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Dakota Reference Manual
Version 6.16
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.