The paper is devoted to the analysis of ill-posed operator equations Ax = y with injective linear operator A and solution x0 in a Hilbert space setting. We present some new ideas and results for finding convergence rates in Tikhonov regularization based on the concept of approximate source conditions by means of using distance functions with a general benchmark. For the case of compact operator A and bench-mark functions of power-type we can show that there is a one-to-one correspondence between the maximal power-type decay rate of the distance function and the best possible Hölder exponent for the noise-free convergence rate in Tikhonov regular-ization. As is well-known this exponent coincides with the supremum of exponents in power-type s...
In this paper we analyze two regularization methods for nonlinear ill-posed problems. The first is a...
We construct with the aid of regularizing filters a new class of improved regularization methods, ca...
We revisit in L2-spaces the autoconvolution equation x ∗ x = y with solutions which are real-valued ...
Abstract In the recent past the authors, with collaborators, have published convergence rate results...
We present some new ideas and results for finding convergence rates in Tikhonov regularization for i...
For Tikhonov regularization of ill-posed nonlinear operator equations, convergence is studied in a H...
There exists a vast literature on convergence rates results for Tikhonov regularized minimizers. We ...
There are various ways to regularize ill-posed operator equations in Hilbert space. If the underlyin...
We study compact operator equations with noisy data in Hilbert space. Instead of assuming that the e...
When deriving rates of convergence for the approximations generated by the application of Tikhonov r...
We study compact operator equations with noisy data in Hilbert space. Instead of assuming that the e...
In the analysis of ill-posed inverse problems the impact of solution smoothness on accuracy and conv...
The stable approximate solution of ill-posed linear operator equations in Hilbert spaces requires re...
In recent years, a series of convergence rates conditions for regulariza-tion methods has been devel...
We study the application of Tikhonov regularization to ill-posed nonlinear operator equations. The o...
In this paper we analyze two regularization methods for nonlinear ill-posed problems. The first is a...
We construct with the aid of regularizing filters a new class of improved regularization methods, ca...
We revisit in L2-spaces the autoconvolution equation x ∗ x = y with solutions which are real-valued ...
Abstract In the recent past the authors, with collaborators, have published convergence rate results...
We present some new ideas and results for finding convergence rates in Tikhonov regularization for i...
For Tikhonov regularization of ill-posed nonlinear operator equations, convergence is studied in a H...
There exists a vast literature on convergence rates results for Tikhonov regularized minimizers. We ...
There are various ways to regularize ill-posed operator equations in Hilbert space. If the underlyin...
We study compact operator equations with noisy data in Hilbert space. Instead of assuming that the e...
When deriving rates of convergence for the approximations generated by the application of Tikhonov r...
We study compact operator equations with noisy data in Hilbert space. Instead of assuming that the e...
In the analysis of ill-posed inverse problems the impact of solution smoothness on accuracy and conv...
The stable approximate solution of ill-posed linear operator equations in Hilbert spaces requires re...
In recent years, a series of convergence rates conditions for regulariza-tion methods has been devel...
We study the application of Tikhonov regularization to ill-posed nonlinear operator equations. The o...
In this paper we analyze two regularization methods for nonlinear ill-posed problems. The first is a...
We construct with the aid of regularizing filters a new class of improved regularization methods, ca...
We revisit in L2-spaces the autoconvolution equation x ∗ x = y with solutions which are real-valued ...