  We analyze a fast incremental aggregated gradient method for optimizing
nonconvex problems of the form $\min_x \sum_i f_i(x)$. Specifically, we analyze
the SAGA algorithm within an Incremental First-order Oracle framework, and show
that it converges to a stationary point provably faster than both gradient
descent and stochastic gradient descent. We also discuss a Polyak's special
class of nonconvex problems for which SAGA converges at a linear rate to the
global optimum. Finally, we analyze the practically valuable regularized and
minibatch variants of SAGA. To our knowledge, this paper presents the first
analysis of fast convergence for an incremental aggregated gradient method for
nonconvex problems.
