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We study dual-based algorithms for distributed convex optimization problems over networks, where the objective is to minimize a sum $\sum_{i=1}^{m}f_i(z)$ of functions over in a network.
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P. Dvurechenskii, D. Dvinskikh, A. Gasnikov, C. Uribe, and A. Nedich, Decentralize and randomize: Faster algorithm for wasserstein barycenters , in Advances in Neural Information Processing Systems 31 , 2018, pp. 10760–10770
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M. Maros and J. Jaldén, PANDA: A Dual Linearly Converging Method for Distributed Optimization Over Time-Varying Undirected Graphs , in 2018 IEEE Conference on Decision and Control (CDC) , Dec, 2018, pp. 6520–6525
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A. Nedić, A. Olshevsky, and M.G. Rabbat, Network topology and communication-computation tradeoffs in decentralized optimization , Proceedings of the IEEE 106 (2018), pp. 953–976
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C.A. Uribe, D. Dvinskikh, P. Dvurechensky, A. Gasnikov, and A. Nedić, Distributed Computation of Wasserstein Barycenters Over Networks , in 2018 IEEE Conference on Decision and Control (CDC) , Dec, 2018, pp. 6544–6549
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D. Jakovetić, A unification and generalization of exact distributed first-order methods , IEEE Transactions on Signal and Information Processing over Networks 5 (2019), pp. 31–46
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