Discrepancy principles for Tikhonov regularization of ill-posed problems leading to optimal convergence rates (Q1071480)

We propose a class of a posteriori parameter choice strategies for Tikhonov regularization (including variants of Morozov's and Arcangeli's methods) that lead to optimal convergence rates toward the minimal-norm least-squares solution of an ill-posed linear operator equation in the presence of noisy data.