Gupta, Anupam
[VerfasserIn];
Guruganesh, Guru
[VerfasserIn];
Schmidt, Melanie
[VerfasserIn]
;
Anupam Gupta and Guru Guruganesh and Melanie Schmidt
[MitwirkendeR]
Approximation Algorithms for Aversion k-Clustering via Local k-Median
Anmerkungen:
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Beschreibung:
In the aversion k-clustering problem, given a metric space, we want to cluster the points into k clusters. The cost incurred by each point is the distance to the furthest point in its cluster, and the cost of the clustering is the sum of all these per-point-costs. This problem is motivated by questions in generating automatic abstractions of extensive-form games. We reduce this problem to a "local" k-median problem where each facility has a prescribed radius and can only connect to clients within that radius. Our main results is a constant-factor approximation algorithm for the aversion k-clustering problem via the local k-median problem. We use a primal-dual approach; our technical contribution is a non-local rounding step which we feel is of broader interest.