Search Machine Learning Repository: @inproceedings{icml2014c1_azadi14,
    Publisher = {JMLR Workshop and Conference Proceedings},
    Title = {Towards an optimal stochastic alternating direction method of multipliers},
    Url = {http://jmlr.org/proceedings/papers/v32/azadi14.pdf},
    Abstract = {We study regularized stochastic convex optimization subject to linear equality constraints. This class of problems was recently also studied by Ouyang et al. (2013) and Suzuki (2013); both introduced similar stochastic alternating direction method of multipliers (SADMM) algorithms. However, the analysis of both papers led to suboptimal convergence rates. This paper presents two new SADMM methods: (i) the first attains the minimax optimal rate of O(1/k) for nonsmooth strongly-convex stochastic problems; while (ii) the second progresses towards an optimal rate by exhibiting an O(1/k^2) rate for the smooth part. We present several experiments with our new methods; the results indicate improved performance over competing ADMM methods.},
    Author = {Samaneh Azadi and Suvrit Sra},
    Editor = {Tony Jebara and Eric P. Xing},
    Year = {2014},
    Booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
    Pages = {620-628}
   }