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arXiv · 1106.4574

Better Mini-Batch Algorithms via Accelerated Gradient Methods

Andrew Cotter·Ohad Shamir·Nathan Srebro·Karthik Sridharan
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Abstract

Mini-batch algorithms have been proposed as a way to speed-up stochastic convex optimization problems. We study how such algorithms can be improved using accelerated gradient methods. We provide a novel analysis, which shows how standard gradient methods may sometimes be insufficient to obtain a significant speed-up and propose a novel accelerated gradient algorithm, which deals with this deficiency, enjoys a uniformly superior guarantee and works well in practice.

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Andrew Cotter, Ohad Shamir, Nathan Srebro, Karthik Sridharan. 2011-06-22. Better Mini-Batch Algorithms via Accelerated Gradient Methods. https://arxiv.org/abs/1106.4574

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