Given a graph G=(V,E), suppose we are interested in selecting a sequence of vertices (x_j)_{j=1}^n such that \left\{x_1, \dots, x_k\right\} is `well-distributed' uniformly in k. We describe a greedy algorithm motivated by potential theory and corresponding developments in the continuous setting. The algorithm performs nicely on graphs and may be of use for sampling problems. We can interpret the algorithm as trying to greedily minimize a negative Sobolev norm; we explain why this is related to Wasserstein distance by establishing a purely spectral bound on the Wasserstein distance on graphs that mirrors R. Peyr\'e 's estimate in the continuous setting. We illustrate this with many examples and discuss several open problems.
Given a graph G=(V,E), suppose we are interested in selecting a sequence of vertices (x_j)_{j=1}^n such that \left\{x_1, \dots, x_k\right\} is `well-distributed' uniformly in k. We describe a greedy algorithm motivated by potential theory and corresponding developments in the continuous setting. The algorithm performs nicely on graphs and may be of use for sampling problems. We can interpret the algorithm as trying to greedily minimize a negative Sobolev norm; we explain why this is related to Wasserstein distance by establishing a purely spectral bound on the Wasserstein distance on graphs that mirrors R. Peyre 's estimate in the continuous setting. We illustrate this with many examples and discuss several open problems.
R
Peyr\'e
Peyre