A gradient method on the initial partition of Fiduccia-Mattheyses algorithm

Abstract

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 descent 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.