A parallel decomposition method for nonconvex stochastic multi-agent optimization problems

Yang, Yang; Scutari, Gesualdo; Palomar, Daniel; Pesavento, Marius

doi:10.1109/TSP.2016.2531627

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Article (Scientific journals)

A parallel decomposition method for nonconvex stochastic multi-agent optimization problems

Yang, Yang; Scutari, Gesualdo; Palomar, Daniel et al.

2016 • In IEEE Transactions on Signal Processing, 64 (11), p. 2949-2964

Peer Reviewed verified by ORBi

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https://hdl.handle.net/10993/33752

DOI
10.1109/TSP.2016.2531627

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Disciplines :

Electrical & electronics engineering

Author, co-author :

Yang, Yang ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)

Scutari, Gesualdo

Palomar, Daniel

Pesavento, Marius

External co-authors :

yes

Language :

English

Title :

A parallel decomposition method for nonconvex stochastic multi-agent optimization problems

Publication date :

16 February 2016

Journal title :

IEEE Transactions on Signal Processing

ISSN :

1053-587X

Publisher :

IEEE

Volume :

Issue :

Pages :

2949-2964

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

http://ieeexplore.ieee.org/document/7412752/

European Projects :

FP7 - 619647 - ADEL - Advanced Dynamic spectrum 5G mobile networks Employing Licensed shared access

Funders :

CE - Commission Européenne [BE]

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since 18 December 2017

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