A one-layer recurrent neural network for constrained pseudoconvex optimization and its application for dynamic portfolio optimization
In this paper, a one-layer recurrent neural network is proposed for solving pseudoconvex optimization problems subject to linear equality and bound constraints. Compared with the existing neural networks for optimization (e.g., the projection neural networks), the proposed neural network is capable...
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| Published in | Neural networks Vol. 26; pp. 99 - 109 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Kidlington
Elsevier Ltd
01.02.2012
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0893-6080 1879-2782 1879-2782 |
| DOI | 10.1016/j.neunet.2011.09.001 |
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| Abstract | In this paper, a one-layer recurrent neural network is proposed for solving pseudoconvex optimization problems subject to linear equality and bound constraints. Compared with the existing neural networks for optimization (e.g., the projection neural networks), the proposed neural network is capable of solving more general pseudoconvex optimization problems with equality and bound constraints. Moreover, it is capable of solving constrained fractional programming problems as a special case. The convergence of the state variables of the proposed neural network to achieve solution optimality is guaranteed as long as the designed parameters in the model are larger than the derived lower bounds. Numerical examples with simulation results illustrate the effectiveness and characteristics of the proposed neural network. In addition, an application for dynamic portfolio optimization is discussed. |
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| AbstractList | In this paper, a one-layer recurrent neural network is proposed for solving pseudoconvex optimization problems subject to linear equality and bound constraints. Compared with the existing neural networks for optimization (e.g., the projection neural networks), the proposed neural network is capable of solving more general pseudoconvex optimization problems with equality and bound constraints. Moreover, it is capable of solving constrained fractional programming problems as a special case. The convergence of the state variables of the proposed neural network to achieve solution optimality is guaranteed as long as the designed parameters in the model are larger than the derived lower bounds. Numerical examples with simulation results illustrate the effectiveness and characteristics of the proposed neural network. In addition, an application for dynamic portfolio optimization is discussed. In this paper, a one-layer recurrent neural network is proposed for solving pseudoconvex optimization problems subject to linear equality and bound constraints. Compared with the existing neural networks for optimization (e.g., the projection neural networks), the proposed neural network is capable of solving more general pseudoconvex optimization problems with equality and bound constraints. Moreover, it is capable of solving constrained fractional programming problems as a special case. The convergence of the state variables of the proposed neural network to achieve solution optimality is guaranteed as long as the designed parameters in the model are larger than the derived lower bounds. Numerical examples with simulation results illustrate the effectiveness and characteristics of the proposed neural network. In addition, an application for dynamic portfolio optimization is discussed.In this paper, a one-layer recurrent neural network is proposed for solving pseudoconvex optimization problems subject to linear equality and bound constraints. Compared with the existing neural networks for optimization (e.g., the projection neural networks), the proposed neural network is capable of solving more general pseudoconvex optimization problems with equality and bound constraints. Moreover, it is capable of solving constrained fractional programming problems as a special case. The convergence of the state variables of the proposed neural network to achieve solution optimality is guaranteed as long as the designed parameters in the model are larger than the derived lower bounds. Numerical examples with simulation results illustrate the effectiveness and characteristics of the proposed neural network. In addition, an application for dynamic portfolio optimization is discussed. |
| Author | Wang, Jun Guo, Zhishan Liu, Qingshan |
| Author_xml | – sequence: 1 givenname: Qingshan surname: Liu fullname: Liu, Qingshan email: qsliu@seu.edu.cn organization: School of Automation, Southeast University, Nanjing 210096, China – sequence: 2 givenname: Zhishan surname: Guo fullname: Guo, Zhishan email: zsguo@cs.unc.edu organization: Department of Computer Science, University of North Carolina - Chapel Hill, Chapel Hill, NC 27599, USA – sequence: 3 givenname: Jun surname: Wang fullname: Wang, Jun email: jwang@mae.cuhk.edu.hk organization: Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong |
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| Keywords | Recurrent neural networks Differential inclusion Pseudoconvex optimization Convergence Lyapunov function Lower bound Recurrent neural nets Network management Neural network Modeling Constrained optimization Efficiency Equality constraint Dynamic programming Fractional programming Rational function Portfolio management Optimal flow Mathematical programming |
| Language | English |
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| SubjectTerms | Algorithms Animals Applied sciences Artificial intelligence Computer science; control theory; systems Computer Simulation Connectionism. Neural networks Convergence Differential inclusion Exact sciences and technology Flows in networks. Combinatorial problems Humans Lyapunov function Mathematical programming Neural Networks (Computer) Nonlinear Dynamics Operational research and scientific management Operational research. Management science Pattern Recognition, Automated Portfolio theory Pseudoconvex optimization Recurrent neural networks |
| Title | A one-layer recurrent neural network for constrained pseudoconvex optimization and its application for dynamic portfolio optimization |
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