Global optimization via inverse distance weighting and radial basis functions

Global optimization problems whose objective function is expensive to evaluate can be solved effectively by recursively fitting a surrogate function to function samples and minimizing an acquisition function to generate new samples. The acquisition step trades off between seeking for a new optimizat...

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Veröffentlicht in:	Computational optimization and applications Jg. 77; H. 2; S. 571 - 595
1. Verfasser:	Bemporad, Alberto
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.11.2020 Springer Nature B.V
Schlagworte:	Algorithms Convex and Discrete Geometry Global optimization Management Science Mathematics Mathematics and Statistics Operations Research Operations Research/Decision Theory Optimization Radial basis function Statistics Weighting Global optimization Radial basis functions Black-box optimization Surrogate models Bayesian optimization Inverse distance weighting Derivative-free algorithms
ISSN:	0926-6003, 1573-2894
Online-Zugang:	Volltext
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