High-Level Interfaces for the MAD (Matlab Automatic Differentiation) Package.

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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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