Automatic Differentiation of Algorithms

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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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.

• Hardware
• algorithms
• computer
• control
• dynamische Systeme
• management
• mechanics
• modeling
• optimization
• parallelism
• performance
• programming
• simulation
• system modeling
• technology

• 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

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