Dakota Reference Manual  Version 6.12
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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

(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

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.

See Also

These keywords may also be of interest: