Linearly Convergent Decentralized Consensus Optimization with the Alternating Direction Method of Multipliers

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dc.contributor.authorShi, W.en_US
dc.contributor.authorLing, Q.en_US
dc.contributor.authorYuan, K.en_US
dc.contributor.authorWu, G.en_US
dc.contributor.authorYin, W.en_US
dc.date.accessioned2018-06-19T17:48:49Zen_US
dc.date.available2018-06-19T17:48:49Zen_US
dc.date.issued2013-04en_US
dc.date.noteApril 2013en_US
dc.description.abstractIn a decentralized consensus optimization problem, a network of agents minimizes the summation of their local objective functions on a common set of decision variables, allowing only information exchange among neighbors. The alternating direction method of multipliers (ADMM) has been shown to be a powerful tool for solving the problem with empirically fast convergence. This paper establishes the linear convergence rate of the ADMM in decentralized consensus optimization. The theoretical convergence rate is a function of the network topology, properties of the local objective functions, and the algorithm parameter. This result not only gives a performance guarantee for the ADMM but also provides a guideline to accelerate its convergence for decentralized consensus optimization problems.en_US
dc.format.extent5 ppen_US
dc.identifier.citationShi, W., Ling, Q., Yuan, K., et al.. "Linearly Convergent Decentralized Consensus Optimization with the Alternating Direction Method of Multipliers." (2013) <a href="https://hdl.handle.net/1911/102219">https://hdl.handle.net/1911/102219</a>.en_US
dc.identifier.digitalTR13-07en_US
dc.identifier.urihttps://hdl.handle.net/1911/102219en_US
dc.language.isoengen_US
dc.titleLinearly Convergent Decentralized Consensus Optimization with the Alternating Direction Method of Multipliersen_US
dc.typeTechnical reporten_US
dc.type.dcmiTexten_US
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