A Trust-Region Projection Neural Network for Nonlinear Programming.

Journal: IEEE transactions on neural networks and learning systems
Published Date: Jun 12, 2025

Abstract

The trust-region method and projection neural networks are two branches of optimization approaches with different operational principles and characteristics. In this article, a trust-region projection neural network (TRPNN) is proposed by integrating the trust-region method and projection neural networks. TRPNN is a discrete-time neurodynamic optimization model that inherits the exploration-exploitation capability of the trust-region method and the local search capability of projection neural networks. TRPNN is theoretically proven to be convergent to a Karush-Kuhn-Tuchker (KKT) point of nonlinear programming problems. The efficacy of TRPNNs leveraged in a collaborative neurodynamic framework is numerically demonstrated for global optimization in the presence of nonconvexity in objective functions or constraints.

Authors

  • Haoen Huang
    College of Electronic and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China.
  • Zhigang Zeng
  • Jun Wang
    Department of Speech, Language, and Hearing Sciences and the Department of Neurology, The University of Texas at Austin, Austin, TX 78712, USA.

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