Preference Swaps for the Stable Matching Problem

TL;DR

This paper extends the analysis of minimal swaps needed to achieve stability in the Stable Matching Problem to the many-to-many case, introduces a new representation, and proves computational hardness results.

Contribution

It generalizes previous polynomial-time algorithms to the many-to-many SMP and establishes NP-hardness and W[1]-hardness for related decision problems.

Findings

Generalization of swap-based stability algorithms to many-to-many SMP

NP-hardness of deciding minimal swaps for stability

W[1]-hardness and exponential time lower bounds for the problem

Abstract

An instance $I$ of the Stable Matching Problem (SMP) is given by a bipartite graph with a preference list of neighbors for every vertex. A swap in $I$ is the exchange of two consecutive vertices in a preference list. A swap can be viewed as a smallest perturbation of $I$. Boehmer et al. (2021) designed a polynomial-time algorithm to find the minimum number of swaps required to turn a given maximal matching into a stable matching. We generalize this result to the many-to-many version of SMP. We do so first by introducing a new representation of SMP as an extended bipartite graph and subsequently by reducing the problem to submodular minimization. It is a natural problem to establish the computational complexity of deciding whether at most $k$ swaps are enough to turn $I$ into an instance where one of the maximum matchings is stable. Using a hardness result of Gupta et al. (2020), we prove that this problem is NP-hard and, moreover, this problem parameterised by $k$ is W[1]-hard. We also obtain a lower bound on the running time for solving the problem using the Exponential Time Hypothesis.