Envy-freeness and Relaxed Stability for Lower-Quotas : A Parameterized Perspective
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We consider the problem of assigning agents to resources under the two-sided preference list model with upper and lower-quotas on resources. Krishnaa et al. [17] explore two optimality notions for this setting – envy-freeness and relaxed stability. They investigate the problem of computing a maximum size envy-free matching (MAXEFM) and a maximum size relaxed stable matching (MAXRSM) that satisfies the lower-quotas. They show that both these optimization problems cannot be approximated within a constant factor unless P = NP. In this work, we investigate parameterized complexity of MAXEFM and MAXRSM. We consider several parameters derived from the instance – the number of resources with non-zero lower-quota, deficiency of the instance, maximum length of the preference list of a resource with non-zero lower-quota, among others. We show that MAXEFM problem is W [1]-hard for several interesting parameters and MAXRSM problem is para-NP-hard for two natural parameters. We present kernelization results and FPT algorithms on a combination of parameters for both problems.
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