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Dakota Reference Manual
Version 6.15
Explore and Predict with Confidence
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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
Child Keywords:
Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description | |
---|---|---|---|---|
Optional | samples | Number of samples for sampling-based methods | ||
Optional | seed | Seed of the random number generator | ||
Optional | max_iterations | Number of iterations allowed for optimizers and adaptive UQ methods | ||
Optional | convergence_tolerance | Stopping criterion based on objective function or statistics convergence | ||
Optional | max_function_evaluations | Number of function evaluations allowed for optimizers | ||
Optional (Choose One) | Solution Approach (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 | 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: