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The SOR-like method with two real parameters ω and α is considered for solving the augmented system. The new method is called the modified SOR-like method (MSOR-like method). The MSOR-like method becomes the SOR-like method when α = 0. The functional equation relating the parameters and eigenvalues of the iteration matrix of the MSOR-like method is obtained. Hence the necessary and sufficient condition for the convergence of the GSOR-like method is derived. It is shown that when α is negative, the convergence domain for the parameter ω for the MSOR-like method is larger than that for the SOR-like method. Finally, a numerical computation based on a particular linear system is given which clearly shows that the MSOR-like method outperforms the SOR-like method. [ABSTRACT FROM AUTHOR]

A new iterative method is given for the augmented system of equations. Similarly to the GSOR and SOR-like methods, the new method involves two iteration parameters and one preconditioning matrix. The convergence analysis and the determination of the optimum parameters are studied first. Then the explicit formulae for the optimum parameters and the associated spectral radius are derived. Finally, numerical computations are presented which show clearly that the new method has a very good numerical performance and is much faster than the SOR-like method. [ABSTRACT FROM AUTHOR]

There are several proposals for the generalization of Young’s successive over-relaxation (SOR) method to solve the saddle-point problem or augmented system. The most practical version is the SOR-like method (G.H. Golub et al ., BIT , 41, 71–85, 2001), which was further studied by Li et al . ( Int. J. Comput. Math. , 81, 749–765, 2004) who found that the iteration matrix of the SOR-like method has no complex eigenvalues only under certain conditions. Motivated by the results of Li and co-authors, we consider the Chebyshev acceleration of the SOR-like method (GSOR-SI). First, the convergence of the GSOR-SI method is given. Secondly, it is shown that the asymptotic rate of the convergence of the GSOR-SI method is much larger than that of the SOR-like method, which indicates that the GSOR-SI method has a faster rate of convergence than the SOR-like method. Finally, numerical comparisons are given which show the GSOR-SI method is indeed faster than the SOR-like method. [ABSTRACT FROM AUTHOR]

Recently, several proposals for the generalization of Young's SOR method to the saddle point problem or the augmented system has been presented. One of the most practical versions is the SOR-like method given by Golub et al. , [(2001). SOR-like methods for augmented systems. BIT , 41 , 71-85.], where the convergence and the determination of its optimum parameters were given. In this article, a full characterization of the spectral radius of the SOR-like iteration matrix is given, and an explicit expression for the optimum parameter is given in each case. The new results also lead to different results to that of Golub et al. Besides, it is shown that by the choices of the preconditioning matrix, the optimum SOR-like iteration matrix has no complex eigenvalues, therefore, it can be accelerated by semi-iterative methods. [ABSTRACT FROM AUTHOR]

In this article, the accelerated over-relaxation (AOR) method is generalized for solving the saddle point problem or the augmented system. The successive over-relaxation (SOR)-like method is the special case of the generalized AOR (GAOR) method. The connection between the parameters and the eigenvalues of the iteration matrix of the GAOR method is given. Therefore, a necessary and sufficient condition for the convergence of the GAOR method is derived. Numerical examples are also given to show that the GAOR method is better than the SOR-like method in certain cases. [ABSTRACT FROM AUTHOR]