Accelerated generalized SOR method for a class of complex systems of linear equations

For solving a broad class of complex symmetric linear systems, recently Salkuyeh et al. recast the system in a real formulation and studied a generalized successive overrelaxation (GSOR) iterative method. In this paper, we introduce an accelerated GSOR (AGSOR) iterative method which involves two iteration parameters. Then, we theoretically study its convergence properties and determine its optimal iteration parameters and corresponding optimal convergence factor. Finally, some numerical computations are presented to validate the theoretical results and compare the performance of the AGSOR method with those of the GSOR and MHSS methods

A variant of the AOR method for augmented systems

A variant of the AOR method for augmented system

Quasi-Perron-Frobenius property of a class of saddle point matrices

The saddle point matrices arising from many scientific computing fields have block structure $W= \left(\begin{array}{cc} A & B\\ B^T & C \end{array} \right)$, where the sub-block $A$ is symmetric and positive definite, and $C$ is symmetric and semi-nonnegative definite. In this article we report a unobtrusive but potentially theoretically valuable conclusion that under some conditions, especially when $C$ is a zero matrix, the spectral radius of $W$ must be the maximum eigenvalue of $W$. This characterization approximates to the famous Perron-Frobenius property, and is called quasi-Perron-Frobenius property in this paper. In numerical tests we observe the saddle point matrices derived from some mixed finite element methods for computing the stationary Stokes equation. The numerical results confirm the theoretical analysis, and also indicate that the assumed condition to make the saddle point matrices possess quasi-Perron-Frobenius property is only sufficient rather than necessary