Automatic Differentiation of Algorithms

From Simulation to Optimization

George Corliss, Christele Faure, Andreas Griewank, Laurent Hascoet, Uwe Naumann (Herausgeber)

Buch | Softcover

432 Seiten

2014 | Softcover reprint of the original 1st ed. 2002
Springer-Verlag New York Inc.
978-1-4612-6543-6 (ISBN)

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Automatic Differentiation (AD) is a maturing computational technology and has become a mainstream tool used by practicing scientists and computer engineers. The rapid advance of hardware computing power and AD tools has enabled practitioners to quickly generate derivative-enhanced versions of their code for a broad range of applications in applied research and development.
Automatic Differentiation of Algorithms provides a comprehensive and authoritative survey of all recent developments, new techniques, and tools for AD use. The book covers all aspects of the subject: mathematics, scientific programming (i.e., use of adjoints in optimization) and implementation (i.e., memory management problems). A strong theme of the book is the relationships between AD tools and other software tools, such as compilers and parallelizers. A rich variety of significant applications are presented as well, including optimum-shape design problems, for which AD offers more efficient tools and techniques.

Part titles: Invited Contributions.- Parameter Identification and Least Squares.- Applications in Ode's and Optimal Control.- Applications in PDE's.- Applications in Science and Engineering.- Maintaining and Enhancing Parallelism.- Exploiting Structure and Sparsity.- Space-Time Tradeoffs in the Reverse Mode.- Use of Second and Higher Derivatives.- Error Estimates and Inclusions.

Zusatzinfo	84 Illustrations, black and white
Verlagsort	New York, NY
Sprache	englisch
Maße	155 x 235 mm
Themenwelt	Mathematik / Informatik ► Informatik ► Theorie / Studium
ISBN-10	1-4612-6543-6 / 1461265436
ISBN-13	978-1-4612-6543-6 / 9781461265436
Zustand	Neuware