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Dakota Reference Manual
Version 6.15
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.