Numerical Integration on Graphs: where to sample and how to weigh
Statistics Theory
2018-03-20 v1 Machine Learning
Numerical Analysis
Machine Learning
Statistics Theory
Abstract
Let be a finite, connected graph with weighted edges. We are interested in the problem of finding a subset of vertices and weights such that for functions that are `smooth' with respect to the geometry of the graph. The main application are problems where is known to somehow depend on the underlying graph but is expensive to evaluate on even a single vertex. We prove an inequality showing that the integration problem can be rewritten as a geometric problem (`the optimal packing of heat balls'). We discuss how one would construct approximate solutions of the heat ball packing problem; numerical examples demonstrate the efficiency of the method.
Keywords
Cite
@article{arxiv.1803.06989,
title = {Numerical Integration on Graphs: where to sample and how to weigh},
author = {George C. Linderman and Stefan Steinerberger},
journal= {arXiv preprint arXiv:1803.06989},
year = {2018}
}