An Improved Local Search Algorithm for k-Median

TL;DR

This paper introduces a novel local search algorithm for the k-median problem that achieves a better approximation ratio of approximately 2.836, utilizing a new potential function based on closest and second-closest facilities to improve solution quality.

Contribution

The paper proposes a new local search algorithm with an innovative potential function, improving the approximation guarantee for k-median clustering over previous methods.

Findings

Achieves a $(2.836+\epsilon)$-approximation ratio.

Outperforms the previous $(3+\epsilon)$-approximate local-search algorithm.

Potential function based on closest and second-closest facilities enhances solution quality.

Abstract

We present a new local-search algorithm for the $k$-median clustering problem. We show that local optima for this algorithm give a $(2.836+\epsilon)$-approximation; our result improves upon the $(3+\epsilon)$-approximate local-search algorithm of Arya et al. [STOC 01]. Moreover, a computer-aided analysis of a natural extension suggests that this approach may lead to an improvement over the best-known approximation guarantee for the problem. The new ingredient in our algorithm is the use of a potential function based on both the closest and second-closest facilities to each client. Specifically, the potential is the sum over all clients, of the distance of the client to its closest facility, plus (a small constant times) the truncated distance to its second-closest facility. We move from one solution to another only if the latter can be obtained by swapping a constant number of facilities, and has a smaller potential than the former. This refined potential allows us to avoid the bad local optima given by Arya et al. for the local-search algorithm based only on the cost of the solution.