  Stochastic alternating direction method of multipliers (ADMM), which visits
only one sample or a mini-batch of samples each time, has recently been proved
to achieve better performance than batch ADMM. However, most stochastic methods
can only achieve a convergence rate $O(1/\sqrt T)$ on general convex
problems,where T is the number of iterations. Hence, these methods are not
scalable with respect to convergence rate (computation cost). There exists only
one stochastic method, called SA-ADMM, which can achieve convergence rate
$O(1/T)$ on general convex problems. However, an extra memory is needed for
SA-ADMM to store the historic gradients on all samples, and thus it is not
scalable with respect to storage cost. In this paper, we propose a novel
method, called scalable stochastic ADMM(SCAS-ADMM), for large-scale
optimization and learning problems. Without the need to store the historic
gradients, SCAS-ADMM can achieve the same convergence rate $O(1/T)$ as the best
stochastic method SA-ADMM and batch ADMM on general convex problems.
Experiments on graph-guided fused lasso show that SCAS-ADMM can achieve
state-of-the-art performance in real applications
