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Local optimization of black-box functions with high or infinite-dimensional inputs: application to nuclear safety

Abstract : Black-box optimization problems when the input space is a high-dimensional space or a function space appear in more and more applications. In this context, the methods available for finite-dimensional data do not apply. The aim is then to propose a general method for optimization involving dimension reduction techniques. Different dimension reduction basis are considered (including data-driven basis). The methodology is illustrated on simulated functional data. The choice of the different parameters, in particular the dimension of the approximation space, is discussed. The method is finally applied to a problem of nuclear safety.
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https://hal.archives-ouvertes.fr/hal-01144628
Contributor : Angelina Roche <>
Submitted on : Tuesday, November 17, 2015 - 4:54:29 PM
Last modification on : Friday, November 13, 2020 - 2:08:03 PM

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Angelina Roche. Local optimization of black-box functions with high or infinite-dimensional inputs: application to nuclear safety. Computational Statistics, Springer Verlag, 2018, 33 (1), pp.467-485. ⟨10.1007/s00180-017-0751-1⟩. ⟨hal-01144628v3⟩

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