A neurodynamic optimization approach to bilevel linear programming

Sitian Qin, Xinyi Le, Jun Wang

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Pages (from-to)418-425
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9377 LNCS
Publication statusPublished - 2015
Externally publishedYes
Event12th International Symposium on Neural Networks, ISNN 2015 - Jeju, Korea, Republic of
Duration: 15 Oct 201518 Oct 2015


  • Bilevel linear programming problem
  • Convergence in finite time
  • Sub-gradient recurrent neural network


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