Towards Non-Uniform k-Center with Constant Types of Radii

Xinrui Jia, Lars Rohwedder, K. Sheth, O. Svensson
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引用次数: 4

Abstract

In the Non-Uniform k-Center problem we need to cover a finite metric space using k balls of different radii that can be scaled uniformly. The goal is to minimize the scaling factor. If the number of different radii is unbounded, the problem does not admit a constant-factor approximation algorithm but it has been conjectured that such an algorithm exists if the number of radii is constant. Yet, this is known only for the case of two radii. Our first contribution is a simple black box reduction which shows that if one can handle the variant of t− 1 radii with outliers, then one can also handle t radii. Together with an algorithm by Chakrabarty and Negahbani for two radii with outliers, this immediately implies a constant-factor approximation algorithm for three radii; thus making further progress on the conjecture. Furthermore, using algorithms for the k-center with outliers problem, that is the one radii with outliers case, we also get a simple algorithm for two radii. The algorithm by Chakrabarty and Negahbani uses a top-down approach, starting with the larger radius and then proceeding to the smaller one. Our reduction, on the other hand, looks only at the smallest radius and eliminates it, which suggests that a bottom-up approach is promising. In this spirit, we devise a modification of the Chakrabarty and Negahbani algorithm which runs in a bottom-up fashion, and in this way we recover their result with the advantage of having a simpler analysis. ∗Supported by the Swiss National Science Foundation project 200021-184656 “Randomness in Problem Instances and Randomized Algorithms.”
关于半径为常数型的非均匀k中心问题
在非均匀k-中心问题中,我们需要用k个不同半径的球覆盖一个有限的度量空间,这些球可以均匀缩放。目标是最小化比例因子。当不同半径的数目无界时,该问题不允许采用常因子近似算法,但在半径数目为常数的情况下,已推测存在常因子近似算法。然而,这只适用于两个半径的情况。我们的第一个贡献是一个简单的黑盒简化,它表明如果一个人可以处理t - 1半径的异常值的变化,那么一个人也可以处理t半径。与Chakrabarty和Negahbani针对两个有离群值的半径的算法一起,这立即隐含了一个针对三个半径的常因子近似算法;从而在猜想上取得了进一步的进展。此外,利用k中心带离群值问题的算法,即一个半径带离群值的情况,我们也得到了一个简单的两个半径的算法。Chakrabarty和Negahbani的算法使用了一种自上而下的方法,从较大的半径开始,然后继续到较小的半径。另一方面,我们的还原只关注最小的半径并将其消除,这表明自下而上的方法很有希望。本着这种精神,我们设计了一种对Chakrabarty和Negahbani算法的修改,该算法以自下而上的方式运行,通过这种方式,我们恢复了他们的结果,并具有更简单的分析的优势。*由瑞士国家科学基金会项目200021-184656“问题实例和随机算法中的随机性”支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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