Automatic Differentiation of Algorithms: From Simulation to Optimization
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...
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| Other Authors: | , , , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
New York, NY
Springer New York
2002
|
| Edition: | 1st ed. 2002 |
| Subjects: | |
| Online Access: | UBY01 Volltext |
| Summary: | 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 |
| Physical Description: | 1 Online-Ressource (XXVII, 432 p. 84 illus) |
| ISBN: | 9781461300755 |
| DOI: | 10.1007/978-1-4613-0075-5 |
Staff View
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| discipline | Informatik Mathematik |
| discipline_str_mv | Informatik Mathematik |
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| edition | 1st ed. 2002 |
| format | Electronic eBook |
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| spelling | Automatic Differentiation of Algorithms From Simulation to Optimization edited by George Corliss, Christele Faure, Andreas Griewank, Laurent Hascoet, Uwe Naumann 1st ed. 2002 New York, NY Springer New York 2002 1 Online-Ressource (XXVII, 432 p. 84 illus) txt rdacontent c rdamedia cr rdacarrier 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 Computer Science, general Computer science Automatische Differentiation (DE-588)4314524-3 gnd rswk-swf (DE-588)1071861417 Konferenzschrift 2000 Nizza gnd-content Automatische Differentiation (DE-588)4314524-3 s DE-604 Corliss, George edt Faure, Christele edt Griewank, Andreas edt Hascoet, Laurent edt Erscheint auch als Druck-Ausgabe 9781461265436 Erscheint auch als Druck-Ausgabe 9780387953052 Erscheint auch als Druck-Ausgabe 9781461300762 https://doi.org/10.1007/978-1-4613-0075-5 Verlag URL des Eerstveröffentlichers Volltext |
| spellingShingle | Automatic Differentiation of Algorithms From Simulation to Optimization Computer Science, general Computer science Automatische Differentiation (DE-588)4314524-3 gnd |
| subject_GND | (DE-588)4314524-3 (DE-588)1071861417 |
| title | Automatic Differentiation of Algorithms From Simulation to Optimization |
| title_auth | Automatic Differentiation of Algorithms From Simulation to Optimization |
| title_exact_search | Automatic Differentiation of Algorithms From Simulation to Optimization |
| title_exact_search_txtP | Automatic Differentiation of Algorithms From Simulation to Optimization |
| title_full | Automatic Differentiation of Algorithms From Simulation to Optimization edited by George Corliss, Christele Faure, Andreas Griewank, Laurent Hascoet, Uwe Naumann |
| title_fullStr | Automatic Differentiation of Algorithms From Simulation to Optimization edited by George Corliss, Christele Faure, Andreas Griewank, Laurent Hascoet, Uwe Naumann |
| title_full_unstemmed | Automatic Differentiation of Algorithms From Simulation to Optimization edited by George Corliss, Christele Faure, Andreas Griewank, Laurent Hascoet, Uwe Naumann |
| title_short | Automatic Differentiation of Algorithms |
| title_sort | automatic differentiation of algorithms from simulation to optimization |
| title_sub | From Simulation to Optimization |
| topic | Computer Science, general Computer science Automatische Differentiation (DE-588)4314524-3 gnd |
| topic_facet | Computer Science, general Computer science Automatische Differentiation Konferenzschrift 2000 Nizza |
| url | https://doi.org/10.1007/978-1-4613-0075-5 |
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