Reverse Automatic Differentiation for Optimum Design:
from Adjoint State Assembly to Gradient Computation
Francois Courty
Alain Dervieux
Bruno Koobus
Laurent Hascoët
(INRIA, BP93, 06902 Sophia-Antipolis, France)
INRIA Research Report #4363, january 2002 (33 pages)
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.
Keywords:
Automatic Differentiation, Optimal shape design, Computational Fluid Dynamics,
Euler Equations, Compressible flow, Adjoint,
Gradient, Reverse mode, Checkpointing, Data Dependence Analysis,
Memory optimization
Full text (pdf)
@techreport{CDKH02,
author = {Courty, F. and Dervieux, A. and Koobus, B. and Hasco{\"e}t, L.},
title = {Reverse Automatic Differentiation for Optimum Design:
from Adjoint State Assembly to Gradient Computation},
institution = {INRIA},
type = {Research Report},
number=4363,
url = "http://www.inria.fr/rrrt/rr-4363.html",
year=2001
}