Scaling hypothesis for the Euclidean bipartite matching problem
classification
❄️ cond-mat.dis-nn
cs.DMcs.GR
keywords
costdimensionscalingbipartitecorrectioneuclideanexponentmatching
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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 $N$ limit of the average cost in dimension $d=1,2$ and of the subleading correction in higher dimension. A non-trivial scaling exponent, $\gamma_d=\frac{d-2}{d}$, 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 $d>2$.
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