Preconditioning techniques for large linear systems: a survey
Journal of Computational Physics
A Parallel Strongly Implicit Algorithm for Solving of Diffusion Equations
ParNum '99 Proceedings of the 4th International ACPC Conference Including Special Tracks on Parallel Numerics and Parallel Computing in Image Processing, Video Processing, and Multimedia: Parallel Computation
Ground Water Flow Modelling in PVM
Proceedings of the 6th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface
The effect of orderings on sparse approximate inverse preconditioners for non-symmetric problems
Advances in Engineering Software - Engineering computational technology
Stabilized approximate inverse preconditioners for indefinite matrices
Proceedings of the 46th Annual Southeast Regional Conference on XX
A two-phase preconditioning strategy of sparse approximate inverse for indefinite matrices
Computers & Mathematics with Applications
Adaptive Pattern Research for Block FSAI Preconditioning
SIAM Journal on Scientific Computing
A generalization of the optimal diagonal approximate inverse preconditioner
Computers & Mathematics with Applications
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We investigate the use of sparse approximate-inverse preconditioners for the iterative solution of unsymmetric linear systems of equations. We consider the approximations proposed by Cosgrove, Diaz, and Griewank [Internat. J. Comput. Math., 44 (1992), pp. 91--110] and Huckle and Grote [A New Approach to Parallel Preconditioning with Sparse Approximate Inverses, Tech. report SCCM-94-03, Stanford University, 1994] which are based on norm-minimization techniques. Such methods are of particular interest because of the considerable scope for parallelization. We propose a number of enhancements which may improve their performance. When run in a sequential environment, these methods can perform unfavorably when compared with other techniques. However, they can be successful when other methods fail and simulations indicate that they can be competitive when considered in a parallel environment.