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If you are okay with an approximated solution (not necessarily the best one possible), you could try genetic approach. 

Find a so-so solution as a starting point - using some simlesimple algorithm such as the one suggested by @Carra - then make, say, a 1000 copies of this initial solution and keep on mutating them randomly. Switching points, trying to add more triangles etc. 

Reward the best specimen and throw away poor copies, eg. after each generation you could overwrite the entire gene pool with clones of the best 5%. For best results decrease the temperature with time - it means use aggressive mutation at the beginning, but slower mutation rate towards the end in order to finetune the results once they reach high quality level.

After some iterations you should end up with a fairly good solution.

If you are okay with an approximated solution (not necessarily the best one possible), you could try genetic approach. Find a so-so solution as a starting point - using some simle algorithm such as the one suggested by @Carra - then make, say, a 1000 copies of this initial solution and keep on mutating them randomly. Switching points, trying to add more triangles etc. Reward the best specimen and throw away poor copies, eg. after each generation you could overwrite the entire gene pool with clones of the best 5%. After some iterations you should end up with a fairly good solution.

If you are okay with an approximated solution (not necessarily the best one possible), you could try genetic approach. 

Find a so-so solution as a starting point - using some simple algorithm such as the one suggested by @Carra - then make, say, a 1000 copies of this initial solution and keep on mutating them randomly. Switching points, trying to add more triangles etc. 

Reward the best specimen and throw away poor copies, eg. after each generation you could overwrite the entire gene pool with clones of the best 5%. For best results decrease the temperature with time - it means use aggressive mutation at the beginning, but slower mutation rate towards the end in order to finetune the results once they reach high quality level.

After some iterations you should end up with a fairly good solution.

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source | link

If you are okay with an approximated solution (not necessarily the best one possible), you could try genetic approach. Find a so-so solution as a starting point - using some simle algorithm such as the one suggested by @Carra - then make, say, a 1000 copies of this initial solution and keep on mutating them randomly. Switching points, trying to add more triangles etc. Reward the best specimen and throw away poor copies, eg. after each generation you could overwrite the entire gene pool with clones of the best 5%. After some iterations you should end up with a fairly good solution.