Stochastic gradient descent algorithms for strongly convex functions at O(1/T) convergence rates

Shenghuo Zhu

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arXiv · 1305.2218

Stochastic gradient descent algorithms for strongly convex functions at O(1/T) convergence rates

Shenghuo Zhu

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Abstract

With a weighting scheme proportional to t, a traditional stochastic gradient descent (SGD) algorithm achieves a high probability convergence rate of O({\kappa}/T) for strongly convex functions, instead of O({\kappa} ln(T)/T). We also prove that an accelerated SGD algorithm also achieves a rate of O({\kappa}/T).

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Shenghuo Zhu. 2013-05-09. Stochastic gradient descent algorithms for strongly convex functions at O(1/T) convergence rates. https://arxiv.org/abs/1305.2218

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