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Dakota Reference Manual
Version 6.2
Large-Scale Engineering Optimization and Uncertainty Analysis
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Dakota Reference Manual
Version 6.2
Large-Scale Engineering Optimization and Uncertainty Analysis
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Interval analysis using global optimization methods
This keyword is related to the topics:
Alias: nond_global_interval_est
Argument(s): none
| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description | |
|---|---|---|---|---|
| Optional (Choose One) | Group 1 | sbo | Use the surrogate based optimization method | |
| ego | Use the Efficient Global Optimization method | |||
| ea | Use an evolutionary algorithm | |||
| lhs | Uses Latin Hypercube Sampling (LHS) to sample variables | |||
| Optional | rng | Selection of a random number generator | ||
| Optional | samples | Number of samples for sampling-based methods | ||
| Optional | seed | Seed of the random number generator | ||
| Optional | model_pointer | Identifier for model block to be used by a method | ||
In the global approach to interval estimation, one uses either a global optimization method or a sampling method to assess the bounds of the responses.
global_interval_est allows the user to specify several approaches to calculate interval bounds on the output responses.
lhs - note: this takes the minimum and maximum of the samples as the bounds ego sbo ea Additional Resources
Refer to variable_support for information on supported variable types.
These keywords may also be of interest:
1.8.4