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Hsia, Y., Wang, S. and Xu, Z. (2015) Improved Semidefinite Approximation Bounds for Nonconvex Nonhomogeneous Quadratic Optimization with Ellipsoid Constraints. Operations Research Letters, 43, 378-383.

has been cited by the following article:

• TITLE: An Effective Algorithm for Quadratic Optimization with Non-Convex Inhomogeneous Quadratic Constraints

  AUTHORS: Kaiyao Lou

  KEYWORDS: Nonconvex Inhomogeneous Quadratic Constrained Quadratic Optimization, Semidefinite Programming Relaxation, Np-Hard

  JOURNAL NAME: Advances in Pure Mathematics, Vol.7 No.4, April 30, 2017

  ABSTRACT: This paper considers the NP (Non-deterministic Polynomial)-hard problem of finding a minimum value of a quadratic program (QP), subject to m non-convex inhomogeneous quadratic constraints. One effective algorithm is proposed to get a feasible solution based on the optimal solution of its semidefinite programming (SDP) relaxation problem.