Halton sampling is a low-discrepancy sampling method. It is a deterministic sampling method based on the Halton sequence, a sequence constructed by a set of co-prime bases. The Halton sequence is an n-dimensional extension of the Van der Corput sequence; each individual Halton sequence is based on a radix inverse function defined on a prime number.
The pysmo.sampling.HaltonSampling method carries out Halton sampling. This can be done in two modes:
- The samples can be selected from a user-provided dataset, or
- The samples can be generated from a set of provided bounds.
The Halton sampling method is only available for low-dimensional problems n ≤ 10. At higher dimensions, the performance of the sampling method has been shown to degrade.
idaes.surrogate.pysmo.sampling.HaltonSampling
[1] Loeven et al paper titled "A Probabilistic Radial Basis Function Approach for Uncertainty Quantification" https://pdfs.semanticscholar.org/48a0/d3797e482e37f73e077893594e01e1c667a2.pdf
[2] Webpage on low discrepancy sampling methods: http://planning.cs.uiuc.edu/node210.html