Higher order semidefinite relaxations for quadratic programming - Decision and Control, 2000. Proceedings of the 39th IEEE Conference on

In this paper, we present improved versions of the standard semidefinite relaxation for quadratic programming, that underlies many important results in robustness analysis and combinatorial optimization. It is shown that the proposed polynomial time convex conditions are at least as strong as the standard ones, and usually better, but at a higher computational cost. Several applications of the new relaxations are provided, including less conservative upper bounds for the structured singular value p and enhanced solutions for the MAX CUT graph partitioning problem.