On the two-stage stochastic graph partitioning problem

Neng Fan, Qipeng P. Zheng, Panos M. Pardalos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

In this paper we introduce the two-stage stochastic graph partitioning problem and present the stochastic mixed integer programming formulation for this problem with finite explicit scenarios. For solving this problem, we present an equivalent integer linear programming formulation where some binary variables are relaxed to continuous ones. Additionally, for some specific graphs, we present a more simplified linear programming formulation. All formulations are tested on randomly generated graphs with different densities and different numbers of scenarios.

Original languageEnglish (US)
Title of host publicationCombinatorial Optimization and Applications - 5th International Conference, COCOA 2011, Proceedings
Pages500-509
Number of pages10
DOIs
StatePublished - Aug 29 2011
Externally publishedYes
Event5th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2011 - Zhangjiajie, China
Duration: Aug 4 2011Aug 6 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6831 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other5th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2011
CountryChina
CityZhangjiajie
Period8/4/118/6/11

Keywords

  • Graph Partitioning
  • Integer Programming
  • Stochastic Optimization

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Fan, N., Zheng, Q. P., & Pardalos, P. M. (2011). On the two-stage stochastic graph partitioning problem. In Combinatorial Optimization and Applications - 5th International Conference, COCOA 2011, Proceedings (pp. 500-509). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6831 LNCS). https://doi.org/10.1007/978-3-642-22616-8_39