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A Gradient-Descent Neurodynamic Approach for Distributed Linear Programming

  • Xinrui Jiang
  • Sitian QinEmail author
  • Ping Guo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11555)

Abstract

In this paper, a gradient-descent neurodynamic approach is proposed for the distributed linear programming problem with affine equality constraints. It is rigorously proved that the state solution of the proposed gradient-descent approach with an arbitrary initial point reaches agreement and is convergent to an optimal solution of the considered optimization problem at the same time. In the end, some numerical experiments are conducted to verify the effectiveness of the proposed gradient-descent approach.

Keywords

Distributed linear programming Gradient-descent neurodynamic approach Reach agreement Convergence to an optimal solution 

Notes

Acknowledgments

This research is supported by the National Natural Science Foundation of China (61773136, 11471088) and the NSFC and CAS project in China with granted No. U1531242.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of MathematicsHarbin Institute of TechnologyHarbinChina
  2. 2.Department of MathematicsHarbin Institute of TechnologyWeihaiChina
  3. 3.Laboratory of Image Processing and Pattern RecognitionBeijing Normal UniversityBeijingChina

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