Partitioning through projections: Strong SDP bounds for large graph partition problems

Frank de Meijer*, Renata Sotirov, Angelika Wiegele, Shudian Zhao

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)

Abstract

The graph partition problem (GPP) aims at clustering the vertex set of a graph into a fixed number of disjoint subsets of given sizes such that the sum of weights of edges joining different sets is minimized. This paper investigates the quality of doubly nonnegative (DNN) relaxations, i.e., relaxations having matrix variables that are both positive semidefinite and nonnegative, strengthened by additional polyhedral cuts for two variations of the GPP: the k-equipartition and the graph bisection problem. After reducing the size of the relaxations by facial reduction, we solve them by a cutting-plane algorithm that combines an augmented Lagrangian method with Dykstra's projection algorithm. Since many components of our algorithm are general, the algorithm is suitable for solving various DNN relaxations with a large number of cutting planes. We are the first to show the power of DNN relaxations with additional cutting planes for the GPP on large benchmark instances up to 1,024 vertices. Computational results show impressive improvements in strengthened DNN bounds.

Original languageEnglish
Article number106088
JournalComputers & Operations Research
Volume151
DOIs
Publication statusPublished - Mar 2023

Keywords

  • Graph partition problems
  • Semidefinite programming
  • Cutting planes
  • Dykstra's projection algorithm
  • Augmented Lagrangian methods

Fingerprint

Dive into the research topics of 'Partitioning through projections: Strong SDP bounds for large graph partition problems'. Together they form a unique fingerprint.

Cite this