Recent Advances in Algorithmic Differentiation

Shaun Forth, Paul Hovland, Eric Phipps, Jean Utke, Andrea Walther (Herausgeber)

Buch | Softcover

XVIII, 362 Seiten

2014 | 2012
Springer Berlin (Verlag)
978-3-642-43991-9 (ISBN)

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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.

Erscheint lt. Verlag	9.8.2014
Reihe/Serie	Lecture Notes in Computational Science and Engineering
Zusatzinfo	XVIII, 362 p.
Verlagsort	Berlin
Sprache	englisch
Maße	155 x 235 mm
Gewicht	581 g
Themenwelt	Mathematik / Informatik ► Mathematik ► Analysis
	Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
	Naturwissenschaften
Schlagworte	adjoint computation • Algorithmic Differentiation • Optimization • Sensitivity Analysis • uncertainty quantification
ISBN-10	3-642-43991-8 / 3642439918
ISBN-13	978-3-642-43991-9 / 9783642439919
Zustand	Neuware