Nonlinear Least Squares for Inverse Problems

1. Front Matter

   Pages i-xiv

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2. Nonlinear Least Squares

   1. Front Matter

      Pages 1-3

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   2. Nonlinear Inverse Problems: Examples and Difficulties

      • Guy Chavent

      Pages 5-28

   3. Computing Derivatives

      • Guy Chavent

      Pages 29-78

   4. Choosing a Parameterization

      • Guy Chavent

      Pages 79-159

   5. Output Least Squares Identifiability and Quadratically Wellposed NLS Problems

      • Guy Chavent

      Pages 161-207

   6. Regularization of Nonlinear Least Squares Problems

      • Guy Chavent

      Pages 209-270

3. A generalization of convex sets

   1. Front Matter

      Pages 272-273

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   2. Quasi-Convex Sets

      • Guy Chavent

      Pages 275-297

   3. Strictly Quasi-Convex Sets

      • Guy Chavent

      Pages 299-320

   4. Deflection Conditions for the Strict Quasi-convexity of Sets

      • Guy Chavent

      Pages 321-343

4. Back Matter

   Pages 345-360

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The domain of inverse problems has experienced a rapid expansion, driven by the increase in computing power and the progress in numerical modeling. When I started working on this domain years ago, I became somehow fr- tratedtoseethatmyfriendsworkingonmodelingwhereproducingexistence, uniqueness, and stability results for the solution of their equations, but that I was most of the time limited, because of the nonlinearity of the problem, to provethatmyleastsquaresobjectivefunctionwasdi?erentiable....Butwith my experience growing, I became convinced that, after the inverse problem has been properly trimmed, the ?nal least squares problem, the one solved on the computer, should be Quadratically (Q)-wellposed,thatis,both we- posed and optimizable: optimizability ensures that a global minimizer of the least squares function can actually be found using e?cient local optimization algorithms, and wellposedness that this minimizer is stable with respect to perturbation of the data. But the vast majority of inverse problems are nonlinear, and the clas- cal mathematical tools available for their analysis fail to bring answers to these crucial questions: for example, compactness will ensure existence, but provides no uniqueness results, and brings no information on the presence or absenceofparasiticlocalminimaorstationarypoints....

• analysis of NLS problems
• analysis of nonlinear least square problems
• choice of parametrization
• inverse problem
• inversion code
• inversion method
• inversion methods
• least square method
• least square methods
• least square problem
• least square problems;

From the reviews:

“This comprehensive treatise on the nonlinear inverse problem, written by a mathematician with extensive experience in exploration geophysics, deals primarily with the nonlinear least squares (NLS) methods to solve such problems. … Chavent has authored a book with appeal to both the practitioner of the arcane art of NLS inversion as well as to the theorist seeking a rigorous and formal development of what is currently known about this subject.” (Sven Treitel, The Leading Edge, April, 2010)

“The book is organized so that readers interested in the more practical aspects can easily dip into the appropriate chapters of the book without having to work through the more theoretical details. … is recommended for readers who are interested in applying the OLS approach to nonlinear inverse problems. … This material is relatively accessible even to readers without a very strong background in analysis. The book will also be of interest to readers who want to learn more about … quasi-convex sets and Q-wellposedness.” (Brain Borchers, The Mathematical Association of America, July, 2010)

• Bellefontaine, France

  Guy Chavent

Background: Ecole Polytechnique (Paris, 1965),

Ecole Nationale Supérieure des Télécommunications (Paris,1968),

Paris-6 University (Ph. D., 1971).

Professor Chavent joined the Faculty of Paris 9-Dauphine in 1971. He is now an emeritus professor from this university. During the same span of time, he ran a research project at INRIA (Institut National de Recherche en Informatique et en Automatique), focused on industrial inverse problems (oil production and exploration, nuclear reactors, ground water management…).

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