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Methods for distributed optimization have received significant attention in recent years owing to their wide applicability in various domains.
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2015
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2015
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2016
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K. Yuan, Q. Ling, and W. Yin, “On the convergence of decentralized gradient descent,” SIAM Journal on Optimization , vol. 26, no. 3, pp. 1835–1854, 2016
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M. Eisen, A. Mokhtari, and A. Ribeiro, “Decentralized quasi-newton methods,” IEEE Transactions on Signal Processing , vol. 65, no. 10, pp. 2613–2628, 2017
2017
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F. Mansoori and E. Wei, “Superlinearly Convergent Asynchronous Distributed Network Newton Method,” Proceedings of IEEE Conference on Decision and Control (CDC) , 2017
2017
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2017
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G. Qu and N. Li, “Harnessing smoothness to accelerate distributed optimization,” IEEE Transactions on Control of Network Systems , 2017
2017
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2017
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2017
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A. Mokhtari, Q. Ling, and A. Ribeiro, “Network newton distributed optimization methods,” IEEE Transactions on Signal Processing , vol. 65, no. 1, pp. 146–161, 2017
2017
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C. C. Chang and C. J. Lin, “Libsvm: a library for support vector machines,” ACM transactions on intelligent systems and technology (TIST) , vol. 2, no. 3, p. 27, 2011
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