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Poster
UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization
Ali Kavis · Kfir Y. Levy · Francis Bach · Volkan Cevher

Tue Dec 10 05:30 PM -- 07:30 PM (PST) @ East Exhibition Hall B + C #133
in Optimization -- Stochastic Optimization »

We propose a novel adaptive, accelerated algorithm for the stochastic constrained convex optimization setting.Our method, which is inspired by the Mirror-Prox method, \emph{simultaneously} achieves the optimal rates for smooth/non-smooth problems with either deterministic/stochastic first-order oracles. This is done without any prior knowledge of the smoothness nor the noise properties of the problem. To the best of our knowledge, this is the first adaptive, unified algorithm that achieves the optimal rates in the constrained setting. We demonstrate the practical performance of our framework through extensive numerical experiments.

Keywords: Online Learning · Algorithms · Optimization · Stochastic Optimization
[ Paper]  [ Slides] 

Author Information

Ali Kavis (EPFL)
Kfir Y. Levy (Technion)
Francis Bach (INRIA - Ecole Normale Superieure)
Volkan Cevher (EPFL)

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