@inproceedings{f52c0ecbc5ba42099005d6543fb5a72f,
title = "Distributed Optimization of Quadratic Costs with a Group-Sparsity Regularization Via PDMM",
abstract = "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.",
keywords = "Distributed optimization, group-sparsity, norm regularization, PDMM, primal-dual algorithm",
author = "Kangwei Hu and Danqi Jin and Wen Zhang and Jie Chen",
note = "Publisher Copyright: {\textcopyright} 2018 APSIPA organization.; 10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 ; Conference date: 12-11-2018 Through 15-11-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.23919/APSIPA.2018.8659752",
language = "英语",
series = "2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1825--1830",
booktitle = "2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings",
}