Canonical Primal-Dual Method for Solving Non-convex Minimization   Problems
  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. 
Changzhi Wu, Chaojie Li, and David Yang Gao
cs.NA cs.DS math.OC