Adaptive Sampling Strategies for Stochastic Optimization

30 Oct 2017Raghu BollapragadaRichard ByrdJorge Nocedal

In this paper, we propose a stochastic optimization method that adaptively controls the sample size used in the computation of gradient approximations. Unlike other variance reduction techniques that either require additional storage or the regular computation of full gradients, the proposed method reduces variance by increasing the sample size as needed... (read more)

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