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I have a locus `L`

of points (lat, long). And I would like to find N=10 points (let's call them warehouses) such that:

$$loss = \sum_{l \in L} maximum_{w \in W}(distance(l, w))^2 $$

is minimized.

Is there a documented algorithm or approach that solves this problem? Right now I am thinking Excel may be able to handle this task. However I have too much data for Excel and will need to implement this in Python / Pandas.

You haven't really defined what you mean by distance. If the points are close together then you may be alright using Euclidean distance but technically distance on a sphere (as with lat,lon) should use Haversine Distance.

– Eumenedies – 2017-08-14T08:18:41.240