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Gaussian process optimization with failures: classification and convergence proof

Abstract : We consider the optimization of a computer model where each simulation either fails or returns a valid output performance. We first propose a new joint Gaussian process model for classification of the inputs (computation failure or success) and for regression of the performance function. We provide results that allow for a computationally efficient maximum likelihood estimation of the covariance parameters, with a stochastic approximation of the likelihood gradient. We then extend the classical improvement criterion to our setting of joint classification and regression. We provide an efficient computation procedure for the extended criterion and its gradient. We prove the almost sure convergence of the global optimization algorithm following from this extended criterion. We also study the practical performances of this algorithm, both on simulated data and on a real computer model in the context of automotive fan design.
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https://hal.archives-ouvertes.fr/hal-02100819
Contributor : Céline Helbert <>
Submitted on : Wednesday, January 29, 2020 - 11:02:18 AM
Last modification on : Monday, May 25, 2020 - 3:38:04 PM

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  • HAL Id : hal-02100819, version 2

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François Bachoc, Céline Helbert, Victor Picheny. Gaussian process optimization with failures: classification and convergence proof. Journal of Global Optimization, Springer Verlag, In press. ⟨hal-02100819v2⟩

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