Tikhonov regularization with oversmoothing penalty for non-linear ill-posed problems in Hilbert scales
It addresses a gap in regularization theory for non-linear inverse problems where the solution lacks the smoothness assumed by the penalty, which is relevant for practitioners applying Tikhonov methods.
The paper extends Tikhonov regularization theory for non-linear ill-posed problems in Hilbert scales to the oversmoothing penalty case, showing that order optimal reconstruction remains possible under appropriate assumptions.
We study the Tikhonov regularization for ill-posed non-linear operator equations in Hilbert scales. Our focus is on the interplay between the smoothness-promoting properties of the penalty and the smoothness inherent in the solution. The objective is to study the situation when the unknown solution fails to have a finite penalty value, hence when the penalty is oversmoothing. By now this case was only studied for linear operator equations in Hilbert scales. We extend those results to certain classes of non-linear problems. The main result asserts that under appropriate assumptions order optimal reconstruction is still possible. In an appendix we highlight that the non-linearity assumption underlying the present analysis is met for specific applications.