Lecture Notes in Computational Science and Engineering

Recent Advances in Algorithmic Differentiation

Editors: Forth, S., Hovland, P., Phipps, E., Utke, J., Walther, A. (Eds.)

  • Easily accessible explanations that do not require a priori in-depth expertise Covers topics for users, researchers, and tool developers in the algorithmic differentiation area This collection is the most comprehensive and recent source of information on the subject since the AD2008 proceedings

Buy this book

eBook $149.00
price for USA (gross)
  • ISBN 978-3-642-30023-3
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $189.00
price for USA
  • ISBN 978-3-642-30022-6
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $189.00
price for USA
valid through November 5, 2017
  • ISBN 978-3-642-43991-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Rent the eBook  
  • Rental duration: 1 or 6 month
  • low-cost access
  • online reader with highlighting and note-making option
  • can be used across all devices
About this book

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.

Table of contents (31 chapters)

  • A Leibniz Notation for Automatic Differentiation

    Christianson, Bruce

    Pages 1-9

  • Sparse Jacobian Construction for Mapped Grid Visco-Resistive Magnetohydrodynamics

    Reynolds, Daniel R. (et al.)

    Pages 11-21

  • Combining Automatic Differentiation Methods for High-Dimensional Nonlinear Models

    Reed, James A. (et al.)

    Pages 23-33

  • Application of Automatic Differentiation to an Incompressible URANS Solver

    Özkaya, Emre (et al.)

    Pages 35-45

  • Applying Automatic Differentiation to the Community Land Model

    Mametjanov, Azamat (et al.)

    Pages 47-57

Buy this book

eBook $149.00
price for USA (gross)
  • ISBN 978-3-642-30023-3
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $189.00
price for USA
  • ISBN 978-3-642-30022-6
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $189.00
price for USA
valid through November 5, 2017
  • ISBN 978-3-642-43991-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Rent the eBook  
  • Rental duration: 1 or 6 month
  • low-cost access
  • online reader with highlighting and note-making option
  • can be used across all devices
Loading...

Recommended for you

Loading...

Bibliographic Information

Bibliographic Information
Book Title
Recent Advances in Algorithmic Differentiation
Editors
  • Shaun Forth
  • Paul Hovland
  • Eric Phipps
  • Jean Utke
  • Andrea Walther
Series Title
Lecture Notes in Computational Science and Engineering
Series Volume
87
Copyright
2012
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer-Verlag Berlin Heidelberg
eBook ISBN
978-3-642-30023-3
DOI
10.1007/978-3-642-30023-3
Hardcover ISBN
978-3-642-30022-6
Softcover ISBN
978-3-642-43991-9
Series ISSN
1439-7358
Edition Number
1
Number of Pages
XVIII, 362
Topics