M-矩阵线性互补问题模系多分裂迭代方法的收敛性

首先证明了M-矩阵的H-相容分裂都是正则分裂,反之不成立.这表明对于M-矩阵而言,其正则分裂包含H-相容分裂.然后针对系数矩阵为M-矩阵的线性互补问题,建立了两个收敛定理:一是模系多分裂迭代方法关于正则分裂的收敛定理;二是模系二级多分裂迭代方法关于外迭代为正则分裂和内迭代为弱正则分裂的收敛定理.     

一类弱非线性互补问题的广义模系矩阵多分裂多参数加速松弛迭代方法

本文提出一类求解弱非线性互补问题的广义模系矩阵多分裂多参数加速松弛迭代方法，并给出了系数矩阵为H₊-矩阵时该方法的收敛性分析.数值实验表明新方法是有效的.     

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.     

二阶锥线性互补问题的广义模系矩阵分裂迭代算法

通过将二阶锥线性互补问题转化为等价的不动点方程,介绍了一种广义模系矩阵分裂迭代算法,并研究了该算法的收敛性.进一步,数值结果表明广义模系矩阵分裂迭代算法能够有效地求解二阶锥线性互补问题.     

迭代求解非Hermitian正定线性方程组的衍生多分裂方法[英文]

本文研究迭代求解非Hermitian正定线性方程组的问题.在系数矩阵HS分裂的基础上,提出了一种新的衍生并行多分裂迭代方法.通过参数调节分配反Hermitian部分给Hermitian部分的多分裂来衍生出非Hermitian正定系数矩阵的并行多分裂迭代格式,并利用优化技巧来获得权矩阵.同时,建立算法的收敛理论.最后用数值实验表明了新方法的有效性和可行性.     

求解一类隐式互补问题的加速模系矩阵分裂迭代方法

本文提出求解一类隐式互补问题的加速模系矩阵分裂迭代法.通过将隐式互补问题重新表述为一个等价的不动点方程,建立一类新的基于模系的两步矩阵分裂方法,并在一定条件下证明了方法的收敛性.数值实验表明,该方法在迭代步数上优于传统的模系矩阵分裂迭代方法.     

求解增广线性系统的局部多分裂迭代法

为了在高性能计算机上求解增广线性系统,基于并行多分裂的两种技巧,本文提出一种局部多分裂迭代格式,给出当增广线性系统的矩阵为M-矩阵和H-矩阵时新方法的收敛性理论.并讨论预条件矩阵的特征值情形.     

对称正定线性方程组具有任意权矩阵的多分裂迭代求解

本文利用优化模型研究求解对称正定线性方程组Ax=6的多分裂并行算法的权矩阵.在我们的多分裂并行算法中,m个分裂仅要求其中之一为P-正则分裂而其余的则可以任意构造,这不仅大大降低了构造多分裂的难度,而且也放宽了对权矩阵的限制(不像标准的多分裂迭代方法中要求权矩阵为预先给定的非负数量矩阵).并且证明了新的多分裂迭代法是收敛的.最后,通过数值例子展示了新算法的有效性.     

矩阵多分裂迭代法的收敛条件

本文给出了求解非奇异线性方程组的矩阵多分裂并行迭代法的一些新的收敛结果.当系数矩阵单调和多分裂序列为弱正则分裂时,得到了几个与已有的收敛准则等价的条件,并且证明了异步迭代法在较弱条件下的收敛性.对于同步迭代,给出了与异步迭代不同且较为宽松的收敛条件.     

水平线性互补问题投影迭代法和模系矩阵分裂迭代法的等价性

水平线性互补问题(HLCP)是著名线性互补问题(LCP)的重要推广形式之一,投影迭代法和模系矩阵分裂迭代法是最近提出的求解HLCP两类非常有效的热点方法.本文研究表明,尽管这两类方法导出原理不同,但在一定条件下是等价的.特别地,当模系矩阵分裂迭代法中参数矩阵Ω取为特定的正对角矩阵时,投影Jacobi法、投影Gauss-Seidel法和投影SOR法分别等价于模系Jacobi迭代法、加速的模系Gauss-Seidel迭代法和加速的模系SOR迭代法.此外,对一般的正对角矩阵Ω,本文也研究了两类方法的等价性.最后,通过数值算例验证了本文的理论结果.     

Modulus‐based synchronous multisplitting iteration methods for linear complementarity problems

By an equivalent reformulation of the linear complementarity problem into a system of fixed‐point equations, we construct modulus‐based synchronous multisplitting iteration methods based on multiple splittings of the system matrix. These iteration methods are suitable to high‐speed parallel multiprocessor systems and include the multisplitting relaxation methods such as Jacobi, Gauss–Seidel, successive overrelaxation, and accelerated overrelaxation of the modulus type as special cases. We establish the convergence theory of these modulus‐based synchronous multisplitting iteration methods and their relaxed variants when the system matrix is an H ₊ ‐matrix. Numerical results show that these new iteration methods can achieve high parallel computational efficiency in actual implementations. Copyright © 2012 John Wiley & Sons, Ltd.     

Two-Step Modulus-Based Synchronous Multisplitting Iteration Methods for Linear Complementarity Problems

To reduce the communication among processors and improve the computing time for solving linear complementarity problems, we present a two-step modulus-based synchronous multisplitting iteration method and the corresponding symmetric modulus-based multisplitting relaxation methods. The convergence theorems are established when the system matrix is an $H_+$-matrix, which improve the existing convergence theory. Numerical results show that the symmetric modulus-based multisplitting relaxation methods are effective in actual implementation.     

Asynchronous multisplitting iteration with different weighting schemes

The multisplitting iteration method was presented by O’Leary and White [5] for solving large sparse linear systems on parallel multiprocessor system. In this paper, we further set up an asynchronous variant for the multisplitting iteration method with different weighting schemes studied by White [8]. Moreover, we establish a general convergence criterion for asynchronous iteration framework, and then prove the convergence of the new asynchronous multisplitting iteration method with different weighting schemes by making use of this general criterion.     

Parallel multisplitting two-stage iterative methods for large sparse systems of weakly nonlinear equations

The finite difference or the finite element discretizations of many differential or integral equations often result in a class of systems of weakly nonlinear equations. In this paper, by reasonably applying both the multisplitting and the two-stage iteration techniques, and in accordance with the special properties of this system of weakly nonlinear equations, we first propose a general multisplitting two-stage iteration method through the two-stage multiple splittings of the system matrix. Then, by applying the accelerated overrelaxation (AOR) technique of the linear iterative methods, we present a multisplitting two-stage AOR method, which particularly uses the AOR-like iteration as inner iteration and is substantially a relaxed variant of the afore-presented method. These two methods have a forceful parallel computing function and are much more suitable to the high-speed multiprocessor systems. For these two classes of methods, we establish their local convergence theories, and precisely estimate their asymptotic convergence factors under some suitable assumptions when the involved nonlinear mapping is only directionally differentiable. When the system matrix is either an H-matrix or a monotone matrix, and the nonlinear mapping is a P-bounded mapping, we thoroughly set up the global convergence theories of these new methods. Moreover, under the assumptions that the system matrix is monotone and the nonlinear mapping is isotone, we discuss the monotone convergence properties of the new multisplitting two-stage iteration methods, and investigate the influence of the multiple splittings as well as the relaxation parameters upon the convergence behaviours of these methods. Numerical computations show that our new methods are feasible and efficient for parallel solving of the system of weakly nonlinear equations. This revised version was published online in August 2006 with corrections to the Cover Date.     

Global relaxed modulus-based synchronous block multisplitting multi-parameters methods for linear complementarity problems

Recently, Bai and Zhang [Numerical Linear Algebra with Applications, 20(2013):425439] constructed modulus-based synchronous multisplitting methods by an equivalent reformulation of the linear complementarity problem into a system of ?xed-point equations and studied the convergence of them; Li et al. [Journal of Nanchang University (Natural Science), 37(2013):307-312] studied synchronous block multisplitting iteration methods; Zhang and Li [Computers and Mathematics with Application, 67(2014):1954-1959] analyzed and obtained the weaker convergence results for linear complementarity problems. In this paper, we generalize their algorithms and further study global relaxed modulus-based synchronous block multisplitting multi-parameters methods for linear complementarity problems. Furthermore, we give the weaker convergence results of our new method in this paper when the system matrix is a block H+?matrix. Therefore, new results provide a guarantee for the optimal relaxation parameters, please refer to [A. Hadjidimos, M. Lapidakis and M. Tzoumas, SIAM Journal on Matrix Analysis and Applications, 33(2012):97-110, (dx.doi.org/10.1137/100811222)], where optimal parameters are determined.     

Improved convergence theorems of the two-step modulus-based matrix splitting and synchronous multisplitting iteration methods for solving linear complementarity problems

Abstract

In this paper, the convergence conditions of the two-step modulus-based matrix splitting and synchronous multisplitting iteration methods for solving linear complementarity problems of H-matrices are weakened. The convergence domain given by the proposed theorems is larger than the existing ones.     

异步并行矩阵多分裂块松弛迭代算法

1 引言 众所周知,许多微分方程经过差分或有限元离散,即可归结为线性代数方程组 Ax=b,A∈L(R~n)非奇异,x,b∈R~n.(1.1)缘于原问题的物理特性,系数矩阵A∈L(R~n)通常是大型稀疏的,并且具有规则的分块结构。鉴此,文[1]基于矩阵多重分裂的概念,并运用线性迭代法的松弛加速技巧,提出了求解这类大型稀疏分块线性代数方程组的并行矩阵多分裂块松弛迭代算法,并在适当的条件下建立了算法的收敛理论。对于SIMD多处理机系统,这类算法是颇为适用和行之有效的。     

A class of asynchronous multisplitting two-stage iterations for large sparse block systems of weakly nonlinear equations

For the block system of weakly nonlinear equations Ax=G(x), where is a large sparse block matrix and is a block nonlinear mapping having certain smoothness properties, we present a class of asynchronous parallel multisplitting block two-stage iteration methods in this paper. These methods are actually the block variants and generalizations of the asynchronous multisplitting two-stage iteration methods studied by Bai and Huang (Journal of Computational and Applied Mathematics 93(1) (1998) 13–33), and they can achieve high parallel efficiency of the multiprocessor system, especially, when there is load imbalance. Under quite general conditions that is a block H-matrix of different types and is a block P-bounded mapping, we establish convergence theories of these asynchronous multisplitting block two-stage iteration methods. Numerical computations show that these new methods are very efficient for solving the block system of weakly nonlinear equations in the asynchronous parallel computing environment.     

Two-Stage Multisplitting Iteration Methods Using Modulus-Based Matrix Splitting as Inner Iteration for Linear Complementarity Problems

The matrix multisplitting iteration method is an effective tool for solving large sparse linear complementarity problems. However, at each iteration step we have to solve a sequence of linear complementarity sub-problems exactly. In this paper, we present a two-stage multisplitting iteration method, in which the modulus-based matrix splitting iteration and its relaxed variants are employed as inner iterations to solve the linear complementarity sub-problems approximately. The convergence theorems of these two-stage multisplitting iteration methods are established. Numerical experiments show that the two-stage multisplitting relaxation methods are superior to the matrix multisplitting iteration methods in computing time, and can achieve a satisfactory parallel efficiency.