Convergence rates for distributed stochastic optimization over random networks

S. Kar and J. M. F. Moura, “Convergence rate analysis of distributed gossip (linear parameter) estimation: Fundamental limits and tradeoffs,” IEEE Journal of Selected Topics in Signal Processing, Signal Processing in Gossiping Algorithms Design and Applications , vol. 5, no. 4, pp. 674––690, Aug. 2011

2011

Earlier work this paper cites.

I. Lobel and A. E. Ozdaglar, “Distributed subgradient methods for convex optimization over random networks,” IEEE Trans. Automat. Contr. , vol. 56, no. 6, pp. 1291–1306, Jan. 2011

2011

Earlier work this paper cites.

I. Lobel, A. Ozdaglar, and D. Feijer, “Distributed multi-agent optimization with state-dependent communication,” Mathematical Programming , vol. 129, no. 2, pp. 255–284, 2011

2011

Earlier work this paper cites.

K. Tsianos and M. Rabbat, “Distributed strongly convex optimization,” 50th Annual Allerton Conference onCommunication, Control, and Computing , Oct. 2012

2012

Earlier work this paper cites.

S. Kar, J. M. F. Moura, and H. V. Poor, “Distributed linear parameter estimation: Asymptotically efficient adaptive strategies,” SIAM J. Control and Optimization , vol. 51, no. 3, pp. 2200–2229, 2013

2013

Cited alongside, same era.

D. Jakovetic, J. Xavier, and J. M. F. Moura, “Convergence rates of distributed Nesterov-like gradient methods on random networks,” IEEE Transactions on Signal Processing , vol. 62, no. 4, pp. 868–882, February 2014

2014

Cited alongside, same era.

A. Daneshmand, F. Facchinei, V. Kungurtsev, and G. Scutari, “Hybrid random/deterministic parallel algorithms for convex and nonconvex big data optimization,” IEEE Transactions on Signal Processing , vol. 63, no. 15, pp. 3914–3929, 2015

2015

Cited alongside, same era.

Z. J. Towfic, J. Chen, and A. H. Sayed, “Excess-risk of distributed stochastic learners,” IEEE Transactions on Information Theory , vol. 62, no. 10, Oct. 2016

2016

Cited alongside, same era.

A. Nedic and A. Olshevsky, “Stochastic gradient-push for strongly convex functions on time-varying directed graphs,” IEEE Transactions on Automatic Control , vol. 61, no. 12, pp. 3936–3947, Dec. 2016

2016

Later among the works it cites.

N. D. Vanli, M. O. Sayin, and S. S. Kozat, “Stochastic subgradient algorithms for strongly convex optimization over distributed networks,” IEEE Transactions on network science and engineering , vol. 4, no. 4, pp. 248–260, Oct.-Dec. 2017

2017

Later among the works it cites.

D. Yuan, Y. Hong, D. W. C. Ho, and G. Jiang, “Optimal distributed stochastic mirror descent for strongly convex optimization,” Automatica , vol. 90, pp. 196–203, April 2018

2018

Closest in time.

A. K. Sahu, D. Jakovetic, D. Bajovic, and S. Kar, “Distributed zeroth order optimization over random networks: A Kiefer-Wolfowitz stochastic approximation approach,” 2018, available at https://www.dropbox.com/s/kfc2hgbfcx5yhr8/MainCDC2018KWSA.pdf

2018

Closest in time.