  Several optimization schemes have been known for convex optimization
problems. However, numerical algorithms for solving nonconvex optimization
problems are still underdeveloped. A progress to go beyond convexity was made
by considering the class of functions representable as differences of convex
functions. In this paper, we introduce a generalized proximal point algorithm
to minimize the difference of a nonconvex function and a convex function. We
also study convergence results of this algorithm under the main assumption that
the objective function satisfies the Kurdyka - \L ojasiewicz property.
