An improved lower bound for one-dimensional online unit clustering
Jun Kawahara, Koji M. Kobayashi

TL;DR
This paper establishes a new lower bound of 1.625 on the competitive ratio for any deterministic online algorithm solving the one-dimensional unit clustering problem, improving previous bounds and supporting conjectures about the optimal ratio.
Contribution
It improves the known lower bound for the competitive ratio of one-dimensional online unit clustering algorithms from 1.6 to 1.625.
Findings
New lower bound of 1.625 on competitive ratio
Improves previous lower bound of 1.6
Supports conjecture that the optimal ratio is 13/8
Abstract
The online unit clustering problem was proposed by Chan and Zarrabi-Zadeh (WAOA2007 and Theory of Computing Systems 45(3), 2009), which is defined as follows: "Points" are given online in the -dimensional Euclidean space one by one. An algorithm creates a "cluster," which is a -dimensional rectangle. The initial length of each edge of a cluster is 0. An algorithm can extend an edge until it reaches unit length independently of other dimensions. The task of an algorithm is to cover a new given point either by creating a new cluster and assigning it to the point, or by extending edges of an existing cluster created in past times. The goal is to minimize the total number of created clusters. Chan and Zarrabi-Zadeh proposed some method to obtain a competitive algorithm for the -dimensional case using an algorithm for the one-dimensional case, and thus the one-dimensional case has…
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Taxonomy
TopicsOptimization and Search Problems · Complexity and Algorithms in Graphs · Computational Geometry and Mesh Generation
