Automatic Differentiation of Algorithms: From Simulation to Optimization

Corliss, George; Faure, Christele; Griewank, Andreas; Hascoet, Laurent; Naumann, Uwe

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.

Details

Published by: Springer

Publication Date: 2014-01-27

Format: Paperback

ISBN-10: 9781461265436

ISBN-13: 9781461265436

DOI: 10.1007/978-1-4613-0075-5

Dimensions: 235cm x155cm

Pages: 432