hide
Free keywords:
Regularization, nonlinear inverse problems, iteratively regularized Gauss–Newton method, non-Gaussian noise
Abstract:
We investigate a generalization of the well-known iteratively regularized Gauss–Newton method
where the Newton equations are regularized variationally using general data delity and penalty terms.
To obtain convergence rates, we use a general error assumption which has recently been shown to be useful
for impulsive and Poisson noise. We restrict the nonlinearity of the forward operator only by a Lipschitztype
condition and compare our results to other convergence rates results proven in the literature. Finally we
explicitly state our convergence rates for the aforementioned case of Poisson noise to shed some light on the
structure of the posed error assumption.
