Paper:
Stochastic Proximal Gradient Descent with Acceleration Techniques
Proximal gradient descent (PGD) and stochastic proximal gradient descent (SPGD) are popular methods for solving regularized risk minimization problems in machine learning and statistics. In this paper, we propose and analyze an accelerated variant of these methods in the mini-batch setting. This method incorporates two acceleration techniques: one is Nesterov's acceleration method, and the other is a variance reduction for the stochastic gradient. Accelerated proximal gradient descent (APG) and proximal stochastic variance reduction gradient (Prox-SVRG) are in a trade-off relationship. We show that our method, with the appropriate mini-batch size, achieves lower overall complexity than both APG and Prox-SVRG.

Review:
The paper presents an accelerated mini-batch randomized incremental gradient descent algorithm for composite minimization. The composite objective function is supposed to decompose into the sum of differentiable, Lipschitz-continuous terms and a proximal term. The contributions can be summarized as a two-fold extension of stochastic proximal variance reduction gradient descent (Prox-SVRG; Johnson&Zhang, Xiao and Zhang), using both an accelerated-gradient-type acceleration (Nesterov's) and a mini-batch setting. The obtained complexity estimate is in between  O((n+ alpha*kappa)log(1/epsilon))  and O((n+gamma*sqrt(kappa))log(1/epsilon)) depending on the appropriate choice of the mini-batch size wrt to the "optimal" (knowing the true condition number) mini-batch size (n is the number of terms in the diff.-part of the objective, kappa the condition number, and epsilon the accuracy). At worst, the proposed method performs as well as the accelerated proximal gradient decent and the Prox-SVRG. With appropriate mini-batch size, it potentially outperform both. Illustrative preliminary experiments are presented for learning with a multinomial logistic loss and an L1 regularization penalty. These experiments suggest that the proposed algorithm can outperform the competing methods with appropriate choice of the mini-batch size. Detailed comments:- the term "stochastic" is misleading here, since the composite optimization problem in Eq. 1 is completely deterministic and does not involve any expectation. Algorithms proceeding by randomly picking subset of terms in a large sum appearing the optimization objective are usually referred to "randomized gradient descent" algorithms. See [Bertsekas, "Nonlinear Programming", 2003] for reference in terminology. This stands in contrast to "stochastic gradient descent" algorithms whose purpose it to minimize an objective involving an expectation. - last of paragraph of page 2 is unclear, probably because of unfortunate copy-paste or edits; please correct. - throughout the paper, expectations are used, sometimes without conditioning to any filtration, sometimes with conditioning to a particular filtration, but always with the same notation. While the derivations seem to be correct, this is very confusing. Please use a less ambiguous notation, for instance E_t for E ( . | F_t). - Section 3 could start with a proof outline. Once the 4 lemmas are proved, the mechanics of the proof of Theorem 1 are quite clear and easy to follow. It would probably make sense to state the lemmas without proof (with proofs deferred to Appendix), and use the space to comment and illustrate what's going on in the proof. In particular, it would be interesting to include illustrations of the interpretation of the behaviour of the estimate sequences along the iterations for the convergence of the proposed algorithm. - Section 4 should be improved. A major issue of mini-batch approaches that limited their use in practice is the tuning of the mini-batch size. Several heuristics could be explored and compared here, since this parameter has clearly a major impact on relative performance wrt competing methods. It would also be interesting to compare the algorithms for several values of the regularization parameter. A default option could be the value that leads to the best validation error. It's quite shocking here (in a submission to a ML conference) that the value was set to a default value uniformly over all datasets, regardless of the generalization performance. Finally, it would be interesting to investigate the behaviour of the algorithms on a large dataset (n >> 1) with high-dimensional and dense features (p >>1, and dense vectors). This case is not covered by the datasets considered, but would clearly be worthwhile to consider in order to compare the algorithms.