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Complexity of Highly Parallel Non-Smooth Convex Optimization
Sebastien Bubeck · Qijia Jiang · Yin-Tat Lee · Yuanzhi Li · Aaron Sidford

Tue Dec 10 05:30 PM -- 07:30 PM (PST) @ East Exhibition Hall B + C #107
A landmark result of non-smooth convex optimization is that gradient descent is an optimal algorithm whenever the number of computed gradients is smaller than the dimension $d$. In this paper we study the extension of this result to the parallel optimization setting. Namely we consider optimization algorithms interacting with a highly parallel gradient oracle, that is one that can answer $\mathrm{poly}(d)$ gradient queries in parallel. We show that in this case gradient descent is optimal only up to $\tilde{O}(\sqrt{d})$ rounds of interactions with the oracle. The lower bound improves upon a decades old construction by Nemirovski which proves optimality only up to $d^{1/3}$ rounds (as recently observed by Balkanski and Singer), and the suboptimality of gradient descent after $\sqrt{d}$ rounds was already observed by Duchi, Bartlett and Wainwright. In the latter regime we propose a new method with improved complexity, which we conjecture to be optimal. The analysis of this new method is based upon a generalized version of the recent results on optimal acceleration for highly smooth convex optimization.

Author Information

Sebastien Bubeck (Microsoft Research)
Qijia Jiang (Stanford University)
Yin-Tat Lee
Yuanzhi Li (Princeton)
Aaron Sidford (Stanford)

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