We present general convergence results on the variational regularization and the Landweber iteration for inverse problems. First, we consider the variational regularization for inverse problems in a general form. We propose a heuristic parameter choice rule for choosing the regularization parameter which does not require the information on the noise level and is therefore purely data driven. Under variational source conditions, we obtain a posteriori error estimates. A variant of the same...[Show more]
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