Pardalos, P.M. and Schnitger, G. (1988) Checking Glocal Optimality in Constrained Quadratic Programming is NP-hard. Operations Research Letters, 7, 33-35.
https://doi.org/10.1016/0167-6377(88)90049-1

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.