In this paper we consider the problem of approximating the solution of infinite linear systems, finitely expressed by a sparse coefficient matrix. We analyse an algorithm based on Krylov subspace methods embedded in an adaptive enlargement scheme. The management of the algorithm is not trivial, due to the irregular convergence behaviour frequently displayed by Krylov subspace methods for nonsymmetric systems. Numerical experiments, carried out on several test problems, indicate that the more robust methods, such as GMRES and QMR, embedded in the adaptive enlargement scheme, exhibit good performances. (C) 2006 Elsevier B.V. All rights reserved
Abstract. We give two important generalizations of the Induced Dimension Reduction (IDR) approach fo...
There is a class of linear problems for which the computation of the matrix-vector product is very ...
AbstractKrylov subspace methods have been recently considered to solve singular linear systems Ax=b....
In this paper we consider the problem of approximating the solution of infinite linear systems, fini...
AbstractIn this paper we consider the problem of approximating the solution of infinite linear syste...
AbstractIn this paper we consider the problem of approximating the solution of infinite linear syste...
AbstractThe problem of approximating the solution of infinite linear systems finitely expressed by a...
Projection methods are the most widely used methods for computing a few of the extreme eigenvalues o...
Projection methods are the most widely used methods for computing a few of the extreme eigenvalues o...
The problem of approximating the solution of infinite linear systems finitely expressed by a sparse ...
Projection methods are the most widely used methods for computing a few of the extreme eigenvalues o...
Abstract. There is a class of linear problems for which the computation of the matrix-vector product...
Projection methods are the most widely used methods for computing a few of the extreme eigenvalues o...
Abstract. There is a class of linear problems for which the computation of the matrix-vector product...
Consider solving a sequence of linear systems A_{(i)}x^{(i)}=b^{(i)}, i=1, 2, ... where A₍ᵢ₎ ϵℂⁿᵡⁿ ...
Abstract. We give two important generalizations of the Induced Dimension Reduction (IDR) approach fo...
There is a class of linear problems for which the computation of the matrix-vector product is very ...
AbstractKrylov subspace methods have been recently considered to solve singular linear systems Ax=b....
In this paper we consider the problem of approximating the solution of infinite linear systems, fini...
AbstractIn this paper we consider the problem of approximating the solution of infinite linear syste...
AbstractIn this paper we consider the problem of approximating the solution of infinite linear syste...
AbstractThe problem of approximating the solution of infinite linear systems finitely expressed by a...
Projection methods are the most widely used methods for computing a few of the extreme eigenvalues o...
Projection methods are the most widely used methods for computing a few of the extreme eigenvalues o...
The problem of approximating the solution of infinite linear systems finitely expressed by a sparse ...
Projection methods are the most widely used methods for computing a few of the extreme eigenvalues o...
Abstract. There is a class of linear problems for which the computation of the matrix-vector product...
Projection methods are the most widely used methods for computing a few of the extreme eigenvalues o...
Abstract. There is a class of linear problems for which the computation of the matrix-vector product...
Consider solving a sequence of linear systems A_{(i)}x^{(i)}=b^{(i)}, i=1, 2, ... where A₍ᵢ₎ ϵℂⁿᵡⁿ ...
Abstract. We give two important generalizations of the Induced Dimension Reduction (IDR) approach fo...
There is a class of linear problems for which the computation of the matrix-vector product is very ...
AbstractKrylov subspace methods have been recently considered to solve singular linear systems Ax=b....