Semidefinite Programming Based Convex Relaxation for Nonconvex Quadratically Constrained Quadratic Programming

  • Rujun Jiang
  • Duan LiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 991)


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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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Data ScienceFudan UniversityShanghaiChina
  2. 2.School of Data ScienceCity University of Hong KongHong KongChina

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