  We investigate an inertial algorithm of gradient type in connection with the
minimization of a nonconvex differentiable function. The algorithm is
formulated in the spirit of Nesterov's accelerated convex gradient method. We
prove some abstract convergence results which applied to our numerical scheme
allows us to show that the generated sequences converge to a critical point of
the objective function, if a regularization of the objective function satisfies
the Kurdyka-{\L}ojasiewicz property. Further, we provide convergence rates for
the generated sequences and the objective function values formulated in terms
of the {\L}ojasiewicz exponent. Finally, we obtain sublinear convergence rates
for the objective function values in the iterates in the case when the
objective function is convex.
