Fetching the paper…
Reading the bibliography…
In this paper, we provide a distributed optimization algorithm, termed as TV-$\mathcal{AB}$, that minimizes a sum of convex functions over time-varying, random directed graphs.
A. Kolmogoroff, “Zur theorie der markoffschen ketten,”
1936
Earlier work this paper cites.
M. Powell, “Some global convergence properties of a variable metric algorithm for minimization without exact line searches,”
1976
Earlier work this paper cites.
J. N. Tsitsiklis, “Problems in decentralized decision making and computation,” Ph.D., Massachusetts Institute of Technology, Cambridge, MA, 1984
1984
Earlier work this paper cites.
J. Tsitsiklis, D. P. Bertsekas, and M. Athans, “Distributed asynchronous deterministic and stochastic gradient optimization algorithms,”
1986
Earlier work this paper cites.
R. Byrd, J. Nocedal, and Y. Yuan, “Global convergence of a class of quasi-Newton methods on convex problems,”
1987
Earlier work this paper cites.
D. Kempe, A. Dobra, and J. Gehrke, “Gossip-based computation of aggregate information,” in
2003
Earlier work this paper cites.
C. C. Moallemi and B. V. Roy, “Distributed optimization in adaptive networks,” in
2004
Earlier work this paper cites.
M. Rabbat and R. Nowak, “Distributed optimization in sensor networks,” in
2004
Earlier work this paper cites.
J. Nocedal and S. J. Wright,
2006
Earlier work this paper cites.
E. Seneta,
2006
Earlier work this paper cites.
A. Nedić and A. Ozdaglar, “Distributed subgradient methods for multi-agent optimization,”
2009
Earlier work this paper cites.
A. Agarwal, M. J. Wainwright, and J. C. Duchi, “Distributed dual averaging in networks,” in
2010
Earlier work this paper cites.
F. Benezit, V. Blondel, P. Thiran, J. Tsitsiklis, and M. Vetterli, “Weighted gossip: Distributed averaging using non-doubly stochastic matrices,” in
2010
Earlier work this paper cites.
M. Zhu and S. Martínez, “Discrete-time dynamic average consensus,”
2010
Earlier work this paper cites.
A. Nedić, A. Ozdaglar, and P. A. Parrilo, “Constrained consensus and optimization in multi-agent networks,”
2010
Earlier work this paper cites.
I. Lobel and A. Ozdaglar, “Distributed subgradient methods for convex optimization over random networks,”
2011
Earlier work this paper cites.
I. Lobel, A. Ozdaglar, and D. Feijer, “Distributed multi-agent optimization with state-dependent communication,”
2011
Earlier work this paper cites.
P. D. Powell, “Calculating determinants of block matrices,”
2011
Earlier work this paper cites.
J. C. Duchi, A. Agarwal, and M. J. Wainwright, “Dual averaging for distributed optimization: Convergence analysis and network scaling,”
2012
Earlier work this paper cites.
E. Wei and A. Ozdaglar, “Distributed alternating direction method of multipliers,” in
2012
Earlier work this paper cites.
K. I. Tsianos, S. Lawlor, and M. G. Rabbat, “Push-sum distributed dual averaging for convex optimization,” in
2012
Cited alongside, same era.
K. Cai and H. Ishii, “Average consensus on general strongly connected digraphs,”
2012
Cited alongside, same era.
T. S. Rappaport, S. Sun, R. Mayzus, H. Zhao, Y. Azar, K. Wang, G. N. Wong, J. K. Schulz, M. Samimi, and F. Gutierrez, “Millimeter wave mobile communications for 5G cellular: It will work!”
2013
Cited alongside, same era.
J. F. C. Mota, J. M. F. Xavier, P. M. Q. Aguiar, and M. Puschel, “D-ADMM: A communication-efficient distributed algorithm for separable optimization,”
2013
Cited alongside, same era.
K. I. Tsianos, “The role of the network in distributed optimization algorithms: Convergence rates, scalability, communication/computation tradeoffs and communication delays,” Ph.D. dissertation, Dept. Elect. Comp. Eng. McGill University, 2013
G. Qu and N. Li, “Accelerated distributed Nesterov gradient descent,”
2017
Later among the works it cites.
2017
Later among the works it cites.
M. Eisen, A. Mokhtari, and A. Ribeiro, “Decentralized quasi-Newton methods,”
2017
Later among the works it cites.
A. Nedić and J. Liu, “On convergence rate of weighted-averaging dynamics for consensus problems,”
2017
Later among the works it cites.
2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2013
Cited alongside, same era.
A. Nedić and A. Olshevsky, “Distributed optimization of strongly convex functions on directed time-varying graphs,” in
2013
Cited alongside, same era.
R. A. Horn and C. R. Johnson,
2013
Cited alongside, same era.
D. Jakovetić, J. M. F. Xavier, and J. M. F. Moura, “Convergence rates of distributed Nesterov-like gradient methods on random networks,”
2014
Cited alongside, same era.
S. Bubeck, “Convex optimization: Algorithms and complexity,”
2014
Cited alongside, same era.
A. Nedić and A. Olshevsky, “Distributed optimization over time-varying directed graphs,”
2015
Cited alongside, same era.
W. Shi, Q. Ling, G. Wu, and W. Yin, “EXTRA: An exact first-order algorithm for decentralized consensus optimization,”
2015
Cited alongside, same era.
H. Raja and W. U. Bajwa, “Cloud K-SVD: A collaborative dictionary learning algorithm for big, distributed data,”
2016
Cited alongside, same era.
S. Safavi, U. A. Khan, S. Kar, and J. M. F. Moura, “Distributed localization: A linear theory,”
2018
Closest in time.
C. Xi, R. Xin, and U. A. Khan, “ADD-OPT: Accelerated distributed directed optimization,”
2018
Closest in time.
C. Xi, V. S. Mai, R. Xin, E. Abed, and U. A. Khan, “Linear convergence in optimization over directed graphs with row-stochastic matrices,”
2018
Closest in time.
2018
Closest in time.
R. Xin and U. A. Khan, “A linear algorithm for optimization over directed graphs with geometric convergence,”
2018
Closest in time.
2018
Closest in time.
2018
Closest in time.
Q. Lü, H. Li, and D. Xia, “Geometrical convergence rate for distributed optimization with time-varying directed graphs and uncoordinated step-sizes,”
2018
Closest in time.
J. Xu, S. Zhu, Y. C. Soh, and L. Xie, “Convergence of asynchronous distributed gradient methods over stochastic networks,”
2018
Closest in time.
2018
Closest in time.
T. Wu, K. Yuan, Q. Ling, W. Yin, and A. H. Sayed, “Decentralized consensus optimization with asynchrony and delays,”
2018
Closest in time.
M. Assran and M. Rabbat, “Asynchronous subgradient-push,”
2018
Closest in time.
S. Shahrampour, S. Rakhlin, and A. Jadbabaie, “Online learning of dynamic parameters in social networks,” in
2021
Closest in time.
J. Xu, S. Zhu, Y. C. Soh, and L. Xie, “Augmented distributed gradient methods for multi-agent optimization under uncoordinated constant stepsizes,” in
2060
Closest in time.