计算实对称矩阵广义特征值问题的并行算法

矩阵广义特征值问题是科学计算与工程应用中的一个重要的研究课题。文章探讨了近年来计算对称矩阵广义特征值问题的并行算法,并着重介绍了二分法、分治算法、同伦连续法和迭代算法。     

求解对称带状广义特征值问题的扩展分治算法

提出了分布式环境下计算对称带状广义特征值问题的一种扩展分治算法,给出了特征值分割定理及其证明．算法在扩展分治的基础上,利用二分压缩结合广义Rayleigh商迭代计算广义特征对．理论分析和数值实验表明,对于窄带宽大规模的广义特征值问题,该分治算法明显优于LAPACK软件包．结合并行性好的多分法,在分布式环境下获得了很好的并行效果．     

用神经网络计算矩阵特征值与特征向量

该文研究用神经网格求解一般实对称矩阵的全部特征向量的问题。详细讨论了网络的平均态度合的结构并建立了平衡态集合的构造定理。通过求解简单的一维微分方程求出了网络的解析表达式。这一表达式是由对称矩阵的特征值与特征向量表达的、因而非常清晰利用解的解析表达式分析了网络的解的全局渐近行为。提出了用一些单位向量作为网络初始值计算对称矩阵的全部特征值与特征向量的具体算法。     

由特征值构造实双对称矩阵

本文提出了实双对称矩阵的中心主子矩阵的概念,并且证明了存在一个实双对称矩阵,其各阶中心主子矩阵具有指定的特征值．文中提供了构造矩阵的算法,数值例子显示该算法是有效的．     

求实对称矩阵部分特征值的并行算法

提出了并行求解实对称稠密矩阵部分特征值的反幂法的预处理方法.该方法基于带状矩阵特征问题反幂法的信息传递复杂度低的特点,采用Householder变换并行算法约化大型实对称稠密矩阵为一定带宽的带状矩阵,针对带状矩阵用反幂法求解矩阵的在某一点的近似特征值;其中针对反幂法迭代中遇到的线性方程组,采用文献中的并行预处理共轭梯度算法求解.最后在Lenovo深腾1800集群上进行数值实验,并与预处理前反幂法的计算结果进行了比较,实验结果表明,经过预处理后的并行性远高于直接采用反幂法的并行性.     

用割线法迭代求解对称三对角矩阵特征值问题

为对称三对角矩阵特征值问题，提出一种新的分而治之的算法。新算法以二分法，割线法迭代为基础，不同于Ｃｕｐｐｅｎ的方法和Ｌａｎｇｕｅｒｒｅ迭代法。理论分析和数据实验的结果表明：新算法的收敛速度明显比文［１］中的Ｌａｇｕｅｒｒｅ迭代法快。     

矩阵多项式特征值的计算

提出计算矩阵多项式特征值的两种方法,搜索法和逐步递推求出全部特征值法,所得结果为研究线性多变量系统动态特性提供了一种有效的工程计算方法。     

基于LDLT分解求实对称矩阵特征值的递归算法

基于线性代数与矩阵理论,给出利用LDLT分解计算实对称矩阵特征值的递归算法。该算法可求出实对称矩阵在给定区间内的特征值的个数,并可计算满足精度要求的特征值。理论分析和实际测试证明该算法是有效的。     

基于两步对角化的对称稠密矩阵特征值问题快速求解算法

计算对称矩阵中的某些特定的特征值和特征向量问题是很多科学计算领域中都存在的重要课题。特别在电子结构的计算中,特征值计算成为计算瓶颈。以往在需要求解大部分特征值和特征向量的应用场合,一般使用直接求解的方式。为了更好地利用存储器性能优势,我们设计了对角化算法,对规约与逆变换过程进行拆分处理,通过对整个过程的重新设计,充分利用存储器结构上的优势,提升单核计算速度,同时改进并行效率。本文中我们重点讨论三对角矩阵到带状矩阵逆变换过程。本文中所提及到的算法应用于MESIA电子结构计算软件包之中,取得了一定的性能提升。     

一种计算矩阵特征值特征向量的神经网络方法

当把Oja学习规则描述的连续型全反馈神经网络(Oja-N)用于求解矩阵特征值特征向量时,网络初始向量需位于单位超球面上,这给应用带来不便.由此,提出一种求解矩阵特征值特征向量的神经网络(1yNN)方法.在lyNN解析解基础上得到了以下结果:初始向量属于任意特征值对应特征向量张成的子空间,则网络平衡向量也将属于该空间;分析了lyNN收敛于矩阵最大特征值对应特征向量的初始向量取值条件;明确了lyNN收敛于矩阵不同特征值的特征子空间时,网络初始向量的最大取值空间;网络初始向量与已知特征向量垂直,则lyNN平衡解向量将垂直于该特征向量;证明了平衡解向量位于由非零初始向量确定的超球面上的结论.基于以上分析,设计了用lyNN求矩阵特征值特征向量的具体算法,实例演算验证了该算法的有效性.1yNN不出现有限溢,而基于Oja-N的方法在矩阵负定、初始向量位于单位超球面外时必出现有限溢,算法失效.与基于优化的方法相比,lyNN实现容易,计算量较小.     

Simultaneous band reduction of two symmetric matrices

In this paper, we consider simultaneous band reduction of two dense symmetric matrices by congruent transformations. The ideas of simultaneous tridiagonalization are generalized to propose an efficient algorithm for the simultaneous band reduction. In contrast to the algorithms of simultaneous tridiagonalization which are mainly based on matrix–vector operations, the proposed algorithm of simultaneous band reduction has the advantage that matrix–matrix operations can be fully used to achieve better performance on modern computer architecture. Numerical results are presented to illustrate the effectiveness of our proposed algorithm.     

A guided genetic algorithm for diagonalization of symmetric and Hermitian matrices

The eigenvalues and eigenvectors of a matrix have many applications in engineering and science, such us studying and solving structural problems in both the treatment of signal or image processing, and the study of quantum mechanics. One of the most important aspects of an algorithm is the speed of execution, especially when it is used in large arrays. For this reason, in this paper the authors propose a new methodology using a genetic algorithm to compute all the eigenvectors and eigenvalues in real symmetric and Hermitian matrices. The algorithm uses a general-purpose library developed by the authors for genetic algorithms (GALGA). The speed of execution and the influence of population size have been studied. Moreover, the algorithm has been tested in different matrices and population sizes by comparing the speed of execution to the number of the eigenvectors. This new methodology is faster than the previous algorithm developed by the authors and all eigenvectors can be obtained with it. In addition, the performance using the Coope matrix has been tested contrasting the results with another technique published in the scientific literature.     

A comparison of two parallel algorithms for the symmetric eigenproblem

Two algorithms for the eigensolution of real symmetric matrices [8] are discussed. The first is a variant of the classical Jacobi method and the second is also based on orthogonal transforms. Both algorithms may be readily expressed in forms which allow the power of SIMD (single instruction multiple data) machines to be exploited. The performances of the algorithms on the ICL DAP (distributed array processor) and the CRAY-1 are compared.     

A uniform object-oriented solution to the eigenvalue problem for real symmetric and Hermitian matrices

We present a system of classes, SHMatrix, to deal in a unified way with the computation of eigenvalues and eigenvectors in real symmetric and Hermitian matrices. Thus, two descendant classes, one for the real symmetric and other for the Hermitian cases, override the abstract methods defined in a base class. The use of the inheritance relationship and polymorphism allows handling objects of any descendant class using a single reference of the base class. The system of classes is intended to be the core element of more sophisticated methods to deal with large eigenvalue problems, as those arising in the variational treatment of realistic quantum mechanical problems. The present system of classes allows computing a subset of all the possible eigenvalues and, optionally, the corresponding eigenvectors. Comparison with well established solutions for analogous eigenvalue problems, as those included in LAPACK, shows that the present solution is competitive against them.

Program summary

Program title: SHMatrixCatalogue identifier: AEHZ_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEHZ_v1_0.htmlProgram obtainable from: CPC Program Library, Queen?s University, Belfast, N. IrelandLicensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 2616No. of bytes in distributed program, including test data, etc.: 127 312Distribution format: tar.gzProgramming language: Standard ANSI C++.Computer: PCs and workstations.Operating system: Linux, Windows.Classification: 4.8.Nature of problem: The treatment of problems involving eigensystems is a central topic in the quantum mechanical field. Here, the use of the variational approach leads to the computation of eigenvalues and eigenvectors of real symmetric and Hermitian Hamiltonian matrices. Realistic models with several degrees of freedom leads to large (sometimes very large) matrices. Different techniques, such as divide and conquer, can be used to factorize the matrices in order to apply a parallel computing approach. However, it is still interesting to have a core procedure able to tackle the computation of eigenvalues and eigenvectors once the matrix has been factorized to pieces of enough small size. Several available software packages, such as LAPACK, tackled this problem under the traditional imperative programming paradigm. In order to ease the modelling of complex quantum mechanical models it could be interesting to apply an object-oriented approach to the treatment of the eigenproblem. This approach offers the advantage of a single, uniform treatment for the real symmetric and Hermitian cases.Solution method: To reach the above goals, we have developed a system of classes: SHMatrix. SHMatrix is composed by an abstract base class and two descendant classes, one for real symmetric matrices and the other for the Hermitian case. The object-oriented characteristics of inheritance and polymorphism allows handling both cases using a single reference of the base class. The basic computing strategy applied in SHMatrix allows computing subsets of eigenvalues and (optionally) eigenvectors. The tests performed show that SHMatrix is competitive, and more efficient for large matrices, than the equivalent routines of the LAPACK package.Running time: The examples included in the distribution take only a couple of seconds to run.     

Parallel computation of the eigenvalues of symmetric Toeplitz matrices through iterative methods

This paper presents a new procedure to compute many or all of the eigenvalues and eigenvectors of symmetric Toeplitz matrices. The key to this algorithm is the use of the “Shift–and–Invert” technique applied with iterative methods, which allows the computation of the eigenvalues close to a given real number (the “shift”). Given an interval containing all the desired eigenvalues, this large interval can be divided in small intervals. Then, the “Shift–and–Invert” version of an iterative method (Lanczos method, in this paper) can be applied to each subinterval. Since the extraction of the eigenvalues of each subinterval is independent from the other subintervals, this method is highly suitable for implementation in parallel computers. This technique has been adapted to symmetric Toeplitz problems, using the symmetry exploiting Lanczos process proposed by Voss [H. Voss, A symmetry exploiting Lanczos method for symmetric Toeplitz matrices, Numerical Algorithms 25 (2000) 377–385] and using fast solvers for the Toeplitz linear systems that must be solved in each Lanczos iteration. The method compares favourably with ScaLAPACK routines, specially when not all the spectrum must be computed.     

Two algorithms for the parallel computation of eigenvalues and eigenvectors of large symmetric matrices using the ICL DAP

The implementation and evaluation of the performances on the ICL DAP of two algorithms for the parallel computation of eigenvalues and eigenvectors of moderately large real symmetric matrices of order N, where 64 < N 256, is reported. The first of the algorithms is a modified form of a Parallel Orthogonal Transformation algorithm proposed by Clint et al., which has already been implemented on the DAP for matrices of order N, where N < 65. The second, which has also been implemented on the DAP for matrices of order N, where N < 65, is Jacobi's algorithm, in the modified form proposed by Modi and Pryce. A comparison of the efficiency of the two algorithms for the solution of a variety of large matrices is given.     

An optimal algorithm for Gaussian elimination of band matrices on an MIMD computer

This paper describes a parallel algorithm for the LU decomposition of band matrices using Gaussian elimination. The matrix dimension is n × n with 2r−1 diagonals. In the case when 1 r 2 p an optimal number of the processors, , is determined according to the equation . When 2 p r n a number of processors, p, statged by Veldhorst is adopted (see [7]). For band matrix with 2r-1 diagonals (1 r 2p) the task scheduling procedure with the aim to obtain maximal parallelism in system operation, i.e. good load balancing, is defined. The architecture of the system is of MIMD type. The connection between the processors is realised via a common bus. Communication and synchronization is performed by message passing technique.     

An efficient two-step iterative method for solving a class of complex symmetric linear systems

In this paper, a new two-step iterative method called the two-step parameterized (TSP) iteration method for a class of complex symmetric linear systems is developed. We investigate its convergence conditions and derive the quasi-optimal parameters which minimize the upper bound of the spectral radius of the iteration matrix of the TSP iteration method. Meanwhile, some more practical ways to choose iteration parameters for the TSP iteration method are proposed. Furthermore, comparisons of the TSP iteration method with some existing ones are given, which show that the upper bound of the spectral radius of the TSP iteration method is smaller than those of the modified Hermitian and skew-Hermitian splitting (MHSS), the preconditioned MHSS (PMHSS), the combination method of real part and imaginary part (CRI) and the parameterized variant of the fixed-point iteration adding the asymmetric error (PFPAE) iteration methods proposed recently. Inexact version of the TSP iteration (ITSP) method and its convergence properties are also presented. Numerical experiments demonstrate that both TSP and ITSP are effective and robust when they are used either as linear solvers or as matrix splitting preconditioners for the Krylov subspace iteration methods and they have comparable advantages over some known ones for the complex symmetric linear systems.