## Abstract

A recurrent neural network called the dual neural network is proposed in this Letter for solving the strictly convex quadratic programming problems. Compared to other recurrent neural networks, the proposed dual network with fewer neurons can solve quadratic programming problems subject to equality, inequality, and bound constraints. The dual neural network is shown to be globally exponentially convergent to optimal solutions of quadratic programming problems. In addition, compared to neural networks containing high-order nonlinear terms, the dynamic equation of the proposed dual neural network is piecewise linear, and the network architecture is thus much simpler. The global convergence behavior of the dual neural network is demonstrated by an illustrative numerical example.

Original language | English |
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Pages (from-to) | 271-278 |

Number of pages | 8 |

Journal | Physics Letters, Section A: General, Atomic and Solid State Physics |

Volume | 298 |

Issue number | 4 |

DOIs | |

Publication status | Published - 10 Jun 2002 |

Externally published | Yes |

## Keywords

- Dual neural network
- Global convergence
- Linear constraint
- Projection operator
- Quadratic programming