For solving linear ill-posed problems with noisy data, regularization methods are required. In the present paper, regularized approximations in Hilbert scales are obtained by a general regularization scheme. The analysis of such schemes is based on new results for interpolation in Hilbert scales. Error bounds are obtained under general smoothness conditions
Kernel based regularized interpolation is one of the most important methods for approximating functi...
The straightforward solution of discrete ill-posed linear systems of equations or least-squares prob...
We study the efficiency of the approximate solution of ill-posed problems, based on discretized obse...
For solving linear ill-posed problems with noisy data regularization methods are required. In the p...
For solving linear ill-posed problems, regularization methods are required when the right-hand side ...
Based on the variable Hilbert scale interpolation inequality, bounds for the error of regularisation...
AbstractVariable Hilbert scales are an important tool for the recent analysis of inverse problems in...
Abstract. Variable Hilbert scales are an important tool for the recent analysis of inverse problems ...
We study the efficiency of the approximate solution of ill-posed problems, based on discretized nois...
Simplified regularization using finite-dimensional approximations in the setting of Hilbert scales h...
Abstract. Regularization of ill-posed problems is only possible if certain bounds on the data noise ...
We consider a class of inexact Newton regularization methods for solving nonlinear inverse problems ...
AbstractRichardson's “extrapolation to the limit” idea is applied to the method of regularization fo...
In the analysis of ill-posed inverse problems the impact of solution smoothness on accuracy and conv...
Abstract. This paper is concerned with a novel regularisation technique for solving linear ill-posed...
Kernel based regularized interpolation is one of the most important methods for approximating functi...
The straightforward solution of discrete ill-posed linear systems of equations or least-squares prob...
We study the efficiency of the approximate solution of ill-posed problems, based on discretized obse...
For solving linear ill-posed problems with noisy data regularization methods are required. In the p...
For solving linear ill-posed problems, regularization methods are required when the right-hand side ...
Based on the variable Hilbert scale interpolation inequality, bounds for the error of regularisation...
AbstractVariable Hilbert scales are an important tool for the recent analysis of inverse problems in...
Abstract. Variable Hilbert scales are an important tool for the recent analysis of inverse problems ...
We study the efficiency of the approximate solution of ill-posed problems, based on discretized nois...
Simplified regularization using finite-dimensional approximations in the setting of Hilbert scales h...
Abstract. Regularization of ill-posed problems is only possible if certain bounds on the data noise ...
We consider a class of inexact Newton regularization methods for solving nonlinear inverse problems ...
AbstractRichardson's “extrapolation to the limit” idea is applied to the method of regularization fo...
In the analysis of ill-posed inverse problems the impact of solution smoothness on accuracy and conv...
Abstract. This paper is concerned with a novel regularisation technique for solving linear ill-posed...
Kernel based regularized interpolation is one of the most important methods for approximating functi...
The straightforward solution of discrete ill-posed linear systems of equations or least-squares prob...
We study the efficiency of the approximate solution of ill-posed problems, based on discretized obse...
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