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This paper describes a novel algorithmic framework to minimize a finite-sum of functions available over a network of nodes.
“Distributed asynchronous deterministic and stochastic gradient optimization algorithms,”
J. Tsitsiklis, D. Bertsekas, and M. Athans, · 1986
Earlier work this paper cites.
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A. Nedich and A. Ozdaglar, · 2009
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S. S. Ram, A. Nedić, and V. V. Veeravalli, · 2010
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Matrix analysis
R. A. Horn and C. R. Johnson, · 2012
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“EXTRA: an exact first-order algorithm for decentralized consensus optimization,”
W. Shi, Q. Ling, G. Wu, and W. Yin, · 2015
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“DLM: decentralized linearized alternating direction method of multipliers,”
Q. Ling, W. Shi, G. Wu, and A. Ribeiro, · 2015
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“An accelerated randomized proximal co-ordinate gradient method and its application to regularized empirical risk minimization,”
Q. Lin, Z. Lu, and L. Xiao, · 2015
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“NEXT: In-network nonconvex optimization,”
P. Di Lorenzo and G. Scutari, · 2016
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“DSA: decentralized double stochastic averaging gradient algorithm,”
A. Mokhtari and A. Ribeiro, · 2016
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“A simple practical accelerated method for finite sums,”
A. Defazio, · 2016
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“Minimizing finite sums with the stochastic average gradient,”
M. Schmidt, N. Le Roux, and F. Bach, · 2017
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X. Lian, C. Zhang, H. Zhang, C. Hsieh, W. Zhang, and J. Liu, · 2017
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