lets say I have a 5 dimensional grid where each dimension has 10 points. There are 100000 combinations. Let's say I want a subset of 10000, is there a deterministic algorithm that will choose a set of points that "covers" the region the best and converges to the limiting case of choosing every point?
If I had a weighting function for each point, would there be a deterministic algorithm that would tend to choose points that are equidistant in a weighting sense as well?
I can think of some possible ways in lower dimension, just wondering if there is a well defined solution (area of math) for these types of problems that extends to higher dimensions
I've tried searching sampling methods, multi-dimensional sampling and the like, but no luck