[1] SHALEV S-S. Online learning and online convex optimization [J]. Foundations and Trends in Machine Learning, 2012, 4(2):107-194. [2] WANG H, BANERJEE A. Online alternating direction method [C/OL]//Proceedings of the 2012 29th International Conference on Machine Learning. [2014-12-02]. http://arxiv.org/abs/1206.6448?context=stat.ML. [3] LUO L. Research on large-scale machine learning [J]. Ship Electronic Engineering, 2013, 33(2): 9-12(罗霖.大规模机器学习问题研究[J].舰船电子工程, 2013, 33(2):9-12.) [4] GAO Q. A new online optimization algorithm for non-smooth losses based on ADMM [J]. Computer Technology and Development, 2014, 24(2): 96-100(高乾坤.一种基于ADMM 的非光滑损失在线优化算法[J].计算机技术与发展, 2014, 24(2):96-100.) [5] SCHIZAS I, RIBEIRO A, GIANNAKIS G. Consensus in Ad Hoc WSNs with noisy links — Part I: distributed estimation of deterministic signals [J]. IEEE Transactions on Signal Processing, 2008, 56(1): 350-364. [6] NEDIC A, OZDAGLAR A. Distributed subgradient methods for multi-Agent optimization [J]. IEEE Transactions on Automatic Control, 2009, 54(1): 48-61. [7] DUCHI J, AGARWAL A, WAINWRIGHT M. Dual averaging for distributed optimization: Convergence analysis and network scaling [J]. IEEE Transactions on Automatic Control, 2012, 57(3): 592–606. [8] ZINKEVICH M. Online convex programming and generalized infinitesimal gradient ascent [EB/OL]. [2014-12-06]. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.110.6680&rep=rep1&type=pdf. [9] YAN F, SUNDARAM S, VISHWANATHAN S, et al. Distributed autonomous online learning: Regrets and intrinsic privacy-preserving properties [J]. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(11): 2483-2493. [10] HOSSEINI S, CHAPMAN A, MESBAHI M. Online distributed optimization via dual averaging [C]//Proceedings of the 2013 IEEE 52nd Annual Conference on Decision and Control.Piscataway: IEEE, 2013: 1484-1489. [11] BAZERQUE J A, MATEOS G, GIANNAKIS G B. Group-Lasso on splines for spectrum cartography [J]. IEEE Transactions on Signal Processing, 2011, 59(10): 4648-4663. [12] SHI W, LING Q, YUAN K, at el. On the linear convergence of the ADMM in decentralized consensus optimization [J]. IEEE Transactions on Signal Processing, 2014, 62(7): 1750-1761. [13] KEKATOS V, GIANNAKIS G. Distributed robust power system state estimation [J]. IEEE Transactions on Power Systems, 2013, 28(2):1617-1626. [14] MOTA J F C, XAVIER J M F, AGUIAR P M Q, at el. D-ADMM: a communication-efficient distributed algorithm for separable optimization [J]. IEEE Transactions on Signal Processing, 2013, 61(10): 2718-2723. [15] PlATT J C. Fast training of support vector machines using sequential minimal optimization [M]. Cambridge: MIT Press, 1999: 185-208. |