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Bregman Distances in Inverse Problems and Partial Differential Equations

In: Advances in Mathematical Modeling, Optimization and Optimal Control

Author

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  • Martin Burger

    (Westfälische Wilhelms-Universität (WWU) Münster. Einsteinstr. 62)

Abstract

The aim of this paper is to provide an overview of recent development related to Bregman distances outside its native areas of optimization and statistics. We discuss approaches in inverse problems and image processing based on Bregman distances, which have evolved to a standard tool in these fields in the last decade. Moreover, we discuss related issues in the analysis and numerical analysis of nonlinear partial differential equations with a variational structure. For such problems Bregman distances appear to be of similar importance, but are currently used only in a quite hidden fashion. We try to work out explicitly the aspects related to Bregman distances, which also lead to novel mathematical questions and may also stimulate further research in these areas.

Suggested Citation

  • Martin Burger, 2016. "Bregman Distances in Inverse Problems and Partial Differential Equations," Springer Optimization and Its Applications, in: Jean-Baptiste Hiriart-Urruty & Adam Korytowski & Helmut Maurer & Maciej Szymkat (ed.), Advances in Mathematical Modeling, Optimization and Optimal Control, pages 3-33, Springer.
    • Handle: RePEc:spr:spochp:978-3-319-30785-5_2
    DOI: 10.1007/978-3-319-30785-5_2
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    Cited by:

    1. Marc Hallin, 2018. "From Mahalanobis to Bregman via Monge and Kantorovich towards a “General Generalised Distance”," Working Papers ECARES 2018-12, ULB -- Universite Libre de Bruxelles.
    2. Marc Hallin, 2018. "From Mahalanobis to Bregman via Monge and Kantorovich," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(1), pages 135-146, December.

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