The Non-Uniform k-Center Problem
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Abstract (arXiv)
In this paper, we introduce and study the Non-Uniform k-Center problem (NUkC). Given a finite metric space (X,d) and a collection of balls of radii r1≥ cdots ≥ rk, the NUkC problem is to find a placement of their centers on the metric space and find the minimum dilation α, such that the union of balls of radius α· ri around the ith center covers all the points in X. This problem naturally arises as a min-max vehicle routing problem with fleets of different speeds. The NUkC problem generalizes the classic k-center problem when all the k radii are the same (which can be assumed to be 1 after scaling). It also generalizes the k-center with outliers (kCwO) problem when there are k balls of radius 1 and ℓ balls of radius 0. There are 2-approximation and 3-approximation algorithms known for these problems respectively; the former is best possible unless P=NP and the latter remains unimproved for 15 years. We first observe that no O(1)-approximation is to the optimal dilation is possible unless P=NP, implying that the NUkC problem is more non-trivial than the above two problems. Our main algorithmic result is an (O(1),O(1))-bi-criteria approximation result: we give an O(1)-approximation to the optimal dilation, however, we may open Theta(1) centers of each radii. Our techniques also allow us to prove a simple (uni-criteria), optimal 2-approximation to the kCwO problem improving upon the long-standing 3-factor. Our main technical contribution is a connection between the NUkC problem and the so-called firefighter problems on trees which have been studied recently in the TCS community.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 May 2016.
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Citing papers per year: 2021: 4; 2022: 2; 2023: 1; 2024: 1.
8 papers.
- 2021How to Find a Good Explanation for Clustering?cited by 16
- 2021Revisiting Priority k-Center: Fairness and Outlierscited by 4
- 2021Tight FPT Approximation for Constrained k-Center and k-Suppliercited by 4
- 2022End-to-End Pareto Set Prediction with Graph Neural Networks for Multi-objective Facility Locationcited by 3
- 2021Is Simple Uniform Sampling Effective for Center-Based Clustering with Outliers: When and Why?cited by 1
- 2023Randomized Greedy Algorithms and Composable Coreset for k-Center Clustering with Outlierscited by 1
- 2024Clustering to Minimize Cluster-Aware Norm Objectivescited by 1
- 2022k-Center Clustering with Outliers in Sliding Windowscited by 0
- 2024Clustering to Minimize Cluster-Aware Norm Objectives31 Oct 2024
- 2023Randomized Greedy Algorithms and Composable Coreset for k-Center Clustering with Outliers7 Jan 2023
- 2022End-to-End Pareto Set Prediction with Graph Neural Networks for Multi-objective Facility Location27 Oct 2022
- 2022k-Center Clustering with Outliers in Sliding Windows7 Jan 2022
- 2021How to Find a Good Explanation for Clustering?13 Dec 2021
- 2021Tight FPT Approximation for Constrained k-Center and k-Supplier27 Oct 2021
- 2021Revisiting Priority k-Center: Fairness and Outliers4 Mar 2021
- 2021Is Simple Uniform Sampling Effective for Center-Based Clustering with Outliers: When and Why?28 Feb 2021