Automatic Differentiation of Algorithms
From Simulation to Optimization
Produktform: Buch / Einband - flex.(Paperback)
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.
Topics and features:
* helpful introductory AD survey chapter for brief overview of the field
*extensive applications chapters, i.e., for circuit simulation, optimization and optimal-control shape design, structural mechanics, and multibody dynamical systems modeling
*comprehensive bibliography for all current literature and results for the field
*performance issues
*optimal control sensitivity analysis
*AD use with object oriented software tool kits
The book is an ideal and accessible survey of recent developments and applications of AD tools and techniques for a broad scientific computing and computer engineering readership. Practitioners, professionals, and advanced graduates working in AD development will find the book a
useful reference and essential resource for their work.weiterlesen
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