Factorized sparse approximate inverse preconditionings I: theory
SIAM Journal on Matrix Analysis and Applications
Constraint Preconditioning for Indefinite Linear Systems
SIAM Journal on Matrix Analysis and Applications
Reducing the bandwidth of sparse symmetric matrices
ACM '69 Proceedings of the 1969 24th national conference
Preconditioning Indefinite Systems in Interior Point Methods for Optimization
Computational Optimization and Applications
Inexact constraint preconditioners for linear systems arising in interior point methods
Computational Optimization and Applications
Mixed Constraint Preconditioners for the iterative solution of FE coupled consolidation equations
Journal of Computational Physics
A comparison of projective and direct solvers for finite elements in elastostatics
Advances in Engineering Software
A massively parallel exponential integrator for advection-diffusion models
Journal of Computational and Applied Mathematics
Computational Optimization and Applications
Parallel acceleration of krylov solvers by factorized approximate inverse preconditioners
VECPAR'04 Proceedings of the 6th international conference on High Performance Computing for Computational Science
Banded target matrices and recursive FSAI for parallel preconditioning
Numerical Algorithms
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In this paper we propose a parallel implementation of the FSAI preconditioner to accelerate the PCG method in the solution of symmetric positive definite linear systems of very large size. This preconditioner is used as building block for the construction of an indefinite Inexact Constraint Preconditioner (ICP) for saddle point-type linear systems arising from Finite Element (FE) discretization of 3D coupled consolidation problems. The FSAI-ICP preconditioner, based on an efficient approximation of the inverse of the (1, 1) block proves very effective in the acceleration of the BiCGSTAB iterative solver in parallel environments. Numerical results on a number of realistic test cases of size up to 6×106 unknowns and 3×108 nonzeros show the almost perfect scalability of the overall code up to 512 processors.