Abstract
This paper explores three concepts: the k-center problem, some of its variants, and asymmetry. The k-center problem is a fundamental clustering problem, similar to the k-median problem. Variants of k-center may more accurately model real-life problems than the original formulation. Asymmetry is a significant impediment to approximation in many graph problems, such as k-center, facility location, k-median and the TSP.
We demonstrate an O(log* n)-approximation algorithm for the asymmetric weightedk-center problem. Here, the vertices have weights and we are given a total budget for opening centers. In the p-neighbor variant each vertex must have p (unweighted) centers nearby: we give an O(log* k)-bicriteria algorithm using 2k centers, for small p.
Finally, the following three versions of the asymmetric k-center problem we show to be inapproximable: priorityk-center, k-supplier, and outliers with forbidden centers.
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Li Gørtz, I., Wirth, A. (2003). Asymmetry in k-Center Variants. In: Arora, S., Jansen, K., Rolim, J.D.P., Sahai, A. (eds) Approximation, Randomization, and Combinatorial Optimization.. Algorithms and Techniques. RANDOM APPROX 2003 2003. Lecture Notes in Computer Science, vol 2764. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45198-3_6
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DOI: https://doi.org/10.1007/978-3-540-45198-3_6
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