Paperback. 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. A survey book focusing on the key relationships and synergies between automatic differentiation (AD) tools and other software tools, such as compilers and parallelizers, as well as their applications. They cover the scientific programming aspects, such as the use of adjoints in optimization and the propagation of rounding errors. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Dati bibliografici

Titolo: Automatic Differentiation of Algorithms (Paperback)
Editore: Springer-Verlag New York Inc., New York, NY
Data di pubblicazione: 2014
Lingua: Inglese
ISBN 10: 1461265436
ISBN 13: 9781461265436
Rilegatura: Paperback
Condizione: new

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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

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