Automatic Differentiation of Algorithms
From Simulation to Optimization
Editors: Corliss, G., Faure, C., Griewank, A., Hascoet, L., Naumann, U. (Eds.)
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- About this book
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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.
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Automatic Differentiation of Algorithms
- Book Subtitle
- From Simulation to Optimization
- Editors
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- George Corliss
- Christele Faure
- Andreas Griewank
- Laurent Hascoet
- Uwe Naumann
- Copyright
- 2002
- Publisher
- Springer-Verlag New York
- Copyright Holder
- Springer Science+Business Media New York
- eBook ISBN
- 978-1-4613-0075-5
- DOI
- 10.1007/978-1-4613-0075-5
- Hardcover ISBN
- 978-0-387-95305-2
- Softcover ISBN
- 978-1-4612-6543-6
- Edition Number
- 1
- Number of Pages
- XXVII, 432
- Number of Illustrations
- 84 b/w illustrations
- Topics
