Matroid and Knapsack Center Problems
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Abstract (arXiv)
In the classic k-center problem, we are given a metric graph, and the objective is to open k nodes as centers such that the maximum distance from any vertex to its closest center is minimized. In this paper, we consider two important generalizations of k-center, the matroid center problem and the knapsack center problem. Both problems are motivated by recent content distribution network applications. Our contributions can be summarized as follows: 1. We consider the matroid center problem in which the centers are required to form an independent set of a given matroid. We show this problem is NP-hard even on a line. We present a 3-approximation algorithm for the problem on general metrics. We also consider the outlier version of the problem where a given number of vertices can be excluded as the outliers from the solution. We present a 7-approximation for the outlier version. 2. We consider the (multi-)knapsack center problem in which the centers are required to satisfy one (or more) knapsack constraint(s). It is known that the knapsack center problem with a single knapsack constraint admits a 3-approximation. However, when there are at least two knapsack constraints, we show this problem is not approximable at all. To complement the hardness result, we present a polynomial time algorithm that gives a 3-approximate solution such that one knapsack constraint is satisfied and the others may be violated by at most a factor of 1+ε. We also obtain a 3-approximation for the outlier version that may violate the knapsack constraint by 1+ε.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Jan 2013.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 1; 2024: 4; 2025: 0; 2026: 2.
11 papers.
- 2021Improved Approximation Algorithms for Individually Fair Clusteringcited by 33
- 2021Approximation Algorithms for Socially Fair Clusteringcited by 25
- 2023Approximation Algorithms for Fair Range Clusteringcited by 10
- 2021Revisiting Priority k-Center: Fairness and Outlierscited by 4
- 2022Streaming Algorithms for Diversity Maximization with Fairness Constraintscited by 3
- 2024Diversity-aware clustering: Computational Complexity and Approximation Algorithmscited by 3
- 2024Fair Clustering for Data Summarization: Improved Approximation Algorithms and Complexity Insightscited by 2
- 2024Scalable Algorithms for Individual Preference Stable Clusteringcited by 1
- 2026Constant-Factor Approximations for Doubly Constrained Fair k-Center, k-Median and k-Meanscited by 1
- 2024A Polynomial-Time Approximation for Pairwise Fair k-Median Clusteringcited by 0
- 2026On the Hardness of Approximation of the Fair k-Center Problemcited by 0
- 2026Constant-Factor Approximations for Doubly Constrained Fair k-Center, k-Median and k-Means17 Apr 2026
- 2026On the Hardness of Approximation of the Fair k-Center Problem18 Feb 2026
- 2024Fair Clustering for Data Summarization: Improved Approximation Algorithms and Complexity Insights16 Oct 2024
- 2024A Polynomial-Time Approximation for Pairwise Fair k-Median Clustering16 May 2024
- 2024Scalable Algorithms for Individual Preference Stable Clustering15 Mar 2024
- 2024Diversity-aware clustering: Computational Complexity and Approximation Algorithms10 Jan 2024
- 2023Approximation Algorithms for Fair Range Clustering11 Jun 2023
- 2022Streaming Algorithms for Diversity Maximization with Fairness Constraints30 Jul 2022
- 2021Improved Approximation Algorithms for Individually Fair Clustering26 Jun 2021
- 2021Revisiting Priority k-Center: Fairness and Outliers4 Mar 2021
- 2021Approximation Algorithms for Socially Fair Clustering3 Mar 2021