GMRES: a generalized minimal residual algorithm for solving nonsymmetric linear systems
SIAM Journal on Scientific and Statistical Computing
LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares
ACM Transactions on Mathematical Software (TOMS)
Preconditioned Conjugate Gradient Methods for General Sparse Matrices on Shared Memory Machines
Proceedings of the Third SIAM Conference on Parallel Processing for Scientific Computing
Iterative methods for large, sparse, nonsymmetric systems of linear equations
Iterative methods for large, sparse, nonsymmetric systems of linear equations
Schur complement preconditioned conjugate gradient methods for spline collocation equations
ICS '90 Proceedings of the 4th international conference on Supercomputing
| Hi-index | 0.00 |
An iterative method for the solution of nonsymmetric linear systems of equations is described and tested. The method, block symmetric successive over-relaxation with conjugate gradient acceleration (BSSOR), is remarkably robust and when applied to block tridiagonal systems allows parallelism in the computations. BSSOR compares favorably to unpreconditioned conjugate gradient-like algorithms in efficiency, and although generally slower than preconditioned methods it is far more reliable. The concept behind BSSOR can, in general, be applied to sparse linear systems (even if they are singular), sparse nonlinear systems of equations and least squares problems.