The authors describe and test spai_1.1, a parallel MPI implementation of the sparse approximate inverse (SPAI) preconditioner. They show that SPAI can be very effective for solving a set of very large and difficult problems on a Cray T3E. The results clearly show the value of SPAI (and approximate inverse methods in general) as the viable alternative to ILU-type methods when facing very large and difficult problems. The authors strengthen this conclusion by showing that spai_1.1 also has very good scaling behavior
Preconditioning techniques based on ILU decomposition, on Frobenius norm minimization and on factori...
A sparse approximate inverse technique is introduced to solve general sparse linear systems. The spa...
Simulation with models based on partial differential equations often requires the solution of (seque...
A parallel implementation of a sparse approximate inverse (spai) preconditioner for distributed memo...
We introduce a novel strategy for parallel preconditioning of large-scale linear systems by means of...
We investigate the use of sparse approximate inverse techniques in a multilevel block ILU preconditi...
A number of recently proposed preconditioning techniques based on sparse approximate inverses are co...
We present the results of numerical experiments aimed at comparing two recently proposed sparse appr...
The efficient parallel solution to large sparse linear systems of equations Ax = b is a central issu...
Abstract. We investigate the use of sparse approximate-inverse preconditioners for the iterative sol...
AbstractAn enhanced version of a stochastic SParse Approximate Inverse (SPAI) preconditioner for gen...
Solving sparse triangular systems is the building block for incomplete LU- (ILU-) based precondition...
In this paper, we analyze the properties of the sparse approximate inverse precon-ditioner, and prov...
Accelerating numerical algorithms for solving sparse linear systems on parallel architectures has at...
A novel parallel preconditioner combining a generalized Factored Sparse Approximate Inverse (FSAI) w...
Preconditioning techniques based on ILU decomposition, on Frobenius norm minimization and on factori...
A sparse approximate inverse technique is introduced to solve general sparse linear systems. The spa...
Simulation with models based on partial differential equations often requires the solution of (seque...
A parallel implementation of a sparse approximate inverse (spai) preconditioner for distributed memo...
We introduce a novel strategy for parallel preconditioning of large-scale linear systems by means of...
We investigate the use of sparse approximate inverse techniques in a multilevel block ILU preconditi...
A number of recently proposed preconditioning techniques based on sparse approximate inverses are co...
We present the results of numerical experiments aimed at comparing two recently proposed sparse appr...
The efficient parallel solution to large sparse linear systems of equations Ax = b is a central issu...
Abstract. We investigate the use of sparse approximate-inverse preconditioners for the iterative sol...
AbstractAn enhanced version of a stochastic SParse Approximate Inverse (SPAI) preconditioner for gen...
Solving sparse triangular systems is the building block for incomplete LU- (ILU-) based precondition...
In this paper, we analyze the properties of the sparse approximate inverse precon-ditioner, and prov...
Accelerating numerical algorithms for solving sparse linear systems on parallel architectures has at...
A novel parallel preconditioner combining a generalized Factored Sparse Approximate Inverse (FSAI) w...
Preconditioning techniques based on ILU decomposition, on Frobenius norm minimization and on factori...
A sparse approximate inverse technique is introduced to solve general sparse linear systems. The spa...
Simulation with models based on partial differential equations often requires the solution of (seque...