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This paper proposes a novel family of primal-dual-based distributed algorithms for smooth, convex, multi-agent optimization over networks that uses only gradient information and gossip communications.
Convergence rate of distributed optimization algorithms based on gradient tracking
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Dual averaging for distributed optimization: Convergence analysis and network scaling
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Lian, X., Zhang, C., Zhang, H., Hsieh, C.-J., Zhang, W., and Liu, J. (2017) · 2017
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Optimal algorithms for smooth and strongly convex distributed optimization in networks
Scaman, K., Bach, F., Bubeck, S., Lee, Y. T., and Massoulié, L. (2017) · 2017
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A sharp convergence rate analysis for distributed accelerated gradient methods
Li, H., Fang, C., Yin, W., and Lin, Z. (2018) · 2018
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Next: In-network nonconvex optimization
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