Fetching the paper…
Reading the bibliography…
We propose and analyze a new stochastic gradient method, which we call Stochastic Unbiased Curvature-aided Gradient (SUCAG), for finite sum optimization problems.
H. Robbins and S. Monro, “A stochastic approximation method,” The annals of mathematical statistics , pp. 400–407, 1951
1951
Earlier work this paper cites.
J. Tsitsiklis, “Problems in decentralized decision making and computation,” Ph.D. dissertation, Dept. of Electrical Engineering and Computer Science, M.I.T., Boston, MA, 1984
1984
Earlier work this paper cites.
D. P. Bertsekas and J. Tsitsiklis, Parallel and distributed computation: numerical methods . Prentice hall Englewood Cliffs, NJ, 1989, vol. 23
1989
Earlier work this paper cites.
Y. Nesterov, “Introductory lectures on convex programming volume I: Basic course,” Lecture notes , 1998
1998
Earlier work this paper cites.
V. Vapnik, “An overview of statistical learning theory,” IEEE Transactions on Neural Networks , vol. 10, no. 5, pp. 988–999, 1999
1999
Earlier work this paper cites.
M. Morari and J. H. Lee, “Model predictive control: past, present and future,” Computers & Chemical Engineering , vol. 23, no. 4-5, pp. 667–682, 1999
1999
Earlier work this paper cites.
A. Nedić, D. P. Bertsekas, and V. S. Borkar, “Distributed asynchronous incremental subgradient methods,” Studies in Computational Mathematics , vol. 8, no. C, pp. 381–407, 2001
2001
Earlier work this paper cites.
A. Nedić and D. P. Bertsekas, “Incremental subgradient methods for nondifferentiable optimization,” SIAM Journal on Optimization , vol. 12, no. 1, pp. 109–138, 2001
2001
Earlier work this paper cites.
X. Lin, N. B. Shroff, and R. Srikant, “A tutorial on cross-layer optimization in wireless networks,” IEEE Journal on Selected areas in Communications , vol. 24, no. 8, pp. 1452–1463, 2006
2006
Earlier work this paper cites.
S. Boyd, A. Ghosh, B. Prabhakar, and D. Shah, “Randomized gossip algorithms,” IEEE transactions on information theory , vol. 52, no. 6, pp. 2508–2530, 2006
2006
Earlier work this paper cites.
A. Nedić and A. Ozdaglar, “Distributed subgradient methods for multi-agent optimization,” IEEE Transactions on Automatic Control , vol. 54, no. 1, pp. 48–61, 2009
2009
Cited alongside, same era.
B. Johansson, M. Rabi, and M. Johansson, “A randomized incremental subgradient method for distributed optimization in networked systems,” SIAM Journal on Optimization , vol. 20, no. 3, pp. 1157–1170, 2009
2009
Cited alongside, same era.
S. S. Ram, A. Nedić, and V. V. Veeravalli, “Incremental stochastic subgradient algorithms for convex optimization,” SIAM Journal on Optimization , vol. 20, no. 2, pp. 691–717, 2009
2009
Cited alongside, same era.
N. Freris, H. Kowshik, and P. R. Kumar, “Fundamentals of large sensor networks: Connectivity, capacity, clocks, and computation,” Proceedings of the IEEE , vol. 98, no. 11, pp. 1828–1846, 2010
2010
Cited alongside, same era.
N. Freris, O. Oçal, and M. Vetterli, “Compressed sensing of streaming data,” in Proc. Allerton , 2013, pp. 1242–1249
2013
Later among the works it cites.
A. Defazio, F. Bach, and S. Lacoste-Julien, “SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives,” in NIPS , 2014, pp. 1646–1654
2014
Later among the works it cites.
S. Roch, “Modern discrete probability: An essential toolkit,” Lecture notes , 2015
2015
Later among the works it cites.
P. Sopasakis, N. Freris, and P. Patrinos, “Accelerated reconstruction of a compressively sampled data stream,” in Proc. EUSIPCO , 2016, pp. 1078–1082
2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S. Boyd, N. Parikh, E. Chu, B. Peleato, J. Eckstein et al. , “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Foundations and Trends® in Machine learning , vol. 3, no. 1, pp. 1–122, 2011
2011
Cited alongside, same era.
N. Freris, S. Graham, and P. Kumar, “Fundamental limits on synchronizing clocks over networks,” IEEE Transactions on Automatic Control , vol. 56, no. 6, pp. 1352–1364, 2011
2011
Cited alongside, same era.
E. Moulines and F. R. Bach, “Non-asymptotic analysis of stochastic approximation algorithms for machine learning,” in NIPS , 2011, pp. 451–459
2011
Cited alongside, same era.
N. Freris and A. Zouzias, “Fast distributed smoothing of relative measurements,” in Proc. CDC , 2012, pp. 1411–1416
2012
Cited alongside, same era.
K.-D. Kim and P. R. Kumar, “Cyber–physical systems: A perspective at the centennial,” Proceedings of the IEEE , vol. 100, no. Special Centennial Issue, pp. 1287–1308, 2012
2012
Cited alongside, same era.
R. Johnson and T. Zhang, “Accelerating stochastic gradient descent using predictive variance reduction,” in NIPS , 2013, pp. 315–323
2013
Cited alongside, same era.
2017
Later among the works it cites.
M. Schmidt, N. Le Roux, and F. Bach, “Minimizing finite sums with the stochastic average gradient,” Mathematical Programming , vol. 162, no. 1-2, pp. 83–112, 2017
2017
Later among the works it cites.
H.-T. Wai, W. Shi, A. Nedić, and A. Scaglione, “Curvature-aided incremental aggregated gradient method,” in Proc. Allerton , 2017
2017
Later among the works it cites.
2017
Later among the works it cites.
A. Nedić, A. Olshevsky, and W. Shi, “Achieving geometric convergence for distributed optimization over time-varying graphs,” SIAM Journal on Optimization , vol. 27, no. 4, pp. 2597–2633, 2017
2017
Later among the works it cites.