We study the application of Tikhonov regularization to ill-posed nonlinear operator equations. The objective of this work is to prove low order convergence rates for the discrepancy principle under low order source conditions of logarithmic type. We work within the framework of Hilbert scales and extend existing studies on this subject to the oversmoothing case. The latter means that the exact solution of the treated operator equation does not belong to the domain of definition of the penalty term. As a consequence, the Tikhonov functional fails to have a finite value
The paper is devoted to the analysis of ill-posed operator equations Ax = y with injective linear op...
summary:We give a derivation of an a-posteriori strategy for choosing the regularization parameter i...
We study the efficiency of the approximate solution of ill-posed problems, based on discretized obse...
For Tikhonov regularization of ill-posed nonlinear operator equations, convergence is studied in a H...
It is shown that Tikhonov regularization for ill- posed operator equation \(Kx = y\) using a possib...
The stable solution of ill-posed non-linear operator equations in Banach space requires regularizati...
AbstractWe discuss adaptive strategies for choosing regularization parameters in Tikhonov–Phillips r...
In the present work, we discuss variational regularization for ill-posed nonlinear problems with foc...
Abstract In the recent past the authors, with collaborators, have published convergence rate results...
We revisit in L2-spaces the autoconvolution equation x ∗ x = y with solutions which are real-valued ...
Conditional stability estimates require additional regularization for obtaining stable approximate s...
Tikhonov regularization is one of the most popular methods for computing approximate solutions of l...
There are various ways to regularize ill-posed operator equations in Hilbert space. If the underlyin...
Recently, Tautenhahn and Hämarik (1999) have considered a monotone rule as a parameter choice strate...
For solving linear ill-posed problems with noisy data regularization methods are required. In the p...
The paper is devoted to the analysis of ill-posed operator equations Ax = y with injective linear op...
summary:We give a derivation of an a-posteriori strategy for choosing the regularization parameter i...
We study the efficiency of the approximate solution of ill-posed problems, based on discretized obse...
For Tikhonov regularization of ill-posed nonlinear operator equations, convergence is studied in a H...
It is shown that Tikhonov regularization for ill- posed operator equation \(Kx = y\) using a possib...
The stable solution of ill-posed non-linear operator equations in Banach space requires regularizati...
AbstractWe discuss adaptive strategies for choosing regularization parameters in Tikhonov–Phillips r...
In the present work, we discuss variational regularization for ill-posed nonlinear problems with foc...
Abstract In the recent past the authors, with collaborators, have published convergence rate results...
We revisit in L2-spaces the autoconvolution equation x ∗ x = y with solutions which are real-valued ...
Conditional stability estimates require additional regularization for obtaining stable approximate s...
Tikhonov regularization is one of the most popular methods for computing approximate solutions of l...
There are various ways to regularize ill-posed operator equations in Hilbert space. If the underlyin...
Recently, Tautenhahn and Hämarik (1999) have considered a monotone rule as a parameter choice strate...
For solving linear ill-posed problems with noisy data regularization methods are required. In the p...
The paper is devoted to the analysis of ill-posed operator equations Ax = y with injective linear op...
summary:We give a derivation of an a-posteriori strategy for choosing the regularization parameter i...
We study the efficiency of the approximate solution of ill-posed problems, based on discretized obse...