Refinement of Multiparameters Overrelaxation (RMPOR) Method

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.

Refinement of Extended Accelerated Over-Relaxation Method for Solution of Linear Systems

Given any linear stationary iterative methods in the form z^(i+1)=Jz^(i)+f, where J is the iteration matrix, a significant improvements of the iteration matrix will decrease the spectral radius and enhances the rate of convergence of the particular method while solving system of linear equations in the form Az=b. This motivates us to refine the Extended Accelerated Over-Relaxation (EAOR) method called Refinement of Extended Accelerated Over-Relaxation (REAOR) so as to accelerate the convergence rate of the method. In this paper, a refinement of Extended Accelerated Over-Relaxation method that would minimize the spectral radius, when compared to EAOR method, is proposed. The method is a 3-parameter generalization of the refinement of Accelerated Over-Relaxation (RAOR) method, refinement of Successive Over-Relaxation (RSOR) method, refinement of Gauss-Seidel (RGS) method and refinement of Jacobi (RJ) method. We investigated the convergence of the method for weak irreducible diagonally dominant matrix, matrix or matrix and presented some numerical examples to check the performance of the method. The results indicate the superiority of the method over some existing methods.

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

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.

Criteria for H-Matrices Based on γ−Diagonally Dominant Matrix

In this paper, we present a new practical criteria for H-matrix based on γ-diagonally dominant matrix. In order to make the judgment conditions convenient and effective, we give two new definitions, one is called strong and weak diagonally dominant degree, the other is called the sum of non-principal diagonal element for the matrix. Further, we obtain a new practical method for the determination of the H-matrix by combining the properties of γ-diagonally dominant matrix, constructing positive diagonal matrix, and adding the appropriate parameters. Finally, we offer numerical examples to verify the validity of the judgment conditions, corresponding numerical examples compared the new criteria and the existing results are presented to verify the advantages of the new determination method.

An event-triggered approach to finite-time observer-based control for Markov jump systems with repeated scalar nonlinearities

This paper studies the issue of finite-time observer-based control via an event-triggered scheme for Markov jump repeated scalar nonlinear systems. An observer-based controller via an event-triggered scheme is proposed, which can save the limited network communication bandwidth effectively, so that the resulting error system is stochastically finite-time bounded. Based on the positive definite diagonally dominant matrix and the Lyapunov function technique, a sufficient condition is presented for the solvability of the addressed problem, and the desired observer-based controller can be constructed via a convex optimization problem. In the end, a simulation example is employed to show the validity and practicability of the proposed design method.

Characterization of diagonally dominant H-matrices

We show that the diagonal dominance nonsingularity result of Shivakumar and Chew from 1974, and the diagonal dominance nonsingularity result of Farid from 1995, and the result of Huang on characterization of diagonally dominant H-matrices from 1995, all proven independently and in different contexts are, in fact, equivalent. We also offer the fourth and the fifth simpler equivalent conditions, for a diagonally dominant matrix to be an H-matrix.

Practical Criteria for H-Matrices

Generalized strictly diagonally dominant matrix is very important in computing mathematics and matrix theory, many articles are searching simple and practical identification of generalized strictly diagonally dominant matrix. In this paper, using the concept of diagonal dominance, some sufficient conditions for generalized strictly diagonally dominant matrices are given.