Ebook: Advances in Automatic Differentiation
Author: Adrian Sandu (auth.) Christian H. Bischof H. Martin Bücker Paul Hovland Uwe Naumann Jean Utke (eds.)
- Genre: Mathematics // Computational Mathematics
- Tags: Computational Science and Engineering, Computational Mathematics and Numerical Analysis, Electrical Engineering, Mathematics of Computing
- Series: Lecture Notes in Computational Science and Engineering 64
- Year: 2008
- Publisher: Springer-Verlag Berlin Heidelberg
- City: Berlin; London
- Edition: 1
- Language: English
- pdf
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.
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.