  In this paper, we study the proximal incremental aggregated gradient(PIAG)
algorithm for minimizing the sum of L-smooth nonconvex component functions and
a proper closed convex function. By exploiting the L-smooth property and with
the help of an error bound condition, we can show that the PIAG method still
enjoys some nice linear convergence properties even for nonconvex minimization.
To illustrate this, we first demonstrate that the generated sequence globally
converges to the stationary point set. Then, there exists a threshold such that
the objective function value sequence and the iterate point sequence are
R-linearly convergent when the stepsize is chosen below this threshold.

Optimization and Control