Dakota Reference Manual  Version 6.4
Large-Scale Engineering Optimization and Uncertainty Analysis
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Design of Computer Experiments - Centroidal Voronoi Tessellation


This keyword is related to the topics:


Alias: none

Argument(s): none

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 latinize Adjust samples to improve the discrepancy of the marginal distributions
Optional quality_metrics Calculate metrics to assess the quality of quasi-Monte Carlo samples
Optional variance_based_decomp

Activates global sensitivity analysis based on decomposition of response variance into contributions from variables

Optional fixed_seed

Reuses the same seed value for multiple random sampling sets

Optional trial_type Specify how the trial samples are generated
Optional num_trials The number of secondary sample points generated to adjust the location of the primary sample points
Optional max_iterations

Stopping criterion based on number of iterations

Optional model_pointer

Identifier for model block to be used by a method


The FSU Centroidal Voronoi Tessellation method (fsu_cvt) produces a set of sample points that are (approximately) a Centroidal Voronoi Tessellation. The primary feature of such a set of points is that they have good volumetric spacing; the points tend to arrange themselves in a pattern of cells that are roughly the same shape.

To produce this set of points, an almost arbitrary set of initial points is chosen, and then an internal set of iterations is carried out. These iterations repeatedly replace the current set of sample points by an estimate of the centroids of the corresponding Voronoi subregions. [17].

The user may generally ignore the details of this internal iteration. If control is desired, however, there are a few variables with which the user can influence the iteration. The user may specify:

  • max_iterations, the number of iterations carried out
  • num_trials, the number of secondary sample points generated to adjust the location of the primary sample points
  • trial_type, which controls how these secondary sample points are generated

This method generates sets of uniform random variables on the interval [0,1]. If the user specifies lower and upper bounds for a variable, the [0,1] samples are mapped to the [lower, upper] interval.


This method is designed to generate samples with the goal of low discrepancy. Discrepancy refers to the nonuniformity of the sample points within the hypercube.

Discrepancy is defined as the difference between the actual number and the expected number of points one would expect in a particular set B (such as a hyper-rectangle within the unit hypercube), maximized over all such sets. Low discrepancy sequences tend to cover the unit hypercube reasonably uniformly.

Centroidal Voronoi Tessellation does very well volumetrically: it spaces the points fairly equally throughout the space, so that the points cover the region and are isotropically distributed with no directional bias in the point placement. There are various measures of volumetric uniformity which take into account the distances between pairs of points, regularity measures, etc. Note that Centroidal Voronoi Tessellation does not produce low-discrepancy sequences in lower dimensions. The lower-dimension (such as 1-D) projections of Centroidal Voronoi Tessellation can have high discrepancy.

See Also

These keywords may also be of interest: