UID:
almahu_9948621317902882
Format:
XXVII, 432 p. 84 illus.
,
online resource.
Edition:
1st ed. 2002.
ISBN:
9781461300755
Content:
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.
Note:
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.
In:
Springer Nature eBook
Additional Edition:
Printed edition: ISBN 9781461265436
Additional Edition:
Printed edition: ISBN 9780387953052
Additional Edition:
Printed edition: ISBN 9781461300762
Language:
English
DOI:
10.1007/978-1-4613-0075-5
URL:
https://doi.org/10.1007/978-1-4613-0075-5
