NeurIPS 2019
Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
Paper ID:7769
Title:Complexity of Highly Parallel Non-Smooth Convex Optimization

Reviewer 1

Quality: The submission is purely theoretical supported by solid analysis. I do not check the proofs in the appendix, but the claims sound correct to me. However, the constants in the almost all theorems seem to be too complicated to be the inherent constants for the problem. Originality: The lower bound for parallel non-smooth optimization has not been very well-studied and the submission presents a fairly thorough answer to that for both upper and lower bound. Clarity: Although the problem setting is clear, it is hard to understand the proof techniques in the lower bound part. More explanations perhaps with a figure for the wall function will be helpful. Significance: Obtaining the lower bound and constructing the random wall function are non-trivial in theory. Although the proposed algorithm seems to be impractical, the results provide a guideline on the lower bound for other algorithms fallen in the regime. Updates after rebuttal: In Theorem 7, usually \eps<1, then it requires \delta<10^{-20}, which suggests the algorithm needs an exact gradient oracle.

Reviewer 2

The work is very original and provides novel approaches for both the lower bound problem, where the new idea of a wall function is introduced, and for the upper bound side, where the authors deploy a new framework for the design of accelerated algorithms. Related work is adequately cited and the key ideas in previous works are properly summarized and explained, which makes it easier to follow the novel technical arguments. The results are significant. The authors also provide a number of open problems and conjectures to build on.

Reviewer 3

# Setting Suppose we want to minimize a non-smooth convex function over d dimensions. At each round, we can query poly(d) function values in parallel (i.e. highly parallel). The goal is to reduce the number of total interactive rounds. # Significance (see also the contributions section) 1. The lower bound construction which shows that that subgradient descent is optimal up to O(d^{1/2}) is a refinement of (and the result an improvement upon) a decades-old lower bound by Nemirovski which shows up to T = O(d^{1/3}). This closes the question of up to what T is subgradient descent optimal (i.e. parallelization does not help). 2. The upper bound result of d^{1/2}/\eps^{2/3} interpolates between subgradient descent (with the complexity of 1/\eps^{2}) and interior-point methods with the complexity of d\log(\eps). This improves upon the previous best upper bound of d^{1/4}/\eps, and the authors conjecture to be optimal. # Things to improve 1. [Clarity] The lower bound construction is explained very well and is easy to read. A formal statement of the theorem in the main paper would add further clarity. On the other hand, the writing of the upper bound seems a little hurried---while oracles (ll 238--249) are defined their significance is never discussed and neither is their implementation, (ll 208--233) discusses in detail a method which does not work, the actual algorithm which does work is never explicitly stated. In general, section 3 discusses many pieces which are used for the final algorithm but does not talk about the actual method. The authors should consider reorganizing their writing so that the high-level details of the method are apparent in the main paper and the implementation details of the pieces are moved to the appendix. 2. It is unclear from the discussion if for \eps smaller than (1/d) if center of gravity method is optimal. Does parallelization not help in this case either (this seems to be implied by the discussion in Sec 1.2)? 3. Line 63 "in the range [d^{-1}, d^{-1/2}]" -> "in the range [d^{-1}, d^{-1/4}]" 4. For \eps in the range [d^{-1}, d^{-1/4}], authors conjecture that d^{1/3}/\eps^{2/3} is optimal. A discussion of their intuitions (perhaps in the appendix) on why this is true would be helpful. 5. It is unclear why the function \Chi is used in algorithm 1 (especially since it increases the bias of the estimator). The proof in the appendix does not make it clear what properties of \Chi are necessary (e.g. why would a gaussian kernel not work...). ==== Update after reading rebuttal and discussion === I thank the authors for their reply. I have one additional concern/recommendation for the writing: lemma 8 only establishes only 1-order smoothness of the function and not p-th order as is required for the acceleration framework. Adding this would aid understanding. I also realized I made an error of assuming that the oracle used is a zero-order one instead of a first order one. However using d parallel function queries one can compute the gradient of the smoothed function to arbitrary accuracy and so leaves their results (and my review) unchanged.