TOR,GAOR和GSAOR迭代法收敛准则

熟知,解线性方程组的TOR迭代法包括了Jacobi,Gauss-Seidel,SOR,AOR等迭代法.而GAOR和GSAOR迭代法则包括了GSOR,SSOR,SAOR,GSSOR和MSOR等迭代法。 本文给出了一些新的,易于检验的迭代法收敛准则,它能用来判别一类矩阵A之Jacobi矩阵B=I-D~(-1)A(或矩阵B=I-AD~(-1))的模B≥1,以及A为(行或列)弱对角占优矩阵     

Jacobi和Gauss-Seidel迭代法收敛性的判定

§1 引言 解线代数方程组 AX=b 的Jacobi迭代法和Gauss-Seidel迭代法收敛的充要条件是Jacobi迭代矩阵B=D(-1)(E F)的谱半径ρ(B)小于1,但验证这一充要条件需要求阵B的特征值,使用很不方便。因此促使人们去寻找使用方便、计算简单判定两迭代法收敛的充分条件。如大家所熟知,两迭代法收敛的一充分条件是:     

GSOR,GAOR,GSSOR和GSAOR

M.M.Martins于1986年提出了解线性代数方程组的MSOR方法,其实这种方法就是[2]中GAOR方法的特例,而且在[2]中还讨论了GSAOR方法,收敛性条件只含Jacobi迭代矩阵的谱半径,不含方程组的系数,特别是建立了GAOR或GSAOR收敛和方程组系数A为H阵的等价性,故所得结果比较好.又[1]中的定理1也是[4]中一个     

求解混合型Lyapunov矩阵方程的参数迭代法

采用参数迭代法求一类混合型Lyapunov矩阵方程A~TX XA B~TXB=C的对称解.在方程相容的条件下,给出了迭代法收敛的充要条件和一些充分条件,以及参数的选取方法.最后,利用数值算例对有关结果进行了验证.     

H-矩阵非线性互补问题基于模的矩阵分裂迭代法改进的收敛性定理北大核心CSCD

该文在较弱的条件下,证明了解一类H-矩阵非线性互补问题基于模的矩阵分裂迭代法和相应的加速迭代法的收敛性定理.这意味着对于分裂A=M-N有更多的选择,使得基于模的矩阵分裂迭代法得以收敛.改进的收敛性定理扩展了基于模的矩阵分裂迭代法的应用范围.     

契比雪夫半迭代法的收敛性

其中,G是一个依赖于A的N阶矩阵,h是一个依赖于A和b的N阶向量。 方法(2)收敛的充要条件是迭代矩阵G的谱半径小于1。这个结论适合于任一线性定常迭代方法。但对非定常迭代方法,收敛性问题比较复杂,一般很难运用谱半径进行收敛性分析,Young给出的一个例子(见[1pp.298])便说明了这一点。 然而,对一种特殊的非定常迭代方法——契比雪夫半迭代法(下文简称CSI方法),却可以提供基于谱半径的收敛性条件。这正是本文的核心内容。     

关于PSD迭代法收敛的充分必要性定理

本文在线性方程组系数矩阵A为相容次序矩阵及A的Jacobi迭代矩阵的特征值μ_j均为实数且μ_j~2<1的条件下,得出了PSD迭代法收敛的充分必要性定理,并由此而得到了一个易于判别的PSD法收敛性定理。     

关于USSOR迭代法收敛的充要条件和最佳松弛因子

本文在线性方程组系数矩阵A为相容次序矩阵及A的Jacobi迭代矩阵的特征值μj均为实数的条件下,得出了USSOR迭代法收敛的充分必要性定理.并给出了USSOR迭代矩阵之谱半径ρ(ψω,-ω)的表达式及ρ(ψω,-ω)的最佳松弛因子.     

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．     

相容次序矩阵PSD迭代法的收敛性

本文是在求解大型线性方程组Ax=b的系数矩阵A为(1,1)相容次序矩阵且其Jacobi迭代矩阵的特征值均为纯虚数或零的条件下,得到PSD迭代法收敛的充分必要性定理,并在特殊情况下得到了相应的最优参数.     

求解加权线性最小二乘问题的一类预处理GAOR方法

为了快速求解一类来自加权线性最小二乘问题的2×2块线性系统,本文提出一类新的预处理子用以加速GAOR方法,也就是新的预处理GAOR方法.得到了一些比较结果,这些结果表明当GAOR方法收敛时,新方法比原GAOR方法和之前的一些预处理GAOR方法有更好的收敛性.而且,数值算例也验证了新预处理子的有效性．     

Preconditioned GAOR methods for solving weighted linear least squares problems

In this paper, we present the preconditioned generalized accelerated overrelaxation (GAOR) method for solving linear systems based on a class of weighted linear least square problems. Two kinds of preconditioning are proposed, and each one contains three preconditioners. We compare the spectral radii of the iteration matrices of the preconditioned and the original methods. The comparison results show that the convergence rate of the preconditioned GAOR methods is indeed better than the rate of the original method, whenever the original method is convergent. Finally, a numerical example is presented in order to confirm these theoretical results.     

求解加权线性最小二乘问题的预处理迭代方法

给出了求解一类加权线性最小二乘问题的预处理迭代方法,也就是预处理的广义加速超松弛方法（GAOR）,得到了一些收敛和比较结果．比较结果表明当原来的迭代方法收敛时,预处理迭代方法会比原来的方法具有更好的收敛率．而且,通过数值算例也验证了新预处理迭代方法的有效性．     

Convergence of generalized AOR iterative method for linear systems with strictly diagonally dominant matrices

In this paper, some improvements on Darvishi and Hessari [On convergence of the generalized AOR method for linear systems with diagonally dominant coefficient matrices, Appl. Math. Comput. 176 (2006) 128–133] are presented for bounds of the spectral radius of _lω,r$l_{\omega ,r}$, which is the iterative matrix of the generalized AOR (GAOR) method. Subsequently, some new sufficient conditions for convergence of GAOR method will be given, which improve some results of Darvishi and Hessari [On convergence of the generalized AOR method for linear systems with diagonally dominant coefficient matrices, Appl. Math. Comput. 176 (2006) 128–133].     

解线性区间方程组的并行多分裂GAOR方法

本文引入区间三角多分裂来包含集合Ｓ＝｛Ａ－１ｂ｜Ａ∈Ｅ［Ａ］，ｂ∈［ｂ］｝，给出解区间线性方程组的并行多分裂ＧＡＯＲ方法，讨论方法的收敛性、收敛速度以及其极限包含集合Ｓ的性质．     

关于“求解加权线性最小二乘问题的一类预处理GAOR方法”一文的注记

在"求解加权线性最小二乘问题的一类预处理GAOR方法"一文中,作者提出了求解加权线性最小二乘问题等价$2\times 2$块线性系统的一类预处理GAOR方法,并给出了几个比较定理来说明新提出预处理GAOR方法的优越性.本文我们将指出该文中几个比较定理的不完善之处和证明的错误之处,并给出正确的证明.     

On the Convergence Regions of Generalized Accelerated Overrelaxation Method for Linear Complementarity Problems

In this paper, we use a generalized Accelerated Overrelaxation (GAOR) method and analyze the convergence of this method for solving linear complementarity problems. Furthermore, we improve on the convergence region of this method with acknowledgement of the maximum norm. A numerical example is also given, to illustrate the efficiency of our results.     

Two class of synchronous matrix multisplitting schemes for solving linear complementarity problems

Many problems in the areas of scientific computing and engineering applications can lead to the solution of the linear complementarity problem LCP (M,q). It is well known that the matrix multisplitting methods have been found very useful for solving LCP (M,q). In this article, by applying the generalized accelerated overrelaxation (GAOR) and the symmetric successive overrelaxation (SSOR) techniques, we introduce two class of synchronous matrix multisplitting methods to solve LCP (M,q). Convergence results for these two methods are presented when M is an H-matrix (and also an M-matrix). Also the monotone convergence of the new methods is established. Finally, the numerical results show that the introduced methods are effective for solving the large and sparse linear complementary problems.     

Convergence of GAOR method for doubly diagonally dominant matrices

In this paper, we obtain bounds for the spectral radius of the matrix l_ω,r which is the iterative matrix of the generalized accelerated overrelaxation (GAOR) iterative method. Moreover, we present one convergence theorem of the GAOR method. Finally, we present two numerical examples.     

Convergence analysis of time-point relaxation iterates for linear systems of differential equations

We study convergence properties of time-point relaxation (TR) Runge-Kutta methods for linear systems of ordinary differential equations. TR methods are implemented by decoupling systems in Gauss-Jacobi, Gauss-Seidel and successive overrelaxation modes (continuous-time iterations) and then solving the resulting subsystems by means of continuous extensions of Runge-Kutta (CRK) methods (discretized iterations). By iterating to convergence, these methods tend to the same limit called diagonally split Runge-Kutta (DSRK) method. We prove that TR methods are equivalent to decouple in the same modes the linear algebraic system obtained by applying DSRK limit method. This issue allows us to study the convergence of TR methods by using standard principles of convergence of iterative methods for linear algebraic systems. For a particular problem regions of convergence are plotted.