A Neurodynamic Optimization Approach to Bilevel Linear Programming

  • Sitian QinEmail author
  • Xinyi Le
  • Jun Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9377)


This paper presents new results on neurodynamic optimization approach to solve bilevel linear programming problems (BLPPs) with linear inequality constraints. A sub-gradient recurrent neural network is proposed for solving the BLPPs. It is proved that the state convergence time period is finite and can be quantitatively estimated. Compared with existing recurrent neural networks for BLPPs, the proposed neural network does not have any design parameter and can solve the BLPPs in finite time. Some numerical examples are introduced to show the effectiveness of the proposed neural network.


Bilevel linear programming problem sub-gradient recurrent neural network convergence in finite time 


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© Springer International Publishing Switzerland 2015

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Authors and Affiliations

  1. 1.Department of MathematicsHarbin Institute of TechnologyWeihaiChina
  2. 2.Department of Mechanical and Automation EngineeringThe Chinese University of Hong KongShatinHong Kong
  3. 3.School of Control Science and EngineeringDalian University of TechnologyDalianChina

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