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Iterated Lavrentiev regularization under random noise: variational source conditions and rates in expectation
The convergence theory of iterated Lavrentiev regularization for nonlinear monotone ill-posed equations is deterministic: the data error is bounded by a known level that the discrepancy principle uses explicitly. Under Gaussian noise no such bound exists. For a discretized two-dimensional nonlinear
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