Convergence properties of the expected improvement algorithm with fixed mean and covariance functions

This paper deals with the convergence of the expected improvement algorithm, a popular global optimization algorithm based on a Gaussian process model of the function to be optimized. The first result is that under some mild hypotheses on the covariance function k of the Gaussian process, the expect...

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Vydané v:	Journal of statistical planning and inference Ročník 140; číslo 11; s. 3088 - 3095
Hlavní autori:	Vazquez, Emmanuel, Bect, Julien
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Kidlington Elsevier B.V 01.11.2010 Elsevier
Predmet:	Bayesian optimization Calculus of variations and optimal control Computer experiments Exact sciences and technology Gaussian process General topics Global optimization Machine Learning Mathematical analysis Mathematics Numerical analysis Numerical analysis. Scientific computation Numerical methods in mathematical programming, optimization and calculus of variations Numerical methods in optimization and calculus of variations Optimization and Control Probability Probability and statistics Probability theory and stochastic processes RKHS Sciences and techniques of general use Sequential design Statistics Statistics Theory Stochastic processes Computer experiments Global optimization RKHS Gaussian process Sequential design Bayesian optimization Density estimation Covariance function Optimization method Gaussian distribution Probability distribution Optimization Convergence Sequential method Variational calculus Hilbert space Mathematical programming Bayes estimation Prior distribution Continuous function Statistical decision Prior probability Algorithm Mean estimation Statistical method Numerical analysis Gaussian processes sequential design computer experiments global optimization
ISSN:	0378-3758, 1873-1171
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