Authors: Aaron J. Defazio,Tibério S. Caetano,Justin Domke
ArXiv: 1407.2710
Document:
PDF
DOI
Abstract URL: http://arxiv.org/abs/1407.2710v1
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.