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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xiao-Min Li Zheng Peng Wenxing Zhu |
| Copyright Year | 2014 |
| Description | Author affiliation: Coll. of Math. & Comput. Sci., Fuzhou Univ. Fuzhou, Fuzhou, China (Zheng Peng; Wenxing Zhu) || Dept. of Public Educ., Shijiazhang Inst. of Technol., Shijiazhang, China (Xiao-Min Li) |
| Abstract | Probabilistic Boolean network (PBN) is widely used in modeling genetic regulatory networks, which main task is to construct a sparse probabilistic Boolean networks (PBNs) based on a given transition-probability matrix and a set of Boolean networks (BNs). In this paper, a new alternating direction method of multipliers is proposed for achieving this purpose. At each iteration of the proposed method, three subproblems need to be solved and a multiplier updating with closed form needs to be performed. The former two subproblems are solved in a parallel fashion, while the last subproblem is handled in an alternative fashion with the former two. The proposed method can be interpreted as a classical alternating direction method of multipliers with an operator splitting. All subproblem solvers do not involve matrix computation, and consequently, the proposed method can be directly used to solve very large scale problem. Some numerical experiments demonstrate that efficiency and validity of the proposed method with comparison to some existing methods. |
| Starting Page | 790 |
| Ending Page | 796 |
| File Size | 889920 |
| Page Count | 7 |
| File Format | |
| ISSN | 21579563 |
| e-ISBN | 9781479951512 |
| DOI | 10.1109/ICNC.2014.6975938 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-08-19 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Alternating direction method of multipliers L1/2-regularization Genetic regulatory networks Sparse probabilistic Boolean networks Separable minimization |
| Content Type | Text |
| Resource Type | Article |
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