In this preprint we deal with convergence rates for regularizing linear and
nonlinear ill-posed problems with operators mapping from a Hilbert space into a
Banach space. Thereby 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 non-linear problems when the reference source condition is not
satisfied. The way of applying distance functions transfers 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 as well as to more general penalty terms using Bregman
distances. Moreover, the application of the discrepancy principle as a
posteriori choice strategy of the regularization parameter is discussed for
both linear and nonlinear problems.