dc.contributor.author |
Forth, Shaun A. |
- |
dc.contributor.author |
Ketzscher, Robert |
- |
dc.date.accessioned |
2011-11-21T23:01:11Z |
|
dc.date.available |
2011-11-21T23:01:11Z |
|
dc.date.issued |
2004-01-01T00:00:00Z |
- |
dc.identifier.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. |
- |
dc.identifier.isbn |
951-39-1869-6 |
- |
dc.identifier.uri |
http://dspace.lib.cranfield.ac.uk/handle/1826/3139 |
|
dc.description.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. |
en_UK |
dc.subject |
Automatic Differentiation |
en_UK |
dc.subject |
Numerical Optimization |
en_UK |
dc.subject |
Numerical Solution of Stiff ODEs |
en_UK |
dc.title |
High-Level Interfaces for the MAD (Matlab Automatic Differentiation) Package. |
en_UK |
dc.type |
Article |
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