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https://issues.apache.org/jira/browse/MATH-1220?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Otmar Ertl updated MATH-1220:
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Attachment: patch_v2
I have found some time to read the paper you mentioned:
http://epub.wu.ac.at/1176/. The algorithm described there is superior to the
method I have proposed. The only drawback is that it is restricted to exponents
larger than 1. However, I have found a way to transform the algorithm so that
it should work for any non-negative exponents (see patch_v2).
> More efficient sample() method for ZipfDistribution
> ---------------------------------------------------
>
> Key: MATH-1220
> URL: https://issues.apache.org/jira/browse/MATH-1220
> Project: Commons Math
> Issue Type: Improvement
> Reporter: Otmar Ertl
> Fix For: 4.0, 3.6
>
> Attachments: patch_v1, patch_v2
>
>
> Currently, sampling from a ZipfDistribution is very inefficient. Random
> values are generated by inverting the CDF. However, the current
> implementation uses O(N) power function evaluations to calculate the CDF for
> some point. (Here N is the number of points of the Zipf distribution.) I
> propose to use rejection sampling instead, which allows the generation of a
> single random value in constant time.
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