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Accelerated Stochastic ADMM with Variance Reduction
Alternating Direction Method of Multipliers (ADMM) is a popular method in
solving Machine Learning problems. Stochastic ADMM was firstly proposed in
order to reduce the per iteration computational complexity, which is more
suitable for big data problems. Recently, variance reduction techniques have
been integrated with stochastic ADMM in order to get a fast convergence rate,
such as SAG-ADMM and SVRG-ADMM,but the convergence is still suboptimal w.r.t
the smoothness constant. In this paper, we propose a new accelerated stochastic
ADMM algorithm with variance reduction, which enjoys a faster convergence than
all the other stochastic ADMM algorithms. We theoretically analyze its
convergence rate and show its dependence on the smoothness constant is optimal.
We also empirically validate its effectiveness and show its priority over other
stochastic ADMM algorithms