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S Jun 20, 2014 at 10:49 history suggested VividD CC BY-SA 3.0
added explanations in order to achieve even better clarity and usability for potential new readers
Jun 20, 2014 at 10:26 review Suggested edits
S Jun 20, 2014 at 10:49
Jun 20, 2014 at 9:25 review Suggested edits
Jun 20, 2014 at 10:01
Jun 20, 2014 at 8:46 review Suggested edits
Jun 20, 2014 at 9:10
May 14, 2014 at 10:57 comment added Simon Woods The second version improves things by using constraints to insist that the circles on connected vertices touch each other. The original vertex positions go in as a starting point to FindMaximum, which seeks a local maximum (whereas NMaxmimize looks for a global optimum). It can still give unsatisfactory results, with overlapping circles for non-edges. I think this means that FindMaximum has found a local maximum which is not a global optimum. I guess this could be remedied with more constraints.
May 14, 2014 at 10:57 comment added Simon Woods The original algorithm is not very good - it doesn't constrain the circles on connected vertices to touch, it just attempts to minimize the gaps. At the same time it is trying to minimize the movement of the vertices from their initial positions (which is not really what we want to do). So it reaches a point where it will sacrifice the circle-packing in order to avoid rearranging the vertex positions.
May 14, 2014 at 10:21 comment added VividD for your first method, I tried g = GraphData[{"BananaTree", {7, 4}}] , with all other code the same, the resulting circle pac is not entirely correct for some reason...
May 14, 2014 at 9:40 comment added VividD Great application of Mathematica built-in optimisations! Thanks a lot! I am also curious why do you think that in FindMaximum vs NMaximize, FindMaximum wins for larger graphs? (Or maybe this is not the case?)
May 13, 2014 at 12:09 history edited Simon Woods CC BY-SA 3.0
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May 12, 2014 at 22:15 history answered Simon Woods CC BY-SA 3.0