Key points are not available for this paper at this time.
Geometric matching is an important topic in computational geometry and has been extensively studied over decades. In this paper, we study a geometric-matching problem, known as geometric many-to-many matching. In this problem, the input is a set S of n colored points in Rᵈ, which implicitly defines a graph G = (S, E (S) ) where E (S) = \ (p, q): p, q S have different colors\, and the goal is to compute a minimum-cost subset E^* E (S) of edges that cover all points in S. Here the cost of E^* is the sum of the costs of all edges in E^*, where the cost of a single edge e is the Euclidean distance (or more generally, the Lₚ-distance) between the two endpoints of e. Our main result is a (1+) -approximation algorithm with an optimal running time O_ (n n) for geometric many-to-many matching in any fixed dimension, which works under any Lₚ-norm. This is the first near-linear approximation scheme for the problem in any d 2. Prior to this work, only the bipartite case of geometric many-to-many matching was considered in R¹ and R², and the best known approximation scheme in R² takes O_ (n^1. 5 poly (n) ) time.
Building similarity graph...
Analyzing shared references across papers
Loading...
Bandyapadhyay et al. (Sat,) studied this question.
synapsesocial.com/papers/68e77c8eb6db6435876f0a5f — DOI: https://doi.org/10.48550/arxiv.2402.15837
Sayan Bandyapadhyay
Portland State University
Jie Xue
Ningbo University
Building similarity graph...
Analyzing shared references across papers
Loading...