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dc.contributor.authorXiu, X.
dc.contributor.authorLiu, Wan-Quan
dc.contributor.authorLi, L.
dc.contributor.authorKong, L.
dc.date.accessioned2019-02-19T04:15:41Z
dc.date.available2019-02-19T04:15:41Z
dc.date.created2019-02-19T03:58:21Z
dc.date.issued2019
dc.identifier.citationXiu, X. and Liu, W. and Li, L. and Kong, L. 2019. Alternating direction method of multipliers for nonconvex fused regression problems. Computational Statistics and Data Analysis.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/74066
dc.identifier.doi10.1016/j.csda.2019.01.002
dc.description.abstract

© 2019 Elsevier B.V. It is well-known that the fused least absolute shrinkage and selection operator (FLASSO) has been playing an important role in signal and image processing. Recently, the nonconvex penalty is extensively investigated due to its success in sparse learning. In this paper, a novel nonconvex fused regression model, which integrates FLASSO and the nonconvex penalty nicely, is proposed. The developed alternating direction method of multipliers (ADMM) approach is shown to be very efficient owing to the fact that each derived subproblem has a closed-form solution. In addition, the convergence is discussed and proved mathematically. This leads to a fast and convergent algorithm. Extensive numerical experiments show that our proposed nonconvex fused regression outperforms the state-of-the-art approach FLASSO.

dc.publisherElsevier Science
dc.titleAlternating direction method of multipliers for nonconvex fused regression problems
dc.typeJournal Article
dcterms.source.issn0167-9473
dcterms.source.titleComputational Statistics and Data Analysis
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Science (EECMS)
curtin.accessStatusFulltext not available


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