For Tikhonov regularization of ill-posed nonlinear operator equations, convergence is studied in a Hilbert scale setting. We include the case of oversmoothing penalty terms, which means that the exact solution does not belong to the domain of definition of the considered penalty functional. In this case, we try to close a gap in the present theory, where Hölder-type convergence rates results have been proven under corresponding source conditions, but assertions on norm convergence for regularized solutions without source conditions are completely missing. A result of the present work is to provide sufficient conditions for convergence under a priori and a posteriori regularization parameter choice strategies without any additional smoothnes...
In the last decade l1-regularization became a powerful and popular tool for the regularization of In...
In the last decade l1-regularization became a powerful and popular tool for the regularization of In...
We construct with the aid of regularizing filters a new class of improved regularization methods, ca...
There exists a vast literature on convergence rates results for Tikhonov regularized minimizers. We ...
Abstract In the recent past the authors, with collaborators, have published convergence rate results...
Conditional stability estimates require additional regularization for obtaining stable approximate s...
Conditional stability estimates require additional regularization for obtaining stable approximate s...
We study the application of Tikhonov regularization to ill-posed nonlinear operator equations. The o...
When deriving rates of convergence for the approximations generated by the application of Tikhonov r...
In this paper we analyze two regularization methods for nonlinear ill-posed problems. The first is a...
The paper is devoted to the analysis of ill-posed operator equations Ax = y with injective linear op...
International audienceWe study a non-linear statistical inverse problem, where we observe the noisy ...
International audienceWe study a non-linear statistical inverse problem, where we observe the noisy ...
summary:We give a derivation of an a-posteriori strategy for choosing the regularization parameter i...
In the last decade l1-regularization became a powerful and popular tool for the regularization of In...
In the last decade l1-regularization became a powerful and popular tool for the regularization of In...
In the last decade l1-regularization became a powerful and popular tool for the regularization of In...
We construct with the aid of regularizing filters a new class of improved regularization methods, ca...
There exists a vast literature on convergence rates results for Tikhonov regularized minimizers. We ...
Abstract In the recent past the authors, with collaborators, have published convergence rate results...
Conditional stability estimates require additional regularization for obtaining stable approximate s...
Conditional stability estimates require additional regularization for obtaining stable approximate s...
We study the application of Tikhonov regularization to ill-posed nonlinear operator equations. The o...
When deriving rates of convergence for the approximations generated by the application of Tikhonov r...
In this paper we analyze two regularization methods for nonlinear ill-posed problems. The first is a...
The paper is devoted to the analysis of ill-posed operator equations Ax = y with injective linear op...
International audienceWe study a non-linear statistical inverse problem, where we observe the noisy ...
International audienceWe study a non-linear statistical inverse problem, where we observe the noisy ...
summary:We give a derivation of an a-posteriori strategy for choosing the regularization parameter i...
In the last decade l1-regularization became a powerful and popular tool for the regularization of In...
In the last decade l1-regularization became a powerful and popular tool for the regularization of In...
In the last decade l1-regularization became a powerful and popular tool for the regularization of In...
We construct with the aid of regularizing filters a new class of improved regularization methods, ca...