Computational Statistics

, Volume 33, Issue 1, pp 467–485 | Cite as

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

  • Angelina RocheEmail author
Original Paper


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.


Experimental design Response surface methods Black-box optimization Functional data analysis 


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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Université Paris-Dauphine, PSL Research University, CNRS, UMR [7534], CEREMADEParis Cedex 16France

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