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Dakota Reference Manual
Version 6.16
Explore and Predict with Confidence
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Use the surrogate based optimization method
Alias: none
Argument(s): none
Child Keywords:
| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description | |
|---|---|---|---|---|
| Optional | gaussian_process | Gaussian Process surrogate model | ||
| Optional | use_derivatives | Use derivative data to construct surrogate models | ||
| Optional | import_build_points_file | File containing points you wish to use to build a surrogate | ||
| Optional | export_approx_points_file | Output file for surrogate model value evaluations | ||
A surrogate-based optimization method will be used. The surrogate employed in sbo is a Gaussian process surrogate.
The main difference between ego and the sbo approach is the objective function being optimized. ego relies on an expected improvement function, while in sbo, the optimization proceeds using an evolutionary algorithm (coliny_ea) on the Gaussian process surrogate: it is a standard surrogate-based optimization. Also note that the sbo option can support optimization over discrete variables (the discrete variables are relaxed) while ego cannot.
This is not the same as surrogate_based_global.