AbstractThe major drawback of GMRES is that the storage demands and the number of operations per iteration increase with the number of iterations. It is important to avoid that so many iterations are needed that the work per iteration and the storage requirements become unacceptably high. This paper describes a polynomial preconditioner with which this can be achieved efficiently. The polynomial preconditioner is constructed so that it has a minimization property in an area of the complex plane. A suitable area, and hence the preconditioning polynomial, can be obtained from eigenvalue estimates. The polynomial preconditioner is very simple and easy to implement
The GMRES(m) method is often used to compute Krylov subspace solutions of large sparse linear system...
AbstractPrecondition plays a critical role in the numerical methods for large and sparse linear syst...
Solving triangular systems is the building block for preconditioned GMRES algorithm. Inexact precond...
AbstractThe major drawback of GMRES is that the storage demands and the number of operations per ite...
Krylov subspace methods are often used to solve large, sparse systems of linear equations Ax=b. Prec...
We look at solving large nonsymmetric systems of linear equations using polynomial preconditioned Kr...
AbstractThis paper presents a new preconditioning technique for the restarted GMRES algorithm. It is...
AbstractThis paper presents a new preconditioning technique for the restarted GMRES algorithm. It is...
This paper presents a new preconditioning technique for solving linear systems. It is based on an in...
In a large number of scientific applications, the solution of sparse linear systems is the stage tha...
This paper studies polynomials used in polynomial preconditioning for solving linear systems of equa...
The GMRES(m) method is often used to compute Krylov subspace solutions of large sparse linear system...
The GMRES(m) method is often used to compute Krylov subspace solutions of large sparse linear system...
The GMRES(m) method is often used to compute Krylov subspace solutions of large sparse linear system...
The GMRES(m) method is often used to compute Krylov subspace solutions of large sparse linear system...
The GMRES(m) method is often used to compute Krylov subspace solutions of large sparse linear system...
AbstractPrecondition plays a critical role in the numerical methods for large and sparse linear syst...
Solving triangular systems is the building block for preconditioned GMRES algorithm. Inexact precond...
AbstractThe major drawback of GMRES is that the storage demands and the number of operations per ite...
Krylov subspace methods are often used to solve large, sparse systems of linear equations Ax=b. Prec...
We look at solving large nonsymmetric systems of linear equations using polynomial preconditioned Kr...
AbstractThis paper presents a new preconditioning technique for the restarted GMRES algorithm. It is...
AbstractThis paper presents a new preconditioning technique for the restarted GMRES algorithm. It is...
This paper presents a new preconditioning technique for solving linear systems. It is based on an in...
In a large number of scientific applications, the solution of sparse linear systems is the stage tha...
This paper studies polynomials used in polynomial preconditioning for solving linear systems of equa...
The GMRES(m) method is often used to compute Krylov subspace solutions of large sparse linear system...
The GMRES(m) method is often used to compute Krylov subspace solutions of large sparse linear system...
The GMRES(m) method is often used to compute Krylov subspace solutions of large sparse linear system...
The GMRES(m) method is often used to compute Krylov subspace solutions of large sparse linear system...
The GMRES(m) method is often used to compute Krylov subspace solutions of large sparse linear system...
AbstractPrecondition plays a critical role in the numerical methods for large and sparse linear syst...
Solving triangular systems is the building block for preconditioned GMRES algorithm. Inexact precond...