A Two-Layer Distributed Algorithm Using Neurodynamic System for Solving L 1 -Minimization

This brief considers a distributed algorithm for solving [Formula Omitted]-minimization problem based on nonlinear neurodynamic system. Compared with centralized algorithms, distributed algorithms have great potential in data privacy protection, distributed storage and processing of data. In this br...

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Vydáno v:IEEE transactions on circuits and systems. II, Express briefs Ročník 69; číslo 8; s. 3490 - 3494
Hlavní autoři: Xu, Junpeng, He, Xing, Han, Xin, Wen, Hongsong
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 01.08.2022
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ISSN:1549-7747, 1558-3791
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Abstract This brief considers a distributed algorithm for solving [Formula Omitted]-minimization problem based on nonlinear neurodynamic system. Compared with centralized algorithms, distributed algorithms have great potential in data privacy protection, distributed storage and processing of data. In this brief, [Formula Omitted]-minimization problem is transformed into a distributed problem by using multiagent consensus theory. For the distributed optimization problem, a two-layer distributed algorithm is designed by utilizing neurodynamic system, projection matrix and derivative feedback technique. Compared with the existing distributed neurodynamic algorithm, the proposed algorithm has a simpler structure and has fewer neurons on the premise that the calculation error does not increase. Besides, the proposed algorithm converges to a minimal point of [Formula Omitted]-minimization problem and is Lyapunov stable. Finally, the comparative examples of sparse signal reconstruction show that the proposed distributed algorithm is effective and superior.
AbstractList This brief considers a distributed algorithm for solving [Formula Omitted]-minimization problem based on nonlinear neurodynamic system. Compared with centralized algorithms, distributed algorithms have great potential in data privacy protection, distributed storage and processing of data. In this brief, [Formula Omitted]-minimization problem is transformed into a distributed problem by using multiagent consensus theory. For the distributed optimization problem, a two-layer distributed algorithm is designed by utilizing neurodynamic system, projection matrix and derivative feedback technique. Compared with the existing distributed neurodynamic algorithm, the proposed algorithm has a simpler structure and has fewer neurons on the premise that the calculation error does not increase. Besides, the proposed algorithm converges to a minimal point of [Formula Omitted]-minimization problem and is Lyapunov stable. Finally, the comparative examples of sparse signal reconstruction show that the proposed distributed algorithm is effective and superior.
Author He, Xing
Xu, Junpeng
Han, Xin
Wen, Hongsong
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crossref_primary_10_1109_TCSII_2023_3291728
Cites_doi 10.1109/TNNLS.2019.2920880
10.1109/TCSII.2021.3128416
10.1109/TIT.2005.858979
10.1002/cpa.20132
10.1109/TCYB.2019.2895885
10.1109/TNNLS.2021.3085314
10.1109/TNNLS.2021.3088535
10.1109/TCSII.2021.3132392
10.1515/9781400831470
10.1007/s00034-020-01445-3
10.1017/cbo9781107282094
10.1016/j.acha.2009.04.002
10.1109/TNNLS.2019.2917137
10.1109/TNNLS.2015.2481006
10.1109/TNNLS.2011.2181867
10.1137/S003614450037906X
10.1109/TIT.2007.909108
10.1137/S0097539792240406
10.1109/TIT.2006.871582
10.1109/TCSII.2019.2930648
10.1109/TFUZZ.2020.3009730
10.1109/TNNLS.2019.2944388
10.1109/TCSII.2020.3009291
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References ref13
ref12
ref15
ref14
Koh (ref4) 2007; 8
ref11
Evans (ref25) 1982; 41
ref10
ref2
ref1
ref17
ref16
ref19
ref18
ref24
ref23
ref20
ref22
ref21
ref8
ref7
ref9
ref3
ref6
ref5
References_xml – ident: ref17
  doi: 10.1109/TNNLS.2019.2920880
– ident: ref6
  doi: 10.1109/TCSII.2021.3128416
– ident: ref21
  doi: 10.1109/TIT.2005.858979
– volume: 41
  issue: 3
  year: 1982
  ident: ref25
  article-title: An introduction to variational inequalities and their applications
  publication-title: Amer. Sci.
– volume: 8
  start-page: 1519
  issue: 8
  year: 2007
  ident: ref4
  article-title: An interior-point method for large-scale $l_{1}$ -regularized logistic regression
  publication-title: J. Mach. Learn. Res.
– ident: ref2
  doi: 10.1002/cpa.20132
– ident: ref9
  doi: 10.1109/TCYB.2019.2895885
– ident: ref19
  doi: 10.1109/TNNLS.2021.3085314
– ident: ref15
  doi: 10.1109/TNNLS.2021.3088535
– ident: ref8
  doi: 10.1109/TCSII.2021.3132392
– ident: ref20
  doi: 10.1515/9781400831470
– ident: ref14
  doi: 10.1007/s00034-020-01445-3
– ident: ref24
  doi: 10.1017/cbo9781107282094
– ident: ref5
  doi: 10.1016/j.acha.2009.04.002
– ident: ref11
  doi: 10.1109/TNNLS.2019.2917137
– ident: ref13
  doi: 10.1109/TNNLS.2015.2481006
– ident: ref16
  doi: 10.1109/TNNLS.2011.2181867
– ident: ref23
  doi: 10.1137/S003614450037906X
– ident: ref3
  doi: 10.1109/TIT.2007.909108
– ident: ref22
  doi: 10.1137/S0097539792240406
– ident: ref1
  doi: 10.1109/TIT.2006.871582
– ident: ref18
  doi: 10.1109/TCSII.2019.2930648
– ident: ref12
  doi: 10.1109/TFUZZ.2020.3009730
– ident: ref10
  doi: 10.1109/TNNLS.2019.2944388
– ident: ref7
  doi: 10.1109/TCSII.2020.3009291
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SubjectTerms Algorithms
Multiagent systems
Optimization
Signal reconstruction
Title A Two-Layer Distributed Algorithm Using Neurodynamic System for Solving L 1 -Minimization
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