  In this work, we present and analyze C-SAGA, a (deterministic) cyclic variant
of SAGA. C-SAGA is an incremental gradient method that minimizes a sum of
differentiable convex functions by cyclically accessing their gradients. Even
though the theory of stochastic algorithms is more mature than that of cyclic
counterparts in general, practitioners often prefer cyclic algorithms. We prove
C-SAGA converges linearly under the standard assumptions. Then, we compare the
rate of convergence with the full gradient method, (stochastic) SAGA, and
incremental aggregated gradient (IAG), theoretically and experimentally.
