Automatic Differentiation of Algorithms

From Simulation to Optimization
Herausgegeben:Corliss, George; Faure, Christele; Griewank, Andreas; Hascoet, Laurent; Naumann, Uwe

• Broschiertes 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.

Produktdetails

• Verlag: Springer / Springer New York / Springer, Berlin
• Artikelnr. des Verlages: 978-1-4612-6543-6
• Softcover reprint of the original 1st ed. 2002
• Seitenzahl: 464
• Erscheinungstermin: 27. Januar 2014
• Englisch
• Abmessung: 235mm x 155mm x 25mm
• Gewicht: 698g
• ISBN-13: 9781461265436
• ISBN-10: 1461265436
• Artikelnr.: 41323005

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.