Conditional Accelerated Lazy Stochastic Gradient Descent

03/16/2017

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by   Guanghui Lan, et al.

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In this work we introduce a conditional accelerated lazy stochastic gradient descent algorithm with optimal number of calls to a stochastic first-order oracle and convergence rate O(1/ε^2) improving over the projection-free, Online Frank-Wolfe based stochastic gradient descent of Hazan and Kale [2012] with convergence rate O(1/ε^4).