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Dakota Reference Manual
Version 6.15
Explore and Predict with Confidence
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Use the Efficient Global 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 |
In the case of ego
, the efficient global optimization (EGO) method is used to calculate bounds. By default, the Surfpack GP (Kriging) model is used, but the Dakota implementation may be selected instead. If use_derivatives
is specified the GP model will be built using available derivative data (Surfpack GP only).
See efficient_global for more information.