Breaking the Span Assumption Yields Fast Finite-Sum Minimization

NeurIPS 2018 Robert HannahYanli LiuDaniel O'ConnorWotao Yin

In this paper, we show that SVRG and SARAH can be modified to be fundamentally faster than all of the other standard algorithms that minimize the sum of $n$ smooth functions, such as SAGA, SAG, SDCA, and SDCA without duality. Most finite sum algorithms follow what we call the ``span assumption'': Their updates are in the span of a sequence of component gradients chosen in a random IID fashion... (read more)

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