A Multi-Batch L-BFGS Method for Machine Learning

NeurIPS 2016 Albert S. BerahasJorge NocedalMartin Takáč

The question of how to parallelize the stochastic gradient descent (SGD) method has received much attention in the literature. In this paper, we focus instead on batch methods that use a sizeable fraction of the training set at each iteration to facilitate parallelism, and that employ second-order information... (read more)

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