Distributed Optimization of Quadratic Costs with a Group-Sparsity Regularization Via PDMM

Kangwei Hu, Danqi Jin, Wen Zhang, Jie Chen

科研成果: 书/报告/会议事项章节会议稿件同行评审

4 引用 (Scopus)

摘要

Structural sparsity is useful for variable and node selection in distributed networks. In this paper, we propose a distributed algorithm to solve the problem of a quadratic cost function with mixed \ell-{1,2}-norm regularization to promote the group-sparsity of the solution. By introducing virtual pair nodes to each actual node and by decomposing the cost function to each nodes, we obtain a distributed optimization problem on an extended graph model, which is further solved via the PDMM algorithm. Numerical simulation results illustrate the accurate convergence of the proposed algorithm to the centralized solution.

源语言英语
主期刊名2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings
出版商Institute of Electrical and Electronics Engineers Inc.
1825-1830
页数6
ISBN(电子版)9789881476852
DOI
出版状态已出版 - 2 7月 2018
活动10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Honolulu, 美国
期限: 12 11月 201815 11月 2018

出版系列

姓名2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings

会议

会议10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018
国家/地区美国
Honolulu
时期12/11/1815/11/18

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