scholarly journals Least-squares fitting applied to nuclear mass formulas. Solution by the Gauss–Seidel method

2021 ◽  
Author(s):  
B. Mohammed-Azizi ◽  
H. Mouloudj
Keyword(s):  
Linear System ◽  
Least Squares ◽  
Statistical Data ◽  
Large Data ◽  
Positive Definite ◽  
Nuclear Mass ◽  
Positive Definite Matrices ◽  
Seidel Method ◽  
Semi Empirical ◽  
Mass Formulas

In this paper, a numerical method optimizing the coefficients of the semi empirical mass formula or those of similar mass formulas is presented. The optimization is based on the least-squares adjustments method and leads to the resolution of a linear system which is solved by iterations according to the Gauss–Seidel scheme. The steps of the algorithm are given in detail. In practice, the method is very simple to implement and is able to treat large data in a very fast way. In fact, although this method has been illustrated here by specific examples, it can be applied without difficulty to any experimental or statistical data of the same type, i.e. those leading to linear system characterized by symmetric and positive-definite matrices.

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 The Preconditioned SOR Iterative Method for Positive Definite Matrices

2013 ◽  
Vol 2013 ◽  
pp. 1-4 ◽  
Author(s):  
Yu-Qin Bai ◽  
Yan-Ping Xiao ◽  
Wei-Yuan Ma
Keyword(s):  
Linear System ◽  
Iterative Method ◽  
Coefficient Matrix ◽  
Positive Definite ◽  
Positive Definite Matrices ◽  
Numerical Examples

We present several iterations for preconditioners introduced by Tarazaga and Cuellar (2009), and study the convergence of the method for solving a linear system whose coefficient matrix is positive definite matrices, and we also find that they complete very well with the SOR iteration, which is shown through numerical examples.

scholarly journals Splitting least squares and collocation procedures for two-point boundary value problems

1985 ◽  
Vol 27 (2) ◽  
pp. 167-193
Author(s):  
John Locker ◽  
P. M. Prenter
Keyword(s):  
Linear System ◽  
Boundary Value Problem ◽  
Numerical Solution ◽  
Least Squares ◽  
Boundary Value ◽  
Linear Operators ◽  
Point Boundary ◽  
System Error ◽  
Densely Defined ◽  
Split System

AbstractLet L, T, S, and R be closed densely defined linear operators from a Hubert space X into X where L can be factored as L = TS + R. The equation Lu = f is equivalent to the linear system Tv + Ru = f and Su = v. If Lu = f is a two-point boundary value problem, numerical solution of the split system admits cruder approximations than the unsplit equations. This paper develops the theory of such splittings together with the theory of the Methods of Least Squares and of Collocation for the split system. Error estimates in both L2 and L∞ norms are obtained for both methods.

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