Publication: Sensitivity Analysis Using Parallel ODE Solvers and Automatic Differentiation in C: SensPVODE and ADIC
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Sensitivity Analysis Using Parallel ODE Solvers and Automatic Differentiation in C: SensPVODE and ADIC

- incollection -
 

Author(s)
Steven L. Lee , Paul D. Hovland

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
PVODE is a high-performance ordinary differential equation solver for the types of initial value problems (IVPs) that arise in large-scale computational simulations. Often, one wants to compute sensitivities with respect to certain parameters in the IVP. We discuss the use of automatic differentiation (ad) to compute these sensitivities in the context of PVODE. Results on a simple test problem indicate that the use of ad-generated derivative code can reduce the time to solution over finite difference approximations.

Cross-References
Corliss2002ADo

BibTeX
@INCOLLECTION{
         Lee2002SAU,
       author = "Steven L. Lee and Paul D. Hovland",
       title = "Sensitivity Analysis Using Parallel {ODE} Solvers and Automatic Differentiation in
         {C}: {SensPVODE} and {ADIC}",
       pages = "223--229",
       chapter = "26",
       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 = "PVODE is a high-performance ordinary differential equation solver for the types of
         initial value problems (IVPs) that arise in large-scale computational simulations. Often, one wants
         to compute sensitivities with respect to certain parameters in the IVP. We discuss the use of
         automatic differentiation (AD) to compute these sensitivities in the context of PVODE. Results on a
         simple test problem indicate that the use of AD-generated derivative code can reduce the time to
         solution over finite difference approximations.",
       comment = "Also appeared as Mathematics and Computer Science Division, Argonne National
         Laboratory preprint ANL/MCS-P818-0500.",
       referred = "[Abate2002IAw], [Carle2002ADM]."
}


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