Recent Advances in Algorithmic Differentiation

  • Shaun Forth
  • Paul Hovland
  • Eric Phipps
  • Jean Utke
  • Andrea Walther

Part of the Lecture Notes in Computational Science and Engineering book series (LNCSE, volume 87)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. James A. Reed, Jean Utke, Hany S. Abdel-Khalik
    Pages 23-33
  3. Emre Özkaya, Anil Nemili, Nicolas R. Gauger
    Pages 35-45
  4. Azamat Mametjanov, Boyana Norris, Xiaoyan Zeng, Beth Drewniak, Jean Utke, Mihai Anitescu et al.
    Pages 47-57
  5. Claire Lauvernet, Laurent Hascoët, François-Xavier Le Dimet, Frédéric Baret
    Pages 59-69
  6. Peder A. Olsen, Steven J. Rennie, Vaibhava Goel
    Pages 71-81
  7. Valérie Pascual, Laurent Hascoët
    Pages 83-92
  8. Markus Beckers, Viktor Mosenkis, Uwe Naumann
    Pages 103-113
  9. Johannes Willkomm, Christian H. Bischof, H. Martin Bücker
    Pages 127-138
  10. Benjamin Letschert, Kshitij Kulshreshtha, Andrea Walther, Duc Nguyen, Assefaw Gebremedhin, Alex Pothen
    Pages 151-161
  11. David C. Carothers, Stephen K. Lucas, G. Edgar Parker, Joseph D. Rudmin, James S. Sochacki, Roger J. Thelwell et al.
    Pages 175-185
  12. Johannes Lotz, Uwe Naumann, Jörn Ungermann
    Pages 187-196
  13. Heather Cole-Mullen, Andrew Lyons, Jean Utke
    Pages 197-207

About these proceedings

Introduction

The proceedings represent the state of knowledge in the area of algorithmic differentiation (AD).  The 31 contributed papers presented at the AD2012 conference cover the application of AD to many areas in science and engineering as well as aspects of AD theory and its implementation in tools. For all papers the referees, selected from the program committee and the greater community, as well as the editors have emphasized accessibility of the presented ideas also to non-AD experts. In the AD tools arena new implementations are introduced covering, for example, Java and graphical modeling environments or join the set of existing tools for Fortran. New developments in AD algorithms target the efficiency of matrix-operation derivatives, detection and exploitation of sparsity, partial separability, the treatment of nonsmooth functions, and other high-level mathematical aspects of the numerical computations to be differentiated. Applications stem from the Earth sciences, nuclear engineering, fluid dynamics, and chemistry, to name just a few. In many cases the applications in a given area of science or engineering share characteristics that require specific approaches to enable AD capabilities or provide an opportunity for efficiency gains in the derivative computation. The description of these characteristics and of the techniques for successfully using AD should make the proceedings a valuable source of information for users of AD tools.

Keywords

adjoint computation algorithmic differentiation optimization sensitivity analysis uncertainty quantification

Editors and affiliations

  • Shaun Forth
    • 1
  • Paul Hovland
    • 2
  • Eric Phipps
    • 3
  • Jean Utke
    • 4
  • Andrea Walther
    • 5
  1. 1.Shrivenham Campus, Applied Mathematics & Scientific ComputiCranfield UniversitySwindonUnited Kingdom
  2. 2.Mathematics and Computer Science Div.Argonne National LaboratoryArgonneUSA
  3. 3.Sandia National LaboratoryAlbuquerqueUSA
  4. 4.Mathematics and Computer Science Div.Argonne National LaboratoryArgonneUSA
  5. 5., MathematicsUniversity of PaderbornPaderbornGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-30023-3
  • Copyright Information Springer-Verlag Berlin Heidelberg 2012
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-642-30022-6
  • Online ISBN 978-3-642-30023-3
  • Series Print ISSN 1439-7358
  • About this book