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Automatic Differentiation of Algorithms: From Simulation to...

Automatic Differentiation of Algorithms: From Simulation to Optimization

Wolfram Klein, Andreas Griewank, Andrea Walther (auth.), George Corliss, Christèle Faure, Andreas Griewank, Laurent Hascoët, Uwe Naumann (eds.)
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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.

Tahun:
2002
Edisi:
1
Penerbit:
Springer-Verlag New York
Bahasa:
english
Halaman:
432
ISBN 10:
1461300754
ISBN 13:
9781461300755
File:
PDF, 11.99 MB
IPFS:
CID , CID Blake2b
english, 2002
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