AbstractFor the solution of full-rank ill-posed linear systems a new approach based on the Arnoldi algorithm is presented. Working with regularized systems, the method theoretically reconstructs the true solution by means of the computation of a suitable function of matrix. In this sense, the method can be referred to as an iterative refinement process. Numerical experiments arising from integral equations and interpolation theory are presented. Finally, the method is extended to work in connection with the standard Tikhonov regularization with the right-hand side contaminated by noise
Abstract. Large linear discrete ill-posed problems with contaminated data are often solved with the ...
The technique we propose for solving ill-conditioned linear systems consists of two steps. First we ...
In the framework of iterative regularization techniques for large-scale linear ill-posed problems, t...
For the solution of full-rank ill-posed linear systems a new approach based on the Arnoldi algorithm...
For the solution of full-rank ill-posed linear systems a new approach based on the Arnoldi algorithm...
AbstractTikhonov regularization for large-scale linear ill-posed problems is commonly implemented by...
This paper is devoted to the numerical solution of large-scale linear ill-posed systems. A multileve...
A novel preconditioned iterative method for solving discrete ill-posed problems, based on the Arnold...
A novel preconditioned iterative method for solving discrete ill-posed problems, based on the Arnold...
A novel preconditioned iterative method for solving discrete ill-posed problems, based on the Arnold...
A novel preconditioned iterative method for solving discrete ill-posed problems, based on the Arnold...
AbstractTikhonov regularization for large-scale linear ill-posed problems is commonly implemented by...
This paper introduces a new strategy for setting the regularization parameter when solving large-sca...
AbstractThis paper discusses the solution of large-scale linear discrete ill-posed problems with a n...
This paper introduces a new strategy for setting the regularization parameter when solving large-sca...
Abstract. Large linear discrete ill-posed problems with contaminated data are often solved with the ...
The technique we propose for solving ill-conditioned linear systems consists of two steps. First we ...
In the framework of iterative regularization techniques for large-scale linear ill-posed problems, t...
For the solution of full-rank ill-posed linear systems a new approach based on the Arnoldi algorithm...
For the solution of full-rank ill-posed linear systems a new approach based on the Arnoldi algorithm...
AbstractTikhonov regularization for large-scale linear ill-posed problems is commonly implemented by...
This paper is devoted to the numerical solution of large-scale linear ill-posed systems. A multileve...
A novel preconditioned iterative method for solving discrete ill-posed problems, based on the Arnold...
A novel preconditioned iterative method for solving discrete ill-posed problems, based on the Arnold...
A novel preconditioned iterative method for solving discrete ill-posed problems, based on the Arnold...
A novel preconditioned iterative method for solving discrete ill-posed problems, based on the Arnold...
AbstractTikhonov regularization for large-scale linear ill-posed problems is commonly implemented by...
This paper introduces a new strategy for setting the regularization parameter when solving large-sca...
AbstractThis paper discusses the solution of large-scale linear discrete ill-posed problems with a n...
This paper introduces a new strategy for setting the regularization parameter when solving large-sca...
Abstract. Large linear discrete ill-posed problems with contaminated data are often solved with the ...
The technique we propose for solving ill-conditioned linear systems consists of two steps. First we ...
In the framework of iterative regularization techniques for large-scale linear ill-posed problems, t...