In this paper, first, by using the diagonally compensated reduction and incomplete Cholesky factorization methods, we construct a constraint preconditioner for solving symmetric positive definite linear systems and then we apply the preconditioner to solve the Helmholtz equations and Poisson equations. Second, according to theoretical analysis, we prove that the preconditioned iteration method is convergent. Third, in numerical experiments, we plot the distribution of the spectrum of the preconditioned matrix M−1A and give the solution time and number of iterations comparing to the results of [5, 19]. First published online: 09 Jun 201
Abstract. We introduce a novel strategy for constructing symmetric positive definite (SPD) precondit...
3siIn this paper, preconditioners for the conjugate gradient method are studied to solve the Newton ...
In this paper, we study the use of an incomplete Cholesky factorization (ICF) as a preconditioner fo...
International audienceA new domain decomposition preconditioner is introduced for efficiently solvin...
This work studies limited memory preconditioners for linear symmetric positive definite systems of e...
We present a preconditioning method for the iterative solution of large sparse systems of equations....
We consider an incomplete Cholesky factorization preconditioner for the iterative solution of large ...
A novel parallel preconditioner for symmetric positive definite matrices is developed coupling a gen...
We describe a novel technique for computing a sparse incomplete factorization of a general symmetric...
Iterative methods for solving large-scale linear systems have been gaining popularity in many areas ...
In this report we give new insights into the properties of invertible and singular deflated and prec...
We consider the problem of solving a symmetric, positive def-inite system of linear equations. The m...
Abstract. Most discretizations of the Helmholtz equation result in a system of linear equations that...
Abstract: In the paper we consider the iterative solution of linear systemby the conjugate...
Abstract. We develop a simple algorithmic framework to solve large-scale symmetric positive definite...
Abstract. We introduce a novel strategy for constructing symmetric positive definite (SPD) precondit...
3siIn this paper, preconditioners for the conjugate gradient method are studied to solve the Newton ...
In this paper, we study the use of an incomplete Cholesky factorization (ICF) as a preconditioner fo...
International audienceA new domain decomposition preconditioner is introduced for efficiently solvin...
This work studies limited memory preconditioners for linear symmetric positive definite systems of e...
We present a preconditioning method for the iterative solution of large sparse systems of equations....
We consider an incomplete Cholesky factorization preconditioner for the iterative solution of large ...
A novel parallel preconditioner for symmetric positive definite matrices is developed coupling a gen...
We describe a novel technique for computing a sparse incomplete factorization of a general symmetric...
Iterative methods for solving large-scale linear systems have been gaining popularity in many areas ...
In this report we give new insights into the properties of invertible and singular deflated and prec...
We consider the problem of solving a symmetric, positive def-inite system of linear equations. The m...
Abstract. Most discretizations of the Helmholtz equation result in a system of linear equations that...
Abstract: In the paper we consider the iterative solution of linear systemby the conjugate...
Abstract. We develop a simple algorithmic framework to solve large-scale symmetric positive definite...
Abstract. We introduce a novel strategy for constructing symmetric positive definite (SPD) precondit...
3siIn this paper, preconditioners for the conjugate gradient method are studied to solve the Newton ...
In this paper, we study the use of an incomplete Cholesky factorization (ICF) as a preconditioner fo...