a gradient method on the initial partition of fiduccia-mattheyses algorithm
In this paper, a Fiduccia-Mattheyses (FM) algorithm incorporating a novel initial partition generating method is proposed. The proposed algorithm applies to both bipartitioning and multi-way partitioning problems with or without replication. The initial partition generating method is based on a gradient decent algorithm. On partitioning without replication, our algorithm achieves an average of 17\% improvement over the analytical method, PARABOLI, on bipartitioning, 10\% better than Primal-Dual method on 4-way partitioning and 51\% better than net-based method. On partitioning allowing replication, our algorithm achieves an average of 23\% improvement over the directed Fiduccia-Mattheyses algorithm on Replication Graph (FMRG) method on bipartitioning.
method partitioning algorithm paper replication gradient decent direct graph integer programming