BibTeX
@INCOLLECTION{
Bischof2002ADf,
author = "C. H. Bischof and H. M. B{\"u}cker and B. Lang",
title = "Automatic Differentiation for Computational Finance",
booktitle = "Computational Methods in DecisionMaking, Economics and Finance",
publisher = "Kluwer Academic Publishers",
editor = "E. J. Kontoghiorghes and B. Rustem and S. Siokos",
pages = "297310",
address = "Dordrecht",
series = "Applied Optimization",
abstract = "Automatic differentiation (AD) is a powerful technique allowing to compute
derivatives of a function given by a (potentially very large) piece of code. The basic principles of
AD and some available tools implementing this technology are reviewed. AD is superior to divided
differences because ADgenerated derivative values are free of approximation errors, and superior to
symbolic differentiation because code of very high complexity can be handled, in contrast to
computer algebra systems whose applicability is limited to rather simple functions. In addition, the
cost for computing gradients of scalarvalued functions with either divided differences or symbolic
differentiation grows linearly with the number of variables, whereas the socalled reverse mode of
AD can compute such gradients at constant cost.",
ad_area = "Finance",
ad_theotech = "Introduction",
year = "2002",
volume = "74",
chapter = "15",
doi = "10.1007/9781475736137_15"
}
