Matchings under distance constraints I

TL;DR

This paper studies the $d$-distance matching problem in bipartite graphs, providing complexity results, approximation algorithms, and insights into its integrality gap, with applications in scheduling.

Contribution

It introduces the $d$-distance matching problem, proves NP-completeness, and offers multiple approximation algorithms and fixed-parameter tractable solutions.

Findings

NP-complete in general

3-approximation algorithm

LP-based approximation with ratio $2-rac{1}{2d-1}$

Abstract

This paper introduces the \emph{$d$-distance matching problem}, in which we are given a bipartite graph $G=(S,T;E)$ with $S=\{s_1,\dots,s_n\}$, a weight function on the edges and an integer $d\in\mathbb Z_+$. The goal is to find a maximum weight subset $M\subseteq E$ of the edges satisfying the following two conditions: i) the degree of every node of $S$ is at most one in $M$, ii) if $s_it,s_jt\in M$, then $|j-i|\geq d$. The question arises naturally, for example, in various scheduling problems. We show that the problem is NP-complete in general and admits a simple $3$-approxi\-mation. We give an FPT algorithm parameterized by $d$ and also settle the case when the size of $T$ is constant. From an approximability point of view, we show that the integrality gap of the natural integer programming model is at most $2-\frac{1}{2d-1}$, and give an LP-based approximation algorithm for the weighted case with the same guarantee. A combinatorial $(2-\frac{1}{d})$-approximation algorithm is also presented. Several greedy approaches are considered, in particular, a local search algorithm that achieves an approximation ratio of $3/2+\epsilon$ for any constant $\epsilon>0$ in the unweighted case. The novel approaches used in the analysis of the integrality gap and the approximation ratio of locally optimal solutions might be of independent combinatorial interest.