George Corliss / Christele Faure / Andreas Griewank / Laurent Hascoet / Uwe Naumann (eds.)

Automatic Differentiation of Algorithms

From Simulation to Optimization
Mitarbeit:Corliss, George; Faure, Christine; Griewank, Andreas

• Gebundenes Buch

Produktbeschreibung

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.

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Produktdetails

• Verlag: Springer / Springer New York / Springer, Berlin
• Artikelnr. des Verlages: 978-0-387-95305-2
• 2002 edition
• Seitenzahl: 432
• Erscheinungstermin: 8. Januar 2002
• Englisch
• Abmessung: 243mm x 162mm x 27mm
• Gewicht: 1038g
• ISBN-13: 9780387953052
• ISBN-10: 0387953051
• Artikelnr.: 10499261

Autorenporträt

George Corliss, Marquette University, Milwaukee, WI, USA / Christel Faure, INRIA, Sophia, France / Andreas Griewank, Technische Universität, Dresden, Germany / Laurent Hascoet, INRIA, Sophia, France

Inhaltsangabe

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.