Due to the current gap in continued funding from the U.S. National Science Foundation (NSF), the NSF Unidata Program Center has temporarily paused most operations. See NSF Unidata Pause in Most Operations for details.
Due to the current gap in continued funding from the U.S. National Science Foundation (NSF), the NSF Unidata Program Center has temporarily paused most operations. See NSF Unidata Pause in Most Operations for details.
Hi Ian, If your triangulations are limited to 2-D, you could try using DelaunayFast. It is an imperfect divide-and-conquer triangulation algorithm I wrote, for use with large numbers of points, when speed is more important than precision. It may not be accurate enough for your needs, but I suggest giving it a quick look. Also of interest is the Delaunay.improve() method, which uses edge-flipping to bring an imperfect triangulation closer to the optimal one. -Curtis On Tue, 29 Apr 2003, Ian Graham wrote: >> I _would_ like to understand where the faster algorithms fail, however, >> because this is a very small dataset in my world, and I don't need >> precision. I already make sure I don't have identical x,y coordinates, but >> that doesn't seem to be enough, and I thought only the Clarkson algorithm >> rounds to integers.
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