  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
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 function values formulated in terms of the
{\L}ojasiewicz exponent.
