  In this paper, we consider the nonconvex quadratically constrained quadratic
programming (QCQP) with one quadratic constraint. By employing the conjugate
gradient method, an efficient algorithm is proposed to solve QCQP that exploits
the sparsity of the involved matrices and solves the problem via solving a
sequence of positive definite system of linear equations after identifying
suitable generalized eigenvalues. Some numerical experiments are given to show
the effectiveness of the proposed method and to compare it with some recent
algorithms in the literature.
