Effective heuristics for matchings in hypergraphs

Fanny Dufossé 1 Kamer Kaya 2 Ioannis Panagiotas 3 Bora Uçar 3
1 DATAMOVE - Data Aware Large Scale Computing
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
3 ROMA - Optimisation des ressources : modèles, algorithmes et ordonnancement
Inria Grenoble - Rhône-Alpes, LIP - Laboratoire de l'Informatique du Parallélisme
Abstract : The problem of finding a maximum cardinality matching in a d-partite d-uniform hypergraph is an important problem in combinatorial optimization and has beentheoretically analyzed by several researchers. In this work, we first devise heuristics for this problem by generalizing the existing cheap graph matching heuristics. Then, we propose a novel heuristic based on tensor scaling to extend the matching via judicious hyperedge selections. Experiments on random, synthetic and real-life hypergraphs show that this new heuristic is highly practical and superior to the others on finding a matching with large cardinality.
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Fanny Dufossé, Kamer Kaya, Ioannis Panagiotas, Bora Uçar. Effective heuristics for matchings in hypergraphs. [Research Report] RR-9224, Inria Grenoble Rhône-Alpes. 2018, pp.1-20. ⟨hal-01924180v5⟩

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