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"
}