High-Level Interfaces for the MAD (Matlab Automatic Differentiation) Package
Shaun A Forth & Robert Ketzscher
Published in
4th European Congress on Computational Methods in Applied Sciences &
Engineering (ECCOMAS) eds. P Neittaanmaki, T Rossi, S Korotov, E Onate, J
Periaux and D Knorzer, 2004, CD-ROM published by University of Jyvaskyla,
Department of Mathematical Information Technology, Finland, ISBN 951-39-1869-6,
Abstract
Presently, the MAD Automatic Differentiation
package for matlab comprises an overloaded implementation of forward mode AD via
the fmad class. A key design feature of the fmad class is a separation of
the storage and manipulation of directional derivatives into a separate derivvec
class. Within the derivvec class, directional derivatives are stored as matrices
(2-D arrays) allowing for the use of either full or sparse matrix storage. All
manipulation of directional derivatives is performed using high-level matrix
operations - thus assuring efficiency. In this paper: we briefly review
implementation of the fmad class; we then present our implementation of
high-level interfaces allowing users to utilise MAD in conjunction with stiff
ODE solvers and numerical optimization routines; we then demonstrate the ease
and utility of this approach via several examples; we conclude with a road-map
for future developments.
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