性质,解决了形如A=k1H1 k2H2 … knHn(ki∈R,Hi为n阶Householder矩阵,i=1,2,…n)的实对称阵的特性值与特征向量的问题,且任一实对称矩阵A均可表示为上述形式.     

二阶矩阵的特征值和特征向量常考题型例析

矩阵的特征值和特征向量是矩阵与变换的一个非常重要的内容,利用矩阵的特征值和特征向量,可以方便地计算多次矩阵变换的结果,而且在实际工程计算和工程控制中也发挥着重要作用.二阶矩阵的特征值和特征向量有两个基本内容.一是二阶矩阵的特征值和特征向量的概念：设A是一个二阶矩阵,如果对于实数λ,存在一个非零向量α,使得Aα=λα,那么λ称为A的一个特征值,而α称为A的属于特征值λ的一个特征向量.     

Jacobi-Davidson方法中的修正方程和对应的精化方法

Jacobi-Davidson方法的核心之一是求解用以合理扩展投影子空间的线性修正方程组,众多文献均认为该方程是自然有解的.本文详细研究了修正方程,证明它可能无解,并给出了解存在的条件.同时,为克服近似特征向量的可能不收敛性,提出了精化的Jacobi-Davidson方法,建立了对应的修正方程.     

乘幂法求矩阵特征向量与特征值的初始向量及循环控制

矩阵迭代法是求矩阵的第一阶特征值与特征向量的一种数值方法 .本文讨论了用矩阵迭代法求解矩阵的特征值与特征向量时的初始向量选取和循环控制条件     

正多边形的一个向量性质及推广

笔者发现正多边形的一个向量性质加以推广后 ,可以将文 [1 ],[2 ],[3]的结论统一起来 ,进一步体现了数学的和谐 .性质 1　正n多边形A1A2 …An 的圆心为O ,则∑ni=1OAi=0 .此性质证明略去 ,下面给出它的推广 .性质 2　正n多边形A1A2 …An 的圆心为O ,半径为R ,P是平面上的任一点 ,则∑ni=1PA2i =nPO2 +nR2 .证　∑ni=1PA2i =∑ni=1PA2i =∑ni=1(PO +OAi) 2 =∑ni=1PO2 + 2PO ∑ni=1OAi +∑ni=1OA2i =nPO2 +nR2 .性质 3　已知中心对称的多边形A1A2…A2n的外接圆O的半径为R ,P是圆O上的任一点 ,Mi 与Mi+n为…     

x_0-Φ满射环上酉群的构造

设 R 为 X_0-φ满射环,则在 Witt 指数 i(H)≥3时,R 上酉群 U_n(R,H)的满阶正规子群包含酉群的换位子群 E_n(R);在 Witt 指数 i(H)≥1及2,3为单位时,U_n(R,H)的子群 G 为E_n(R)-正规子群的充要条件为 E_n(R,A)(R,A),其中 A 由 G 唯一确定.特别当 R为交换环时,A 为 G 的阶理想.     

(0,1)实对称矩阵特征值的图论意义

A为元素只取 0 ,1且主对角线元素均为 0的 n阶实对称方阵 ,n维列向量 J=( 1 ,1 ,1 ,… ,1 ) T ,且 AJ=( d1,d2 ,d3,… ,dn) T。若 λi 是 A的特征值 ,试证明 :∑ni=1λ2i =∑ni=1di ( 0 )　　这是一道典型的线性代数中关于实对称矩阵特征值方面的问题。对它的求解如下 :设 n维非零向量 x是 A的对应于特征值λi 的特征向量 ,则有 Ax=λix.两边同时左乘 A,得A2 x =A(λix) =λi( Ax) =λ2ix ( 1 )而上式说明 λ2i 即方阵 A2 的特征值。由 [1 ],对任一 n阶方阵 A=[aij]n× n,若 λi 是 A的特征值 ,则有 ∑ni=1λi=tr( A) =∑ni=1aii 。…     

IMPROVING EIGENVECTORS IN ARNOLDI'S METHOD

1. IntroductionArnoldi's method [1, 12] is used for computing.,a few selected eigenpairs of largeunsymmetric matrices. It hajs been investigated since the 1980s; see, e-g., [3--15].It is well known that the m--step Arnoldi processt as described in detail in Section 2,generates an orthonormal basis {yi}7=1 of the Krylov subspace Km(vi, A) spanned byvil Avi,... 5 Am--'v,. Here yi is an initial unit norm vector. The projected matrix ofA onto Km(vi, A) is represented by an m x m upper Hessenb…     

A VARIATION ON THE BLOCK ARNOLDIMETHOD FOR LARGE UNSYMMETRIC MATRIX EIGENPROBLEMS

1.IntroductionLarge-scalematrixeigenproblemsariseinappliedsciencesandmanyengineeringapplications.Arnoldi'smethod[1'2]anditsblockversion[3--6]areverypopularforsolvingthem.Thesemethodshavebeenintensivelyinvestigatedsincethe1980s,bothintheoryandinalgorithms;wereferto[7--17]fordetails.WhenmstepsoftheblockArnoldiprocessareperformed,anorthonormalbasis{K}7=1oftheblockKrylovsubspaceK.(VI,A)spannedbyVI5AVI,'IAm--1VIisgenerated,whereVIisaninitialNxporthogonalmatrix,andtherestrictionofAtoKm(V…     

A modified harmonic block Arnoldi algorithm with adaptive shifts for large interior eigenproblems

The harmonic block Arnoldi method can be used to find interior eigenpairs of large matrices. Given a target point or shift τ$\tau$ to which the needed interior eigenvalues are close, the desired interior eigenpairs are the eigenvalues nearest τ$\tau$ and the associated eigenvectors. However, it has been shown that the harmonic Ritz vectors may converge erratically and even may fail to do so. To do a better job, a modified harmonic block Arnoldi method is coined that replaces the harmonic Ritz vectors by some modified harmonic Ritz vectors. The relationships between the modified harmonic block Arnoldi method and the original one are analyzed. Moreover, how to adaptively adjust shifts during iterations so as to improve convergence is also discussed. Numerical results on the efficiency of the new algorithm are reported.     

Ritz and harmonic Ritz values and the convergence of FOM and GMRES

The Ritz and harmonic Ritz values are approximate eigenvalues, which can be computed cheaply within the FOM and GMRES Krylov subspace iterative methods for solving non‐symmetric linear systems. They are also the zeros of the residual polynomials of FOM and GMRES, respectively. In this paper we show that the Walker–Zhou interpretation of GMRES enables us to formulate the relation between the harmonic Ritz values and GMRES in the same way as the relation between the Ritz values and FOM. We present an upper bound for the norm of the difference between the matrices from which the Ritz and harmonic Ritz values are computed. The differences between the Ritz and harmonic Ritz values enable us to describe the breakdown of FOM and stagnation of GMRES. Copyright © 1999 John Wiley & Sons, Ltd.     

A framework of the harmonic Arnoldi method for evaluating <Emphasis Type="Italic">φ</Emphasis>-functions with applications to exponential integrators

In recent years, a great deal of attention has been focused on exponential integrators. The important ingredient to the implementation of exponential integrators is the efficient and accurate evaluation of the so called φ-functions on a given vector. The Krylov subspace method is an important technique for this problem. For this type of method, however, restarts become essential for the sake of storage requirements or due to computational complexities of evaluating matrix function on a reduced matrix of growing size. Another problem in computing φ-functions is the lack of a clear residual notion. The contribution of this work is threefold. First, we introduce a framework of the harmonic Arnoldi method for φ-functions, which is based on the residual and the oblique projection technique. Second, we establish the relationship between the harmonic Arnoldi approximation and the Arnoldi approximation, and compare the harmonic Arnoldi method and the Arnoldi method from a theoretical point of view. Third, we apply the thick-restarting strategy to the harmonic Arnoldi method, and propose a thick-restarted harmonic Arnoldi algorithm for evaluating φ-functions. An advantage of the new algorithm is that we can compute several φ-functions simultaneously in the same search subspace after restarting. The relationship between the error and the residual of the harmonic Arnoldi approximation is also investigated. Numerical experiments show the superiority of our new algorithm over many state-of-the-art algorithms for computing φ-functions.     

A refined iterative algorithm based on the block arnoldi process for large unsymmetric eigenproblems

When the matrix in question is unsymmetric, the approximate eigenvectors or Ritz vectors obtained by orthogonal projection methods including Arnoldi's method and the block Arnoldi method cannot be guaranteed to converge in theory even if the corresponding approximate eigenvalues or Ritz values do. In order to circumvent this danger, a new strategy has been proposed by the author for Arnoldi's method. The strategy used is generalized to the block Arnoldi method in this paper. It discards Ritz vectors and instead computes refined approximate eigenvectors by small-sized singular-value decompositions. It is proved that the new strategy can guarantee the convergence of refined approximate eigenvectors if the corresponding Ritz values do. The resulting refined iterative algorithm is realized by the block Arnoldi process. Numerical experiments show that the refined algorithm is much more efficient than the iterative block Arnoldi algorithm.     

The Rayleigh-Ritz method, refinement and Arnoldi process for periodic matrix pairs

We extend the Rayleigh-Ritz method to the eigen-problem of periodic matrix pairs. Assuming that the deviations of the desired periodic eigenvectors from the corresponding periodic subspaces tend to zero, we show that there exist periodic Ritz values that converge to the desired periodic eigenvalues unconditionally, yet the periodic Ritz vectors may fail to converge. To overcome this potential problem, we minimize residuals formed with periodic Ritz values to produce the refined periodic Ritz vectors, which converge under the same assumption. These results generalize the corresponding well-known ones for Rayleigh-Ritz approximations and their refinement for non-periodic eigen-problems. In addition, we consider a periodic Arnoldi process which is particularly efficient when coupled with the Rayleigh-Ritz method with refinement. The numerical results illustrate that the refinement procedure produces excellent approximations to the original periodic eigenvectors.     

Harmonic and refined Rayleigh–Ritz for the polynomial eigenvalue problem

After reviewing the harmonic Rayleigh–Ritz approach for the standard and generalized eigenvalue problem, we discuss several extraction processes for subspace methods for the polynomial eigenvalue problem. We generalize the harmonic and refined Rayleigh–Ritz approaches which lead to new approaches to extract promising approximate eigenpairs from a search space. We give theoretical as well as numerical results of the methods. In addition, we study the convergence of the Jacobi–Davidson method for polynomial eigenvalue problems with exact and inexact linear solves and discuss several algorithmic details. Copyright © 2008 John Wiley & Sons, Ltd.     

An Investigation of Restarted GMRES Method by Using Flexible Starting Vectors

<正>We discuss a variant of restarted GMRES method that allows changes of the restarting vector at each cycle of iterations.The merit of the variant is that previously generated information can be utilized to select a new starting vector,such that the occurrence of stagnation be mitigated or the convergence be accelerated.The more appealing utilization of the new method is in conjunction with a harmonic Ritz vector as the starting vector,which is discussed in detail.Numerical experiments are carried out to demonstrate that the proposed procedure can effectively mitigate the occurrence of stagnation due to the presence of small eigenvalues in modulus.     

The Use of Refined Harmonic Shifts in the Implicitly Restarted Refined Harmonic Arnoldi Method

This paper proposes a new shift scheme, called refined harmonic shifts, for use in the implicitly restarted refined harmonic Arnoldi method. Numerical experiments show that the implicitly restarted refined harmonic Arnoldi algorithm with refined harmonic shifts is better than the implicitly restarted harmonic Arnoldi algorithm with Morgan's harmonic shifts and the refined harmonic shifts are as efficient as Jia's refined shifts.