Publication: Odyssée versus Hand Differentiation of a Terrain Modelling Application
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Odyssée versus Hand Differentiation of a Terrain Modelling Application

- incollection -
 

Author(s)
Bernard Cappelaere , David Elizondo , Christèle Faure

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
A comparison is made between differentiation alternatives for a terrain modeling problem, a sample application where strong non-linearities are solved iteratively. Investigated methods include automatic differentiation (ad) with the Odyssee software (forward and reverse modes) and manual differentiation (MD) using the model's adjoint equations. The comparison mainly focuses on accuracy and computing efficiency, as well as on development effort. While ad ensures perfect consistency between the computer model and its derivative at a low development cost, MD shows significantly lesser computing costs. We discuss the perturbation method as well as hybrid strategies that combine advantages of ad and MD.

Cross-References
Corliss2002ADo

BibTeX
@INCOLLECTION{
         Cappelaere2002OvH,
       author = "Bernard Cappelaere and David Elizondo and Christ{\`e}le Faure",
       title = "Odyss{\'e}e versus Hand Differentiation of a Terrain Modelling
         Application",
       pages = "75--82",
       chapter = "7",
       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 = "A comparison is made between differentiation alternatives for a terrain modeling
         problem, a sample application where strong non-linearities are solved iteratively. Investigated
         methods include automatic differentiation (AD) with the Odyssee software (forward and reverse modes)
         and manual differentiation (MD) using the model's adjoint equations. The comparison mainly
         focuses on accuracy and computing efficiency, as well as on development effort. While AD ensures
         perfect consistency between the computer model and its derivative at a low development cost, MD
         shows significantly lesser computing costs. We discuss the perturbation method as well as hybrid
         strategies that combine advantages of AD and MD."
}


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