For the solution of linear discrete ill-posed problems, in this paper we consider the Arnoldi-Tikhonov method coupled with the Generalized Cross Validation for the computation of the regularization parameter at each iteration. We study the convergence behavior of the Arnoldi method and its properties for the approximation of the (generalized) singular values, under the hypothesis that Picard condition is satisfied. Numerical experiments on classical test problems and on image restoration are presented
In the framework of iterative regularization techniques for large-scale linear ill-posed problems, t...
In the present work, we study the determination of the regularization parameter and the computation ...
AbstractThis paper discusses the solution of large-scale linear discrete ill-posed problems with a n...
For the solution of linear discrete ill-posed problems, in this paper we consider the Arnoldi-Tikhon...
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...
Tikhonov regularization is commonly used for the solution of linear discrete ill-posed problems with...
Abstract. Large linear discrete ill-posed problems with contaminated data are often solved with the ...
Tikhonov regularization is commonly used for the solution of linear discrete ill-posed problems with...
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...
In the framework of iterative regularization techniques for large-scale linear ill-posed problems, t...
Large linear discrete ill-posed problems with contaminated data are often solved with the aid of Tik...
In the framework of iterative regularization techniques for large-scale linear ill-posed problems, t...
In the present work, we study the determination of the regularization parameter and the computation ...
AbstractThis paper discusses the solution of large-scale linear discrete ill-posed problems with a n...
For the solution of linear discrete ill-posed problems, in this paper we consider the Arnoldi-Tikhon...
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...
Tikhonov regularization is commonly used for the solution of linear discrete ill-posed problems with...
Abstract. Large linear discrete ill-posed problems with contaminated data are often solved with the ...
Tikhonov regularization is commonly used for the solution of linear discrete ill-posed problems with...
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...
In the framework of iterative regularization techniques for large-scale linear ill-posed problems, t...
Large linear discrete ill-posed problems with contaminated data are often solved with the aid of Tik...
In the framework of iterative regularization techniques for large-scale linear ill-posed problems, t...
In the present work, we study the determination of the regularization parameter and the computation ...
AbstractThis paper discusses the solution of large-scale linear discrete ill-posed problems with a n...