Envy-Freeness and Relaxed Stability: Hardness and Approximation Algorithms

We consider the problem of matchings under two-sided preferences in the presence of maximum as well as minimum quota requirements for the agents. When there are no minimum quotas, stability is the de-facto notion of optimality. In the presence of minimum quotas, ensuring stability and simultaneously satisfying lower quotas is not an attainable goal in many instances. To address this, a relaxation of stability known as envy-freeness, is proposed in literature. In our work, we thoroughly investigate envy-freeness from a computational view point. Our results show that computing envy-free matchings that match maximum number of agents is computationally hard and also hard to approximate up to a constant factor. Additionally, it is known that envy-free matchings satisfying lower-quotas may not exist. To circumvent these drawbacks, we propose a new notion called relaxed stability. We show that relaxed stable matchings are guaranteed to exist even in the presence of lower-quotas. Despite the computational intractability of finding a largest matching that is feasible and relaxed stable, we give an efficient algorithm that computes a constant factor approximation to this matching in terms of size.