Automatic Differentiation of Algorithms

George Corliss

ISBN 10: 1461265436 / ISBN 13: 9781461265436

Editore: Springer New York, Springer New York Jan 2014, 2014

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Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 464 pp. Englisch. Codice articolo 9781461265436

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Titolo: Automatic Differentiation of Algorithms

Casa editrice: Springer New York, Springer New York Jan 2014

Data di pubblicazione: 2014

Legatura: Taschenbuch

Condizione: Neu

Descrizione articolo

Riassunto:

A state-of-the-art survey book focusing upon the key applications and relationships and synergies between automatic differentiation (AD) tools and other software tools, such as compilers and parallelizers. Essential reading for practitioners and professionals in scientific computing, as well as for computer engineers.

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