A neurodynamic approach to zero-one quadratic programming

Abstract

This paper proposes a neurodynamic approach to zero-one quadratic programming with linear constraints. Compared with one existing neurodynamic approach to such problems, the proposed one has lower dimensions of state variables and less computational cost, which makes the implementation easier. Under some suitable conditions, the stability and convergence properties of the proposed approach are established. Numerical simulation results and related comparisons show the efficiency of this proposed method in practical computation.

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Acknowledgements

The authors are very grateful to the anonymous referees and the associate editor for their valuable comments and constructive suggestions that greatly improved this paper.

Funding

This work is partially supported by NNSF of China (Nos. 11961018, 61762032) and NSF of Hainan Province (No. 120QN175)

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Correspondence to Yigui Ou.

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Ou, Y., Lin, H. A neurodynamic approach to zero-one quadratic programming. Numer Algor (2021). https://doi.org/10.1007/s11075-021-01075-z

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Keywords

  • Neurodynamic approach
  • 0-1 quadratic programming
  • NCP function
  • Stability analysis
  • Convergence analysis

Mathematics subject classification (2010)

  • 90C30
  • 90C10
  • 49M37