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Volumn , Issue , 2012, Pages 601-608

A fast distributed proximal-gradient method

Author keywords

[No Author keywords available]

Indexed keywords

ACCELERATION TECHNIQUE; COMMUNICATION ROUNDS; CONVERGENCE RATES; CONVEX FUNCTIONS; DISTRIBUTED METHODS; NON-DIFFERENTIABLE; NUMERICAL EXPERIMENTS; OBJECTIVE FUNCTIONS;

EID: 84875742744     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/Allerton.2012.6483273     Document Type: Conference Paper
Times cited : (174)

References (24)
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    • Olfati-Saber, R.1    Murray, R.2
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    • I. Lobel and A. Ozdaglar, "Distributed subgradient methods for convex optimization over random networks," IEEE Transactions on Automatic Control, vol. 56, no. 6, pp. 1291-1306, 2011.
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    • Lobel, I.1    Ozdaglar, A.2
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    • On distributed constrained formation control in operator-vehicle adversarial networks
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    • M. Zhu and S. Martínez, "On distributed constrained formation control in operator-vehicle adversarial networks," Automatica, submitted, 2012.
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    • Zhu, M.1    Martínez, S.2
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    • Distributed optimization and statistical learning via the alternating direction method of multipliers
    • S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, "Distributed optimization and statistical learning via the alternating direction method of multipliers," Foundations and Trends in Machine Learning, vol. 3, no. 1, 2011.
    • (2011) Foundations and Trends in Machine Learning , vol.3 , Issue.1
    • Boyd, S.1    Parikh, N.2    Chu, E.3    Peleato, B.4    Eckstein, J.5
  • 18
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    • Monotone operators and the proximal point algorithm
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.