高阶PageRank问题的一个两步分裂迭代算法

在一般PageRank问题的基础上,Gleich等结合了马尔科夫链的性质提出了高阶PageRank问题.基于Gleich等提出的几个算法,结合两步分裂迭代的思想提出了解高阶PageRank问题的一个两步分裂迭代算法.该算法能增加收敛的范围,并且减少算法的迭代步数.     

求解PageRank问题的多步幂法修正的内外迭代法

引用两种加速计算PageRank的算法,分别为内外迭代法和两步分裂迭代算法.从这两种方法中,得到多步幂法修正的内外迭代方法.首先,详细介绍了算法实施过程.然后,对此算法的收敛性进行证明,并且将此算法的谱半径与两步分裂迭代算法的谱半径进行比较.最后,数值试验说明该算法的计算速度比两步分裂迭代法要快.     

求解PageRank问题改进的多分裂迭代法

引用两种加速计算PageRank的算法,分别为内外迭代法和多分裂迭代算法.从这两种方法中,得到改进的多分裂迭代方法.首先,详细介绍了算法实施过程.然后,对此算法的收敛性进行证明,并且将此算法的谱半径与原有的多分裂迭代算法的谱半径进行比较.最后,数值实验说明我们的算法的计算速度比原有的多分裂迭代法要快.     

基于GMRES的多项式预处理广义极小残差法

求解大型稀疏线性方程组一般采用迭代法,其中GMRES(m)算法是一种非常有效的算法,然而该算法在解方程组时,可能发生停滞．为了克服算法GMRES(m)解线性系统Ax=b过程中可能出现的收敛缓慢或不收敛,本文利用GMRES本身构造出一种有效的多项式预处理因子pk(z),该多项式预处理因子非常简单且易于实现．数值试验表明,新算法POLYGMRES(m)较好地克服了GMRES(m)的缺陷．     

简化全局GMRES算法的扩张及收缩

简化的全局GMRES算法作为求解多右端项线性方程组的方法之一,与标准的全局GMRES算法相比,需要较少的计算量,但对应的重启动方法由于矩阵Krylov子空间维数的限制,收敛会较慢.基于调和Ritz矩阵,提出了简化全局GMRES的扩张及收缩算法.数值实验结果表明,新提出的扩张及收缩算法比标准的全局GMRES算法更为快速高效.     

预条件广义极小残余新算法

研究Krylov子空间广义极小残余算法(GMRES(m))的基本理论，给出GMRES(m)算法透代求解所满足的代数方程组．深入探讨算法的收敛性与方程组系数矩阵的密切关系，提出一种改进GMRES(m)算法收敛性的新的预条件方法，并作出相关论证．     

简化块GMRES的稳定算法

块GMRES算法是求解多右端项线性方程组的经典算法.基于迭代过程中的迭代残量,提出一种基于残量的简化块GMRES算法,有效避免经典算法中块上Hessenberg阵的QR约化过程,比文献(Liu H,Zhong B.Simpler block GMRES for nonsymmetric systems with multiple right-hand sides.Electronic Transactions on Numerical Analysis,2008,30:1-9)提出的简化算法有更好的收敛精度和稳定性.     

求解大型非对称线性方程组的不完全广义最小向后扰动法

本文给出了求解大型非对称线性方程组的广义最小向后扰动法(GMBACK)的截断版本——不完全广义最小向后扰动法(IGMBACK).该方法基于Krylov向量的不完全正交化,从而在Krylov子空间上求出一个近似的或者拟最小向后扰动解.本文对新算法IGMBACK做了一些理论研究,包括算法的有限终止、解的存在性和唯一性等方面的研究;且给出了IGMBACK的执行.数值实验表明:IGMBACK通常比GMBACK和广义最小残量法(GMRES)更有效;且IGMBACK和GMBACK经常比GMRES收敛得更好.特殊地,如果系数矩阵是敏感矩阵,且方程组右侧的向量平行于系数矩阵的最小奇异值对应的左奇异向量时,重新开始的GMRES不一定收敛,而IGMBACK和GMBACK一般收敛,且比GMRES收敛得更好.     

M/M/c休假排队系统稳态分布的数值计算

从数值计算角度研究M/M/c休假排队系统稳定状态的概率分布.采用GMRES方法求解概率分布向量所满足的大型线性方程,构造了一个循环预处理算子加速GMRES方法的收敛.数值实例验证了该算法的优越性.     

广义鞍点问题的松弛维数分解预条件子

本文将Benzi等提出的松弛维数分解(Relaxed dimensionalfactorization, RDF)预条件子进一步推广到广义鞍点问题上,并称为GRDF(Generalized RDF)预条件子.该预条件子可看做是用维数分裂迭代法求解广义鞍点问题而导出的改进维数分裂(Modified dimensional split, MDS)预条件子的松弛形式, 它相比MDS预条件子更接近于系数矩阵, 因而结合Krylov子空间方法(如GMRES)有更快的收敛速度.文中分析了GRDF预处理矩阵特征值的一些性质,并用数值算例验证了新预条件子的有效性.     

Heavy Ball Flexible GMRES Method for Nonsymmetric Linear Systems

Flexible GMRES (FGMRES) is a variant of preconditioned GMRES, which changes preconditioners at every Arnoldi step. GMRES often has to be restarted in order to save storage and reduce orthogonalization cost in the Arnoldi process. Like restarted GMRES, FGMRES may also have to be restarted for the same reason. A major disadvantage of restarting is the loss of convergence speed. In this paper, we present a heavy ball flexible GMRES method, aiming to recoup some of the loss in convergence speed in the restarted flexible GMRES while keep the benefit of limiting memory usage and controlling orthogonalization cost. Numerical tests often demonstrate superior performance of the proposed heavy ball FGMRES to the restarted FGMRES.     

Preconditioning iterative algorithm for the electromagnetic scattering from a large cavity

A preconditioning iterative algorithm is proposed for solving electromagnetic scattering from an open cavity embedded in an infinite ground plane. In this iterative algorithm, a physical model with a vertically layered medium is employed as a preconditioner of the model of general media. A fast algorithm developed in (SIAM J. Sci. Comput. 2005; 27 :553–574) is applied for solving the model of layered media and classical Krylov subspace methods, restarted GMRES, COCG, and BiCGstab are employed for solving the preconditioned system. Our numerical experiments on cavity models with large numbers of mesh points and large wave numbers show that the algorithm is efficient and the number of iterations is independent of the number of mesh points and dependent upon the wave number. Copyright © 2008 John Wiley & Sons, Ltd.     

Generalizations and modifications of the GMRES iterative method

For solving nonsymmetric linear systems, the well-known GMRES method is considered to be a stable method; however, the work per iteration increases as the number of iterations increases. We consider two new iterative methods GGMRES and MGMRES, which are a generalization and a modification of the GMRES method, respectively. Instead of using a minimization condition as in the derivation of GGMRES, we use a Galerkin condition to derive the MGMRES method. We also introduce another new iterative method, LAN/MGMRES, which is designed to combine the reliability of GMRES with the reduced work of a Lanczos-type method. A computer program has been written based on the use of the LAN/MGMRES algorithm for solving nonsymmetric linear systems arising from certain elliptic problems. Numerical tests are presented comparing this algorithm with some other commonly used iterative algorithms. These preliminary tests of the LAN/MGMRES algorithm show that it is comparable in terms of both the approximate number of iterations and the overall convergence behavior. This revised version was published online in June 2006 with corrections to the Cover Date.     

An Arnoldi-Extrapolation algorithm for computing PageRank

The Arnoldi-type algorithm proposed by Golub and Greif [G. Golub, C. Greif, An Arnoldi-type algorithm for computing PageRank, BIT 46 (2006) 759-771] is a restarted Krylov subspace method for computing PageRank. However, this algorithm may not be efficient when the damping factor is high and the dimension of the search subspace is small. In this paper, we first develop an extrapolation method based on Ritz values. We then consider how to periodically knit this extrapolation method together with the Arnoldi-type algorithm. The resulting algorithm is the Arnoldi-Extrapolation algorithm. The convergence of the new algorithm is analyzed. Numerical experiments demonstrate the numerical behavior of this algorithm.     

CGS/GMRES(k): AN ADAPTIVE PRECONDITIONED CGS ALGORITHM FOR NONSYMMETRIC LINEAR SYSTEMS

Recently Y. Saad proposed a flexible inner-outer preconditioned GMRES algorithm for nonsymmetric linear systems [4]. Following their ideas, we suggest an adaptive preconditioned CGS method, called CGS/GMRES (k), in which the preconditioner is constructed in the iteration step of CGS, by several steps of GMRES(k). Numerical experiments show that the residual of the outer iteration decreases rapidly. We also found the interesting residual behaviour of GMRES for the skewsymmetric linear system Ax = b, which gives a convergence result for restarted GMRES (k). For convenience, we discuss real systems.     

一种灵活的混合GMRES算法

1　引　　言考虑线性方程组Ax =b (1 .1 )其中 A∈RN× N是非奇异的 .求解方程组 (1 .1 )的很多迭代方法都可归类于多项式法 ,即满足x(n) =x(0 ) +qn- 1 (A) r(0 ) ,degqn- 1 ≤ n -1这里 x(n) ,n≥ 0为第 n步迭代解 ,r(n) =b-Ax(n) 是对应的迭代残量 .等价地 ,r(n) =pn(A) r(0 ) ,degpn≤ n;pn(0 ) =1 (1 .2 )其中 pn(z) =1 -zqn- 1 (z)称为残量多项式 .或有r(n) -r(0 ) ∈ AKn(r(0 ) ,A)其中 Kn(v,A)≡span{ Aiv} n- 1 i=0 是对应于 v,A的 Krylov子空间 .对于非对称问题 ,可以用正交性条件r(n)⊥ AKn(r(0 ) ,A)来确定 (1 .2 )中的…     

Restarted GMRES preconditioned by deflation

This paper presents a new preconditioning technique for the restarted GMRES algorithm. It is based on an invariant subspace approximation which is updated at each cycle. Numerical examples show that this deflation technique gives a more robust scheme than the restarted algorithm, at a low cost of operations and memory.     

A new relaxed HSS preconditioner for saddle point problems

We present a preconditioner for saddle point problems. The proposed preconditioner is extracted from a stationary iterative method which is convergent under a mild condition. Some properties of the preconditioner as well as the eigenvalues distribution of the preconditioned matrix are presented. The preconditioned system is solved by a Krylov subspace method like restarted GMRES. Finally, some numerical experiments on test problems arisen from finite element discretization of the Stokes problem are given to show the effectiveness of the preconditioner.     

TOTAL GENERALIZED MINIMUM BACKWARD ERROR ALGORITHM FOR SOLVING NONSYMMETRIC LINEAR SYSTEMS

1.IntroductionAnimportantaspectofanyiterativemethodforapproximatingthesolutionofalinearsystemAx~b,(1.1)whereAisannxnrealnonsymmetricmatrixandbisann-vector,istodecideatwhatpointtostoptheiteration.Wecustomarilyusetheresidualerrorasastoppingcondition.Theresidualerrorre=b--AxmcanbeviewedasaperturbationtothevectorbsuchthattheapproximatesolutionisanexactsolutionoftheperturbedlinearsystemAx=b 5,inwhichchangesarepermittedtothevectorbonly.TheGMRESalgorithmisbasedonclassicalKrylovsubspacetechniquesa…