This paper is devoted to the numerical solution of large-scale linear ill-posed systems. A multilevel regularization method is proposed. This method is based on a synthesis of the Arnoldi-Tikhonov regularization technique and the multilevel technique. We show that if the Arnoldi-Tikhonov method is a regularization method, then the multilevel method is also a regularization one. Numerical experiments presented in this paper illustrate the effectiveness of the proposed method
This paper introduces a new strategy for setting the regularization parameter when solving large-sca...
For the solution of linear ill-posed problems, in this paper we introduce a simple algorithm for the...
For the solution of linear ill-posed problems, in this paper we introduce a simple algorithm for the...
AbstractTikhonov regularization for large-scale linear ill-posed problems is commonly implemented by...
Abstract. Large linear discrete ill-posed problems with contaminated data are often solved with the ...
AbstractFor the solution of full-rank ill-posed linear systems a new approach based on the Arnoldi a...
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...
Large linear discrete ill-posed problems with contaminated data are often solved with the aid of Tik...
Large linear discrete ill-posed problems with contaminated data are often solved with the aid of Tik...
Large linear discrete ill-posed problems with contaminated data are often solved with the aid of Tik...
Large linear discrete ill-posed problems with contaminated data are often solved with the aid of Tik...
Large linear discrete ill-posed problems with contaminated data are often solved with the aid of Tik...
Abstract. Several numerical methods for the solution of large linear ill-posed problems combine Tikh...
This paper introduces a new strategy for setting the regularization parameter when solving large-sca...
This paper introduces a new strategy for setting the regularization parameter when solving large-sca...
For the solution of linear ill-posed problems, in this paper we introduce a simple algorithm for the...
For the solution of linear ill-posed problems, in this paper we introduce a simple algorithm for the...
AbstractTikhonov regularization for large-scale linear ill-posed problems is commonly implemented by...
Abstract. Large linear discrete ill-posed problems with contaminated data are often solved with the ...
AbstractFor the solution of full-rank ill-posed linear systems a new approach based on the Arnoldi a...
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...
Large linear discrete ill-posed problems with contaminated data are often solved with the aid of Tik...
Large linear discrete ill-posed problems with contaminated data are often solved with the aid of Tik...
Large linear discrete ill-posed problems with contaminated data are often solved with the aid of Tik...
Large linear discrete ill-posed problems with contaminated data are often solved with the aid of Tik...
Large linear discrete ill-posed problems with contaminated data are often solved with the aid of Tik...
Abstract. Several numerical methods for the solution of large linear ill-posed problems combine Tikh...
This paper introduces a new strategy for setting the regularization parameter when solving large-sca...
This paper introduces a new strategy for setting the regularization parameter when solving large-sca...
For the solution of linear ill-posed problems, in this paper we introduce a simple algorithm for the...
For the solution of linear ill-posed problems, in this paper we introduce a simple algorithm for the...