  The technique of semidefinite programming (SDP) relaxation can be used to
obtain a nontrivial bound on the optimal value of a nonconvex quadratically
constrained quadratic program (QCQP). We explore concave quadratic inequalities
that hold for any vector in the integer lattice ${\bf Z}^n$, and show that
adding these inequalities to a mixed-integer nonconvex QCQP can improve the
SDP-based bound on the optimal value. This scheme is tested using several
numerical problem instances of the max-cut problem and the integer least
squares problem.
