Recent Advances in Algorithmic Differentiation

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Synopsis

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.

Book details

Edition:
2012
Series:
Lecture Notes in Computational Science and Engineering (Book 87)
Author:
Shaun Forth, Paul Hovland, Eric Phipps, Jean Utke and Andrea Walther
ISBN:
9783642300233
Related ISBNs:
9783642300226
Publisher:
Springer Berlin Heidelberg
Pages:
N/A
Reading age:
Not specified
Includes images:
Yes
Date of addition:
2022-08-09
Usage restrictions:
Copyright
Copyright date:
2012
Copyright by:
Springer-Verlag Berlin Heidelberg 
Adult content:
No
Language:
English
Categories:
Computers and Internet, Mathematics and Statistics, Nonfiction