scholarly journals Convergence of GAOR Iterative Method with Strictly Diagonally Dominant Matrices

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Guangbin Wang ◽  
Hao Wen ◽  
Ting Wang
Keyword(s):  
Iterative Method ◽  
Linear Systems ◽  
Numerical Examples ◽  
Diagonally Dominant Matrices ◽  
Diagonally Dominant ◽  
Better Than

We discuss the convergence of GAOR method for linear systems with strictly diagonally dominant matrices. Moreover, we show that our results are better than ones of Darvishi and Hessari (2006), Tian et al. (2008) by using three numerical examples.

scholarly journals A convergence analysis of SOR iterative methods for linear systems with weak H-matrices

2016 ◽  
Vol 14 (1) ◽  
pp. 747-760
Author(s):  
Cheng-yi Zhang ◽  
Zichen Xue ◽  
Shuanghua Luo
Keyword(s):  
Convergence Analysis ◽  
Linear Systems ◽  
Iterative Methods ◽  
Sufficient Conditions ◽  
Necessary And Sufficient Conditions ◽  
Numerical Examples ◽  
Convergence Results ◽  
Diagonally Dominant Matrices ◽  
Diagonally Dominant ◽  
Necessary And Sufficient

AbstractIt is well known that SOR iterative methods are convergent for linear systems, whose coefficient matrices are strictly or irreducibly diagonally dominant matrices and strong H-matrices (whose comparison matrices are nonsingular M-matrices). However, the same can not be true in case of those iterative methods for linear systems with weak H-matrices (whose comparison matrices are singular M-matrices). This paper proposes some necessary and sufficient conditions such that SOR iterative methods are convergent for linear systems with weak H-matrices. Furthermore, some numerical examples are given to demonstrate the convergence results obtained in this paper.

scholarly journals Convergence of Relaxed Matrix Parallel Multisplitting Chaotic Methods forH-Matrices

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Li-Tao Zhang ◽  
Jian-Lei Li ◽  
Tong-Xiang Gu ◽  
Xing-Ping Liu
Keyword(s):  
Numerical Experiments ◽  
Convergence Property ◽  
Numerical Examples ◽  
Convergence Results ◽  
Diagonally Dominant Matrices ◽  
Diagonally Dominant

Based on the methods presented by Song and Yuan (1994), we construct relaxed matrix parallel multisplitting chaotic generalized USAOR-style methods by introducing more relaxed parameters and analyze the convergence of our methods when coefficient matrices areH-matrices or irreducible diagonally dominant matrices. The parameters can be adjusted suitably so that the convergence property of methods can be substantially improved. Furthermore, we further study some applied convergence results of methods to be convenient for carrying out numerical experiments. Finally, we give some numerical examples, which show that our convergence results are applied and easily carried out.

Discussion for Block H-Matrices

2014 ◽  
Vol 1006-1007 ◽  
pp. 1039-1042
Author(s):  
Hui Shuang Gao
Keyword(s):  
Necessary Condition ◽  
Numerical Examples ◽  
Diagonally Dominant Matrices ◽  
Diagonally Dominant ◽  
Sufficient And Necessary Condition

In this paper, a new sufficient and necessary condition for judging block strictly-double diagonally dominant matrices is given firstly. By this theorem, some new practical criteria for nonsingular blockH-matrices are obtained. In the end, the result effectiveness is illustrated by numerical examples.

scholarly journals Convergence on Gauss-Seidel iterative methods for linear systems with general H-matrices

2015 ◽  
Vol 30 ◽  
pp. 843-870 ◽  
Author(s):  
Cheng-yi Zhang ◽  
Dan Ye ◽  
Cong-Lei Zhong ◽  
SHUANGHUA SHUANGHUA
Keyword(s):  
Linear Systems ◽  
Iterative Methods ◽  
Sufficient Conditions ◽  
Numerical Linear Algebra ◽  
Convergence Results ◽  
Diagonally Dominant Matrices ◽  
Sufficient Conditions For Convergence ◽  
Diagonally Dominant ◽  
Necessary And Sufficient ◽  
Theoretical Results

It is well known that as a famous type of iterative methods in numerical linear algebra, Gauss-Seidel iterative methods are convergent for linear systems with strictly or irreducibly diagonally dominant matrices, invertible H−matrices (generalized strictly diagonally dominant matrices) and Hermitian positive definite matrices. But, the same is not necessarily true for linear systems with non-strictly diagonally dominant matrices and general H−matrices. This paper firstly proposes some necessary and sufficient conditions for convergence on Gauss-Seidel iterative methods to establish several new theoretical results on linear systems with nonstrictly diagonally dominant matrices and general H−matrices. Then, the convergence results on preconditioned Gauss-Seidel (PGS) iterative methods for general H−matrices are presented. Finally, some numerical examples are given to demonstrate the results obtained in this paper.

scholarly journals Convergence of the GAOR Method for One Subclass ofH-Matrix

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Guangbin Wang ◽  
Ting Wang
Keyword(s):  
Linear System ◽  
Convergence Theorem ◽  
Coefficient Matrix ◽  
Linear Least Squares ◽  
Numerical Examples ◽  
Linear Least Squares Problem ◽  
Diagonally Dominant Matrix ◽  
Diagonally Dominant ◽  
Dominant Matrix ◽  
Better Than

We discuss the convergence of the GAOR method to solve linear system which occurred in solving the weighted linear least squares problem. Moreover, we present one convergence theorem of the GAOR method when the coefficient matrix is a strictly doublyαdiagonally dominant matrix which is a nonsingularH-matrix. Finally, we show that our results are better than previous ones by using four numerical examples.

scholarly journals Discussion forH-Matrices and It’s Application

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Guichun Han ◽  
Huishuang Gao ◽  
Haitao Yang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network System ◽  
Numerical Examples ◽  
Diagonally Dominant Matrices ◽  
Neural Network System ◽  
Diagonally Dominant ◽  
The Stability

NonsingularH-matrices and positive stable matrices play an important role in the stability of neural network system. In this paper, some criteria for nonsingularH-matrices are obtained by the theory of diagonally dominant matrices and the obtained result is introduced into identifying the stability of neural networks. So the criteria for nonsingularH-matrices are expanded and their application on neural network system is given. Finally, the effectiveness of the results is illustrated by numerical examples.

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