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arXiv · 1407.2710

Finito: A Faster, Permutable Incremental Gradient Method for Big Data Problems

Aaron J. Defazio·Tibério S. Caetano·Justin Domke
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Abstract

Recent advances in optimization theory have shown that smooth strongly convex finite sums can be minimized faster than by treating them as a black box "batch" problem. In this work we introduce a new method in this class with a theoretical convergence rate four times faster than existing methods, for sums with sufficiently many terms. This method is also amendable to a sampling without replacement scheme that in practice gives further speed-ups. We give empirical results showing state of the art performance.

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Aaron J. Defazio, Tibério S. Caetano, Justin Domke. 2014-07-10. Finito: A Faster, Permutable Incremental Gradient Method for Big Data Problems. https://arxiv.org/abs/1407.2710

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