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Automatic Differentiation of Algorithms: From Simulation to Optimization. 2002 ed.
・ISBN 978-0-387-95305-2 hard EUR 49.99
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お気に入り
★★★
| 著者・編者 | Corliss, George / Faure, Christele / Griewank, Andreas / Hascoet, Laurent / Naumann, Uwe (eds.), |
|---|---|
| 出版社 | (Springer-Verlag New York Inc., US) |
| 出版年月 | 2002 |
| ページ数 | 432 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A05-49840＞ |
解説
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.
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.