Citation:
Shaun A Forth & Robert Ketzscher; High-Level Interfaces for the MAD (Matlab
Automatic Differentiation) Package. 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.
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.
