Work

Convergence rates for regularization functionals with polyconvex integrands

Year: 2017

Type: article

Abstract: Convergence rates results for variational regularization methods typically assume the regularization functional to be convex. While this assumption is natural for scalar-valued functions, it can be unnecessarily strong for vector-valued ones. In this paper we focus on regularization functionals with polyconvex integrands. Even though such functionals are nonconvex in general, it is possible to derive linear convergence rates with respect to a generalized Bregman distance, an idea introduced by Grasmair in 2010. As a case example we consider the image registration problem.

Source: Inverse Problems

DOI: 10.1088/1361-6420/aa7a1e

Authors: Clemens Kirisits, Otmar Scherzer

Institutions: Johann Radon Institute for Computational and Applied Mathematics, University of Vienna

Language: English

Topic: Numerical methods in inverse problems

Subfield: Mathematical Physics

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funder: Austrian Science Fund

Awards: G2628568020, Interdisciplinary Coupled Physic Imaging