On the rate of convergence of the preconditioned conjugate gradient method
Numerische Mathematik
Topics in matrix analysis
Journal of the ACM (JACM)
A comparison theorem for the iterative method with the preconditioner (I + Smax)
Journal of Computational and Applied Mathematics
Block Gauss elimination followed by a classical iterative method for the solution of linear systems
Journal of Computational and Applied Mathematics
Journal of Computational and Applied Mathematics - Special Issue: Proceedings of the 10th international congress on computational and applied mathematics (ICCAM-2002)
Comparison results for solving preconditioned linear systems
Journal of Computational and Applied Mathematics
Block Gauss elimination followed by a classical iterative method for the solution of linear systems
Journal of Computational and Applied Mathematics
Journal of Computational and Applied Mathematics
Letter to the editor: Comment on 'A comparison theorem of the SOR iterative method'
Journal of Computational and Applied Mathematics
Hi-index | 7.29 |
Many researchers have considered preconditioners, applied to linear systems, whose matrix coefficient is a Z- or an M-matrix, that make the associated Jacobi and Gauss-Seidel methods converge asymptotically faster than the unpreconditioned ones. Such preconditioners are chosen so that they eliminate the off-diagonal elements of the same column or the elements of the first upper diagonal [Milaszewicz, LAA 93 (1987) 161-170], Gunawardena et al. [LAA 154-156 (1991) 123-143]. In this work we generalize the previous preconditioners to obtain optimal methods. ''Good'' Jacobi and Gauss-Seidel algorithms are given and preconditioners, that eliminate more than one entry per row, are also proposed and analyzed. Moreover, the behavior of the above preconditioners to the Krylov subspace methods is studied.