Compact Mixed Integer Programming Formulations for the Minimum Biclique Cover Problem

ABSTRACT Given a simple graph with vertex set and edge set , the minimum biclique cover problem seeks to cover all edges of the graph with a minimum number of bicliques (i.e., complete bipartite subgraphs). This paper proposes two compact mixed integer programming (MIP) formulations for solving the minimum biclique cover problem on general graphs: (i) A natural formulation in the edge space and (ii) an extended formulation in the edge and vertex spaces. While the natural MIP formulation of Cornaz and Fonlupt ( Discrete Mathematics , 2006) has exponentially many constraints, our natural formulation enjoys only a polynomial number of their exponential “no‐good” cuts, along with another set of polynomial valid inequalities. We also employ bounding and variable fixing procedures that help solve most of our social network instances, which are not solvable to optimality in a one‐hour time limit without the bounding and fixing procedures. The instances that are not solved in the one‐hour time limit are submitted to the 2024 Mixed Integer Programming Library (MIPLIB 2024).

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Publication Details

Journal
Networks
Published
2025-12-20
DOI
https://doi.org/10.1002/net.70020
Primary Topic
Vehicle Routing Optimization Methods
Type
article
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article

Compact Mixed Integer Programming Formulations for the Minimum Biclique Cover Problem

Illya V. Hicks, Hamidreza Validi, Bochuan Lyu, Bruno Burin
Networks
Vehicle Routing Optimization Methods
article

Compact Mixed Integer Programming Formulations for the Minimum Biclique Cover Problem

Illya V. Hicks, Hamidreza Validi, Bochuan Lyu, Bruno Burin
article en

Abstract

ABSTRACT Given a simple graph with vertex set and edge set , the minimum biclique cover problem seeks to cover all edges of the graph with a minimum number of bicliques (i.e., complete bipartite subgraphs). This paper proposes two compact mixed integer programming (MIP) formulations for solving the minimum biclique cover problem on general graphs: (i) A natural formulation in the edge space and (ii) an extended formulation in the edge and vertex spaces. While the natural MIP formulation of Cornaz and Fonlupt ( Discrete Mathematics , 2006) has exponentially many constraints, our natural formulation enjoys only a polynomial number of their exponential “no‐good” cuts, along with another set of polynomial valid inequalities. We also employ bounding and variable fixing procedures that help solve most of our social network instances, which are not solvable to optimality in a one‐hour time limit without the bounding and fixing procedures. The instances that are not solved in the one‐hour time limit are submitted to the 2024 Mixed Integer Programming Library (MIPLIB 2024).

NetworksVol. 87(3)
Texas Tech University (US), Military Institute of Engineering (BR), Rice University (US)
Openalex Percentile: Top 47%
Vehicle Routing Optimization Methods
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