New Approximation Algorithms for Fair $k$-median Problem

TL;DR

This paper introduces the first approximation algorithms for the fair k-median problem without fair violation, achieving a QPTAS in doubling metrics and an O(log k) ratio in general metrics, advancing fair clustering solutions.

Contribution

It provides the first algorithms for fair k-median without violations, including a QPTAS for doubling metrics and an O(log k) approximation for general metrics.

Findings

First QPTAS for fair k-median in doubling metrics.

O(log k) approximation algorithm for fair k-median in general metrics.

New techniques for handling fairness constraints in clustering.

Abstract

The fair $k$-median problem is one of the important clustering problems. The current best approximation ratio is 4.675 for this problem with 1-fair violation, which was proposed by Bercea et al. [APPROX-RANDOM'2019]. As far as we know, there is no available approximation algorithm for the problem without any fair violation. In this paper, we consider the fair $k$-median problem in bounded doubling metrics and general metrics. We provide the first QPTAS for fair $k$-median problem in doubling metrics. Based on the split-tree decomposition of doubling metrics, we present a dynamic programming process to find the candidate centers, and apply min-cost max-flow method to deal with the assignment of clients. Especially, for overcoming the difficulties caused by the fair constraints, we construct an auxiliary graph and use minimum weighted perfect matching to get part of the cost. For the fair $k$-median problem in general metrics, we present an approximation algorithm with ratio $O(\log k)$, which is based on the embedding of given space into tree metrics, and the dynamic programming method. Our two approximation algorithms for the fair $k$-median problem are the first results for the corresponding problems without any fair violation, respectively.