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Recent Advances in Algorithmic Differentiation

  • Kartonierter Einband
  • 380 Seiten
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The proceedings represent the state of knowledge in the area of algorithmic differentiation (AD). The 31 contributed papers presen... Weiterlesen
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Beschreibung

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.

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

Produktinformationen

Titel: Recent Advances in Algorithmic Differentiation
Editor:
EAN: 9783642439919
ISBN: 3642439918
Format: Kartonierter Einband
Herausgeber: Springer Berlin Heidelberg
Genre: Mathematik
Anzahl Seiten: 380
Gewicht: 575g
Größe: H235mm x B155mm x T20mm
Jahr: 2014
Auflage: 2012

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