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Automatic Differentiation of Algorithms

From Simulation to Optimization

  • Book
  • © 2002

Overview

Editors:
  1. George Corliss

    1. Department of Electrical and Computer Engineering, Marquette University, Milwaukee, USA

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  2. Christèle Faure

    1. PolySpace Technologies, Montrouge, France

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  3. Andreas Griewank

    1. Institute of Scientific Computing, Technical University Dresden, Dresden, Germany

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  4. Laurent Hascoët

    1. INRIA, projet Tropics, Sophia Antipolis, France

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  5. Uwe Naumann

    1. Department of Computer Science, University of Hertfordshire, Herts, UK

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Table of contents (45 chapters)

  1. Front Matter

    Pages i-xxvii
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  2. Invited Contributions

    1. Front Matter

      Pages 1-1
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    2. Differentiation Methods for Industrial Strength Problems

      • Wolfram Klein, Andreas Griewank, Andrea Walther
      Pages 3-23

    3. Automatic Differentiation Tools in Optimization Software

      • Jorge J. Moré
      Pages 25-34

    4. Using Automatic Differentiation for Second-Order Matrix-free Methods in PDE-constrained Optimization

      • David E. Keyes, Paul D. Hovland, Lois C. McInnes, Widodo Samyono
      Pages 35-50

    5. Performance Issues in Automatic Differentiation on Superscalar Processors

      • François Bodin, Antoine Monsifrot
      Pages 51-57

    6. Present and Future Scientific Computation Environments

      • Steve Hague, Uwe Naumann
      Pages 59-66

  3. Parameter Identification and Least Squares

    1. Front Matter

      Pages 67-67
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    2. A Case Study of Computational Differentiation Applied to Neutron Scattering

      • Christian H. Bischof, H. Martin Bücker, Dieter an Mey
      Pages 69-74

    3. Odyssée versus Hand Differentiation of a Terrain Modeling Application

      • Bernard Cappelaere, David Elizondo, Christèle Faure
      Pages 75-82

    4. Automatic Differentiation for Modern Nonlinear Regression

      • Mark J. Huiskes
      Pages 83-90

    5. Sensitivity Analysis and Parameter Tuning of a Sea-Ice Model

      • Jong G. Kim, Paul D. Hovland
      Pages 91-98

    6. Electron Paramagnetic Resonance, Optimization and Automatic Differentiation

      • Edgar J. Soulié, Christèle Faure, Théo Berclaz, Michel Geoffroy
      Pages 99-106

  4. Applications in ODE’S and Optimal Control

    1. Front Matter

      Pages 107-107
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    2. Continuous Optimal Control Sensitivity Analysis with AD

      • Jean-Baptiste Caillau, Joseph Noailles
      Pages 109-115

    3. Application of Automatic Differentiation to Race Car Performance Optimisation

      • Daniele Casanova, Robin S. Sharp, Mark Final, Bruce Christianson, Pat Symonds
      Pages 117-124

    4. Globalization of Pantoja’s Optimal Control Algorithm

      • Bruce Christianson, Michael Bartholomew-Biggs
      Pages 125-130

    5. Analytical Aspects and Practical Pitfalls in Technical Applications of AD

      • Florian Dignath, Peter Eberhard, Axel Fritz
      Pages 131-136

    6. Nonlinear Observer Design Using Automatic Differentiation

      • Klaus Röbenack, Kurt J. Reinschke
      Pages 137-142

  5. Applications in PDE’S

    1. Front Matter

      Pages 143-143
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Keywords

About this book

Automatic Differentiation (AD) is a maturing computational technology and has become a mainstream tool used by practicing scientists and computer engineers. The rapid advance of hardware computing power and AD tools has enabled practitioners to quickly generate derivative-enhanced versions of their code for a broad range of applications in applied research and development.
Automatic Differentiation of Algorithms provides a comprehensive and authoritative survey of all recent developments, new techniques, and tools for AD use. The book covers all aspects of the subject: mathematics, scientific programming (i.e., use of adjoints in optimization) and implementation (i.e., memory management problems). A strong theme of the book is the relationships between AD tools and other software tools, such as compilers and parallelizers. A rich variety of significant applications are presented as well, including optimum-shape design problems, for which AD offers more efficient tools and techniques.

Editors and Affiliations

  • Department of Electrical and Computer Engineering, Marquette University, Milwaukee, USA

    George Corliss

  • PolySpace Technologies, Montrouge, France

    Christèle Faure

  • Institute of Scientific Computing, Technical University Dresden, Dresden, Germany

    Andreas Griewank

  • INRIA, projet Tropics, Sophia Antipolis, France

    Laurent Hascoët

  • Department of Computer Science, University of Hertfordshire, Herts, UK

    Uwe Naumann

Bibliographic Information

  • Book Title: Automatic Differentiation of Algorithms

  • Book Subtitle: From Simulation to Optimization

  • Editors: George Corliss, Christèle Faure, Andreas Griewank, Laurent Hascoët, Uwe Naumann

  • DOI: https://doi.org/10.1007/978-1-4613-0075-5

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer Science+Business Media New York 2002

  • Hardcover ISBN: 978-0-387-95305-2

  • Softcover ISBN: 978-1-4612-6543-6

  • eBook ISBN: 978-1-4613-0075-5

  • Edition Number: 1

  • Number of Pages: XXVII, 432

  • Number of Illustrations: 84 b/w illustrations

  • Topics: Computer Science, general

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