Semidefinite Relaxations for Mixed 0-1 Second-Order Cone Program
We investigate semidefinite relaxations for mixed 0-1 Second-Order Cone Programs. Central to our approach is the reformulation of the problem as a non convex Quadratically Constrained Quadratic Program (QCQP), an approach that situates this problem in the framework of binary quadratically constrained quadratic programming. This allows us to apply the well-known semidefinite relaxation for such problems. This relaxation is strengthened by the addition of constraints of the initial problem expressed in the form of semidefinite constraints. We report encouraging computational results indicating that the semidefinite relaxation improves significantly the continuous relaxation (112% on average) and that it often provides a lower bound very close to the optimal value. In addition, the computational time for obtaining these results remains reasonable.
KeywordsCombinatorial Optimization Mixed-Integer Second-Order Cone Program Semidefinite Relaxation Quadratically Constrained Quadratic Program
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