A One-Layer Projection Neural Network for Nonsmooth Optimization Subject to Linear Equalities and Bound Constraints
This paper presents a one-layer projection neural network for solving nonsmooth optimization problems with generalized convex objective functions and subject to linear equalities and bound constraints. The proposed neural network is designed based on two projection operators: linear equality constra...
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| Published in | IEEE transaction on neural networks and learning systems Vol. 24; no. 5; pp. 812 - 824 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York, NY
IEEE
01.05.2013
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | This paper presents a one-layer projection neural network for solving nonsmooth optimization problems with generalized convex objective functions and subject to linear equalities and bound constraints. The proposed neural network is designed based on two projection operators: linear equality constraints, and bound constraints. The objective function in the optimization problem can be any nonsmooth function which is not restricted to be convex but is required to be convex (pseudoconvex) on a set defined by the constraints. Compared with existing recurrent neural networks for nonsmooth optimization, the proposed model does not have any design parameter, which is more convenient for design and implementation. It is proved that the output variables of the proposed neural network are globally convergent to the optimal solutions provided that the objective function is at least pseudoconvex. Simulation results of numerical examples are discussed to demonstrate the effectiveness and characteristics of the proposed neural network. |
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| AbstractList | This paper presents a one-layer projection neural network for solving nonsmooth optimization problems with generalized convex objective functions and subject to linear equalities and bound constraints. The proposed neural network is designed based on two projection operators: linear equality constraints, and bound constraints. The objective function in the optimization problem can be any nonsmooth function which is not restricted to be convex but is required to be convex (pseudoconvex) on a set defined by the constraints. Compared with existing recurrent neural networks for nonsmooth optimization, the proposed model does not have any design parameter, which is more convenient for design and implementation. It is proved that the output variables of the proposed neural network are globally convergent to the optimal solutions provided that the objective function is at least pseudoconvex. Simulation results of numerical examples are discussed to demonstrate the effectiveness and characteristics of the proposed neural network. This paper presents a one-layer projection neural network for solving nonsmooth optimization problems with generalized convex objective functions and subject to linear equalities and bound constraints. The proposed neural network is designed based on two projection operators: linear equality constraints, and bound constraints. The objective function in the optimization problem can be any nonsmooth function which is not restricted to be convex but is required to be convex (pseudoconvex) on a set defined by the constraints. Compared with existing recurrent neural networks for nonsmooth optimization, the proposed model does not have any design parameter, which is more convenient for design and implementation. It is proved that the output variables of the proposed neural network are globally convergent to the optimal solutions provided that the objective function is at least pseudoconvex. Simulation results of numerical examples are discussed to demonstrate the effectiveness and characteristics of the proposed neural network.This paper presents a one-layer projection neural network for solving nonsmooth optimization problems with generalized convex objective functions and subject to linear equalities and bound constraints. The proposed neural network is designed based on two projection operators: linear equality constraints, and bound constraints. The objective function in the optimization problem can be any nonsmooth function which is not restricted to be convex but is required to be convex (pseudoconvex) on a set defined by the constraints. Compared with existing recurrent neural networks for nonsmooth optimization, the proposed model does not have any design parameter, which is more convenient for design and implementation. It is proved that the output variables of the proposed neural network are globally convergent to the optimal solutions provided that the objective function is at least pseudoconvex. Simulation results of numerical examples are discussed to demonstrate the effectiveness and characteristics of the proposed neural network. |
| Author | Jun Wang Qingshan Liu |
| Author_xml | – sequence: 1 givenname: Qingshan surname: Liu fullname: Liu, Qingshan – sequence: 2 givenname: Jun surname: Wang fullname: Wang, Jun |
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| Cites_doi | 10.1016/j.physd.2005.12.006 10.1109/TCSI.2004.834493 10.1109/TNN.2008.2011266 10.1109/TNN.2007.910736 10.1007/978-94-015-7793-9 10.1109/TNN.2011.2109735 10.1109/TNN.2008.2003287 10.1137/100786022 10.1162/neco.2006.18.3.683 10.1109/TCSI.2003.818614 10.1002/047084535X 10.1016/j.neunet.2011.09.001 10.1109/TNN.2005.849834 10.1109/82.160169 10.1109/TNN.2004.824425 10.1109/81.244913 10.1109/TNN.2008.2000273 10.1137/080740544 10.1002/0471787779 10.1109/TCS.1986.1085953 10.1023/A:1022659230603 10.1109/TSMCB.2007.903706 10.1109/TNN.2004.841779 10.1109/72.143372 10.1109/TNN.2009.2016340 10.1109/TNN.2006.881046 10.1007/978-3-642-69512-4 10.1109/81.995659 10.1109/TNN.2004.824252 10.1109/TNN.2006.879774 10.1109/9.802909 10.1016/0893-6080(94)90041-8 10.1162/neco.2007.03-07-488 10.1109/TNN.2005.852862 10.1109/TCSI.2008.920131 10.1109/31.1783 10.1109/72.207617 10.1109/TNN.2011.2104979 10.1109/3477.907574 10.1109/TNN.2006.879775 10.1109/TSMCB.2011.2140395 10.1016/S0375-9601(01)00542-4 10.1109/72.857782 10.1109/72.728383 |
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| Keywords | Convex set Generalized function Network management Modeling Linear operator nonsmooth optimization Optimization Projection method projection neural network Nonsmooth analysis Efficiency Convex function Mathematical programming Recurrent neural nets global convergence Linear programming Neural network Distributed system Set constraint Projection operator Optimal solution Equality constraint Objective function Differential inclusion Lyapunov function |
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| References | ref13 ref12 ref15 ref14 ref11 ref17 ref16 ref19 ref18 aubin (ref1) 1984 clarke (ref9) 1983 ref46 ref45 ref48 ref47 xia (ref41) 2008; 19 ref42 ref44 auslender (ref2) 1976 ref43 ref49 ref8 ref7 ref4 ref3 ref6 ref5 ref40 ref35 ref34 ref37 ref36 ref31 ref33 ref32 ref39 ref38 liu (ref28) 2009 ref24 li (ref23) 2006 ref26 ref25 ref21 liu (ref30) 2011; 41 ref27 liu (ref29) 2011; 22 kinderlehrer (ref22) 1982 filippov (ref10) 1988 hu (ref20) 2009; 20 |
| References_xml | – ident: ref11 doi: 10.1016/j.physd.2005.12.006 – ident: ref14 doi: 10.1109/TCSI.2004.834493 – year: 1976 ident: ref2 publication-title: Optimisation M thodes Num riques – volume: 20 start-page: 654 year: 2009 ident: ref20 article-title: A new recurrent neural network for solving convex quadratic programming problems with an application to the k-winners-take-all problem publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2008.2011266 – year: 1982 ident: ref22 publication-title: An Introduction to Variational Inequalities and Their Applications – ident: ref27 doi: 10.1109/TNN.2007.910736 – year: 1988 ident: ref10 publication-title: Differential Equations with Discontinuous Righthand Sides Mathematics and Its Applications doi: 10.1007/978-94-015-7793-9 – ident: ref7 doi: 10.1109/TNN.2011.2109735 – ident: ref19 doi: 10.1109/TNN.2008.2003287 – ident: ref6 doi: 10.1137/100786022 – ident: ref32 doi: 10.1162/neco.2006.18.3.683 – ident: ref12 doi: 10.1109/TCSI.2003.818614 – start-page: 344 year: 2006 ident: ref23 article-title: A neural network model for non-smooth optimization over a compact convex subset publication-title: Proc 3rd Int Symp Neural Netw – year: 1983 ident: ref9 publication-title: Optimization and Nonsmooth Analysis – ident: ref34 doi: 10.1002/047084535X – start-page: 1003 year: 2009 ident: ref28 article-title: A one-layer recurrent neural network for non-smooth convex optimization subject to linear equality constraints publication-title: Proc 15th Int Conf Neural Inf Proc – ident: ref25 doi: 10.1016/j.neunet.2011.09.001 – ident: ref24 doi: 10.1109/TNN.2005.849834 – ident: ref49 doi: 10.1109/82.160169 – ident: ref16 doi: 10.1109/TNN.2004.824425 – ident: ref39 doi: 10.1109/81.244913 – volume: 19 start-page: 1340 year: 2008 ident: ref41 article-title: A novel recurrent neural network for solving nonlinear optimization problems with inequality constraints publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2008.2000273 – ident: ref35 doi: 10.1137/080740544 – ident: ref3 doi: 10.1002/0471787779 – ident: ref37 doi: 10.1109/TCS.1986.1085953 – ident: ref36 doi: 10.1023/A:1022659230603 – ident: ref18 doi: 10.1109/TSMCB.2007.903706 – ident: ref47 doi: 10.1109/TNN.2004.841779 – ident: ref33 doi: 10.1109/72.143372 – ident: ref4 doi: 10.1109/TNN.2009.2016340 – ident: ref31 doi: 10.1109/TNN.2006.881046 – year: 1984 ident: ref1 publication-title: Differential Inclusions Set-Valued Maps and Viability Theory doi: 10.1007/978-3-642-69512-4 – ident: ref42 doi: 10.1109/81.995659 – ident: ref46 doi: 10.1109/TNN.2004.824252 – ident: ref17 doi: 10.1109/TNN.2006.879774 – ident: ref8 doi: 10.1109/9.802909 – ident: ref40 doi: 10.1016/0893-6080(94)90041-8 – ident: ref26 doi: 10.1162/neco.2007.03-07-488 – ident: ref13 doi: 10.1109/TNN.2005.852862 – ident: ref48 doi: 10.1109/TCSI.2008.920131 – ident: ref21 doi: 10.1109/31.1783 – ident: ref5 doi: 10.1109/72.207617 – volume: 22 start-page: 601 year: 2011 ident: ref29 article-title: Finite-time convergent recurrent neural network with a hard-limiting activation function for constrained optimization with piecewise-linear objective functions publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2011.2104979 – ident: ref45 doi: 10.1109/3477.907574 – ident: ref15 doi: 10.1109/TNN.2006.879775 – volume: 41 start-page: 1323 year: 2011 ident: ref30 article-title: A one-layer recurrent neural network for constrained nonsmooth optimization publication-title: IEEE Trans Syst Man Cybern -B doi: 10.1109/TSMCB.2011.2140395 – ident: ref38 doi: 10.1016/S0375-9601(01)00542-4 – ident: ref44 doi: 10.1109/72.857782 – ident: ref43 doi: 10.1109/72.728383 |
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| Snippet | This paper presents a one-layer projection neural network for solving nonsmooth optimization problems with generalized convex objective functions and subject... |
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| SubjectTerms | Algorithms Applied sciences Artificial intelligence Biological neural networks Computer science; control theory; systems Computer Simulation Connectionism. Neural networks Convergence Differential inclusion Exact sciences and technology global convergence Humans Linear programming Lyapunov function Mathematical model Neural networks Neural Networks (Computer) Nonlinear Dynamics nonsmooth optimization Operations research Optimization Problem Solving projection neural network Recurrent neural networks Studies |
| Title | A One-Layer Projection Neural Network for Nonsmooth Optimization Subject to Linear Equalities and Bound Constraints |
| URI | https://ieeexplore.ieee.org/document/6472077 https://www.ncbi.nlm.nih.gov/pubmed/24808430 https://www.proquest.com/docview/1324478518 https://www.proquest.com/docview/1349463018 https://www.proquest.com/docview/1523404572 |
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