Convergence rates for regularization of ill-posed problems in Banach spaces by approximate source conditions

Abstract

In this paper we deal with convergence rates for regularizing ill-posed problems with operator mapping from a Hilbert space into a Banach space. Since we cannot transfer the well-established convergence rates theory in Hilbert spaces, only few convergence rates results are known in the literature for this situation. Therefore we present an alternative approach for deriving convergence rates. Hereby we deal with so-called distance functions which quantify the violation of a reference source condition. With the aid of these functions we present error bounds and convergence rates for regularized solutions of linear and nonlinear problems when the reference source condition is not satisfied. We show that the approach of applying distance functions carries over the idea of considering generalized source conditions in Hilbert spaces to inverse problems in Banach spaces in a natural way. Introducing this topic for linear ill-posed problems we additionally show that this theory can be easily extended to nonlinear problems.