On the Hardness of Approximation of the Fair k-Center Problem
Suhas Thejaswi

TL;DR
This paper proves that achieving better than a 3-approximation for the fair k-center problem is NP-hard, establishing the optimality of existing algorithms and highlighting a fundamental complexity barrier.
Contribution
It establishes the NP-hardness of approximating fair k-center within any factor less than 3, confirming the optimality of current algorithms and contrasting with related problems.
Findings
No polynomial-time algorithm can approximate fair k-center within (3 - epsilon) for any epsilon > 0.
The hardness result holds even with only two groups and one center per group.
Existing 3-approximation algorithms are essentially optimal under standard complexity assumptions.
Abstract
In this work, we study the hardness of approximation of the fair -center problem. In this problem, we are given a set of data points in a metric space that is partitioned into groups and the task is to choose a subset of -data points, called centers, such that a prescribed number of data points from each group are chosen while minimizing the maximum distance from any point to its closest center. Although a polynomial-time -approximation is known for fair -center in general metrics, it has remained open whether this approximation guarantee is tight or could be further improved, especially since the classical unconstrained -center problem admits a polynomial-time factor- approximation. We resolve this open question by proving that, assuming , for any , no polynomial-time algorithm can approximate fair -center to…
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Taxonomy
TopicsFacility Location and Emergency Management · Computational Geometry and Mesh Generation · Complexity and Algorithms in Graphs
