Gradient descent in a generalised Bregman distance framework

Martin Benning, Marta M. Betcke, Matthias J Ehrhardt, Carola Bibiane Schönlieb

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

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We discuss a special form of gradient descent that in the literature has become known as the so-called linearised Bregman iteration. The idea is to replace the classical (squared) two norm metric in the gradient descent setting with a generalised Bregman distance, based on a more general proper, convex and lower semi-continuous functional. Gradient descent as well as the entropic mirror descent by Nemirovsky and Yudin are special cases, as is a specific form of non-linear Landweber iteration introduced by Bachmayr and Burger. We are going to analyse the linearised Bregman iteration in a setting where the functional we want to minimise is neither necessarily Lipschitz-continuous (in the classical sense) nor necessarily convex, and establish a global convergence result under the additional assumption that the functional we wish to minimise satisfies the so-called Kurdyka-Łojasiewicz property.
Original languageEnglish
Title of host publicationMI Lecture Notes series of Kyushu University
EditorsG. Reinout W. Quispel, Philipp Bader, McLaren David I., Daisuke Tagami
Number of pages5
Publication statusPublished - 31 Mar 2017


  • math.OC
  • 49M37, 65K05, 65K10, 90C26, 90C30
  • G.1.0; G.1.6


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