Lavrentiev Regularization for Linear Ill-Posed Problems under General Source Conditions

In this paper we study the problem of identifying the solution x† of linear ill-posed problems Ax = y with non-negative and self-adjoint operators A on a Hilbert space X where instead of exact data y noisy data y ∈ X are given satisfying ‖y − y‖ ≤ δ with known noise level δ. Regularized approximations xα are obtained by the method of Lavrentiev regularization, that is, xα is the solution of the singularly perturbed operator equation Ax + αx = y, and the regularization parameter α is chosen either a priori or a posteriori by the rule of Raus. Assuming the unknown solution belongs to some general source set M we prove that the regularized approximations provide order optimal error bounds on the set M . Our results cover the special case of finitely smoothing operators A and extend recent results for infinitely smoothing operators. In addition, we generalize our results to the method of iterated Lavrentiev regularization of order m and discuss a special ill-posed problem arising in inverse heat conduction.