We analyze stochastic gradient algorithms for optimizing nonconvex, nonsmooth
finite-sum problems. In particular, the objective function is given by the
summation of a differentiable (possibly nonconvex) component, together with a
possibly non-differentiable but convex component. We propose a proximal
stochastic gradient algorithm based on variance reduction, called ProxSVRG+.
Our main contribution lies in the analysis of ProxSVRG+. It recovers several
existing convergence results and improves/generalizes them (in terms of the
number of stochastic gradient oracle calls and proximal oracle calls). In
particular, ProxSVRG+ generalizes the best results given by the SCSG algorithm,
recently proposed by [Lei et al., 2017] for the smooth nonconvex case.
ProxSVRG+ is also more straightforward than SCSG and yields simpler analysis.
Moreover, ProxSVRG+ outperforms the deterministic proximal gradient descent
(ProxGD) for a wide range of minibatch sizes, which partially solves an open
problem proposed in [Reddi et al., 2016b]. Also, ProxSVRG+ uses much less
proximal oracle calls than ProxSVRG [Reddi et al., 2016b]. Moreover, for
nonconvex functions satisfied Polyak-\L{}ojasiewicz condition, we prove that
ProxSVRG+ achieves a global linear convergence rate without restart unlike
ProxSVRG. Thus, it can \emph{automatically} switch to the faster linear
convergence in some regions as long as the objective function satisfies the PL
condition locally in these regions. ProxSVRG+ also improves ProxGD and
ProxSVRG/SAGA, and generalizes the results of SCSG in this case. Finally, we
conduct several experiments and the experimental results are consistent with
the theoretical results.Comment: 32nd Conference on Neural Information Processing Systems (NeurIPS
2018
