BibTeX
@ARTICLE{
Forth2006AEO,
author = "Shaun A. Forth",
title = "An Efficient Overloaded Implementation of Forward Mode Automatic Differentiation in
{MATLAB}",
journal = "{ACM} Transactions on Mathematical Software",
volume = "32",
number = "2",
pages = "195222",
year = "2006",
URL = "http://doi.acm.org/10.1145/1141885.1141888",
abstract = "The {\sc Mad} package described here facilitates the evaluation of first
derivatives of multidimensional functions that are defined by computer codes written in MATLAB. The
underlying algorithm is the wellknown forward mode of automatic differentiation implemented via
operator overloading on variables of the class {\tt fmad}. The main distinguishing feature of
this MATLAB implementation is the separation of the linear combination of derivative vectors into a
separate derivative vector class {\tt derivvec}. This allows for the straightforward
performance optimisation of the overall package. Additionally by internally using a matrix
(twodimensional) representation of arbitrary dimension directional derivatives we may utilise
MATLAB's sparse matrix class to propagate sparse directional derivatives for MATLAB code which
uses arbitrary dimension arrays. On several examples the package is shown to be more efficient than
Verma's ADMAT package.",
month = "jun",
ad_tools = "TOMLAB /MAD"
}
