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 ℝ^d, 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 L_p-distance) between the two endpoints of e. Our main result is a (1+ε)-approximation algorithm with an optimal running time O_ε(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 ≥ 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_ε(n^{1.5} ⋅ poly(log n)) time.
@InProceedings{bandyapadhyay_et_al:LIPIcs.SoCG.2024.12, author = {Bandyapadhyay, Sayan and Xue, Jie}, title = {{An O(n log n)-Time Approximation Scheme for Geometric Many-To-Many Matching}}, booktitle = {40th International Symposium on Computational Geometry (SoCG 2024)}, pages = {12:1--12:15}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-316-4}, ISSN = {1868-8969}, year = {2024}, volume = {293}, editor = {Mulzer, Wolfgang and Phillips, Jeff M.}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2024.12}, URN = {urn:nbn:de:0030-drops-199577}, doi = {10.4230/LIPIcs.SoCG.2024.12}, annote = {Keywords: many-to-many matching, geometric optimization, approximation algorithms} }
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