### 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 language | English (US) |
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Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |

Pages | 500-509 |

Number of pages | 10 |

Volume | 6831 LNCS |

DOIs | |

State | Published - 2011 |

Externally published | Yes |

Event | 5th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2011 - Zhangjiajie, China Duration: Aug 4 2011 → Aug 6 2011 |

### Publication series

Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 6831 LNCS |

ISSN (Print) | 03029743 |

ISSN (Electronic) | 16113349 |

### Other

Other | 5th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2011 |
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Country | China |

City | Zhangjiajie |

Period | 8/4/11 → 8/6/11 |

### Fingerprint

### Keywords

- Graph Partitioning
- Integer Programming
- Stochastic Optimization

### ASJC Scopus subject areas

- Computer Science(all)
- Theoretical Computer Science

### Cite this

*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)*(Vol. 6831 LNCS, 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

**On the two-stage stochastic graph partitioning problem.** / Fan, Neng; Zheng, Qipeng P.; Pardalos, Panos M.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).*vol. 6831 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6831 LNCS, pp. 500-509, 5th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2011, Zhangjiajie, China, 8/4/11. https://doi.org/10.1007/978-3-642-22616-8_39

}

TY - GEN

T1 - On the two-stage stochastic graph partitioning problem

AU - Fan, Neng

AU - Zheng, Qipeng P.

AU - Pardalos, Panos M.

PY - 2011

Y1 - 2011

N2 - 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.

AB - 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.

KW - Graph Partitioning

KW - Integer Programming

KW - Stochastic Optimization

UR - http://www.scopus.com/inward/record.url?scp=80051966229&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80051966229&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-22616-8_39

DO - 10.1007/978-3-642-22616-8_39

M3 - Conference contribution

AN - SCOPUS:80051966229

SN - 9783642226151

VL - 6831 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 500

EP - 509

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -