Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
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Gaussian Process (GP) models are popular statistical surrogates used for emulating computationally expensive computer simulators. The quality of a GP model fit can be assessed by a goodness of fit measure based on optimized likelihood. Finding the global maximum of the likelihood function for a GP model is typically challenging, as the likelihood surface often has multiple local optima, and an explicit expression for the gradient of the likelihood function may not be available. Previous methods for optimizing the likelihood function have proven to be robust and accurate, though relatively inefficient. Several likelihood optimization techniques are proposed, including two modified multi-start local search techniques, that are equally as reliable, and significantly more efficient than existing methods. A hybridization of the global search algorithm Dividing Rectangles (DIRECT) with the local optimization algorithm BFGS provides a comparable GP model quality for a fraction of the computational cost, and is the preferred optimization technique when computational resources are limited. Several test functions and an application motivated by oil reservoir development are used to test and compare the performance of the proposed methods with the implementation provided in the R library GPfit. The proposed method is implemented in a Matlab package, GPMfit.