Optimal parameterized algorithms for planar facility location problems using Voronoi diagrams

TL;DR

This paper presents improved algorithms for planar facility location problems, reducing the complexity from exponential in k to subexponential, by leveraging Voronoi diagrams and balanced separators.

Contribution

It introduces a novel approach using Voronoi diagrams and balanced separators to achieve $n^{O(\sqrt{k})}$ algorithms for these problems, improving upon previous brute-force methods.

Findings

Achieved $n^{O(\sqrt{k})}$ time algorithms for certain facility location problems.

Demonstrated the effectiveness of Voronoi diagram-based separators in algorithm design.

Provided evidence that packing problems admit similar subexponential algorithms.

Abstract

We study a general family of facility location problems defined on planar graphs and on the 2-dimensional plane. In these problems, a subset of $k$ objects has to be selected, satisfying certain packing (disjointness) and covering constraints. Our main result is showing that, for each of these problems, the $n^{O(k)}$ time brute force algorithm of selecting $k$ objects can be improved to $n^{O(\sqrt{k})}$ time. The algorithm is based on an idea that was introduced recently in the design of geometric QPTASs, but was not yet used for exact algorithms and for planar graphs. We focus on the Voronoi diagram of a hypothetical solution of $k$ objects, guess a balanced separator cycle of this Voronoi diagram to obtain a set that separates the solution in a balanced way, and then recurse on the resulting subproblems. We complement our study by giving evidence that packing problems have $n^{O(\sqrt{k})}$ time algorithms for a much more general class of objects than covering problems have.