Extending the QCR method to general mixed-integer programs
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Let (MQP) be a general mixed integer quadratic program that consists of minimizing a quadratic function subject to linear constraints. In this paper, we present a convex reformulation of (MQP), i.e. we reformulate (MQP) into an equivalent program, with a convex objective function. Such a reformulation can be solved by a standard solver that uses a branch and bound algorithm. We prove that our reformulation is the best one within a convex reformulation scheme, from the continuous relaxation point of view. This reformulation, that we call MIQCR (Mixed Integer Quadratic Convex Reformulation), is based on the solution of an SDP relaxation of (MQP). Computational experiences are carried out with instances of (MQP) including one equality constraint or one inequality constraint. The results show that most of the considered instances with up to 40 variables can be solved in 1 h of CPU time by a standard solver.
KeywordsGeneral integer programming Mixed-integer programming Quadratic programming Convex reformulation Semi-definite programming Experiments
Mathematics Subject Classification (2000)90C11 Mixed integer programming 90C20 Quadratic programming 90C22 Semidefinite programming 90C26 Nonconvex programming
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- 8.Floudas C.A.: Deterministic Global Optimization. Kluwer, Dordrecht (2000)Google Scholar
- 12.Garey M.R., Johnson D.S.: Computers and Intractability: a guide to the theory of NP-Completness. W.H. Freeman, San Francisco (1979)Google Scholar
- 18.ILOG. ILOG CPLEX 11.0 Reference Manual. ILOG CPLEX Division, Gentilly (2008)Google Scholar
- 21.Liberti L., Maculan N.: Global Optimization: From Theory to Implementation, Chapter: Nonconvex Optimization and Its Applications. Springer, New York (2006)Google Scholar
- 23.Saxena A., Bonami P., Lee J.: Disjunctive Cuts for Non-Convex Mixed Integer Quadratically Constrained Programs. IPCO, Bologna (2008)Google Scholar