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JSTSP
2016

Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting

3 years 12 months ago
Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting
We propose a mini-batching scheme for improving the theoretical complexity and practical performance of semi-stochastic gradient descent applied to the problem of minimizing a strongly convex composite function represented as the sum of an average of a large number of smooth convex functions, and simple nonsmooth convex function. Our method first performs a deterministic step (computation of the gradient of the objective function at the starting point), followed by a large number of stochastic steps. The process is repeated a few times with the last iterate
Jakub Konecný, Jie Liu, Peter Richtá
Added 07 Apr 2016
Updated 07 Apr 2016
Type Journal
Year 2016
Where JSTSP
Authors Jakub Konecný, Jie Liu, Peter Richtárik, Martin Takác
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