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Reverse Automatic Differentiation for Optimum Design: from Adjoint State Assembly to Gradient Computation

Abstract : The utilization of reverse mode Automatic Differentiation to the adjoint method for solving an Optimal Design problem is described. Using the reverse mode, we obtain the adjoint system residual in a rather efficient way. But memory requirements may be very large. The family of programs to differentiate involves many independant calculations, typically in parallel loops. Then we propose to apply a reverse differentiation «by iteration». This demands much less memory storage. This methods is used for the computing of the adjoint state and gradient related to the Optimal Design problem.
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https://hal.inria.fr/inria-00072225
Contributor : Rapport de Recherche Inria <>
Submitted on : Tuesday, May 23, 2006 - 8:09:32 PM
Last modification on : Saturday, January 27, 2018 - 1:31:27 AM
Long-term archiving on: : Sunday, April 4, 2010 - 9:05:57 PM

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  • HAL Id : inria-00072225, version 1

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F. Courty, Alain Dervieux, B. Koobus, L. Hascoet. Reverse Automatic Differentiation for Optimum Design: from Adjoint State Assembly to Gradient Computation. RR-4363, INRIA. 2002. ⟨inria-00072225⟩

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