Editors: Victor Pereyra, Godela Scherer

Exponential Data Fitting and its Applications

eBook: US $59 Special Offer (PDF + Printed Copy): US $148
Printed Copy: US $119
Library License: US $236
ISBN: 978-1-60805-345-2 (Print)
ISBN: 978-1-60805-048-2 (Online)
Year of Publication: 2010
DOI: 10.2174/97816080504821100101

Introduction

Real and complex exponential data fitting is an important activity in many different areas of science and engineering, ranging from Nuclear Magnetic Resonance Spectroscopy and Lattice Quantum Chromodynamics to Electrical and Chemical Engineering, Vision and Robotics. The most commonly used norm in the approximation by linear combinations of exponentials is the l2 norm (sum of squares of residuals), in which case one obtains a nonlinear separable least squares problem. A number of different methods have been proposed through the years to solve these types of problems and new applications appear daily. Necessary guidance is provided so that care should be taken when applying standard or simplified methods to it. The described methods take into account the separability between the linear and nonlinear parameters, which have been quite successful. The accessibility of good, publicly available software that has been very beneficial in many different fields is also considered. This Ebook covers the main solution methods (Variable Projections, Modified Prony) and also emphasizes the applications to different fields. It is considered essential reading for researchers and students in this field.

Foreword

Exponential data fitting has a long history theoretically, algorithmically, and in its applications. Despite its widespread use and success there has been no unified presentation of the subject available. This book attempts to fill the gap. The problem and some of the most successful and proven algorithms for its solution are presented in detail. Then a number of experts in different fields describe their applications and specific models. This combination will alert many other researchers to what is out there and how their problems relate to various applications across disciplines. The editors have done a commendable job in assembling this material. They are experts in the field. We thank them for sharing their knowledge.

Michael Saunders
Stanford University


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