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Advances in Automatic Differentiation PDF

366 Pages·2008·6.867 MB·English
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About 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. 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.

Detailed Information

Author:Adrian Sandu (auth.), Christian H. Bischof, H. Martin Bücker, Paul Hovland, Uwe Naumann, Jean Utke (eds.)
Publication Year:2008
ISBN:9783540689423
Pages:366
Language:English
File Size:6.867
Format:PDF
Price:FREE
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