English

Scaling hypothesis for the Euclidean bipartite matching problem

Disordered Systems and Neural Networks 2014-08-25 v2 Discrete Mathematics Graphics

Abstract

We propose a simple yet very predictive form, based on a Poisson's equation, for the functional dependence of the cost from the density of points in the Euclidean bipartite matching problem. This leads, for quadratic costs, to the analytic prediction of the large NN limit of the average cost in dimension d=1,2d=1,2 and of the subleading correction in higher dimension. A non-trivial scaling exponent, γd=d2d\gamma_d=\frac{d-2}{d}, which differs from the monopartite's one, is found for the subleading correction. We argue that the same scaling holds true for a generic cost exponent in dimension d>2d>2.

Keywords

Cite

@article{arxiv.1402.6993,
  title  = {Scaling hypothesis for the Euclidean bipartite matching problem},
  author = {Sergio Caracciolo and Carlo Lucibello and Giorgio Parisi and Gabriele Sicuro},
  journal= {arXiv preprint arXiv:1402.6993},
  year   = {2014}
}

Comments

11 pages

R2 v1 2026-06-22T03:17:17.843Z