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

Jun Hu; Pengzhan Jin

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arXiv · 2302.00538

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

Jun Hu·Pengzhan Jin

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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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Jun Hu, Pengzhan Jin. 2023-02-01. Experimental observation on a low-rank tensor model for eigenvalue problems. https://arxiv.org/abs/2302.00538

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