Asynchronous parallel multisplitting nonlinear gauss-seidel iteration

An asynchronous parallel multisplitting nonlinear Gauss-Seidel iterative method is established for the particularly structured system of nonlinear equations Aφ(x) Bφ(x) = bwith A,B∈(R^n) φ,φtR^n→R^n being diagonal mappings and b ∈ R^n, and the global convergence of it isproved.     

Parallel multisplitting two-stage iterative methods with general weighting matrices for non-symmetric positive definite systems

In this work, we propose a new parallel multisplitting iterative method for non-symmetric positive definite linear systems. Based on optimization theory, the new method has two great improvements; one is that only one splitting needs to be convergent, and the other is that the weighting matrices are not scalar and nonnegative matrices. The convergence of the new parallel multisplitting iterative method is discussed. Finally, the numerical results show that the new method is effective.     

Asynchronous multisplitting methods for nonlinear fixed point problems

Our aim is to present for nonlinear problems asynchronous multisplitting algorithms including both the basic situation of O'Leary and White and the discrete analogue of Schwarz's alternating method and its multisubdomain extensions and moreover their two-stage counterparts. The analysis of these methods is based on El Tarazi’s convergence theorem for asynchronous iterations and leads to a good level of asynchronism in each of the considered situations. This revised version was published online in August 2006 with corrections to the Cover Date.     

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.     

A two-step modulus-based multisplitting iteration method for the nonlinear complementarity problem

In this paper, we construct a two-step modulus-based multisplitting iteration method based on multiple splittings of the system matrix for the nonlinear complementarity problem. And we prove its convergence when the system matrix is an $H$-matrix with positive diagonal elements. Numerical experiments show that the proposed method is efficient.     

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.     

IGAOR and multisplitting IGAOR methods for linear complementarity problems

In this paper, we propose an interval version of the generalized accelerated overrelaxation methods, which we refer to as IGAOR, for solving the linear complementarity problems, LCP (M, q), and develop a class of multisplitting IGAOR methods which can be easily implemented in parallel. In addition, in regards to the H-matrix with positive diagonal elements, we prove the convergence of these algorithms and illustrate their efficiency through our numerical results.     

RELAXED ASYNCHRONOUS ITERATIONS FOR THE LINEAR COMPLEMENTARITY PROBLEM

1. IntroductionConsider the large sparse Linear Complementarity Problem (LCP):where .1I = (mkj) 6 L(R") is a gitren real matrix and q = (qk) E R" a given real vector. Thisproblem arises in many areas of scientific computing. FOr example, it arises from problemsin (linear and) contrex quadratic programming, the prob1em of finding a Nash equilibriumpoint of a bimatrix game (e.g., Cottle and Dantzig[5] and Lemke[13]), a11d also a number of freeboundary problems of fluid mechanics (e.g., Cr…     

A CLASS OF ASYNCHRONOUS PARALLEL MULTISPLITTING RELAXATION METHODS FOR LARGE SPARSE LINEAR COMPLEMENTARITY PROBLEMS

Asynchronous parallel multisplitting relaxation methods for solving large sparse linear complementarity problems are presented, and their convergence is proved when the system matrices are H-matrices having positive diagonal elements. Moreover, block and multi-parameter variants of the new methods, together with their convergence properties,are investigated in detail. Numerical results show that these new methods can achieve high parallel efficiency for solving the large sparse linear complementarity problems on multiprocessor systems.     

Generalizations of the nonstationary multisplitting iterative method for symmetric positive definite linear systems

In this paper, we generalize the nonstationary parallel multisplitting iterative method for solving the symmetric positive definite linear systems. With several choices of variable weighting matrices, the convergence properties of these generalized methods can be improved. Finally, the numerical comparison of several nonstationary parallel multisplitting methods are shown.     

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.     

A CLASS OF GENERALIZED MULTISPLITTING RELAXATION METHODS FOR LINEAR COMPLEMENTARITY PROBLEMS

In this paper,a class of generalized parallel matrix multisplitting relaxation methods for solving linear complementarity problems on the high-speed multiprocessor systems is set up. This class of methods not only includes all the existing relaxation methods for the linear complementarity problems ,but also yields a lot of novel ones in the sense of multisplittlng. We establish the convergence theories of this class of generalized parallel multisplitting relaxation methods under the condition that the system matrix is an H-metrix with positive diagonal elements.     

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.     

广义异步矩阵多分裂向前向后松弛算法

本文建立了一类广义异步矩阵多分裂向前向后松弛算法，并在系数矩阵是Ｈ－矩阵的条件下，证明了这类算法的收敛性．     

On the convergence of parallel chaotic nonlinear multisplitting Newton-type methods

A class of parallel chaotic nonlinear multisplitting Newton-type methods for solving the nonlinear system of equations F(x) = 0(F : D Rⁿ → Rⁿ) is established and its local convergence theory is presented.     

ON THE CONVERGENCE DOMAIN OF THEMATRIX MULTISPLITTING RELAXATION METHODS FOR LINEAR SYSTEMS

The convergence of the parallel matrix multisplitting relaxation methods presented by Wang (Linear Algebra and Its Applications 154/156 (1991) 473-486) is further investigated.The investigations show that these relaxation methods really have considerably larger convergence domains.     

Semiconvergence of parallel multisplitting methods for symmetric positive semidefinite linear systems

In this paper we construct some parallel relaxed multisplitting methods for solving consistent symmetric positive semidefinite linear systems, based on modified diagonally compensated reduction and incomplete factorizations. The semiconvergence of the parallel multisplitting method, relaxed multisplitting method and relaxed two‐stage multisplitting method are discussed. The results generalize some well‐known results for the nonsingular linear systems to the singular systems. Copyright © 2010 John Wiley & Sons, Ltd.     

On parallel multisplitting methods for symmetric positive semidefinite linear systems

In this paper we propose some parallel multisplitting methods for solving consistent symmetric positive semidefinite linear systems, based on modified diagonally compensated reduction. The semiconvergence of the parallel multisplitting method is discussed. The results here generalize some known results for the nonsingular linear systems to the singular systems. Copyright © 2008 John Wiley & Sons, Ltd.     

Pi-sigma神经网络的带动量项的异步批处理梯度算法收敛性

本文将动量项引入到训练Pi-sigma神经网络的异步批处理的梯度算法中,有效的改善了算法的收敛效率,并从理论上对该算法的收敛性进行研究,给出了误差函数的单调性定理及该算法的弱收敛和强收敛性定理.计算机仿真实验亦验证了带动量项的异步批处理梯度算法的有效性和理论分析的正确性.     

线性方程组异步并行迭代法的舍入误差分析

本文对求解大型线性方程组的异步并行迭代法进行了浮点运算的舍入误差分析,给出了算法是向前稳定的充分条件.