Abstract
There are no practical reasons for using the Bayesian approach to optimize convex functions without noise. The well known methods of ‘second order’ based on a quadratic approximation such as variable metrics or conjugate gradients are apparently nearly optimal and usually ensure a superlinear convergence. However, it is only when there is no noise. The presence of even a small amount of noise can change the situation completely.
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© 1989 Kluwer Academic Publishers
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Mockus, J. (1989). The Bayesian Approach to Local Optimization. In: Bayesian Approach to Global Optimization. Mathematics and Its Applications, vol 37. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-0909-0_7
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DOI: https://doi.org/10.1007/978-94-009-0909-0_7
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-6898-7
Online ISBN: 978-94-009-0909-0
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