We consider a class of inexact Newton regularization methods for solving nonlinear inverse problems in Hilbert scales. Under certain conditions we obtain the order optimal convergence rate result
This paper develops truncated Newton methods as an appropriate tool for nonlinear inverse problems w...
Regularization is necessary for solving nonlinear ill-posed inverse problems arising in different fi...
AbstractRecently, a new iterative method, called Newton–Lavrentiev regularization (NLR) method, was ...
Inexact Newton regularization methods have been proposed by Hanke and Rieder for solving nonlinear i...
Inverse problems arise whenever one searches for unknown causes based on observation of their effect...
For solving linear ill-posed problems, regularization methods are required when the right-hand side ...
For solving linear ill-posed problems with noisy data, regularization methods are required. In the p...
In this paper we consider the iteratively regularized Gauss-Newton method for solving nonlinear ill-...
Recently, Vasin and George (2013) considered an iterative scheme for approximately solving an ill-po...
In this paper we investigate convergence of Landweber iteration in Hilbert scales for linear and non...
We consider a regularized Levenberg-Marquardt method for solving nonlinear ill-posed inverse problem...
We study the efficiency of the approximate solution of ill-posed problems, based on discretized nois...
AbstractIn this paper, an inexact Newton-type approach is proposed for solving inverse singular valu...
Abstract. Regularization of ill-posed problems is only possible if certain bounds on the data noise ...
In this paper, an inexact Newton-type approach is proposed for solving inverse singular value proble...
This paper develops truncated Newton methods as an appropriate tool for nonlinear inverse problems w...
Regularization is necessary for solving nonlinear ill-posed inverse problems arising in different fi...
AbstractRecently, a new iterative method, called Newton–Lavrentiev regularization (NLR) method, was ...
Inexact Newton regularization methods have been proposed by Hanke and Rieder for solving nonlinear i...
Inverse problems arise whenever one searches for unknown causes based on observation of their effect...
For solving linear ill-posed problems, regularization methods are required when the right-hand side ...
For solving linear ill-posed problems with noisy data, regularization methods are required. In the p...
In this paper we consider the iteratively regularized Gauss-Newton method for solving nonlinear ill-...
Recently, Vasin and George (2013) considered an iterative scheme for approximately solving an ill-po...
In this paper we investigate convergence of Landweber iteration in Hilbert scales for linear and non...
We consider a regularized Levenberg-Marquardt method for solving nonlinear ill-posed inverse problem...
We study the efficiency of the approximate solution of ill-posed problems, based on discretized nois...
AbstractIn this paper, an inexact Newton-type approach is proposed for solving inverse singular valu...
Abstract. Regularization of ill-posed problems is only possible if certain bounds on the data noise ...
In this paper, an inexact Newton-type approach is proposed for solving inverse singular value proble...
This paper develops truncated Newton methods as an appropriate tool for nonlinear inverse problems w...
Regularization is necessary for solving nonlinear ill-posed inverse problems arising in different fi...
AbstractRecently, a new iterative method, called Newton–Lavrentiev regularization (NLR) method, was ...
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