Proximal-Like Incremental Aggregated Gradient Method with Linear Convergence under Bregman Distance Growth Conditions

We introduce a unified algorithmic framework, called proximal-like incremental aggregated gradient (PLIAG) method, for minimizing the sum of smooth convex component functions and a proper closed convex regularization function that is possibly non-smooth and extendedvalued, with an additional abstract feasible set whose geometry can be captured by using the domain of a Legendre function. The PLIAG method includes many existing algorithms in the literature as special cases such as the proximal gradient (PG) method, the incremental aggregated gradient (IAG) method, the incremental aggregated proximal (IAP) method, and the proximal incremental aggregated gradient (PIAG) method. By making use of special Lyapunov functions constructed by embedding growth-type conditions into descent-type lemmas, we show that the PLIAG method is globally convergent with a linear rate provided that the step-size is not greater than some positive constant. Our results recover existing linear convergence results for incremental aggregated methods even under strictly weaker conditions than the standard assumptions in the literature.