Gilbert–Johnson–Keerthi distance algorithm: Difference between revisions

Revision as of 13:40, 13 April 2017

Revision as of 16:27, 13 April 2017

Line 1:

The '''Gilbert–Johnson–Keerthi distance [[algorithm]]''' is a method of determining the minimum distance between two [[convex set]]s with linear complexity<ref>"The Gilbert-Johnson-Keerthi Distance Algorithm" Patrick Lindemann, 2009</ref>. Unlike many other distance algorithms, it does not require that the geometry data be stored in any specific format, but instead relies solely on a [[support (mathematics)|support function]] to iteratively generate closer [[simplex|simplices]] to the correct answer using the [[Minkowski sum]] (CSO) of two convex shapes.

"Enhanced GJK" algorithms use edge information to speed up the algorithm by following edges when looking for the next simplex. This improves performance substantially for polytopes with large numbers of vertices.