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Section: New Results

Algorithmic Differentiation and Iterative Processes

Participants : Laurent Hascoët, Ala Taftaf.

Adjoint codes naturally propagate partial gradients backwards from the result of the simulation. However, this uses the data flow of the simulation in reverse order, at a cost that increases with the length of the simulation. In the special case of iterative Fixed-Point loops, it is clear that the first iterations operate on a meaningless “initial guess” state vector, and that reversing the corresponding data-flow is wasted effort. An adapted adjoint strategy for the iterative process should consider only the last or the few last iterations. Also the adjoint loop, which is itself a Fixed-Point iteration, must have its own stopping criterion and not merely run as many times as the direct Fixed-Point loop. We selected the strategy proposed by Bruce Christianson  [17] and this year we implemented it in Tapenade. This strategy is triggered by differentiation directives that we defined. We tested this strategy with success on the medium-size testcase provided by Queen Mary University for the AboutFlow project.

Ala Taftaf presented her results at the WCCM congress during the Eccomas conference in Barcelona [13] , july 21-25. Ala Taftaf did a two-months secondment for her Marie Curie PhD grant, with our partner team of Queen Mary University of London, during which she helped them take advantage of the latest developments in Tapenade and of her developments about Fixed-Point adjoints.