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Advances in Automatic Differentiation

  • Conference proceedings
  • © 2008

Overview

Editors:
  1. Christian H. Bischof

    1. Institute for Scientific Computing, RWTH Aachen University, Aachen, Germany

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  2. H. Martin Bücker

    1. Institute for Scientific Computing, RWTH Aachen University, Aachen, Germany

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  3. Paul Hovland

    1. Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, USA

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  4. Uwe Naumann

    1. Software and Tools for Computational Engineering, RWTH Aachen University, Aachen, Germany

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  5. Jean Utke

    1. Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, USA

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Part of the book series: Lecture Notes in Computational Science and Engineering (LNCSE, volume 64)

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Table of contents (31 papers)

  1. Front Matter

    Pages I-XVIII
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  2. Reverse Automatic Differentiation of Linear Multistep Methods

    • Adrian Sandu
    Pages 1-12

  3. Call Tree Reversal is NP-Complete

    • Uwe Naumann
    Pages 13-22

  4. On Formal Certification of AD Transformations

    • Emmanuel M. Tadjouddine
    Pages 23-33

  5. Collected Matrix Derivative Results for Forward and Reverse Mode Algorithmic Differentiation

    • Mike B. Giles
    Pages 35-44

  6. A Modification of Weeks’ Method for Numerical Inversion of the Laplace Transform in the Real Case Based on Automatic Differentiation

    • Salvatore Cuomo, Luisa D’Amore, Mariarosaria Rizzardi, Almerico Murli
    Pages 45-54

  7. A Low Rank Approach to Automatic Differentiation

    • Hany S. Abdel-Khalik, Paul D. Hovland, Andrew Lyons, Tracy E. Stover, Jean Utke
    Pages 55-65

  8. Algorithmic Differentiation of Implicit Functions and Optimal Values

    • Bradley M. Bell, James V. Burke
    Pages 67-77

  9. Using Programming Language Theory to Make Automatic Differentiation Sound and Efficient

    • Barak A. Pearlmutter, Jeffrey Mark Siskind
    Pages 79-90

  10. A Polynomial-Time Algorithm for Detecting Directed Axial Symmetry in Hessian Computational Graphs

    • Sanjukta Bhowmick, Paul D. Hovland
    Pages 91-102

  11. On the Practical Exploitation of Scarsity

    • Andrew Lyons, Jean Utke
    Pages 103-114

  12. Design and Implementation of a Context-Sensitive, Flow-Sensitive Activity Analysis Algorithm for Automatic Differentiation

    • Jaewook Shin, Priyadarshini Malusare, Paul D. Hovland
    Pages 115-125

  13. Efficient Higher-Order Derivatives of the Hypergeometric Function

    • Isabelle Charpentier, Claude Dal Cappello, Jean Utke
    Pages 127-137

  14. The Diamant Approach for an Efficient Automatic Differentiation of the Asymptotic Numerical Method

    • Isabelle Charpentier, Arnaud Lejeune, Michel Potier-Ferry
    Pages 139-149

  15. Tangent-on-Tangent vs. Tangent-on-Reverse for Second Differentiation of Constrained Functionals

    • Massimiliano Martinelli, Laurent Hascoët
    Pages 151-161

  16. Parallel Reverse Mode Automatic Differentiation for OpenMP Programs with ADOL-C

    • Christian Bischof, Niels Guertler, Andreas Kowarz, Andrea Walther
    Pages 163-173

  17. Adjoints for Time-Dependent Optimal Control

    • Jan Riehme, Andrea Walther, Jörg Stiller, Uwe Naumann
    Pages 175-185

  18. Development and First Applications of TAC++

    • Michael Voßbeck, Ralf Giering, Thomas Kaminski
    Pages 187-197

  19. TAPENADE for C

    • Valérie Pascual, Laurent Hascoët
    Pages 199-209

  20. Coping with a Variable Number of Arguments when Transforming MATLAB Programs

    • H. Martin Bücker, Andre Vehreschild
    Pages 211-222
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Keywords

About this book

The Fifth International Conference on Automatic Differentiation held from August 11 to 15, 2008 in Bonn, Germany, is the most recent one in a series that began in Breckenridge, USA, in 1991 and continued in Santa Fe, USA, in 1996, Nice, France, in 2000 and Chicago, USA, in 2004. The 31 papers included in these proceedings re?ect the state of the art in automatic differentiation (AD) with respect to theory, applications, and tool development. Overall, 53 authors from institutions in 9 countries contributed, demonstrating the worldwide acceptance of AD technology in computational science. Recently it was shown that the problem underlying AD is indeed NP-hard, f- mally proving the inherently challenging nature of this technology. So, most likely, no deterministic “silver bullet” polynomial algorithm can be devised that delivers optimum performance for general codes. In this context, the exploitation of doma- speci?c structural information is a driving issue in advancing practical AD tool and algorithm development. This trend is prominently re?ected in many of the pub- cations in this volume, not only in a better understanding of the interplay of AD and certain mathematical paradigms, but in particular in the use of hierarchical AD approaches that judiciously employ general AD techniques in application-speci?c - gorithmic harnesses. In this context, the understanding of structures such as sparsity of derivatives, or generalizations of this concept like scarcity, plays a critical role, in particular for higher derivative computations.

Editors and Affiliations

  • Institute for Scientific Computing, RWTH Aachen University, Aachen, Germany

    Christian H. Bischof, H. Martin Bücker

  • Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, USA

    Paul Hovland, Jean Utke

  • Software and Tools for Computational Engineering, RWTH Aachen University, Aachen, Germany

    Uwe Naumann

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