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We study the problem of minimizing a sum of local objective convex functions over a network of processors/agents.
Multiplier and gradient methods
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Dimitris Bertsimas and John N Tsitsiklis · 1997
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Convergence rate of incremental subgradient algorithms
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Distributed learning in wireless sensor networks
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Information consensus in multivehicle cooperative control
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Distributed subgradient methods for multi-agent optimization
Angelia Nedić and Asuman Ozdaglar · 2009
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Incremental stochastic subgradient algorithms for convex optimization
S Sundhar Ram, A Nedić, and Venugopal V Veeravalli · 2009
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Distributed stochastic subgradient projection algorithms for convex optimization
S Sundhar Ram, Angelia Nedić, and Venugopal V Veeravalli · 2010
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Incremental gradient, subgradient, and proximal methods for convex optimization: A survey
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On the O(1/k) convergence of asynchronous distributed alternating direction method of multipliers
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Decentralized quadratically approximated alternating direction method of multipliers
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Linear convergence rate of a class of distributed augmented lagrangian algorithms
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