scholarly journals Refinement of Multiparameters Overrelaxation (RMPOR) Method

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Gashaye Dessalew ◽  
Tesfaye Kebede ◽  
Gurju Awgichew ◽  
Assaye Walelign
Keyword(s):  
Positive Definite Matrix ◽  
Positive Definite ◽  
Symmetric Positive Definite Matrix ◽  
Convergence Properties ◽  
Linear System Of Equations ◽  
Symmetric Positive Definite ◽  
Diagonally Dominant Matrix ◽  
Diagonally Dominant ◽  
Strictly Diagonally Dominant Matrix ◽  
Dominant Matrix

In this paper, we present refinement of multiparameters overrelaxation (RMPOR) method which is used to solve the linear system of equations. We investigate its convergence properties for different matrices such as strictly diagonally dominant matrix, symmetric positive definite matrix, and M-matrix. The proposed method minimizes the number of iterations as compared with the multiparameter overrelaxation method. Its spectral radius is also minimum. To show the efficiency of the proposed method, we prove some theorems and take some numerical examples.

scholarly journals Second-refinement of Gauss-Seidel iterative method for solving linear system of equations

2020 ◽  
Vol 13 (1) ◽  
pp. 1-15
Author(s):  
Tesfaye Kebede Enyew ◽  
Gurju Awgichew ◽  
Eshetu Haile ◽  
Gashaye Dessalew Abie
Keyword(s):  
Linear System ◽  
Positive Definite ◽  
System Of Equations ◽  
Linear System Of Equations ◽  
Symmetric Positive Definite ◽  
Modified Method ◽  
Seidel Method ◽  
Diagonally Dominant Matrix ◽  
Diagonally Dominant ◽  
Number Of Iterations

Although large and sparse linear systems can be solved using iterative methods, its number of iterations is relatively large. In this case, we need to modify the existing methods in order to get approximate solutions in a small number of iterations. In this paper, the modified method called second-refinement of Gauss-Seidel method for solving linear system of equations is proposed. The main aim of this study was to minimize the number of iterations, spectral radius and to increase rate of convergence. The method can also be used to solve differential equations where the problem is transformed to system of linear equations with coefficient matrices that are strictly diagonally dominant matrices, symmetric positive definite matrices or M-matrices by using finite difference method. As we have seen in theorem 1and we assured that, if A is strictly diagonally dominant matrix, then the modified method converges to the exact solution. Similarly, in theorem 2 and 3 we proved that, if the coefficient matrices are symmetric positive definite or M-matrices, then the modified method converges. And moreover in theorem 4 we observed that, the convergence of second-refinement of Gauss-Seidel method is faster than Gauss-Seidel and refinement of Gauss-Seidel methods. As indicated in the examples, we demonstrated the efficiency of second-refinement of Gauss-Seidel method better than Gauss-Seidel and refinement of Gauss-Seidel methods.

scholarly journals A randomized algorithm for approximating the log determinant of a symmetric positive definite matrix

2017 ◽  
Vol 533 ◽  
pp. 95-117 ◽  
Author(s):  
Christos Boutsidis ◽  
Petros Drineas ◽  
Prabhanjan Kambadur ◽  
Eugenia-Maria Kontopoulou ◽  
Anastasios Zouzias
Keyword(s):  
Randomized Algorithm ◽  
Positive Definite Matrix ◽  
Positive Definite ◽  
Symmetric Positive Definite Matrix ◽  
Symmetric Positive Definite
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