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Automatic Differentiation of Algorithms

From Simulation to Optimization

  • George Corliss
  • Christèle Faure
  • Andreas Griewank
  • Laurent Hascoët
  • Uwe Naumann
Book

Table of contents

  1. Front Matter
    Pages i-xxvii
  2. Invited Contributions

    1. Front Matter
      Pages 1-1
    2. Wolfram Klein, Andreas Griewank, Andrea Walther
      Pages 3-23
    3. David E. Keyes, Paul D. Hovland, Lois C. McInnes, Widodo Samyono
      Pages 35-50
    4. François Bodin, Antoine Monsifrot
      Pages 51-57
    5. Steve Hague, Uwe Naumann
      Pages 59-66
  3. Parameter Identification and Least Squares

    1. Front Matter
      Pages 67-67
    2. Christian H. Bischof, H. Martin Bücker, Dieter an Mey
      Pages 69-74
    3. Bernard Cappelaere, David Elizondo, Christèle Faure
      Pages 75-82
    4. Jong G. Kim, Paul D. Hovland
      Pages 91-98
    5. 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
    2. Jean-Baptiste Caillau, Joseph Noailles
      Pages 109-115
    3. Daniele Casanova, Robin S. Sharp, Mark Final, Bruce Christianson, Pat Symonds
      Pages 117-124
    4. Bruce Christianson, Michael Bartholomew-Biggs
      Pages 125-130
    5. Florian Dignath, Peter Eberhard, Axel Fritz
      Pages 131-136
    6. Klaus Röbenack, Kurt J. Reinschke
      Pages 137-142
  5. Applications in PDE’S

    1. Front Matter
      Pages 143-143
    2. Michael B. Giles
      Pages 145-151
    3. Mark S. Gockenbach, Daniel R. Reynolds, William W. Symes
      Pages 161-166
    4. Engelbert Tijskens, Herman Ramon, Josse De Baerdemaeker
      Pages 167-172
    5. Jason Abate, Steve Benson, Lisa Grignon, Paul Hovland, Lois McInnes, Boyana Norris
      Pages 173-178
  6. Applications in Science and Engineering

    1. Front Matter
      Pages 179-179
    2. Gundolf Haase, Ulrich Langer, Ewald Lindner, Wolfram Mühlhuber
      Pages 181-188
    3. Adel Ben-Haj-Yedder, Eric Cances, Claude Le Bris
      Pages 205-211
  7. Maintaining and Enhancing Parallelism

    1. Front Matter
      Pages 213-213
    2. Andrea Walther, Andreas Griewank
      Pages 237-243
  8. Exploiting Structure and Sparsity

    1. Front Matter
      Pages 245-245
    2. Uwe Naumann
      Pages 247-253
    3. Mohamed Tadjouddine, Shaun A. Forth, John D. Pryce
      Pages 255-261
    4. Shahadat Hossain, Trond Steihaug
      Pages 263-270
    5. Andreas Griewank, Christo Mitev
      Pages 271-279
  9. Space-Time Tradeoffs in the Reverse Mode

    1. Front Matter
      Pages 281-281
    2. Ralf Giering, Thomas Kaminski
      Pages 283-291
    3. Christèle Faure, Uwe Naumann
      Pages 293-298
    4. Laurent Hascoët, Stefka Fidanova, Christophe Held
      Pages 299-304
    5. Pierre Aubert, Nicolas Di Césaré
      Pages 311-315
  10. Use of Second and Higher Derivatives

    1. Front Matter
      Pages 317-317
    2. Yuri G. Evtushenko, E. S. Zasuhina, V. I. Zubov
      Pages 327-333
    3. Jean-Daniel Beley, Stephane Garreau, Frederic Thevenon, Mohamed Masmoudi
      Pages 335-341

About this book

Introduction

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.

Keywords

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

Editors and affiliations

  • George Corliss
    • 1
  • Christèle Faure
    • 2
  • Andreas Griewank
    • 3
  • Laurent Hascoët
    • 4
  • Uwe Naumann
    • 5
  1. 1.Department of Electrical and Computer EngineeringMarquette UniversityMilwaukeeUSA
  2. 2.PolySpace TechnologiesMontrougeFrance
  3. 3.Institute of Scientific ComputingTechnical University DresdenDresdenGermany
  4. 4.INRIA, projet TropicsSophia AntipolisFrance
  5. 5.Department of Computer ScienceUniversity of HertfordshireHertsUK

Bibliographic information