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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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Design of Computer Experiments - Quasi-Monte Carlo sampling
This keyword is related to the topics:
Alias: none
Argument(s): none
| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description | |
|---|---|---|---|---|
| Required (Choose One) | sequence type (Group 1) | halton | Generate samples from a Halton sequence | |
| hammersley | Use Hammersley sequences | |||
| 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 | samples | Number of samples for sampling-based methods | ||
| Optional | fixed_sequence | Reuse the same sequence and samples for multiple sampling sets | ||
| Optional | sequence_start | Choose where to start sampling the sequence | ||
| Optional | sequence_leap | Specify how often the sequence is sampled | ||
| Optional | prime_base | The prime numbers used to generate the sequence | ||
| Optional | model_pointer | Identifier for model block to be used by a method | ||
Quasi-Monte Carlo methods produce low discrepancy sequences, especially if one is interested in the uniformity of projections of the point sets onto lower dimensional faces of the hypercube (usually 1-D: how well do the marginal distributions approximate a uniform?)
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.
The user must first choose the sequence type:
halton or hammersley Then three keywords are used to define the sequence and how it is sampled:
prime_base sequence_start sequence_leap Each of these has defaults, so specification is optional.
The quasi-Monte Carlo sequences of Halton and Hammersley are deterministic sequences determined by a set of prime bases. Generally, we recommend that the user leave the default setting for the bases, which are the lowest primes. Thus, if one wants to generate a sample set for 3 random variables, the default bases used are 2, 3, and 5 in the Halton sequence. To give an example of how these sequences look, the Halton sequence in base 2 starts with points 0.5, 0.25, 0.75, 0.125, 0.625, etc. The first few points in a Halton base 3 sequence are 0.33333, 0.66667, 0.11111, 0.44444, 0.77777, etc. Notice that the Halton sequence tends to alternate back and forth, generating a point closer to zero then a point closer to one. An individual sequence is based on a radix inverse function defined on a prime base. The prime base determines how quickly the [0,1] interval is filled in. Generally, the lowest primes are recommended.
The Hammersley sequence is the same as the Halton sequence, except the values for the first random variable are equal to 1/N, where N is the number of samples. Thus, if one wants to generate a sample set of 100 samples for 3 random variables, the first random variable has values 1/100, 2/100, 3/100, etc. and the second and third variables are generated according to a Halton sequence with bases 2 and 3, respectively.
For more information about these sequences, see[43], [42], and [56].
These keywords may also be of interest:
1.8.4