An a posteriori parameter choice for ordinary and iterated Tikhonov regularization of ill-posed problems leading to optimal convergence rates

Author:

Gfrerer Helmut

Abstract

We propose an a posteriori parameter choice for ordinary and iterated Tikhonov regularization that leads to optimal rates of convergence towards the best approximate solution of an ill-posed linear operator equation in the presence of noisy data. Numerical examples are given.

Publisher

American Mathematical Society (AMS)

Subject

Applied Mathematics,Computational Mathematics,Algebra and Number Theory

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