Dakota Reference Manual: sbo

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sbo

Use the surrogate based optimization method

## Specification

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

## Description

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.