# Space-filling design algorithms from a discrete domain

Given $S = (X_1, \ldots, X_n)$, where each $X_i \in \mathbb{R}^2$, are there algorithms that produce $(X_{(1)}, \ldots, X_{(m)})$, where $m << n$ and the $X_{(i)}$ are sampled from $S$ to fill space (e.g. maximize distances)?

My problem arises from where the $X_i$ are iid bivariate normal, $n=10000$ and $m=100$. I want to sample an unknown noisy function that I'm trying to learn over the domain, so my goal is to sample it at a smaller number of representative locations of $S$.

I've found algorithms in the literature can fill in my domain with new points, but I want pre-existing ones.

So far I'm using a heuristic minimax style algorithm that samples from $S$, and then keeps points only if they are at least $d$ Euclidean distance from all other kept points, where $d$ is chosen by hand. This is sufficient for what I need, but I'm publishing results and I'd prefer use/cite something already established.