An exact solution method for quadratic matching: The one-quadratic-term technique and generalisations

作者：Hupp Lena^*; Klein Laura; Liers Frauke

来源：Discrete Optimization, 2015, 18: 193-216.

DOI：10.1016/j.disopt.2015.10.002

• Binary quadratic optimisation problem
• Reformulation linearisation technique (RLT)
• Quadratic matching problem
• Polyhedral combinatorics

The quadratic matching problem (QMP) asks for a matching in a graph that optimises a quadratic objective in the edge variables. The QMP generalises the quadratic assignment problem as the latter can be seen as a perfect matching on a bipartite graph. In our approach, we strengthen the linearised IP-formulation by cutting planes that are derived from facets of the corresponding matching problem where only one quadratic term is present in the objective QMP1). As the influence of these cutting planes on the root bound decreases for instances with more quadratic terms in the objective, we present different methods to strengthen these cutting planes. Adopting the idea of the reformulation linearisation technique (RLT) we derive valid inequalities for the general QMP from QMP1 facets. Following the approach in Fomeni et al. (2015) we replace cubic terms that appear in a reformulated QMP1 inequality by a quadratic estimator. Based on these methods we design and implement an exact branch-and-cut approach. When compared to solving the standard linearised IP formulation strengthened by cutting planes that result from the application of the RLT to the degree inequalities, we show that root bounds and cpu times for solving instances to optimality can significantly be improved, when the new method is applied.

• 出版日期2015-11