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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.
https://hal.inria.fr/hal-01440209
Contributor : Abderrahmane Habbal <>
Submitted on : Thursday, January 19, 2017 - 9:50:00 AM
Last modification on : Thursday, May 20, 2021 - 9:12:01 AM
Long-term archiving on: : Thursday, April 20, 2017 - 12:59:37 PM
Contributor : Abderrahmane Habbal <>
Submitted on : Thursday, January 19, 2017 - 9:50:00 AM
Last modification on : Thursday, May 20, 2021 - 9:12:01 AM
Long-term archiving on: : Thursday, April 20, 2017 - 12:59:37 PM
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HessianTransfert_Final.pdf
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