Automatic Differentiation for Modern Nonlinear Regression
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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.
KeywordsExecution Trace Automatic Differentiation Spawn Stock Biomass Regressor Variable Function Prototype
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