一类预条件AOR迭代法的比较定理(英文)

本文研究了当线性方程组的系数矩阵是严格对角占优L-矩阵时带有预条件子P1→kα的预条件AOR迭代方法.利用矩阵分裂的相关理论,获得了预条件AOR迭代法的收敛性结论以及参数α和k对收敛速度影响的比较定理.结果表明当α和k取值较大时这类预条件方法更加有效.文中的结论推广了Li等人关于预条件Gauss-Seidel迭代法的相关结论.最后,用数值例子进一步验证了这些结果.     

H-矩阵方程组的预条件迭代法

A．D．Gunawardena等1991年提出的预条件矩阵为I S的预条件Gauss-Seidel方法的收敛率优于基本的迭代法．本文引入了预条件矩阵I Sαβ．证明了若系数矩阵A为H-矩阵,则[I Sαβ]A仍是H-矩阵．     

H-矩阵线性方程组的一类预条件并行多分裂SOR迭代法

基于并行多分裂算法的思想及SOR迭代格式, 本文提出一种求解H-矩阵线性方程组新的并行多分裂SOR迭代法, 新方法某种程度上避免了SOR迭代法中选取最优参数的困难. 同时, 选取Kohno等(1997)提出的预条件子$P=I+S_{\alpha}$对原始线性方程组进行预处理, 进而给出了一种实用的预条件并行多分裂SOR迭代法. 理论分析和数值实验均表明, 新算法是实用而有效的.     

‖B~(-1)A‖的估计及其应用

无论是求解线性方程组的点算法还是区间算法,能较好的估计‖B~(-1)A‖是十分有用的,然而按‖B~(-1)A‖≤‖B~(-1)‖‖A‖来估计又往往不能达到予期目的,为此本文应用[15]中关于区间对角占优矩阵的性质,对区间矩阵B,A给出了一类满足‖B~(-1)A‖<1的条件及判别方法,将这些结果应用到区间线性方程组的诸分裂求解方法如Jacobi、Gauss-Seidel、SOR及正则分裂方法中,不仅改进了已有结果,而且方法简单。     

解线性方程组的松弛方法及其应用

线性方程组在科学和工程领域中有着重要的应用,松弛方法是求解线性方程组的有效算法之一.本文在著名的Gauss-Seidel迭代法的基础上,研究了一种有效的松弛方法.理论分析表明,该方法能收敛到线性方程组的唯一解.此外,我们还将该方法应用在鞍点问题和PageRank问题的求解上,并得出了相应的数值结果.结果表明该方法比现有的松弛方法更有效.     

一类预条件AOR迭代法的收敛性分析

本文研究了线性方程组Ax=b的预条件迭代法.利用新的待定参数加速预条件子的方法,获得了一种带参数的新预条件迭代法,并对参数的选择给出必要条件,证明了对于非奇异不可约M-矩阵,新预条件方法收敛且可以加速AOR迭代法的收敛速度,数值例子表明新预条件方法是有效的,推广了已有文献中的有关结果.     

一类预条件AOR迭代法及其收敛性

给出了一类预条件的AOR迭代法及其收敛性,并给出了松驰因子ω与加速因子γ的选取对收敛速度的影响,同时通过数值实例验证了主要结果.     

解线性方程组迭代法的若干几何研究

从解线性方程组迭代法入手,提出了两个迭代法的基本几何过程,揭示了著名的Jacobi迭代法、Gauss-Seidel迭代法和SOR方法等迭代法的几何实质、重新认识了这些经典的迭代过程,同时揭示了解线性方程组的克兰姆法则与迭代法的关系.同时从几何出发设计了一种解线性方程组的迭代方法.     

求解H-矩阵线性方程组的几种预处理迭代法

在求解H-矩阵线性方程组预处理Gauss-Seidel迭代法的基础上,提出了一种渐变预处理技术,提高了H-阵线性方程组的求解效率,加快了Gauss-Seidel迭代法的收敛速度.同时,讨论了两种特殊形式的预处理子:上Hessenberg预处理矩阵和下Hessenberg预处理矩阵,并证明了算法的收敛性.最后用数值实验验证了算法的可行性及有效性.     

稀疏近似逆与多层块ILU预条件技术

设计了一种求解一般稀疏线性方程组的健壮且有效的可并行化预条件子,这种预条件子涉及在多层块ILU预条件子(BILUM)中使用稀疏近似逆(AINV)技术．所得的预条件子保持了BILUM的健壮性,它比标准的BILUM预条件子有两点优势:控制稀疏性的能力和增强了并行性．数值例子显示了新预条件子的有效性和效率．     

广义鞍点问题基于PSS的约束预条件子

对于(1,1)块为非Hermitian阵的广义鞍点问题,本文给出了一种基于正定和反对称分裂(Positive definite andskew-Hermitian splitting, PSS)的约束预条件子.该预条件子的(1,1)块由求解非Hermitian正定线性方程组时的PSS迭代法所构造得到.文中分析了PSS约束预条件子的一些性质并证明了预处理迭代法的收敛性.最后用数值算例验证了该预条件子的有效性.     

A SHIFT-SPLITTING PRECONDITIONER FOR NON-HERMITIAN POSITIVE DEFINITE MATRICES

A shift splitting concept is introduced and, correspondingly, a shift-splitting iteration scheme and a shift-splitting preconditioner are presented, for solving the large sparse system of linear equations of which the coefficient matrix is an ill-conditioned non-Hermitian positive definite matrix. The convergence property of the shift-splitting iteration method and the eigenvalue distribution of the shift-splitting preconditioned matrix are discussed in depth, and the best possible choice of the shift is investigated in detail. Numerical computations show that the shift-splitting preconditioner can induce accurate, robust and effective preconditioned Krylov subspace iteration methods for solving the large sparse non-Hermitian positive definite systems of linear equations.     

On a type of matrix splitting preconditioners for a class of block two-by-two linear systems

Recently, Bai et al. (2013) proposed an effective and efficient matrix splitting iterative method, called preconditioned modified Hermitian/skew-Hermitian splitting (PMHSS) iteration method, for two-by-two block linear systems of equations. The eigenvalue distribution of the iterative matrix suggests that the splitting matrix could be advantageously used as a preconditioner. In this study, the CGNR method is utilized for solving the PMHSS preconditioned linear systems, and the performance of the method is considered by estimating the condition number of the normal equations. Furthermore, the proposed method is compared with other PMHSS preconditioned Krylov subspace methods by solving linear systems arising in complex partial differential equations and a distributed control problem. The numerical results demonstrate the difference in the performance of the methods under consideration.     

A parallel block overlap preconditioning with inexact submatrix inversion for linear elasticity problems

We present a parallel preconditioned iterative solver for large sparse symmetric positive definite linear systems. The preconditioner is constructed as a proper combination of advanced preconditioning strategies. It can be formally seen as being of domain decomposition type with algebraically constructed overlap. Similar to the classical domain decomposition technique, inexact subdomain solvers are used, based on incomplete Cholesky factorization. The proper preconditioner is shown to be near optimal in minimizing the so‐called K‐condition number of the preconditioned matrix. The efficiency of both serial and parallel versions of the solution method is illustrated on a set of benchmark problems in linear elasticity. Copyright © 2002 John Wiley & Sons, Ltd.     

The effect of block red-black ordering on blockILU preconditioner for sparse matrices

It is well known that the ordering of the unknowns can have a significant effect on the convergence of a preconditioned iterative method and on its implementation on a parallel computer. To do so, we introduce a block red-black coloring to increase the degree of parallelism in the application of the blockILU preconditioner for solving sparse matrices, arising from convection-diffusion equations discretized using the finite difference scheme (five-point operator). We study the preconditioned PGMRES iterative method for solving these linear systems.     

Fast inexact subspace iteration for generalized eigenvalue problems with spectral transformation

We study inexact subspace iteration for solving generalized non-Hermitian eigenvalue problems with spectral transformation, with focus on a few strategies that help accelerate preconditioned iterative solution of the linear systems of equations arising in this context. We provide new insights into a special type of preconditioner with “tuning” that has been studied for this algorithm applied to standard eigenvalue problems. Specifically, we propose an alternative way to use the tuned preconditioner to achieve similar performance for generalized problems, and we show that these performance improvements can also be obtained by solving an inexpensive least squares problem. In addition, we show that the cost of iterative solution of the linear systems can be further reduced by using deflation of converged Schur vectors, special starting vectors constructed from previously solved linear systems, and iterative linear solvers with subspace recycling. The effectiveness of these techniques is demonstrated by numerical experiments.     

解线性互补问题的预处理加速模Gauss-Seidel迭代方法

本文提出了解线性互补问题的预处理加速模系Gauss-Seidel迭代方法,当线性互补问题的系统矩阵是M-矩阵时证明了方法的收敛性,并给出了该预处理方法关于原方法的一个比较定理.数值实验显示该预处理迭代方法明显加速了原方法的收敛.     

Using a hybrid preconditioner for solving large-scale linear systems arising from interior point methods

We devise a hybrid approach for solving linear systems arising from interior point methods applied to linear programming problems. These systems are solved by preconditioned conjugate gradient method that works in two phases. During phase I it uses a kind of incomplete Cholesky preconditioner such that fill-in can be controlled in terms of available memory. As the optimal solution of the problem is approached, the linear systems becomes highly ill-conditioned and the method changes to phase II. In this phase a preconditioner based on the LU factorization is found to work better near a solution of the LP problem. The numerical experiments reveal that the iterative hybrid approach works better than Cholesky factorization on some classes of large-scale problems.     

Strang-Type Preconditioners for Solving Linear Systems from Delay Differential Equations

We consider the solution of delay differential equations (DDEs) by using boundary value methods (BVMs). These methods require the solution of one or more nonsymmetric, large and sparse linear systems. The GMRES method with the Strang-type block-circulant preconditioner is proposed for solving these linear systems. We show that if a P _{k 1},k ₂-stable BVM is used for solving an m-by-m system of DDEs, then our preconditioner is invertible and all the eigenvalues of the preconditioned system are clustered around 1. It follows that when the GMRES method is applied to solving the preconditioned systems, the method may converge fast. Numerical results are given to illustrate the effectiveness of our methods.     

Suitable iterative methods for solving the linear system arising in the three fields domain decomposition method

There are two approaches for applying substructuring preconditioner for the linear system corresponding to the discrete Steklov–Poincaré operator arising in the three fields domain decomposition method for elliptic problems. One of them is to apply the preconditioner in a common way, i.e. using an iterative method such as preconditioned conjugate gradient method [S. Bertoluzza, Substructuring preconditioners for the three fields domain decomposition method, I.A.N.-C.N.R, 2000] and the other one is to apply iterative methods like for instance bi-conjugate gradient method, conjugate gradient square and etc. which are efficient for nonsymmetric systems (the preconditioned system will be nonsymmetric). In this paper, second approach will be followed and extensive numerical tests will be presented which imply that the considered iterative methods are efficient.