Iterative Regularization Methods for Nonlinear Ill-Posed Problems

Nonlinear inverse problems appear in many applications, and typically they lead to mathematical models that are ill-posed, i.e., they are unstable under data perturbations. Those problems require a regularization, i.e., a special numerical treatment. This book presents regularization schemes which are based on iteration methods, e.g., nonlinear Landweber iteration, level set methods, multilevel methods and Newton type methods.

Auteur

Barbara Kaltenbacher, University Stuttgart; Andreas Neubauer, Johannes-Kepler-University Linz, Austria; Otmar Scherzer, University Linz, Austria.

Texte du rabat

The series is devoted to the publication of high-level monographs, surveys and proceedings which cover the whole spectrum of computational and applied mathematics. The books of this series are addressed to both specialists and advanced students. Interested authors may submit book proposals to the Managing Editor or to any member of the Editorial Board. Managing Editor Otmar Scherzer, RICAM, Linz, Austria; University of Vienna, Austria Editorial Board Hansjörg Albrecher, University of Lausanne, Switzerland Ronald H. W. Hoppe, University of Houston, USA Karl Kunisch, RICAM, Linz, Austria; University of Graz, Austria Harald Niederreiter, RICAM, Linz, Austria Christian Schmeiser, University of Vienna, Austria

Résumé

"This well written monograph may become a standard reference on regularization theory for nonlinear inverse problems."
Thorsten Hohage in: Mathematical Reviews 2010c