Publication: On the Limits of Current Implementations of Algorithmic Differentiation
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On the Limits of Current Implementations of Algorithmic Differentiation

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Author(s)
E. Slusanschi , H. M. Bücker

Published in
Proceedings of the 6th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC04, Timisoara, Romania, September 26--30, 2004

Editor(s)
D. Petcu, V. Negru, D. Zaharie, T. Jebelean

Year
2004

Publisher
MIRTON

Abstract
The computation of derivatives is a crucial part in various computational techniques used in science and engineering. In many applications, like parameter identification, design optimization or data assimilation problems, different optimization tasks have to be performed. Since most numerical optimization algorithms require the use of either gradient or Jacobian derivative information, the accurate evaluation of these derivatives is essential. The technique of automatic differentiation provides an efficient way of computing derivatives without truncation error. In the present note we investigate various issues that arise in the GRADIENT, TAMC and TAPENADE implementations of algorithmic differentiation for programs written in Maple and Fortran.

AD Tools
TAMC, TAPENADE, GRADIENT(Maple)

AD Theory and Techniques
General, Iteration

BibTeX
@INPROCEEDINGS{
         Slusanschi2004OtL,
       author = "E.~Slusanschi and H. M. B{\"u}cker",
       title = "On the Limits of Current Implementations of Algorithmic Differentiation",
       booktitle = "Proceedings of the 6th International Symposium on Symbolic and Numeric Algorithms
         for Scientific Computing, SYNASC04, Timisoara, Romania, September~26--30, 2004",
       editor = "D. Petcu and V. Negru and D. Zaharie and T. Jebelean",
       publisher = "MIRTON",
       pages = "295--306",
       address = "Timisoara",
       abstract = "The computation of derivatives is a crucial part in various computational
         techniques used in science and engineering. In many applications, like parameter identification,
         design optimization or data assimilation problems, different optimization tasks have to be
         performed. Since most numerical optimization algorithms require the use of either gradient or
         Jacobian derivative information, the accurate evaluation of these derivatives is essential. The
         technique of automatic differentiation provides an efficient way of computing derivatives without
         truncation error. In the present note we investigate various issues that arise in the GRADIENT, TAMC
         and Tapenade implementations of algorithmic differentiation for programs written in Maple and
         Fortran.",
       ad_tools = "TAMC, Tapenade, GRADIENT(Maple)",
       ad_theotech = "General, Iteration",
       year = "2004"
}


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