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Minimizing Finite Sums with the Stochastic Average Gradient.

Mathematical Programming: Series A and B, no. 1-2 (2017): 83-112

Cited: 859|Views72

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convex optimization

Abstract

We analyze the stochastic average gradient (SAG) method for optimizing the sum of a finite number of smooth convex functions. Like stochastic gradient (SG) methods, the SAG method's iteration cost is independent of the number of terms in the sum. However, by incorporating a memory of previous gradient values the SAG method achieves a fast...More

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Mark W. Schmidt

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Nicolas Le Roux

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Francis Bach

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