Publication: Electron Paramagnetic Resonance, Optimization and Automatic Differentiation
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Electron Paramagnetic Resonance, Optimization and Automatic Differentiation

- incollection -
 

Author(s)
Edgar J. Soulié , Christèle Faure , Théo Berclaz , Michel Geoffroy

Published in
Automatic Differentiation of Algorithms: From Simulation to Optimization

Editor(s)
George Corliss, Christèle Faure, Andreas Griewank, Laurent Hascoët, Uwe Naumann

Year
2002

Publisher
Springer

Abstract
This paper describes an optimization problem applied to electron paramagnetic resonance spectroscopy. Levenberg-Marquardt fails to converge using a divided differences Jacobian approximation in single precision, while it succeeds using Odyssée-generated forward mode Jacobian values. In double precision, the optimizer returns a smaller ``minimum″ objective function with ad compared to DD in 46% more CPU time.

Cross-References
Corliss2002ADo

BibTeX
@INCOLLECTION{
         Soulie2002EPR,
       author = "Edgar J. Souli{\'e} and Christ{\`e}le Faure and Th{\'e}o
         Berclaz and Michel Geoffroy",
       title = "Electron Paramagnetic Resonance, Optimization and Automatic Differentiation",
       pages = "99--106",
       chapter = "10",
       crossref = "Corliss2002ADo",
       booktitle = "Automatic Differentiation of Algorithms: From Simulation to Optimization",
       year = "2002",
       editor = "George Corliss and Christ{\`e}le Faure and Andreas Griewank and Laurent
         Hasco{\"e}t and Uwe Naumann",
       series = "Computer and Information Science",
       publisher = "Springer",
       address = "New York, NY",
       abstract = "This paper describes an optimization problem applied to electron paramagnetic
         resonance spectroscopy. Levenberg-Marquardt fails to converge using a divided differences Jacobian
         approximation in single precision, while it succeeds using Odyss{\'e}e-generated forward
         mode Jacobian values. In double precision, the optimizer returns a smaller ``minimum''
         objective function with AD compared to DD in 46\% more CPU time.",
       referred = "[Klein2002DMf]."
}


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