New SOR-like methods for solving the Sylvester equation

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Jakub Kierzkowski
Keyword(s):  
Linear Systems ◽  
Iterative Methods ◽  
Relaxation Method ◽  
Sylvester Equation ◽  
Numerical Experimentation ◽  
Successive Over Relaxation ◽  
Theoretical Results ◽  
Methods Numerical

AbstractWe present new iterative methods for solving the Sylvester equation belonging to the class of SOR-like methods, based on the SOR (Successive Over-Relaxation) method for solving linear systems. We discuss convergence characteristics of the methods. Numerical experimentation results are included, illustrating the theoretical results and some other noteworthy properties of the Methods.

scholarly journals A Modified SSOR Preconditioning Strategy for Helmholtz Equations

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Shi-Liang Wu ◽  
Cui-Xia Li
Keyword(s):  
Convergence Rate ◽  
Linear Systems ◽  
Iterative Methods ◽  
Low Cost ◽  
Computational Cost ◽  
Coefficient Matrix ◽  
Computational Time ◽  
Helmholtz Equations ◽  
Speed Up ◽  
Methods Numerical

The finite difference method discretization of Helmholtz equations usually leads to the large spare linear systems. Since the coefficient matrix is frequently indefinite, it is difficult to solve iteratively. In this paper, a modified symmetric successive overrelaxation (MSSOR) preconditioning strategy is constructed based on the coefficient matrix and employed to speed up the convergence rate of iterative methods. The idea is to increase the values of diagonal elements of the coefficient matrix to obtain better preconditioners for the original linear systems. Compared with SSOR preconditioner, MSSOR preconditioner has no additional computational cost to improve the convergence rate of iterative methods. Numerical results demonstrate that this method can reduce both the number of iterations and the computational time significantly with low cost for construction and implementation of preconditioners.

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 Optimization by parameters in the iterative methods for solving non-linear equations

2021 ◽  
pp. 16-22
Author(s):  
Tugal Zhanlav ◽  
Khuder Otgondorj
Keyword(s):  
Iterative Methods ◽  
Linear Equations ◽  
Real Life ◽  
Robust Methods ◽  
Life Problems ◽  
Optimal Values ◽  
Non Linear Equations ◽  
Theoretical Results ◽  
High Computational Efficiency ◽  
Methods Numerical

In this paper, we used the necessary optimality condition for parameters in a two-point iterations for solving nonlinear equations. Optimal values of these parameters fully coincide with those obtained in [6] and allow us to increase the convergence order of these iterative methods. Numerical experiments and the comparison of existing robust methods are included to confirm the theoretical results and high computational efficiency. In particular, we considered a variety of real life problems from different disciplines, e.g., Kepler’s equation of motion, Planck’s radiation law problem, in order to check the applicability and effectiveness of our proposed methods.

scholarly journals SOR-like methods for solving the Sylvester equation

2015 ◽  
Author(s):  
Jakub Kierzkowski
Keyword(s):  
Iterative Methods ◽  
Numerical Experiments ◽  
Sufficient Conditions ◽  
Sylvester Equation ◽  
Modification Method ◽  
Stationary Iterative Methods ◽  
Theoretical Results

We present the SOR-like methods and highlight some of their known properties. We give the SOR-like method as proposed by Z. Wo źnicki and propose two similar methods based upon it. All three are stationary iterative methods for solving the Sylvester equation (AX-XB=C). We form two sufficient conditions under which one of those methods will converge. In addition, we present a modification method, based on the following fact: if X is a solution of AX-XB=C, it is also a solution of (A-αIm)X-X(B-αIn)=C. We also present numerical experiments to illustrate the theoretical results and some properties of the methods.

scholarly journals On applying weighted seed techniques to GMRES algorithm for solving multiple linear systems

2018 ◽  
Vol 36 (3) ◽  
pp. 155-172
Author(s):  
Lakhdar Elbouyahyaoui ◽  
Mohammed Heyouni
Keyword(s):  
Linear Systems ◽  
Sparse Matrix ◽  
Coefficient Matrix ◽  
University Of Florida ◽  
Block Arnoldi ◽  
The Matrix ◽  
Multiple Linear Systems ◽  
The University ◽  
Theoretical Results ◽  
Methods Numerical

In the present paper, we are concerned by weighted Arnoldi like methods for solving large and sparse linear systems that have different right-hand sides but have the same coefficient matrix. We first give detailed descriptions of the weighted Gram-Schmidt process and of a Ruhe variant of the weighted block Arnoldi algorithm. We also establish some theoretical results that links the iterates of the weighted block Arnoldi process to those of the non weighted one. Then, to accelerate the convergence of the classical restarted block and seed GMRES methods, we introduce the weighted restarted block and seed GMRES methods. Numerical experiments that are done with different matrices coming from the Matrix Market repository or from the university of Florida sparse matrix collection are reported at the end of this work in order to compare the performance and show the effectiveness of the proposed methods.

scholarly journals Efficient High-Order Iterative Methods for Solving Nonlinear Systems and Their Application on Heat Conduction Problems

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Alicia Cordero ◽  
Esther Gómez ◽  
Juan R. Torregrosa
Keyword(s):  
Heat Conduction ◽  
Nonlinear Systems ◽  
Iterative Methods ◽  
Finite Differences ◽  
Diffusion Problem ◽  
Eighth Order ◽  
New Methods ◽  
Theoretical Results ◽  
High Order Iterative Methods ◽  
Methods Numerical

For solving nonlinear systems of big size, such as those obtained by applying finite differences for approximating the solution of diffusion problem and heat conduction equations, three-step iterative methods with eighth-order local convergence are presented. The computational efficiency of the new methods is compared with those of some known ones, obtaining good conclusions, due to the particular structure of the iterative expression of the proposed methods. Numerical comparisons are made with the same existing methods, on standard nonlinear systems and a nonlinear one-dimensional heat conduction equation by transforming it in a nonlinear system by using finite differences. From these numerical examples, we confirm the theoretical results and show the performance of the presented schemes.

scholarly journals Multipreconditioned GMRES for simulating stochastic automata networks

2018 ◽  
Vol 16 (1) ◽  
pp. 986-998
Author(s):  
Chun Wen ◽  
Ting-Zhu Huang ◽  
Xian-Ming Gu ◽  
Zhao-Li Shen ◽  
Hong-Fan Zhang ◽  
...  
Keyword(s):  
Steady State ◽  
Probability Distribution ◽  
Linear Systems ◽  
Iterative Methods ◽  
Communication Systems ◽  
Queueing Systems ◽  
Steady State Probability ◽  
Stochastic Automata ◽  
Automata Networks ◽  
Generator Matrices

AbstractStochastic Automata Networks (SANs) have a large amount of applications in modelling queueing systems and communication systems. To find the steady state probability distribution of the SANs, it often needs to solve linear systems which involve their generator matrices. However, some classical iterative methods such as the Jacobi and the Gauss-Seidel are inefficient due to the huge size of the generator matrices. In this paper, the multipreconditioned GMRES (MPGMRES) is considered by using two or more preconditioners simultaneously. Meanwhile, a selective version of the MPGMRES is presented to overcome the rapid increase of the storage requirements and make it practical. Numerical results on two models of SANs are reported to illustrate the effectiveness of these proposed methods.

On the Flow Fields of Inertial Impactors

1974 ◽  
Vol 96 (4) ◽  
pp. 394-400 ◽  
Author(s):  
V. A. Marple ◽  
B. Y. H. Liu ◽  
K. T. Whitby
Keyword(s):  
Flow Field ◽  
Stokes Equations ◽  
Relaxation Method ◽  
Water Model ◽  
Navier Stokes ◽  
Visualization Technique ◽  
Navier Stokes Equations ◽  
Theoretical Results ◽  
Plate Distance ◽  
Good Agreement

The flow field in an inertial impactor was studied experimentally with a water model by means of a flow visualization technique. The influence of such parameters as Reynolds number and jet-to-plate distance on the flow field was determined. The Navier-Stokes equations describing the laminar flow field in the impactor were solved numerically by means of a finite difference relaxation method. The theoretical results were found to be in good agreement with the empirical observations made with the water model.

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