[ https://issues.apache.org/jira/browse/MATH749?page=com.atlassian.jira.plugin.system.issuetabpanels:alltabpanel
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Thomas Neidhart updated MATH749:

Description:
It would be nice to have convex hull implementations for 2D/3D space. There are several known
algorithms [http://en.wikipedia.org/wiki/Convex_hull_algorithms]:
* Graham scan: O(n log n)
* Incremental: O(n log n)
* Divide and Conquer: O(n log n)
* KirkpatrickSeidel: O(n log h)
* Chan: O(n log h)
The preference would be on an algorithm that is easily extensible for higher dimensions, so
*Incremental* and *Divide and Conquer* would be prefered.
was:
It would be nice to have convex hull implementations for 2D/3D space. There are several known
algorithms [http://en.wikipedia.org/wiki/Convex_hull_algorithms]:
* Graham scan: O(n log n)
* Incremental: O(n log n)
* KirkpatrickSeidel: O(n log h)
* Chan: O(n log h)
The preference would be on an algorithm that is easily extensible for higher dimensions, TBD.
> Convex Hull algorithm
> 
>
> Key: MATH749
> URL: https://issues.apache.org/jira/browse/MATH749
> Project: Commons Math
> Issue Type: New Feature
> Reporter: Thomas Neidhart
> Priority: Minor
> Labels: 2d, geometric
> Fix For: 3.1
>
>
> It would be nice to have convex hull implementations for 2D/3D space. There are several
known algorithms [http://en.wikipedia.org/wiki/Convex_hull_algorithms]:
> * Graham scan: O(n log n)
> * Incremental: O(n log n)
> * Divide and Conquer: O(n log n)
> * KirkpatrickSeidel: O(n log h)
> * Chan: O(n log h)
> The preference would be on an algorithm that is easily extensible for higher dimensions,
so *Incremental* and *Divide and Conquer* would be prefered.

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