
In a graph where vertices have preferences over their neighbors, a matching is called popular if it does not lose a head-to-head election against any other matching when the vertices vote between the matchings. Popular matchings can be seen as an intermediate category between stable matchings and maximum-size matchings. In this article, we aim to maximize the utility of a matching that is popular but admits only a few blocking edges. We observe that, for general graphs, finding a popular matching with at most one blocking edge is already NP -complete. For bipartite instances, we study the problem of finding a maximum-utility popular matching with a bound on the number (or, more generally, the cost) of blocking edges applying a multivariate approach. We show classical and parameterized hardness results for severely restricted instances. By contrast, we design an algorithm for instances where preferences on one side admit a master list and show that this algorithm is roughly optimal.
FOS: Computer and information sciences, Matching models, Discrete Mathematics (cs.DM), stable matching, Parameterized complexity, tractability and kernelization, popular matching, Edge subsets with special properties (factorization, matching, partitioning, covering and packing, etc.), Graph theory (including graph drawing) in computer science, Graph algorithms (graph-theoretic aspects), Computer Science - Data Structures and Algorithms, Computational difficulty of problems (lower bounds, completeness, difficulty of approximation, etc.), master lists, Data Structures and Algorithms (cs.DS), complexity, Computer Science - Discrete Mathematics
FOS: Computer and information sciences, Matching models, Discrete Mathematics (cs.DM), stable matching, Parameterized complexity, tractability and kernelization, popular matching, Edge subsets with special properties (factorization, matching, partitioning, covering and packing, etc.), Graph theory (including graph drawing) in computer science, Graph algorithms (graph-theoretic aspects), Computer Science - Data Structures and Algorithms, Computational difficulty of problems (lower bounds, completeness, difficulty of approximation, etc.), master lists, Data Structures and Algorithms (cs.DS), complexity, Computer Science - Discrete Mathematics
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