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ILP-based Local Search for Graph Partitioning

Authors Alexandra Henzinger, Alexander Noe, Christian Schulz

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Alexandra Henzinger
  • Stanford University, Stanford, CA, USA
Alexander Noe
  • University of Vienna, Vienna, Austria
Christian Schulz
  • University of Vienna, Vienna, Austria

Funding

The research leading to these results has received funding from the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement No. 340506. Moreover, the authors gratefully acknowledge the Gauss Centre for Supercomputing e.V. (http://www.gauss-centre.eu) for funding this project by providing computing time on the GCS Supercomputer SuperMUC at Leibniz Supercomputing Centre (https://www.lrz.de).

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Alexandra Henzinger, Alexander Noe, and Christian Schulz. ILP-based Local Search for Graph Partitioning. In 17th International Symposium on Experimental Algorithms (SEA 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 103, pp. 4:1-4:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018) https://doi.org/10.4230/LIPIcs.SEA.2018.4

Abstract

Computing high-quality graph partitions is a challenging problem with numerous applications. In this paper, we present a novel meta-heuristic for the balanced graph partitioning problem. Our approach is based on integer linear programs that solve the partitioning problem to optimality. However, since those programs typically do not scale to large inputs, we adapt them to heuristically improve a given partition. We do so by defining a much smaller model that allows us to use symmetry breaking and other techniques that make the approach scalable. For example, in Walshaw's well-known benchmark tables we are able to improve roughly half of all entries when the number of blocks is high.

Subject Classification

ACM Subject Classification
  • Mathematics of computing → Graph algorithms
  • Theory of computation → Integer programming
Keywords
  • Graph Partitioning
  • Integer Linear Programming

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