迭代求解复对称线性方程组的收敛性分析(英文)

本文提出求解系数矩阵不是埃尔米特但是对称复矩阵的线性方程组的一种分裂迭代法,详细讨论新方法的迭代矩阵的谱半径,最优参数选择,一些范数性质.证明在合理的假设下新方法是收敛的.最后以数值结果验证了新方法的有效性和可行性.     

求解一类非线性互补问题的松弛two-sweep模系矩阵分裂迭代法

本文构造了求解一类非线性互补问题的松弛two-sweep模系矩阵分裂迭代法. 理论分析建立了新方法在系数矩阵为正定矩阵或H₊矩阵时的收敛性质．数值实验结果表明新方法是行之有效的, 并且在最优参数下松弛two-sweep模系矩阵分裂迭代法在迭代步数和时间上均优于传统的模系矩阵分裂迭代法和two-sweep模系矩阵分裂迭代法.     

一类复对称线性方程组的单步HSS迭代法

基于修正的埃尔米特和反埃尔米特分裂(MHSS)及预处理的MHSS(PMHSS)迭代法,提出了关于一类复对称线性方程组的单步MHSS(SMHSS)和单步PMHSS(SPMHSS)迭代法,进一步利用优化技巧给出了位移参数的动态选择格式,得出相应的带有灵活位移的SMHSS方法及SPMHSS迭代法.理论分析表明,迭代参数α在较弱的约束条件下,SMHSS迭代法收敛于复对称线性方程组的唯一解.同时,得到了SMHSS迭代矩阵的谱半径的上界,并且求得使上述上界最小的最优参数α~*.进一步给出了SPMHSS方法的收敛性分析.MHSS法和SMHSS迭代法之间的数值比较表明,在某些情况下,SMHSS迭代法比MHSS迭代法更优.     

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.     

PSD方法的最优因子及效率分析

在系数矩阵是相容序2循环阵的情况下，本文给出了PSD方法的最优松弛参数和最优收敛因子，分析和讨论了它的实用性，并进而得到了一个新的迭代法，它的最优收敛因子与PSD方法一样，而迭代参数却只有一个．     

块二级迭代法的近似最优内迭代次数

本文讨论线性方程组定常块二级迭代法内迭代次数的选择.对于单调矩阵,证明了块Jacobi矩阵的谱半径ρp(T)为非定常块二级迭代法R_1-因子的下界.对于M-矩阵,用某个单调范数给出了ρ(T_p)的关于p单调下降且收敛于ρ(T)的上界.于是,当系数矩阵为M-矩阵时,我们定义了定常块二级迭代法的近似最优内迭代次数.所定义的近似最优值与模型问题数值计算的实际最优值非常吻合.本文分析表明,实际计算中应该把内迭代次数控制在较小的数目.     

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.     

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.     

外推的MHSS迭代法求解一类大型稀疏的复对称线性系统

本文对改良的Hermitian和反Hermitian分裂迭代方法 (MHSS)使用了外推技术,构造了外推的MHSS(EMHSS)迭代法.从理论上给出了EMHSS迭代方法的迭代矩阵与MHSS迭代方法的迭代矩阵之间的关系,并讨论了EMHSS迭代方法的收敛条件.最后用数值实验验证了所提方法的有效性.     

线性方程组迭代法的几何解释

以2阶矩阵为例,对线性方程组的迭代法进行了深入分析.以矩阵的特征值与谱半径作为分类原则,对迭代法的收敛或发散从几何上进行了解释,旨在加深学生从几何上理解线性方程组的迭代法.     

求解2×2块对称不定线性方程组迭代法的收敛性分析(英文)

本文研究求解系数矩阵为2×2块对称不定矩阵时的线性方程组,提出了一种新的分裂迭代法,并通过研究迭代矩阵的谱半径,详细讨论了新方法的收敛性.最后,我们也讨论了预条件矩阵特征根的几条性质.     

On the spectral radius of the Jacobi iteration matrix for a rectangular region with two different media

The spectral radius of the Jacobi iteration matrix plays an important role to estimate the optimum relaxation factor, when the successive overrelaxation (SOR) method is used for solving a linear system. The specific systems are finite difference forms of the Laplace equation satisfied on a rectanglar region with two different media. Though the potential function for the inhomogeneous closed region is continuous, the first order derivative is not continuous. So this requires internal boundary conditions or interface conditions. In this paper, the spectral radius of the Jacobi iteration matrix for the inhomogeneous rectangular region is formulated and the approximation for the explicit formula, suitable for the computation of the spectral radius, is deduced. It is also found by the proposed formula that the spectral radius and the optimum relaxation factor rigorously depend on the inhomogeneity or the internal boundary conditions in the closed region, and especially vary with the position of the internal boundary. These findings are also confirmed by the numerical results of the power method.The stationary iterative method using the proposed formula for calculating estimates of the spectral radius of the Jacobi iteration matrix is compared with Carré's method, Kulstrud's method and the stationary iterative method using Frankel's theoretical formula, all for the case of some numerical models with two different media. According to the results our stationary iterative method gives the best results ffor the estimate of the spectral radius of the Jacobi iteration matrix, for the required number of iterations to calculate solutions, and for the accuracy of the solutions.As a numerical example the microstrip transmission line is taken, the propating mode of which can be approximated by a TEM mode. The cross section includes inhomogeneous media and a strip conductor. Upper and lower bounds of the spectral radius of the Jacobi iteration matrix are estimated. Our method using these estimates is also compared with the other methods. The upper bound of the spectral radius of the Jacobi iteration matrix for more general closed regions with two different media might be given by the proposed formula.     

Optimum Modified Extrapolated Jacobi Method for Consistently Ordered Matrices

1.IlltroductionandPreliminariesWeconsiderthelineaxsystemAx=b,(1.1)wheteAERn,",bERnanddet(A)/O.WeaJ8oassumethatAhasthef0rmwhereA11,A22axesquarenonsingular(usuallydiagonal)matrices-Asisknow[61,AisaconSisentlyordered2-cyclicmatris.Forsolving(1.1)weintendtousethef0lfowngsimpleiterativemethod:In(1.4)and(1.6),w1,w2arenonzeroparameters(extraP0lati0nparameters)andI1,I2areidelltitymatricesofthesamesizesasA11anA22respectively.Theconstructionofmeth0d(1.3)isbasedonthesplittingA=M-N,whereM=Dfl-1…     

Convergences of splitting iterative methods for symmetric indefinite linear systems

In this paper, we generalize the saddle point problem to general symmetric indefinite systems, we also present a kind of convergent splitting iterative methods for the symmetric indefinite systems. A special divergent splitting is introduced. The sufficient condition is discussed that the eigenvalues of the iteration matrix are real. The spectral radius of the iteration matrix is discussed in detail, the convergence theories of the splitting iterative methods for the symmetric indefinite systems are obtained. Finally, we present a preconditioner and discuss the eigenvalues of preconditioned matrix.     

并行多分裂AOR迭代法的收敛定理

对解非奇异线性方程组的并行多分裂ＡＯＲ方法,本文给出了该方法的收敛性定理,同时也给出了该方法的迭代矩阵的谱半径的上界估计式。     

关于求解常微方程组的离散波形松驰方法的加速收敛

提出一种关于求解常微线性系统的离散波形松驰方法的新的加速收敛技巧。通过对系统矩阵A的分裂，该技巧使迭代矩阵（(zI M)^-1N）具有理想的较小谱半径。在LU分解的基础上给出了一个迭代算法以及用该法与Gauss-Seidel方不垢敛速进行比较的数值例子。     

关于非Hermitian正定线性代数方程组的超松弛HSS方法

本文针对求解大型稀疏非Hermitian正定线性方程组的HSS迭代方法,利用迭代法的松弛技术进行加速,提出了一种具有三个参数的超松弛HSS方法(SAHSS)和不精确的SAHSS方法(ISAHSS),它采用CG和一些Krylov子空间方法作为其内部过程,并研究了SAHSS和ISAHSS方法的收敛性.数值例子验证了新方法的有效性.     

A special Hermitian and skew-Hermitian splitting method for image restoration

In this paper, we consider an ill-posed image restoration problem with a noise contaminated observation, and a known convolution kernel. A special Hermitian and skew-Hermitian splitting (HSS) iterative method is established for solving the linear systems from image restoration. Our approach is based on an augmented system formulation. The convergence and operation cost of the special HSS iterative method for image restoration problems are discussed. The optimal parameter minimizing the spectral radius of the iteration matrix is derived. We present a detailed algorithm for image restoration problems. Numerical examples are given to demonstrate the performance of the presented method. Finally, the SOR acceleration scheme for the special HSS iterative method is discussed.     

Lopsided PMHSS iteration method for a class of complex symmetric linear systems

Based on the preconditioned modified Hermitian and skew-Hermitian splitting (PMHSS) iteration method, we introduce a lopsided PMHSS (LPMHSS) iteration method for solving a broad class of complex symmetric linear systems. The convergence properties of the LPMHSS method are analyzed, which show that, under a loose restriction on parameter α, the iterative sequence produced by LPMHSS method is convergent to the unique solution of the linear system for any initial guess. Furthermore, we derive an upper bound for the spectral radius of the LPMHSS iteration matrix, and the quasi-optimal parameter α ^? which minimizes the above upper bound is also obtained. Both theoretical and numerical results indicate that the LPMHSS method outperforms the PMHSS method when the real part of the coefficient matrix is dominant.     

解不适定算子方程的一个定常二步隐式迭代法

On the basis of an implicit iterative method for ill-posed operator equations,we introduce a relaxation factor and a weighted factor , and obtain a stationarytwo-step implicit iterative method. The range of the factors which guarantee theconvergence of iteration is explored.We also study the convergence properties and ratesfor both non-perturbed andperturbed equations.An implementable algorithm is presented by using Morozov discrepancy principle.The theoretical results show that the convergence rates of the new methods always lead to optimal convergentrates which are superior to those of the original one after choosing suitable relaxation and weightedfactors. Numerical examplesare also given, which coincide well with the theoretical results.