Mathematical Programming: Series A and B
The cut polytope and the Boolean quadric polytope
Discrete Mathematics
The Stanford GraphBase: a platform for combinatorial computing
The Stanford GraphBase: a platform for combinatorial computing
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Using a Mixed Integer Quadratic Programming Solver for the Unconstrained Quadratic 0-1 Problem
Mathematical Programming: Series A and B
A new exact algorithm for the two-sided crossing minimization problem
COCOA'07 Proceedings of the 1st international conference on Combinatorial optimization and applications
A rearrangement of adjacency matrix based approach for solving the crossing minimization problem
Journal of Combinatorial Optimization
Exact bipartite crossing minimization under tree constraints
SEA'10 Proceedings of the 9th international conference on Experimental Algorithms
An SDP approach to multi-level crossing minimization
Journal of Experimental Algorithmics (JEA)
A computational study and survey of methods for the single-row facility layout problem
Computational Optimization and Applications
Exact Approaches to Multilevel Vertical Orderings
INFORMS Journal on Computing
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The quadratic linear ordering problem naturally generalizes various optimization problems such as bipartite crossing minimization or the betweenness problem, which includes linear arrangement. These problems have important applications, e.g., in automatic graph drawing and computational biology. We present a new polyhedral approach to the quadratic linear ordering problem that is based on a linearization of the quadratic objective function. Our main result is a reformulation of the 3-dicycle inequalities using quadratic terms. After linearization, the resulting constraints are shown to be face-inducing for the polytope corresponding to the unconstrained quadratic problem. We use this result both within a branch-and-cut algorithm and within a branch-and-bound algorithm based on semidefinite programming. Experimental results for bipartite crossing minimization show that this approach clearly outperforms other methods.