  A new primal-dual algorithm is presented for solving a class of non-convex
minimization problems. This algorithm is based on canonical duality theory such
that the original non-convex minimization problem is first reformulated as a
convex-concave saddle point optimization problem, which is then solved by a
quadratically perturbed primal-dual method. %It is proved that the popular SDP
method is indeed a special case of the canonical duality theory. Numerical
examples are illustrated. Comparing with the existing results, the proposed
algorithm can achieve better performance.
