Automatic Differentiation of Algorithms: From Simulation to Optimization

ISBN 10: 0387953051 / ISBN 13: 9780387953052

Editore: Springer, 2002

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Codice articolo SHAK86039

Dati bibliografici

• Titolo: Automatic Differentiation of Algorithms: From Simulation to Optimization
• Editore: Springer
• Data di pubblicazione: 2002
• Lingua: Inglese
• ISBN 10: 0387953051
• ISBN 13: 9780387953052
• Rilegatura: Rilegato
• Condizione: nuovo

Descrizione articolo

Riassunto

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.

Contenuti

Part titles: *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