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Synopsis
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.
"synopsis" may belong to another edition of this title.
Synopsis
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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Automatic Differentiation Of Algorithms: From Simulation To Optimization
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Automatic Differentiation Of Algorithms
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Automatic Differentiation Of Algorithms
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Automatic Differentiation of Algorithms (Hardcover)
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Springer-Verlag New York Inc., New York, NY, 2002
ISBN 10: 0387953051
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Hardcover. Condition: new. Hardcover. 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. A survey book focusing upon the key applications and relationships and synergies between automatic differentiation (AD) tools and other software tools, such as compilers and parallelizers. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9780387953052
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Automatic Differentiation of Algorithms: From Simulation to Optimization
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Automatic Differentiation of Algorithms: From Simulation to Optimization
Published by
Springer-Verlag New York Inc., 2002
ISBN 10: 0387953051
ISBN 13: 9780387953052
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Automatic Differentiation of Algorithms: From Simulation to Optimization
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