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...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Bischof, Christian H. (Επιμελητής έκδοσης), Bücker, H. Martin (Επιμελητής έκδοσης), Hovland, Paul (Επιμελητής έκδοσης), Naumann, Uwe (Επιμελητής έκδοσης), Utke, Jean (Επιμελητής έκδοσης)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.
Σειρά:Lecture Notes in Computational Science and Engineering, 64
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Περιγραφή
Περίληψη: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.
Φυσική περιγραφή:XVIII, 368 p. 111 illus. online resource.
ISBN:9783540689423
ISSN:1439-7358 ;