A one-layer recurrent neural network for non-smooth convex optimization subject to linear equality constraints

Qingshan Liu; Jun Wang

doi:10.1007/978-3-642-03040-6_122

A one-layer recurrent neural network for non-smooth convex optimization subject to linear equality constraints

Qingshan Liu
, Jun Wang

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

28 Citations (Scopus)

Abstract

In this paper, a one-layer recurrent neural network is proposed for solving non-smooth convex optimization problems with linear equality constraints. Comparing with the existing neural networks, the proposed neural network has simpler architecture and the number of neurons is the same as that of decision variables in the optimization problems. The global convergence of the neural network can be guaranteed if the non-smooth objective function is convex. Simulation results are provided to show that the state trajectories of the neural network can converge to the optimal solutions of the non-smooth convex optimization problems and show the performance of the proposed neural network. © 2009 Springer Berlin Heidelberg.

Original language	English
Title of host publication	Advances in Neuro-Information Processing
Subtitle of host publication	15th International Conference, ICONIP 2008, Revised Selected Papers
Publisher	Springer Verlag
Pages	1003-1010
Volume	5507 LNCS
ISBN (Print)	3642030394, 9783642030390
DOIs	https://doi.org/10.1007/978-3-642-03040-6_122
Publication status	Published - 2009
Externally published	Yes
Event	15th International Conference on Neuro-Information Processing, ICONIP 2008 - Auckland, New Zealand Duration: 25 Nov 2008 → 28 Nov 2008 https://link.springer.com/book/10.1007/978-3-642-02490-0

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5507 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Neuro-Information Processing, ICONIP 2008
Place	New Zealand
City	Auckland
Period	25/11/08 → 28/11/08
Internet address	https://link.springer.com/book/10.1007/978-3-642-02490-0

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Liu, Q., & Wang, J. (2009). A one-layer recurrent neural network for non-smooth convex optimization subject to linear equality constraints. In Advances in Neuro-Information Processing: 15th International Conference, ICONIP 2008, Revised Selected Papers (Vol. 5507 LNCS, pp. 1003-1010). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5507 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-642-03040-6_122

Liu, Qingshan ; Wang, Jun. / A one-layer recurrent neural network for non-smooth convex optimization subject to linear equality constraints. Advances in Neuro-Information Processing: 15th International Conference, ICONIP 2008, Revised Selected Papers. Vol. 5507 LNCS Springer Verlag, 2009. pp. 1003-1010 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A one-layer recurrent neural network for non-smooth convex optimization subject to linear equality constraints",

abstract = "In this paper, a one-layer recurrent neural network is proposed for solving non-smooth convex optimization problems with linear equality constraints. Comparing with the existing neural networks, the proposed neural network has simpler architecture and the number of neurons is the same as that of decision variables in the optimization problems. The global convergence of the neural network can be guaranteed if the non-smooth objective function is convex. Simulation results are provided to show that the state trajectories of the neural network can converge to the optimal solutions of the non-smooth convex optimization problems and show the performance of the proposed neural network. {\textcopyright} 2009 Springer Berlin Heidelberg.",

author = "Qingshan Liu and Jun Wang",

year = "2009",

doi = "10.1007/978-3-642-03040-6\_122",

language = "English",

isbn = "3642030394",

volume = "5507 LNCS",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "1003--1010",

booktitle = "Advances in Neuro-Information Processing",

address = "Germany",

note = "15th International Conference on Neuro-Information Processing, ICONIP 2008 ; Conference date: 25-11-2008 Through 28-11-2008",

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Liu, Q & Wang, J 2009, A one-layer recurrent neural network for non-smooth convex optimization subject to linear equality constraints. in Advances in Neuro-Information Processing: 15th International Conference, ICONIP 2008, Revised Selected Papers. vol. 5507 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5507 LNCS, Springer Verlag, pp. 1003-1010, 15th International Conference on Neuro-Information Processing, ICONIP 2008, Auckland, New Zealand, 25/11/08. https://doi.org/10.1007/978-3-642-03040-6_122

A one-layer recurrent neural network for non-smooth convex optimization subject to linear equality constraints. / Liu, Qingshan; Wang, Jun.
Advances in Neuro-Information Processing: 15th International Conference, ICONIP 2008, Revised Selected Papers. Vol. 5507 LNCS Springer Verlag, 2009. p. 1003-1010 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5507 LNCS).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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AU - Liu, Qingshan

AU - Wang, Jun

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N2 - In this paper, a one-layer recurrent neural network is proposed for solving non-smooth convex optimization problems with linear equality constraints. Comparing with the existing neural networks, the proposed neural network has simpler architecture and the number of neurons is the same as that of decision variables in the optimization problems. The global convergence of the neural network can be guaranteed if the non-smooth objective function is convex. Simulation results are provided to show that the state trajectories of the neural network can converge to the optimal solutions of the non-smooth convex optimization problems and show the performance of the proposed neural network. © 2009 Springer Berlin Heidelberg.

AB - In this paper, a one-layer recurrent neural network is proposed for solving non-smooth convex optimization problems with linear equality constraints. Comparing with the existing neural networks, the proposed neural network has simpler architecture and the number of neurons is the same as that of decision variables in the optimization problems. The global convergence of the neural network can be guaranteed if the non-smooth objective function is convex. Simulation results are provided to show that the state trajectories of the neural network can converge to the optimal solutions of the non-smooth convex optimization problems and show the performance of the proposed neural network. © 2009 Springer Berlin Heidelberg.

UR - https://www.scopus.com/pages/publications/70349138424

UR - https://www.scopus.com/pages/publications/70349138424#tab=impact

U2 - 10.1007/978-3-642-03040-6_122

DO - 10.1007/978-3-642-03040-6_122

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 3642030394

SN - 9783642030390

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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ER -

Liu Q, Wang J. A one-layer recurrent neural network for non-smooth convex optimization subject to linear equality constraints. In Advances in Neuro-Information Processing: 15th International Conference, ICONIP 2008, Revised Selected Papers. Vol. 5507 LNCS. Springer Verlag. 2009. p. 1003-1010. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-03040-6_122

A one-layer recurrent neural network for non-smooth convex optimization subject to linear equality constraints

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