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<metadata xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dc="http://purl.org/dc/elements/1.1/"><dc:title>Geometric matching and bottleneck problems</dc:title><dc:creator>Cabello,	Sergio	(Avtor)
	</dc:creator><dc:creator>Cheng,	Siu-Wing	(Avtor)
	</dc:creator><dc:creator>Cheong,	Otfried	(Avtor)
	</dc:creator><dc:creator>Knauer,	Christian	(Avtor)
	</dc:creator><dc:subject>many-to-many matching</dc:subject><dc:subject>bipartite</dc:subject><dc:subject>planar</dc:subject><dc:subject>geometric matching</dc:subject><dc:subject>approximation</dc:subject><dc:description>Let $P$ be a set of at most $n$ points and let $R$ be a set of at most $n$ geometric ranges, such as disks or rectangles, where each $p \in P$ has an associated supply $s_{p} &gt; 0$, and each $r \in R$ has an associated demand $d_{r} &gt; 0$. A (many-to-many) matching is a set ${\mathcal A}$ of ordered triples $(p,r,a_{pr}) \in P \times R \times {\mathbb R}_{&gt;0}$ such that $p \in r$ and the $a_{pr}$'s satisfy the constraints given by the supplies and demands. We show how to compute a maximum matching, that is, a matching maximizing $\sum_{(p,r,a_{pr}) \in {\mathcal A}} a_{pr}$. Using our techniques, we can also solve minimum bottleneck problems, such as computing a perfect matching between a set of $n$ red points $P$ and a set of $n$ blue points $Q$ that minimizes the length of the longest edge. For the $L_\infty$-metric, we can do this in time $O(n^{1+\varepsilon})$ in any fixed dimension, for the $L_2$-metric in the plane in time $O(n^{4/3 + \varepsilon})$, for any $\varepsilon &gt; 0$.</dc:description><dc:date>2024</dc:date><dc:date>2024-12-11 10:39:33</dc:date><dc:type>Neznano</dc:type><dc:identifier>20967</dc:identifier><dc:identifier>UDK: 004.42</dc:identifier><dc:identifier>DOI: 10.4230/LIPIcs.SoCG.2024.31</dc:identifier><dc:identifier>COBISS_ID: 218353667</dc:identifier><dc:identifier>OceCobissID: 218546691</dc:identifier><dc:language>sl</dc:language></metadata>
