For a one-dimensional diffusion problem on an refined computational grid we present preconditioners based on the standard approximate inverse technique. Next, we determine its spectral condition number ¿2 and perform numerical calculations which corroborate the theoretical results. Then we perform numerical calculations which show that the standard approximate inverse preconditioners and our modified versions behave in a similar manner. To finish with we show that a combination of the standard approximate inverse with an additional incomplete factorisation leads to an almost optimal order preconditioner in 1–3 dimensions on refined grids, with and without dominant convection
The CG schemes ORTHOMIN(k), GCR(k) and MR properly preconditioned appear to be robust, reliable and ...
AbstractWe study some properties of block-circulant preconditioners for high-order compact approxima...
AbstractThe subject of this paper is an additive multilevel preconditioning approach for convection-...
For a one-dimensional diffusion problem on an refined computational grid we present preconditioners ...
For a one-dimensional diffusion problem on an refined computational grid we present preconditioners ...
The paper is devoted to the spectral analysis of effective preconditioners for linear systems obtain...
AbstractIterative methods preconditioned by incomplete factorizations and sparse approximate inverse...
Abstract. This paper introduces a strategy for automatically generating a block preconditioner for s...
We present a variant of the AINV factorized sparse approximate inverse algorithm which is applicable...
We consider the iterative solution of the linear systems arising from four convection-diffusion mode...
The class of preconditioning that approximates the inverse of the matrix A is studied in the thesis....
The efficient parallel solution to large sparse linear systems of equations Ax = b is a central issu...
This paper is concerned with a new approach to preconditioning for large, sparse linear systems. A p...
Single domain spectral/pseudospectral integration preconditioning matrices have been shown to be eff...
Preconditioned iterative methods have become standard linear solvers in many applications, but their...
The CG schemes ORTHOMIN(k), GCR(k) and MR properly preconditioned appear to be robust, reliable and ...
AbstractWe study some properties of block-circulant preconditioners for high-order compact approxima...
AbstractThe subject of this paper is an additive multilevel preconditioning approach for convection-...
For a one-dimensional diffusion problem on an refined computational grid we present preconditioners ...
For a one-dimensional diffusion problem on an refined computational grid we present preconditioners ...
The paper is devoted to the spectral analysis of effective preconditioners for linear systems obtain...
AbstractIterative methods preconditioned by incomplete factorizations and sparse approximate inverse...
Abstract. This paper introduces a strategy for automatically generating a block preconditioner for s...
We present a variant of the AINV factorized sparse approximate inverse algorithm which is applicable...
We consider the iterative solution of the linear systems arising from four convection-diffusion mode...
The class of preconditioning that approximates the inverse of the matrix A is studied in the thesis....
The efficient parallel solution to large sparse linear systems of equations Ax = b is a central issu...
This paper is concerned with a new approach to preconditioning for large, sparse linear systems. A p...
Single domain spectral/pseudospectral integration preconditioning matrices have been shown to be eff...
Preconditioned iterative methods have become standard linear solvers in many applications, but their...
The CG schemes ORTHOMIN(k), GCR(k) and MR properly preconditioned appear to be robust, reliable and ...
AbstractWe study some properties of block-circulant preconditioners for high-order compact approxima...
AbstractThe subject of this paper is an additive multilevel preconditioning approach for convection-...