Advances in Automatic Differentiation

  • Christian H. Bischof
  • H. Martin Bücker
  • Paul Hovland
  • Uwe Naumann
  • Jean Utke
Conference proceedings

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

Table of contents

  1. Front Matter
    Pages I-XVIII
  2. Uwe Naumann
    Pages 13-22
  3. Emmanuel M. Tadjouddine
    Pages 23-33
  4. Hany S. Abdel-Khalik, Paul D. Hovland, Andrew Lyons, Tracy E. Stover, Jean Utke
    Pages 55-65
  5. Bradley M. Bell, James V. Burke
    Pages 67-77
  6. Andrew Lyons, Jean Utke
    Pages 103-114
  7. Isabelle Charpentier, Claude Dal Cappello, Jean Utke
    Pages 127-137
  8. Isabelle Charpentier, Arnaud Lejeune, Michel Potier-Ferry
    Pages 139-149
  9. Christian Bischof, Niels Guertler, Andreas Kowarz, Andrea Walther
    Pages 163-173
  10. Jan Riehme, Andrea Walther, Jörg Stiller, Uwe Naumann
    Pages 175-185
  11. Michael Voßbeck, Ralf Giering, Thomas Kaminski
    Pages 187-197
  12. Valérie Pascual, Laurent Hascoët
    Pages 199-209
  13. H. Martin Bücker, Andre Vehreschild
    Pages 211-222
  14. H. Martin Bücker, Monika Petera, Andre Vehreschild
    Pages 223-233
  15. Markus Grabner, Thomas Pock, Tobias Gross, Bernhard Kainz
    Pages 259-269
  16. Mattia Padulo, Shaun A. Forth, Marin D. Guenov
    Pages 271-280
  17. Armen Jaworski, Jens-Dominik Müller
    Pages 281-291
  18. Philipp Stumm, Andrea Walther, Jan Riehme, Uwe Naumann
    Pages 339-349
  19. Eric T. Phipps, Roscoe A. Bartlett, David M. Gay, Robert J. Hoekstra
    Pages 351-362
  20. Back Matter
    Pages 363-368

About these proceedings


This collection covers advances in automatic differentiation theory and practice. Computer scientists and mathematicians will learn about recent developments in automatic differentiation theory as well as mechanisms for the construction of robust and powerful automatic differentiation tools. Computational scientists and engineers will benefit from the discussion of various applications, which provide insight into effective strategies for using automatic differentiation for inverse problems and design optimization.


3D AES Computer MATLAB OpenMP algorithms automatic differentiation calculus construction development modeling optimization programming programming language sensitivity analysis

Editors and affiliations

  • Christian H. Bischof
    • 1
  • H. Martin Bücker
    • 1
  • Paul Hovland
    • 2
  • Uwe Naumann
    • 3
  • Jean Utke
    • 2
  1. 1.Institute for Scientific ComputingRWTH Aachen UniversityAachenGermany
  2. 2.Mathematics and Computer Science DivisionArgonne National LaboratoryArgonneUSA
  3. 3.Software and Tools for Computational EngineeringRWTH Aachen UniversityAachenGermany

Bibliographic information

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