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.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer Science+Business Media New York
About this chapter
Cite this chapter
Huiskes, M.J. (2002). Automatic Differentiation for Modern Nonlinear Regression. In: Corliss, G., Faure, C., Griewank, A., Hascoët, L., Naumann, U. (eds) Automatic Differentiation of Algorithms. Springer, New York, NY. https://doi.org/10.1007/978-1-4613-0075-5_8
Download citation
DOI: https://doi.org/10.1007/978-1-4613-0075-5_8
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4612-6543-6
Online ISBN: 978-1-4613-0075-5
eBook Packages: Springer Book Archive