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Sinossi
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.
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Contenuti
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
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