Search Machine Learning Repository: @inproceedings{icml2014c1_zhong14,
    Publisher = {JMLR Workshop and Conference Proceedings},
    Title = {Fast Stochastic Alternating Direction Method of Multipliers},
    Url = {},
    Abstract = {We propose a new stochastic alternating direction method of multipliers (ADMM) algorithm, which incrementally approximates the full gradient in the linearized ADMM formulation. Besides having a low per-iteration complexity as existing stochastic ADMM algorithms, it improves the convergence rate on convex problems from $\mO(1/\sqrt{T})$ to $\mO(1/T)$, where $T$ is the number of iterations. This matches the convergence rate of the batch ADMM algorithm, but without the need to visit all the samples in each iteration. Experiments on the graph-guided fused lasso demonstrate that the new algorithm is significantly faster than state-of-the-art stochastic and batch ADMM algorithms.},
    Author = {Wenliang Zhong and James Kwok},
    Editor = {Tony Jebara and Eric P. Xing},
    Year = {2014},
    Booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
    Pages = {46-54}