  In this paper, we adapt proximal incremental aggregated gradient methods to
saddle point problems, which is motivated by decoupling linear transformations
in regularized empirical risk minimization models. First, the Primal-Dual
Proximal Incremental Aggregated (PD-PIAG) methods with extrapolations were
proposed. We proved that the primal-dual gap of the averaged iteration sequence
sublinearly converges to 0, and the iteration sequence converges to some saddle
point. Under the strong convexity of $f$ and $h^\ast$, we proved that the
iteration sequence linearly converges to the saddle point. Then, we propose a
PD-PIAG method without extrapolations. The primal-dual gap of the iteration
sequence is proved to be sublinearly convergent under strong convexity of $f$.
