Paper:
Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization
We analyze stochastic algorithms for optimizing nonconvex, nonsmooth finite-sum problems, where the nonsmooth part is convex. Surprisingly, unlike the smooth case, our knowledge of this fundamental problem is very limited. For example, it is not known whether the proximal stochastic gradient method with constant minibatch converges to a stationary point. To tackle this issue, we develop fast stochastic algorithms that provably converge to a stationary point for constant minibatches. Furthermore, using a variant of these algorithms, we obtain provably faster convergence than batch proximal gradient descent. Our results are based on the recent variance reduction techniques for convex optimization but with a novel analysis for handling nonconvex and nonsmooth functions. We also prove global linear convergence rate for an interesting subclass of nonsmooth nonconvex functions, which subsumes several recent works.

Review:
The paper studies stochastic algortihms for minimizing a regularized finite sum problem, where the regularizer is nonsmoth but convex; and the functions in the finite sum are smooth but possibly nonconvex. This problem is not well studied and hence understood from a complexity point of view. Previous known results give complexity bounds (to a stationary point defined using the notion of the proximal mapping) for a method with increasing minibatch sizes. The present paper sets out to give the first results which do not require growing minibatches. Linear convergence results are obtained under a Polyak-Łojasiewicz condition. Minibatch proximal versions of the SAGA and SVRG methods are proposed and studied.
The paper is a pleasure to read – excellent writing. The ideas are well explained, the paper proceeds smoothly. The paper is well set in the context of existing results and literature (one small issue is the omission to mention that a minibatch version of Prox SVRG was analysed before in the case of smooth losses – and is known as the mS2GD method; see line 124).   The complexity (in terms of number of stochastic gradient evaluations; IFO) for both is shown to be O(n + n^{2/3}/eps); which is better than O(1/eps^2) rate of ProxSGD and O(n/eps) rate of ProxGD. The complexity is also improved for PL functions. The new results are a good addition to our knowledge of complexity of stochastic methods for  an important class of nonconvex problems relevant to ML. Some previous results are included as special cases of this approach (e.g., [27], [34]).  Experiments with non-negative PCA show these methods work about the same as much better than ProxSGD.  Q: Are any lower bounds known for the IFO complexity for this problem?   Remark: The word “fast” in the title evokes the use of Nesterov’s “acceleration” – but this is not the case. It is tempting to call a method fast, but if this is to be justified, either extensive computational experiments should be performed against all or most competing methods; or the word should have a special  meaning (such as Nesterov’s acceleration). I suggest the word be dropped from the title and replaced by “Complexity of”.   Small issues:  a) Line 40: to -> to be b) Table 1 contains complexity results for proxSGD under PL assumption. Where have these been proved (citation is missing)? Confusingly, the caption says that no specific complexity results exist. Do they exist or not? c) Line 151: For the -> For d) Line 154: is a -> be a e) Line 236: worser -> worse f) References: [9] : capitalize P and L in PL; [22] appeared in ICML 2015; [25] – capitalize journal name; [26] appeared in Math Prog 2016; [33] appeared; [34] – write SVRG and not svrg.   ----- post-rebuttal comments -----  I have read the rebuttal. I have the same opinion of the paper as before and keep my scores.