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<p>In this paper, an iterative algorithm for finding the spectral radius of an irreducible nonnegative tensor is proposed based on the neural network tensor mode. It is proved that the equilibrium point of the tensor model is Lyapunov stable, and the solution of the tensor model converges to the eigenvector corresponding the spectral radius of the tensor. Meanwhile, the spectral radius of the tensor is obtained. The proposed iterative algorithm is capable of computing the spectral radius of tensors under a wider range of conditions. Some numerical results demonstrate that the iterative method achieves high computational efficiency.</p>

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An iterative algorithm for finding the spectral radius of an irreducible nonnegative tensor based on neural network models

  • 期刊论文

  • 发表日期：

    2026年6月20日

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  • Junli Zhang,
  • Fanbo Meng

摘要

In this paper, an iterative algorithm for finding the spectral radius of an irreducible nonnegative tensor is proposed based on the neural network tensor mode. It is proved that the equilibrium point of the tensor model is Lyapunov stable, and the solution of the tensor model converges to the eigenvector corresponding the spectral radius of the tensor. Meanwhile, the spectral radius of the tensor is obtained. The proposed iterative algorithm is capable of computing the spectral radius of tensors under a wider range of conditions. Some numerical results demonstrate that the iterative method achieves high computational efficiency.