Hessian transfer for multilevel and adaptive shape optimization

Abstract : 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.
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Article dans une revue
International Journal for Simulation and Multidisciplinary Design Optimization, EDP sciences/NPU (China), 2017, 8, 18 p. 〈10.1051/smdo/2017002 〉
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https://hal.inria.fr/hal-01440209
Contributeur : Abderrahmane Habbal <>
Soumis le : jeudi 19 janvier 2017 - 09:50:00
Dernière modification le : vendredi 12 janvier 2018 - 01:49:53
Document(s) archivé(s) le : jeudi 20 avril 2017 - 12:59:37

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Badr Abou El Majd, Ouail Ouchetto, Jean-Antoine Désidéri, Abderrahmane Habbal. Hessian transfer for multilevel and adaptive shape optimization . International Journal for Simulation and Multidisciplinary Design Optimization, EDP sciences/NPU (China), 2017, 8, 18 p. 〈10.1051/smdo/2017002 〉. 〈hal-01440209〉

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