Add to ORKGWe consider the solution of large-scale least squares problems where the coefficient matrix comes from the discretization of an ill-posed operator and the right-hand size contains noise. Special techniques known as regularization methods are needed to treat these problems in order to control the effect of the noise on the solution. We pose the regularization problem as a trust-region subproblem and solve it by means of a recently developed method for the large-scale trust-region subproblem. We present numerical results on test problems, an inverse interpolation problem with real data, and a model seismic inversion problem with real data
The computation of an approximate solution of linear discrete illposed problems with contaminated da...
Large linear discrete ill-posed problems with contaminated data are often solved with the aid of Tik...
In this work, we introduce and investigate a class of matrix-free regularization techniques for disc...
A Large--Scale Trust--Region Approach to the Regularization of Discrete Ill--Posed Problems by Marie...
Abstract. Regularization of ill-posed problems is only possible if certain bounds on the data noise ...
The problem min ||x||, s.t. ||b-Ax||≤ ε arises in the regularization of discrete forms of ill-posed ...
International audienceIn this paper we address the stable numerical solution of nonlinear ill-posed ...
The straightforward solution of discrete ill-posed linear systems of equations or least-squares prob...
The trust-region subproblem of minimizing a quadratic function subject to a norm constraint arises i...
Abstract. Straightforward solution of discrete ill-posed linear systems of equations or least-square...
The trust-region subproblem of minimizing a quadratic function subject to a norm constraint arises i...
The trust-region subproblem of minimizing a quadratic function subject to a norm constraint arises i...
The computation of an approximate solution of linear discrete illposed problems with contaminated da...
Large linear discrete ill-posed problems with contaminated data are often solved with the aid of Tik...
In this work, we introduce and investigate a class of matrix-free regularization techniques for disc...
A Large--Scale Trust--Region Approach to the Regularization of Discrete Ill--Posed Problems by Marie...
Abstract. Regularization of ill-posed problems is only possible if certain bounds on the data noise ...
The problem min ||x||, s.t. ||b-Ax||≤ ε arises in the regularization of discrete forms of ill-posed ...
International audienceIn this paper we address the stable numerical solution of nonlinear ill-posed ...
The straightforward solution of discrete ill-posed linear systems of equations or least-squares prob...
The trust-region subproblem of minimizing a quadratic function subject to a norm constraint arises i...
Abstract. Straightforward solution of discrete ill-posed linear systems of equations or least-square...
The trust-region subproblem of minimizing a quadratic function subject to a norm constraint arises i...
The trust-region subproblem of minimizing a quadratic function subject to a norm constraint arises i...
The computation of an approximate solution of linear discrete illposed problems with contaminated da...
Large linear discrete ill-posed problems with contaminated data are often solved with the aid of Tik...
In this work, we introduce and investigate a class of matrix-free regularization techniques for disc...