Fetching the paper…
Reading the bibliography…
We introduce a framework for designing primal methods under the decentralized optimization setting where local functions are smooth and strongly convex.
Augmented lagrangians and applications of the proximal point algorithm in convex programming
R. T. Rockafellar · 1976
Earlier work this paper cites.
New proximal point algorithms for convex minimization
O. Güler · 1992
Earlier work this paper cites.
The complexity of decentralized control of markov decision processes
D. S. Bernstein, R. Givan, N. Immerman, and S. Zilberstein · 2002
Earlier work this paper cites.
Introductory lectures on convex optimization , volume 87
Y. Nesterov · 2004
Earlier work this paper cites.
Cooperative multi-agent learning: The state of the art
L. Panait and S. Luke · 2005
Earlier work this paper cites.
Distributed average consensus with least-mean-square deviation
L. Xiao, S. Boyd, and S.-J. Kim · 2007
Earlier work this paper cites.
Distributed subgradient methods for multi-agent optimization
A. Nedic and A. Ozdaglar · 2009
Earlier work this paper cites.
Variational analysis , volume 317
R. T. Rockafellar and R. J.-B. Wets · 2009
Earlier work this paper cites.
Mnist handwritten digit database
Y. LeCun, C. Cortes, and C. Burges · 2010
Earlier work this paper cites.
Iterative solution of large linear systems
W. Auzinger and J. Melenk · 2011
Earlier work this paper cites.
Dual averaging for distributed optimization: Convergence analysis and network scaling
J. C. Duchi, A. Agarwal, and M. J. Wainwright · 2011
Earlier work this paper cites.
Convergence rates of inexact proximal-gradient methods for convex optimization
M. Schmidt, N. L. Roux, and F. R. Bach · 2011
Earlier work this paper cites.
Constrained optimization and Lagrange multiplier methods
D. P. Bertsekas · 2014
Earlier work this paper cites.
Computational complexity of inexact gradient augmented lagrangian methods: application to constrained mpc
V. Nedelcu, I. Necoara, and Q. Tran-Dinh · 2014
Earlier work this paper cites.
On the linear convergence of the ADMM in decentralized consensus optimization
W. Shi, Q. Ling, K. Yuan, G. Wu, and W. Yin · 2014
Earlier work this paper cites.
Communication complexity of distributed convex learning and optimization
Y. Arjevani and O. Shamir · 2015
Earlier work this paper cites.
Inexact accelerated augmented lagrangian methods
M. Kang, M. Kang, and M. Jung · 2015
Earlier work this paper cites.
Extra: An exact first-order algorithm for decentralized consensus optimization
W. Shi, Q. Ling, G. Wu, and W. Yin · 2015
Cited alongside, same era.
Privacy-preserving deep learning
R. Shokri and V. Shmatikov · 2015
Cited alongside, same era.
On the iteration complexity of oblivious first-order optimization algorithms
Y. Arjevani and O. Shamir · 2016
Cited alongside, same era.
The eu general data protection regulation (gdpr)
GDPR · 2016
Cited alongside, same era.
Distributed learning: developing a predictive model based on data from multiple hospitals without data leaving the hospital–a real life proof of concept
A. Jochems, T. M. Deist, J. Van Soest, M. Eble, P. Bulens, P. Coucke, W. Dries, P. Lambin, and A. Dekker · 2016
Cited alongside, same era.
Federated learning: Strategies for improving communication efficiency
Geometrically convergent distributed optimization with uncoordinated step-sizes
A. Nedić, A. Olshevsky, W. Shi, and C. A. Uribe · 2017
Later among the works it cites.
Harnessing smoothness to accelerate distributed optimization
G. Qu and N. Li · 2017
Later among the works it cites.
Optimal algorithms for smooth and strongly convex distributed optimization in networks
K. Scaman, F. Bach, S. Bubeck, Y. T. Lee, and L. Massoulié · 2017
Later among the works it cites.
A sharp convergence rate analysis for distributed accelerated gradient methods
H. Li, C. Fang, W. Yin, and Z. Lin · 2018
Later among the works it cites.
Optimal algorithms for non-smooth distributed optimization in networks
K. Scaman, F. Bach, S. Bubeck, L. Massoulié, and Y. T. Lee · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. Kone 𝐯 \mathbf{v} cný, H. B. McMahan, F. X. Yu, P. Richtarik, A. T. Suresh, and D. Bacon · 2016
Cited alongside, same era.
End-to-end kernel learning with supervised convolutional kernel networks
J. Mairal · 2016
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
H. B. McMahan, E. Moore, D. Ramage, S. Hampson, et al · 2016
Cited alongside, same era.
Edge computing: Vision and challenges
W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu · 2016
Cited alongside, same era.
On the convergence of decentralized gradient descent
K. Yuan, Q. Ling, and W. Yin · 2016
Cited alongside, same era.
Decentralized computation of effective resistances and acceleration of consensus algorithms
N. S. Aybat and M. Gürbüzbalaban · 2017
Cited alongside, same era.
Developing and validating a survival prediction model for nsclc patients through distributed learning across 3 countries
A. Jochems, T. M. Deist, I. El Naqa, M. Kessler, C. Mayo, J. Reeves, S. Jolly, M. Matuszak, R. Ten Haken, J. van Soest, et al · 2017
Cited alongside, same era.
B. E. Woodworth, J. Wang, A. Smith, B. McMahan, and N. Srebro · 2018
Later among the works it cites.
B. Can, S. Soori, N. S. Aybat, M. M. Dehvani, and M. Gürbüzbalaban · 2019
Later among the works it cites.
D. Dvinskikh and A. Gasnikov · 2019
Later among the works it cites.
Robust distributed accelerated stochastic gradient methods for multi-agent networks
A. Fallah, M. Gürbüzbalaban, A. Ozdaglar, U. Simsekli, and L. Zhu · 2019
Later among the works it cites.
Convergence rate of distributed optimization algorithms based on gradient tracking
Y. Sun, A. Daneshmand, and G. Scutari · 2019
Later among the works it cites.
Achieving acceleration in distributed optimization via direct discretization of the heavy-ball ode
J. Zhang, C. A. Uribe, A. Mokhtari, and A. Jadbabaie · 2019
Later among the works it cites.
A tight convergence analysis for stochastic gradient descent with delayed updates
Y. Arjevani, O. Shamir, and N. Srebro · 2020
Closest in time.
An optimal algorithm for decentralized finite sum optimization, 2020
H. Hendrikx, F. Bach, and L. Massoulie · 2020
Closest in time.
A dual approach for optimal algorithms in distributed optimization over networks
C. A. Uribe, S. Lee, A. Gasnikov, and A. Nedić · 2020
Closest in time.
Accelerated primal-dual algorithms for distributed smooth convex optimization over networks
J. Xu, Y. Tian, Y. Sun, and G. Scutari · 2020
Closest in time.
Bregman augmented lagrangian and its acceleration, 2020
S. Yan and N. He · 2020
Closest in time.