In this paper‎, ‎an efficient dropping criterion has been used to compute the IUL factorization obtained from Backward Factored APproximate INVerse (BFAPINV) and ILU factorization obtained from Forward Factored APproximate INVerse (FFAPINV) algorithms‎. ‎We use different drop tolerance parameters to compute the preconditioners‎. ‎To study the effect of such a dropping on the quality of the ILU ...

in this paper‎, ‎an efficient dropping criterion has been used to compute the iul factorization obtained from backward factored approximate inverse (bfapinv) and ilu factorization obtained from forward factored approximate inverse (ffapinv) algorithms‎. ‎we use different drop tolerance parameters to compute the preconditioners‎. ‎to study the effect of such a dropping on the quality of the ilu ...

‎In this paper‎, ‎we use a complete pivoting strategy to compute the IUL preconditioner obtained as the by-product of the Backward Factored APproximate INVerse process‎. ‎This pivoting is based on the complete pivoting strategy of the Backward IJK version of Gaussian Elimination process‎. ‎There is a parameter $alpha$ to control the complete pivoting process‎. ‎We have studied the effect of dif...

Consider the linear system of equations of the form Ax = b where the coefficient matrix A ∈ Rn×n is nonsingular, large, sparse and nonsymmetric and also x, b ∈ R. We refer to this system as the original system. An explicit preconditioner M for this system is an approximation of matrix A−1. In [1], Lou presented the Backward Factored INVerse or BFINV algorithm which computes the inverse factoriz...

We present an incomplete UL IUL decomposition of matrixAwhich is extracted as a by-product of BFAPINV backward factored approximate inverse process. We term this IUL factorization as IULBF. We have used ILUFF 3 and IULBF as left preconditioner for linear systems. Different versions of ILUFF and IULBF preconditioners are computed by using different dropping techniques. In this paper, we compare ...

The approximate inverse AINV and the factored approximate inverse FAPINV are two known algorithms in the field of preconditioning of linear systems of equations. Both of these algorithms compute a sparse approximate inverse of matrix A in the factored form and are based on computing two sets of vectors which are A-biconjugate. The AINV algorithm computes the inverse factors W and Z of a matrix ...

In this paper we develop new techniques for stabilizing factored approximate inverse preconditioners (AINV) using pivoting. This method yields stable preconditioners in many cases and can provide successful preconditioners in many situations when the underlying system is highly indefinite. Numerical examples illustrate the effectiveness of this approach.

We develop an algorithm for computing a sparse approximate inverse for a nonsymmetric positive definite matrix based upon the FFAPINV algorithm. The sparse approximate inverse is computed in the factored form and used to work with some Krylov subspace methods. The preconditioner is breakdown free and, when used in conjunction with Krylovsubspace-based iterative solvers such as the GMRES algorit...

This paper discusses some relationships between Incomplete LU (ILU) factoriza-tion techniques and factored sparse approximate inverse (AINV) techniques. While ILU factorizations compute approximate LU factors of the coeecient matrix A, AINV techniques aim at building triangular matrices Z and W such that W > AZ is approximately diagonal. The paper shows that certain forms of approximate inverse...