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Advances in Automatic Differentiation

This collection covers advances in automatic differentiation theory and practice. Computer scientists and mathematicians will learn about recent developments in automatic differentiation theory as well as mechanisms for the construction of robust and powerful automatic differentiation tools. Computa...

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Bibliographic Details
Other Authors: Bischof, Christian H. (Editor), Bücker, H. Martin (Editor), Hovland, Paul (Editor), Naumann, Uwe (Editor)
Format: eBook
Language:English
Published: Berlin, Heidelberg Springer Berlin Heidelberg 2008, 2008
Edition:1st ed. 2008
Series:Lecture Notes in Computational Science and Engineering
Subjects:
Optimization
Computer Science
Mathematics Of Computing
Computer Science / Mathematics
Electrical And Electronic Engineering
Electrical Engineering
Computational Mathematics And Numerical Analysis
Mathematics / Data Processing
Computational Science And Engineering
Theory Of Computation
Mathematical Optimization
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
Description
Summary:This collection covers advances in automatic differentiation theory and practice. Computer scientists and mathematicians will learn about recent developments in automatic differentiation theory as well as mechanisms for the construction of robust and powerful automatic differentiation tools. Computational scientists and engineers will benefit from the discussion of various applications, which provide insight into effective strategies for using automatic differentiation for inverse problems and design optimization
Physical Description:XVIII, 368 p. 111 illus online resource
ISBN:9783540689423

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