Stable matchings with diversity constraints: Affirmative action is beyond NP

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Abstract

We investigate the following many-to-one stable matching problem with diversity constraints (SMTI-DIVERSE): Given a set of students and a set of colleges which have preferences over each other, where the students have overlapping types, and the colleges each have a total capacity as well as quotas for individual types (the diversity constraints), is there a matching satisfying all diversity constraints such that no unmatched student-college pair has an incentive to deviate? SMTI-DIVERSE is known to be NP-hard. However, as opposed to the NP-membership claims in the literature [Aziz et al., 2019; Huang, 2010], we prove that it is beyond NP: it is complete for the complexity class SP2. In addition, we provide a comprehensive analysis of the problem's complexity from the viewpoint of natural restrictions to inputs and obtain new algorithms for the problem.

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Chen, J., Ganian, R., & Hamm, T. (2020). Stable matchings with diversity constraints: Affirmative action is beyond NP. In IJCAI International Joint Conference on Artificial Intelligence (Vol. 2021-January, pp. 146–152). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2020/21

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