Ankit Singh

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 inverse source probl...