International audienceIn this paper we present a class of robust and fully algebraic two-level preconditioners for symmetric positive definite (SPD) matrices. We introduce the notion of algebraic local symmetric positive semidefinite splitting of an SPD matrix and we give a characterization of this splitting. This splitting leads to construct algebraically and locally a class of efficient coarse spaces which bound the spectral condition number of the preconditioned system by a number defined a priori. We also introduce the $\tau$-filtering subspace. This concept helps compare the dimension minimality of coarse spaces. Some PDEs-dependant preconditioners correspond to a special case. The examples of the algebraic coarse spaces in this paper ...
. We consider the problem of computing a modest number of the smallest eigenvalues along with orthog...
The use of factorized sparse approximate inverse (FSAI) preconditioners in a standard multilevel fra...
We present support theory, a set of techniques for bounding extreme eigenvalues and condition number...
International audienceIn this paper we present a class of robust and fully algebraic two-level preco...
In this paper we present a class of robust and fully algebraic two-level preconditionersfor SPD matr...
In this article a new family of preconditioners is introduced for symmetric positive definite linear...
International audienceA new domain decomposition preconditioner is introduced for efficiently solvin...
International audienceIn this paper we present a multilevel preconditioner based on overlapping Schw...
Abstract. An abstract setting for robustly preconditioning symmetric positive definite (SPD) operato...
We consider an incomplete Cholesky factorization preconditioner for the iterative solution of large ...
We propose a class of preconditioners for large positive definite linear systems, arising in nonlin...
We present a preconditioning method for the iterative solution of large sparse systems of equations....
A novel parallel preconditioner for symmetric positive definite matrices is developed coupling a gen...
This work studies limited memory preconditioners for linear symmetric positive definite systems of e...
An abstract framework for constructing stable decompositions of the spaces corresponding t...
. We consider the problem of computing a modest number of the smallest eigenvalues along with orthog...
The use of factorized sparse approximate inverse (FSAI) preconditioners in a standard multilevel fra...
We present support theory, a set of techniques for bounding extreme eigenvalues and condition number...
International audienceIn this paper we present a class of robust and fully algebraic two-level preco...
In this paper we present a class of robust and fully algebraic two-level preconditionersfor SPD matr...
In this article a new family of preconditioners is introduced for symmetric positive definite linear...
International audienceA new domain decomposition preconditioner is introduced for efficiently solvin...
International audienceIn this paper we present a multilevel preconditioner based on overlapping Schw...
Abstract. An abstract setting for robustly preconditioning symmetric positive definite (SPD) operato...
We consider an incomplete Cholesky factorization preconditioner for the iterative solution of large ...
We propose a class of preconditioners for large positive definite linear systems, arising in nonlin...
We present a preconditioning method for the iterative solution of large sparse systems of equations....
A novel parallel preconditioner for symmetric positive definite matrices is developed coupling a gen...
This work studies limited memory preconditioners for linear symmetric positive definite systems of e...
An abstract framework for constructing stable decompositions of the spaces corresponding t...
. We consider the problem of computing a modest number of the smallest eigenvalues along with orthog...
The use of factorized sparse approximate inverse (FSAI) preconditioners in a standard multilevel fra...
We present support theory, a set of techniques for bounding extreme eigenvalues and condition number...