Dakota Reference Manual  Version 6.4
Large-Scale Engineering Optimization and Uncertainty Analysis
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samples


Number of samples for sampling-based methods

Specification

Alias: initial_samples

Argument(s): INTEGER

Default: 0

Description

The samples keyword is used to define the number of samples (i.e., randomly chosen sets of variable values) at which to execute a model.

Default Behavior

By default, Dakota will use the minimum number of samples required by the chosen method.

Usage Tips

To obtain linear sensitivities or to construct a linear response surface, at least dim+1 samples should be used, where "dim" is the number of variables. For sensitivities to quadratic terms or quadratic response surfaces, at least (dim+1)(dim+2)/2 samples are needed. For uncertainty quantification, we recommend at least 10*dim samples. For variance_based_decomp, we recommend hundreds to thousands of samples. Note that for variance_based_decomp, the number of simulations performed will be N*(dim+2).

Examples

method
  sampling
    sample_type lhs
    samples = 20