Publication: Automatic Differentiation for Modern Nonlinear Regression
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Automatic Differentiation for Modern Nonlinear Regression

- incollection -
 

Area
Uncertainty Analysis

Author(s)
Mark J. Huiskes

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
For modern nonlinear regression routines, the efficient computation of first and higher order derivatives is highly important. Automatic differentiation constitutes an opportunity to achieve both higher run-time efficiency and an increased feasibility of higher-order uncertainty analysis of complex models. In this article we present an overview of the derivative requirements of nonlinear regression routines. We further describe our experience in developing a C++ library for model analysis that uses the ADOL-C package for automatic differentiation. We show how the model analysis library, named MAP, has benefited from using automatic differentiation. Also a number of experiments are presented to show how more flexible and efficient execution trace management could further enhance the ease-of-use of ADOL-C.

Cross-References
Corliss2002ADo

AD Tools
ADOL-C

BibTeX
@INCOLLECTION{
         Huiskes2002ADf,
       author = "Mark J. Huiskes",
       title = "Automatic Differentiation for Modern Nonlinear Regression",
       pages = "83--90",
       chapter = "8",
       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 = "For modern nonlinear regression routines, the efficient computation of first and
         higher order derivatives is highly important. Automatic differentiation constitutes an opportunity
         to achieve both higher run-time efficiency and an increased feasibility of higher-order uncertainty
         analysis of complex models. In this article we present an overview of the derivative requirements of
         nonlinear regression routines. We further describe our experience in developing a C++ library for
         model analysis that uses the ADOL-C package for automatic differentiation. We show how the model
         analysis library, named MAP, has benefited from using automatic differentiation. Also a number of
         experiments are presented to show how more flexible and efficient execution trace management could
         further enhance the ease-of-use of ADOL-C.",
       referred = "[Klein2002DMf].",
       ad_area = "Uncertainty Analysis",
       ad_tools = "ADOL-C"
}


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