  Under the strongly convex assumption, several recent works studied the global
linear convergence rate of the proximal incremental aggregated gradient (PIAG)
method for minimizing the sum of a large number of smooth component functions
and a non-smooth convex function. In this paper, under \textsl{the quadratic
growth condition}--a strictly weaker condition than the strongly convex
assumption, we derive a new global linear convergence rate result, which
implies that the PIAG method attains global linear convergence rates in both
the function value and iterate point errors. The main idea behind is to
construct a certain Lyapunov function.
