An $O(n \log n)$-Time Approximation Scheme for Geometric Many-to-Many Matching
<|reference_start|>An $O(n \log n)$-Time Approximation Scheme for Geometric Many-to-Many Matching: 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 $\mathbb{R}^d$, which implicitly defines a graph $G = (S,E(S))$ where $E(S) = \{(p,q): p,q \in S \text{ have different colors}\}$, and the goal is to compute a minimum-cost subset $E^* \subseteq E(S)$ 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 $L_p$-distance) between the two endpoints of $e$. Our main result is a $(1+\varepsilon)$-approximation algorithm with an optimal running time $O_\varepsilon(n \log n)$ for geometric many-to-many matching in any fixed dimension, which works under any $L_p$-norm. This is the first near-linear approximation scheme for the problem in any $d \geq 2$. Prior to this work, only the bipartite case of geometric many-to-many matching was considered in $\mathbb{R}^1$ and $\mathbb{R}^2$, and the best known approximation scheme in $\mathbb{R}^2$ takes $O_\varepsilon(n^{1.5} \cdot \mathsf{poly}(\log n))$ time.<|reference_end|>
@article{bandyapadhyay2024an,
    title={An $O(n \log n)$-Time Approximation Scheme for Geometric Many-to-Many
  Matching},
    author={Sayan Bandyapadhyay, Jie Xue},
    journal={arXiv preprint arXiv:2402.15837},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.15837},
    primaryClass={cs.CG cs.DS}
}