Amazon cover image
Image from Amazon.com

Recent Advances in Algorithmic Differentiation [electronic resource] / edited by Shaun Forth, Paul Hovland, Eric Phipps, Jean Utke, Andrea Walther.

Contributor(s): Series: Lecture Notes in Computational Science and Engineering ; 87Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012Description: XVIII, 362 p. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783642300233
Subject(s): Genre/Form: Additional physical formats: Printed edition:: No titleDDC classification:
  • 518 23
LOC classification:
  • QA71-90
Online resources: In: Springer eBooksSummary: 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.
Item type: eBooks
Star ratings
    Average rating: 0.0 (0 votes)
No physical items for this record

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.

Copyright © 2020 Alfaisal University Library. All Rights Reserved.
Tel: +966 11 2158948 Fax: +966 11 2157910 Email:
librarian@alfaisal.edu