Iterative Regularization Methods for Nonlinear Ill-Posed Problems (Radon Series on Computational and Applied Mathematics)

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.

Barbara Kaltenbacher, Universität Stuttgart; Andreas Neubauer, Johannes-Kepler-Universität Linz, Österreich; Otmar Scherzer, Universität Linz, Österreich.

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