Automatic Differentiation of Algorithms
From Simulation to Optimization
George Corliss, Christèle Faure, Andreas Griewank, Laurent Hascoët, Uwe Naumann
432 pages, parution le 27/02/2002
Résumé
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.
Contents
- 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.
Caractéristiques techniques
| PAPIER | |
| Éditeur(s) | Springer |
| Auteur(s) | George Corliss, Christèle Faure, Andreas Griewank, Laurent Hascoët, Uwe Naumann |
| Parution | 27/02/2002 |
| Nb. de pages | 432 |
| Format | 15,5 x 23,5 |
| Couverture | Relié |
| Poids | 827g |
| Intérieur | Noir et Blanc |
| EAN13 | 9780387953052 |
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