# Case studies and a pitfall for nonlinear variational regularization under conditional stability

## TL;DR

This paper investigates the use of conditional stability estimates in nonlinear Tikhonov regularization, highlighting potential pitfalls and demonstrating convergence behavior through theoretical analysis and numerical case studies.

## Contribution

It provides a detailed analysis of convergence and rates in nonlinear Tikhonov regularization under conditional stability, emphasizing the importance of correct stability estimates.

## Key findings

- Oversmoothing penalties can achieve optimal convergence rates with modified assumptions.
- Incorrect stability estimates can lead to failure of convergence.
- Numerical examples illustrate potential pitfalls in applying conditional stability.

## Abstract

Conditional stability estimates are a popular tool for the regularization of ill-posed problems. A drawback in particular under nonlinear operators is that additional regularization is needed for obtaining stable approximate solutions if the validity area of such estimates is not completely known. In this paper we consider Tikhonov regularization under conditional stability estimates for nonlinear ill-posed operator equations in Hilbert scales. We summarize assertions on convergence and convergence rate in three cases describing the relative smoothness of the penalty in the Tikhonov functional and of the exact solution. For oversmoothing penalties, for which the rue solution no longer attains a finite value, we present a result with modified assumptions for a priori choices of the regularization parameter yielding convergence rates of optimal order for noisy data. We strongly highlight the local character of the conditional stability estimate and demonstrate that pitfalls may occur through incorrect stability estimates. Then convergence can completely fail and the stabilizing effect of conditional stability may be lost. Comprehensive numerical case studies for some nonlinear examples illustrate such effects.