Conditional Accelerated Lazy Stochastic Gradient Descent
Guanghui ⋅ Sebastian Pokutta ⋅ Yi Zhou ⋅ Daniel Zink
2017 Poster
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Abstract
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 / epsilon^2) improving over the projection-free, Online Frank-Wolfe based stochastic gradient descent of (Hazan and Kale, 2012) with convergence rate O(1 / epsilon^4).
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