A Novel Neural Network Based on NCP Function for Solving Constrained Nonconvex Optimization Problems
Year
: 2016
Abstract: his article presents a novel neural network (NN) based on NCP function for solving nonconvex nonlinear optimization (NCNO) problem subject to nonlinear inequality constraints. We first apply the p-power convexification of
the Lagrangian function in the NCNO problem. The proposed NN is a gradient model which is constructed by an
NCP function and an unconstrained minimization problem. The main feature of this NN is that its equilibrium
point coincides with the optimal solution of the original problem. Under a proper assumption and utilizing a
suitable Lyapunov function, it is shown that the proposed NN is Lyapunov stable and convergent to an exact optimal solution of the original problem. Finally, simulation results on two numerical examples and two practical
examples are given to show the effectiveness and applicability of the proposed NN.VC 2015 Wiley Periodicals, Inc.
Complexity 000: 00–00, 2015
the Lagrangian function in the NCNO problem. The proposed NN is a gradient model which is constructed by an
NCP function and an unconstrained minimization problem. The main feature of this NN is that its equilibrium
point coincides with the optimal solution of the original problem. Under a proper assumption and utilizing a
suitable Lyapunov function, it is shown that the proposed NN is Lyapunov stable and convergent to an exact optimal solution of the original problem. Finally, simulation results on two numerical examples and two practical
examples are given to show the effectiveness and applicability of the proposed NN.VC 2015 Wiley Periodicals, Inc.
Complexity 000: 00–00, 2015
Keyword(s): neural network,nonconvex optimization,NCP function,p-power convexification method,
stability
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A Novel Neural Network Based on NCP Function for Solving Constrained Nonconvex Optimization Problems
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| contributor author | Mohammad Moghaddas | en |
| contributor author | سهراب عفتی | en |
| contributor author | Sohrab Effati | fa |
| date accessioned | 2020-06-06T13:25:57Z | |
| date available | 2020-06-06T13:25:57Z | |
| date issued | 2016 | |
| identifier uri | https://libsearch.um.ac.ir:443/fum/handle/fum/3354606?locale-attribute=en | |
| description abstract | his article presents a novel neural network (NN) based on NCP function for solving nonconvex nonlinear optimization (NCNO) problem subject to nonlinear inequality constraints. We first apply the p-power convexification of the Lagrangian function in the NCNO problem. The proposed NN is a gradient model which is constructed by an NCP function and an unconstrained minimization problem. The main feature of this NN is that its equilibrium point coincides with the optimal solution of the original problem. Under a proper assumption and utilizing a suitable Lyapunov function, it is shown that the proposed NN is Lyapunov stable and convergent to an exact optimal solution of the original problem. Finally, simulation results on two numerical examples and two practical examples are given to show the effectiveness and applicability of the proposed NN.VC 2015 Wiley Periodicals, Inc. Complexity 000: 00–00, 2015 | en |
| language | English | |
| title | A Novel Neural Network Based on NCP Function for Solving Constrained Nonconvex Optimization Problems | en |
| type | Journal Paper | |
| contenttype | External Fulltext | |
| subject keywords | neural network | en |
| subject keywords | nonconvex optimization | en |
| subject keywords | NCP function | en |
| subject keywords | p-power convexification method | en |
| subject keywords | stability | en |
| journal title | Complexity | fa |
| pages | 130-141 | |
| journal volume | 21 | |
| journal issue | 6 | |
| identifier link | https://profdoc.um.ac.ir/paper-abstract-1049967.html | |
| identifier articleid | 1049967 |