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https://issues.apache.org/jira/browse/MATH-749?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Thomas Neidhart updated MATH-749:
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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)
* Kirkpatrick-Seidel: 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)
* Kirkpatrick-Seidel: 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: MATH-749
> URL: https://issues.apache.org/jira/browse/MATH-749
> 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)
> * Kirkpatrick-Seidel: 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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