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Large-Scale PDE-Constrained Optimization pp 133–147Cite as

Challenges and Opportunities in Using Automatic Differentiation with Object-Oriented Toolkits for Scientific Computing

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Part of the book series: Lecture Notes in Computational Science and Engineering ((LNCSE,volume 30))

Abstract

The increased use of object-oriented toolkits in large-scale scientific simulation presents challenges and new opportunities for the use of automatic (or algorithmic) differentiation (AD) techniques, especially in the context of optimization. Because object-oriented toolkits use well-defined interfaces and data structures, there is potential for simplifying the AD process. Furthermore, derivative computation can be improved by exploiting high-level information about numerical and computational abstractions. However, challenges to the successful use of AD with these toolkits also exist. Among the greatest challenges is balancing the desire to limit the scope of the AD process with the desire to minimize the work required of a user. We discuss our experiences in integrating AD with the PETSc, PVODE, and TAO toolkits and our plans for future research and development in this area.

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Author information

Authors and Affiliations

  1. Mathematics and Computer Science Division, Argonne National Laboratory, 9700 S. Cass Ave., Argonne, IL, 60439-4844

    Paul Hovland, Lois McInnes, Boyana Norris & Barry Smith

  2. Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Box 808, L-560, Livermore, CA, 94551

    Steven Lee

Authors
  1. Paul Hovland
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  2. Steven Lee
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  3. Lois McInnes
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  4. Boyana Norris
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  5. Barry Smith
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Editor information

Editors and Affiliations

  1. Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Lorenz T. Biegler

  2. Department of Computational and Applied Mathematics, Rice University, 6100 S. Main Street — MS134, Houston, TX, 77005, USA

    Matthias Heinkenschloss

  3. Department of Biomedical Engineering and Civil & Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Omar Ghattas

  4. Optimization & Uncertainty Estimation Dept., Sandia National Laboratories — MS 0847, P.O. Box 5800, Albuquerque, NM, 87185, USA

    Bart van Bloemen Waanders

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© 2003 Springer-Verlag Berlin Heidelberg

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Hovland, P., Lee, S., McInnes, L., Norris, B., Smith, B. (2003). Challenges and Opportunities in Using Automatic Differentiation with Object-Oriented Toolkits for Scientific Computing. In: Biegler, L.T., Heinkenschloss, M., Ghattas, O., van Bloemen Waanders, B. (eds) Large-Scale PDE-Constrained Optimization. Lecture Notes in Computational Science and Engineering, vol 30. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55508-4_8

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  • DOI: https://doi.org/10.1007/978-3-642-55508-4_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-05045-2

  • Online ISBN: 978-3-642-55508-4

  • eBook Packages: Springer Book Archive

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