  SAGA is a fast incremental gradient method on the finite sum problem and its
effectiveness has been tested on a vast of applications. In this paper, we
analyze SAGA on a class of non-strongly convex and non-convex statistical
problem such as Lasso, group Lasso, Logistic regression with $\ell_1$
regularization, linear regression with SCAD regularization and Correct Lasso.
We prove that SAGA enjoys the linear convergence rate up to the statistical
estimation accuracy, under the assumption of restricted strong convexity (RSC).
It significantly extends the applicability of SAGA in convex and non-convex
optimization.
