Semidefinite Programming Based Convex Relaxation for Nonconvex Quadratically Constrained Quadratic Programming
In this paper, we review recent development in semidefinite programming (SDP) based convex relaxations for nonconvex quadratically constrained quadratic programming (QCQP) problems. QCQP problems have been well known as NP-hard nonconvex problems. We focus on convex relaxations of QCQP, which forms the base of global algorithms for solving QCQP. We review SDP relaxations, reformulation-linearization technique, SOC-RLT constraints and various other techniques based on lifting and linearization.
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