Fetching the paper…
Reading the bibliography…
A lot of effort has been invested into characterizing the convergence rates of gradient based algorithms for non-linear convex optimization.
P. Diaconis and D. Stroock, “Geometric bounds for eigenvalues of markov chains,” The Annals of Applied Probability , vol. 1, no. 1, pp. 36–61, 1991
1991
Earlier work this paper cites.
O. Reingold, S. Vadhan, and A. Wigderson, “Entropy waves, the zig-zag graph product, and new constant-degree expanders,” Annals of Mathematics , vol. 155, no. 2, pp. 157–187, 2002
2002
Earlier work this paper cites.
M. A. Zinkevich, “Online convex programming and generalized infinitesimal gradient ascent,” in 20th International Conference on Machine Learning (ICML) , 2003
2003
Earlier work this paper cites.
E. Hazan, A. Kalai, S. Kale, and A. Agarwal, “Logarithmic regret algorithms for online convex optimization,” in 19’th COLT , 2006, pp. 499–513
2006
Earlier work this paper cites.
S. Shalev-Shwartz1 and Y. Singer, “Logarithmic regret algorithms for strongly convex repeated games,” in The Hebrew University , 2007
2007
Earlier work this paper cites.
P. L. Bartlett, E. Hazan, and A. Rakhlin, “Adaptive online gradient descent,” in Advances in Neural Information Processing Systems 20 , J. C. Platt, D. Koller, Y. Singer, and S. Roweis, Eds. MIT Press, 2007
2007
Earlier work this paper cites.
P. Tseng, “On accelerated proximal gradient methods for convex-concave optimization,” SIAM Journal on Optimization , vol. (Submitted), 2008
2008
Cited alongside, same era.
Y. Nesterov, “Primal-dual subgradient methods for convex problems,” Mathematical Programming Series B , vol. 120, pp. 221–259, 2009
2009
Cited alongside, same era.
L. Bottou, “Large-scale machine learning with stochastic gradient descent,” in Proceedings of the 19th International Conference on Computational Statistics , Y. Lechevallier and G. Saporta, Eds., Paris, France, August 2010, pp. 177–187
2010
Cited alongside, same era.
A. Nedic, A. Ozdaglar, and P. A. Parrilo, “Constrained consensus and optimization in multi-agent networks,” IEEE Transactions on Automatic Control , vol. 55, no. 4, pp. 922–938, 2010
2010
Cited alongside, same era.
E. Hazan and S. Kale, “Beyond the regret minimization barrier: an optimal algorithm for stochastic strongly-convex optimization,” in 24th Annual Conference on Learning Theory (COLT) , 2011
2011
Later among the works it cites.
S. S. Ram, A. Nedic, and V. V. Veeravalli, “Distributed stochastic subgradient projection algorithms for convex optimization,” Journal of Optimization Theory and Applications , vol. 147, no. 3, pp. 516–545, 2011
2011
Later among the works it cites.
O. Dekel, R. Gilad-Bachrach, O. Shamir, and L. Xiao, “Optimal distributed online prediction using mini-batches.” Journal of Machine Learning Research , vol. 13, pp. 165–202, 2012
2012
Closest in time.
K. I. Tsianos, S. Lawlor, and M. G. Rabbat, “Push-sum distributed dual averaging for convex optimization,” in 51st IEEE Conference on Decision and Control , 2012
2012
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. Duchi, A. Agarwal, and M. Wainwright, “Dual averaging for distributed optimization: Convergence analysis and network scaling,” IEEE Transactions on Automatic Control , vol. 57, no. 3, pp. 592–606, 2011
2011
Cited alongside, same era.
2011
Cited alongside, same era.
K. I. Tsianos and M. G. Rabbat, “Distributed dual averaging for convex optimization under communication delays,” in American Control Conference (ACC) , 2012
2012
Closest in time.