Int. J. Simul. Multidisci. Des. Optim.
Volume 8, 2017
|Number of page(s)||11|
|Published online||01 March 2017|
Hessian transfer for multilevel and adaptive shape optimization
LIMSAD Lab., Faculty of Science Ain Chock, Hassan II University, Casablanca
2 LR2I Lab., FSJES Ain Chock, Hassan II University, Casablanca 20100, Morocco
3 Opale Project-Team, INRIA, Sophia-Antipolis Méditerranée Centre, Sophia Antipolis 06902, France
4 JAD Lab., University of Nice-Sophia-Antipolis, Nice 06000, France
* e-mail: firstname.lastname@example.org
Accepted: 9 January 2017
We have developed a multilevel and adaption parametric strategies solved by optimization algorithms which require only the availability of objective function values but no derivative information. The key success of these hierarchical strategies refer to the quality of the downward and upward transfers of information. In this paper, we extend our approach when using a derivative-based optimization algorithms. The aim is to better re-initialize the Hessian and the gradient during the optimization process based on our construction of the downward and upward operators. The efficiency of this proposed approach is demonstrated by numerical experiments on an inverse shape model.
Key words: Shape parametric optimization / Hessian and gradient transfer / Multilevel / Adaption / BFGS method
© B. A. El Majd et al., Published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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