Solving Hard Stable Matching Problems Involving Groups of Similar Agents
Kitty Meeks, Baharak Rastegari

TL;DR
This paper identifies structural properties in stable matching problems involving a limited number of agent types, enabling efficient algorithms for certain NP-hard problems, especially when exceptions are limited or absent.
Contribution
It demonstrates that several NP-hard stable matching problems become fixed-parameter tractable when parameterized by the number of agent types, under specific restrictions on preferences and exceptions.
Findings
Max SMTI is FPT with respect to the number of agent types without exceptions.
The problem remains NP-hard if multiple exceptions are allowed in preference lists.
FPT results extend to cases with at most one exceptional candidate per agent.
Abstract
Many important stable matching problems are known to be NP-hard, even when strong restrictions are placed on the input. In this paper we seek to identify structural properties of instances of stable matching problems which will allow us to design efficient algorithms using elementary techniques. We focus on the setting in which all agents involved in some matching problem can be partitioned into k different types, where the type of an agent determines his or her preferences, and agents have preferences over types (which may be refined by more detailed preferences within a single type). This situation would arise in practice if agents form preferences solely based on some small collection of agents' attributes. We also consider a generalisation in which each agent may consider some small collection of other agents to be exceptional, and rank these in a way that is not consistent with…
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