Automatic Differentiation of Algorithms (eBook)

From Simulation to Optimization

George Corliss, Christele Faure, Andreas Griewank, Laurent Hascoet, Uwe Naumann (Herausgeber)

eBook Download: PDF

2013
Springer New York (Verlag)
978-1-4613-0075-5 (ISBN)

Infos

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.

Produkt-Details

• Erscheint lt. Verlag: 21.11.2013
• Sprache: englisch
• Themenwelt: Informatik ► Theorie / Studium ► Algorithmen
• ISBN-10: 1-4613-0075-4 / 1461300754
• ISBN-13: 978-1-4613-0075-5 / 9781461300755