
TL;DR
This paper introduces the classified stable matching problem, exploring its computational complexity and polyhedral structure, with applications to academic hiring and insights into stable matchings with class-based constraints.
Contribution
It characterizes the complexity of classified stable matching problems based on classification types and develops a linear programming framework for laminar classifications, solving an open problem.
Findings
Polynomial-time algorithm for downward forest classifications
NP-completeness for non-downward forest classifications
Integral polytope description enabling optimal matchings
Abstract
We introduce the {\sc classified stable matching} problem, a problem motivated by academic hiring. Suppose that a number of institutes are hiring faculty members from a pool of applicants. Both institutes and applicants have preferences over the other side. An institute classifies the applicants based on their research areas (or any other criterion), and, for each class, it sets a lower bound and an upper bound on the number of applicants it would hire in that class. The objective is to find a stable matching from which no group of participants has reason to deviate. Moreover, the matching should respect the upper/lower bounds of the classes. In the first part of the paper, we study classified stable matching problems whose classifications belong to a fixed set of ``order types.'' We show that if the set consists entirely of downward forests, there is a polynomial-time algorithm;…
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Taxonomy
TopicsStatistical Methods and Inference · Bayesian Modeling and Causal Inference
