Experimental observation on a low-rank tensor model for eigenvalue problems

Jun Hu, Pengzhan Jin
2/1/2023
cs.LGphysics.comp-ph

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Abstract

Here we utilize a low-rank tensor model (LTM) as a function approximator, combined with the gradient descent method, to solve eigenvalue problems including the Laplacian operator and the harmonic oscillator. Experimental results show the superiority of the polynomial-based low-rank tensor model (PLTM) compared to the tensor neural network (TNN). We also test such low-rank architectures for the classification problem on the MNIST dataset.

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Cite this paper

@article{hu2023experimental,
  title  = {Experimental observation on a low-rank tensor model for eigenvalue problems},
  author = {Jun Hu and Pengzhan Jin},
  year   = {2023},
  eprint = {2302.00538},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2302.00538v1}
}

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