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arXiv · 2009.04128

Convergence of asymptotic costs for random Euclidean matching problems

Michael Goldman·Dario Trevisan
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Abstract

We investigate the average minimum cost of a bipartite matching between two samples of n independent random points uniformly distributed on a unit cube in d $\ge$ 3 dimensions, where the matching cost between two points is given by any power p $\ge$ 1 of their Euclidean distance. As n grows, we prove convergence, after a suitable renormalization, towards a finite and positive constant. We also consider the analogous problem of optimal transport between n points and the uniform measure. The proofs combine sub-additivity inequalities with a PDE ansatz similar to the one proposed in the context of the matching problem in two dimensions and later extended to obtain upper bounds in higher dimensions.

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BibTeXRIS

Michael Goldman, Dario Trevisan. 2020-09-09. Convergence of asymptotic costs for random Euclidean matching problems. https://doi.org/10.2140/pmp.2021.2.121

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