A one-layer recurrent neural network for constrained pseudoconvex optimization and its application for dynamic portfolio optimization

@article{Liu2012AOR,
  title={A one-layer recurrent neural network for constrained pseudoconvex optimization and its application for dynamic portfolio optimization},
  author={Qingshan Liu and Zhishan Guo and Jun Wang},
  journal={Neural networks : the official journal of the International Neural Network Society},
  year={2012},
  volume={26},
  pages={99-109}
}
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… CONTINUE READING
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