Higher order convergence rates for Bregman iterated variational regularization of inverse problems

Abstract

We study the convergence of variationally regularized solutions to linear ill-posed operator equations in Banach spaces as the noise in the right hand side tends to $0$. The rate of this convergence is determined by abstract smoothness conditions on the solution called source conditions. For non-quadratic data fidelity or penalty terms such source conditions are often formulated in the form of variational inequalities. Such variational source conditions (VSCs) as well as other formulations of such conditions in Banach spaces have the disadvantage of yielding only low-order convergence rates. A first step towards higher order VSCs has been taken by Grasmair (2013) who obtained convergence rates up to the saturation of Tikhonov regularization. For even higher order convergence rates, iterated versions of variational regularization have to be considered. In this paper we introduce VSCs of arbitrarily high order which lead to optimal convergence rates in Hilbert spaces. For Bregman iterated variational regularization in Banach spaces with general data fidelity and penalty terms, we derive convergence rates under third order VSC. These results are further discussed for entropy regularization with elliptic pseudodifferential operators where the VSCs are interpreted in terms of Besov spaces and the optimality of the rates can be demonstrated. Our theoretical results are confirmed in numerical experiments.

References in corpus (6)

• Pointwise multiplication of Besov and Triebel--Lizorkin spaces
• Characterizations of variational source conditions, converse results, and maxisets of spectral regularization methods
• Verification of a variational source condition for acoustic inverse medium scattering problems
• Morozov's principle for the augmented Lagrangian method applied to linear inverse problems
• Convergence Rates for Inverse Problems with Impulsive Noise
• Regularization of Linear Ill-posed Problems by the Augmented Lagrangian Method and Variational Inequalities

Cited by in corpus (1)

• New Pair of Primal Dual Algorithms for Bregman Iterated Variational Regularization