Alternating direction method of multipliers for nonconvex fused regression problems
MetadataShow full item record
© 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.
Showing items related by title, author, creator and subject.
Ruan, Ning (2012)Duality is one of the most successful ideas in modern science  . It is essential in natural phenomena, particularly, in physics and mathematics   . In this thesis, we consider the canonical duality ...
Wang, Y.; Zhou, Guanglu; Zhang, X.; Liu, W.; Caccetta, Louis (2016)The problem of finding a sparse solution for linear equations has been investigated extensively in recent years. This is an NP-hard combinatorial problem, and one popular method is to relax such combinatorial requirement ...
Ko, Ming Hsiao (2009)Fusion is a fundamental human process that occurs in some form at all levels of sense organs such as visual and sound information received from eyes and ears respectively, to the highest levels of decision making such as ...