Popular matchings with two-sided preferences and one-sided ties

We are given a bipartite graph $G = (A \cup B, E)$ where each vertex has a preference list ranking its neighbors: in particular, every $a \in A$ ranks its neighbors in a strict order of preference, whereas the preference lists of $b \in B$ may contain ties. A matching $M$ is popular if there is no matching $M'$ such that the number of vertices that prefer $M'$ to $M$ exceeds the number of vertices that prefer $M$ to~$M'$. We show that the problem of deciding whether $G$ admits a popular matching or not is NP-hard. This is the case even when every $b \in B$ either has a strict preference list or puts all its neighbors into a single tie. In contrast, we show that the problem becomes polynomially solvable in the case when each $b \in B$ puts all its neighbors into a single tie. That is, all neighbors of $b$ are tied in $b$'s list and $b$ desires to be matched to any of them. Our main result is an $O(n^2)$ algorithm (where $n = |A \cup B|$) for the popular matching problem in this model. Note that this model is quite different from the model where vertices in $B$ have no preferences and do not care whether they are matched or not.Comment: A shortened version of this paper has appeared at ICALP 201

Popular and Dominant Matchings with Uncertain, Multilayer and Aggregated Preferences

We study the Popular Matching problem in multiple models, where the preferences of the agents in the instance may change or may be unknown/uncertain. In particular, we study an Uncertainty model, where each agent has a possible set of preferences, a Multilayer model, where there are layers of preference profiles, a Robust model, where any agent may move some other agents up or down some places in his preference list and an Aggregated Preference model, where votes are summed over multiple instances with different preferences. We study both one-sided and two-sided preferences in bipartite graphs. In the one-sided model, we show that all our problems can be solved in polynomial time by utilizing the structure of popular matchings. We also obtain nice structural results. With two-sided preferences, we show that all four above models lead to NP-hard questions for popular matchings. By utilizing the connection between dominant matchings and stable matchings, we show that in the robust and uncertainty model, a certainly dominant matching in all possible prefernce profiles can be found in polynomial-time, whereas in the multilayer and aggregated models, the problem remains NP-hard for dominant matchings too. We also answer an open question about $d$-robust stable matchings

Matchings and Copeland's Method

Given a graph $G = (V,E)$ where every vertex has a weak ranking over its neighbors, we consider the problem of computing an optimal matching as per agent preferences. The classical notion of optimality in this setting is stability. However stable matchings, and more generally, popular matchings need not exist when $G$ is non-bipartite. Unlike popular matchings, Copeland winners always exist in any voting instance -- we study the complexity of computing a matching that is a Copeland winner and show there is no polynomial-time algorithm for this problem unless $\mathsf{P} = \mathsf{NP}$. We introduce a relaxation of both popular matchings and Copeland winners called weak Copeland winners. These are matchings with Copeland score at least $\mu/2$, where $\mu$ is the total number of matchings in $G$; the maximum possible Copeland score is $(\mu-1/2)$. We show a fully polynomial-time randomized approximation scheme to compute a matching with Copeland score at least $\mu/2\cdot(1-\varepsilon)$ for any $\varepsilon > 0$

Popular matchings with two-sided preferences and one-sided ties

We are given a bipartite graph G = (A ∪ B, E) where each vertex has a preference list ranking its neighbors: in particular, every a ∈ A ranks its neighbors in a strict order of preference, whereas the preference lists of b ∈ B may contain ties. A matching M is popular if there is no matching M’ such that the number of vertices that prefer M’ to M exceeds the number that prefer M to M’. We show that the problem of deciding whether G admits a popular matching or not is NP-hard. This is the case even when every b ∈ B either has a strict preference list or puts all its neighbors into a single tie. In contrast, we show that the problem becomes polynomially solvable in the case when each b ∈ B puts all its neighbors into a single tie. That is, all neighbors of b are tied in b’s list and and b desires to be matched to any of them. Our main result is an O(n2) algorithm (where n = |A ∪ B|) for the popular matching problem in this model. Note that this model is quite different from the model where vertices in B have no preferences and do not care whether they are matched or not

Popular matchings with two-sided preferences and one-sided ties

We are given a bipartite graph G = (A ∪ B, E) where each vertex has a preference list ranking its neighbors: in particular, every a ∈ A ranks its neighbors in a strict order of preference, whereas the preference lists of b ∈ B may contain ties. A matching M is popular if there is no matching M’ such that the number of vertices that prefer M’ to M exceeds the number that prefer M to M’. We show that the problem of deciding whether G admits a popular matching or not is NP-hard. This is the case even when every b ∈ B either has a strict preference list or puts all its neighbors into a single tie. In contrast, we show that the problem becomes polynomially solvable in the case when each b ∈ B puts all its neighbors into a single tie. That is, all neighbors of b are tied in b’s list and and b desires to be matched to any of them. Our main result is an O(n2) algorithm (where n = |A ∪ B|) for the popular matching problem in this model. Note that this model is quite different from the model where vertices in B have no preferences and do not care whether they are matched or not