A Priority-based Weighted Inner Products Matching Coarsening Algorithm on Multilevel Hypergraph Partitioning

摘要

Multilevel hypergraph partitioning is an significant and extensively researched problem in combinatorial optimization. Nevertheless, as the primary component of multilevel hypergraph partitioning, coarsening phase has not yet attracted sufficient attention. Meanwhile, the performance of coarsening algorithm is not very satisfying. In this paper, we present a new coarsening algorithm based on multilevel framework to reduce the number of vertices more rapidly. The main contribution is introducing the matching mechanism of weighted inner product and establishing two priority rules of vertices. Finally, the effectiveness of our coarsening algorithm was indicated by experimental results compared with those produced by the combination of different sort algorithms and hMETIS in most of the ISPD98 benchmark suite.