Description: Recent Advances in Algorithmic Differentiation Please note: this item is printed on demand and will take extra time before it can be dispatched to you (up to 20 working days). Author(s): Shaun Forth, Paul Hovland, Eric Phipps, Jean Utke, Andrea Walther Format: Hardback Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Germany Imprint: Springer-Verlag Berlin and Heidelberg GmbH & Co. K ISBN-13: 9783642300226, 978-3642300226 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.
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Book Title: Recent Advances in Algorithmic Differentiation
Number of Pages: 362 Pages
Language: English
Publication Name: Recent Advances in Algorithmic Differentiation
Publisher: Springer-Verlag Berlin AND Heidelberg Gmbh & Co. KG
Publication Year: 2012
Subject: Computer Science, Mathematics
Item Height: 235 mm
Item Weight: 735 g
Type: Textbook
Author: Jean Utke, Paul Hovland, Andrea Walther, Shaun Forth, Eric Phipps
Series: Lecture Notes in Computational Science and Engineering
Item Width: 155 mm
Format: Hardcover
