English

On a linearization of quadratic Wasserstein distance

Numerical Analysis 2022-03-02 v2 Numerical Analysis Classical Analysis and ODEs Probability

Abstract

This paper studies the problem of computing a linear approximation of quadratic Wasserstein distance W2W_2. In particular, we compute an approximation of the negative homogeneous weighted Sobolev norm whose connection to Wasserstein distance follows from a classic linearization of a general Monge-Amp\'ere equation. Our contribution is threefold. First, we provide expository material on this classic linearization of Wasserstein distance including a quantitative error estimate. Second, we reduce the computational problem to solving an elliptic boundary value problem involving the Witten Laplacian, which is a Schr\"odinger operator of the form H=Δ+VH = -\Delta + V, and describe an associated embedding. Third, for the case of probability distributions on the unit square [0,1]2[0,1]^2 represented by n×nn \times n arrays we present a fast code demonstrating our approach. Several numerical examples are presented.

Keywords

Cite

@article{arxiv.2201.13386,
  title  = {On a linearization of quadratic Wasserstein distance},
  author = {Philip Greengard and Jeremy G. Hoskins and Nicholas F. Marshall and Amit Singer},
  journal= {arXiv preprint arXiv:2201.13386},
  year   = {2022}
}

Comments

24 pages, 6 figures