I have \$10^5\$ to \$10^6\$ points on a sphere, and want to choose some points which are uniformly distributed on this sphere. For that reason, I do the following:

But this still has the issue that the pairwise distances are computed each time, and has obvious memory issues for large N. Would love to hear any suggestions how to improve it.

I have \$10^5\$ to \$10^6\$ points on a sphere, and want to choose some points from them which are as close as uniformly distributed as possible. For that reason, I do the following: at each step I add the point in the data which is farthest away from any point which I have already chosen. In order to do this, I find the closest distance from each point in the data to any of the points that are already chosen. Then I pick the point from the data which has the largest minimum distance to any of the points that are already chosen. The algorithm is the following:

But this still has the issue that the pairwise distances are computed each time, and has obvious memory issues for large N. I would like to hear suggestions how I can improve the performance of the algorithm.

I have \$10^5\$ - \$10^6\$ points on a sphere, and want to choose some points which are uniformly distributed on this sphere. For that reason, I do the following:

I have \$10^5\$ to \$10^6\$ points on a sphere, and want to choose some points which are uniformly distributed on this sphere. For that reason, I do the following:

But this still has the issue that the pairwise distances are computer each time, and has obvious memory issues for large N. Would love to hear any suggestions how to improve it.

But this still has the issue that the pairwise distances are computed each time, and has obvious memory issues for large N. Would love to hear any suggestions how to improve it.