  We propose to solve large instances of the non-convex optimization problems
reformulated with canonical duality theory. To this aim we propose an interior
point potential reduction algorithm based on the solution of the primal-dual
total complementarity (Lagrange) function. We establish the global convergence
result for the algorithm under mild assumptions and demonstrate the method on
instances of the Sensor Network Localization problem. Our numerical results are
promising and show the possibility of devising efficient interior points
methods for non-convex duality.
