Unsolvability and Beyond in Many-To-Many Non-Bipartite Stable Matching

TL;DR

This paper extends the classical non-bipartite stable matching problem to many-to-many settings, introducing a generalised stable partition framework, and provides theoretical, algorithmic, and empirical insights into stability and solvability.

Contribution

It introduces the concept of a generalised stable partition for many-to-many stable matchings, extending Tan's work, and develops new algorithms and empirical analyses for these complex systems.

Findings

GSP can be computed efficiently.

A non-bipartite analogue of the Rural Hospitals Theorem is established.

Capacity functions significantly affect solvability likelihood.

Abstract

We study the Stable Fixtures problem, a many-to-many generalisation of the classical non-bipartite Stable Roommates matching problem. Building on the foundational work of Tan on stable partitions, we extend his results to this significantly more general setting and develop a rich framework for understanding stable structures in many-to-many contexts. Our main contribution, the notion of a generalised stable partition (GSP), not only characterises the solution space of this problem, but also serves as a versatile tool for reasoning about ordinal preference systems with capacity constraints. We show that a GSP can be computed efficiently and and can provide an elegant representation of key aspects of a preference system. Leveraging a connection to stable half-matchings, we also establish a non-bipartite analogue of the Rural Hospitals Theorem for stable half-matchings and GSPs, and connect our results to recent work on near-feasible matchings, providing a simpler algorithm and tighter analysis for this problem. Our work also addresses the computational challenges of finding optimal stable half-matchings and GSPs, presenting a flexible integer linear programming model for various objectives. Beyond theoretical insights, we conduct the first empirical analysis of random Stable Fixtures instances, uncovering surprising results, such as the impact of capacity functions on the solvability likelihood. Our work not only unifies and extends classical and recent perspectives on stability in non-bipartite stable matching but also establishes new tools, techniques, and directions for advancing the study of stable matchings and their applications.