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

• URL: http://arxiv.org/abs/2302.00538v1
• Date: Wed, 1 Feb 2023 16:03:41 GMT
• Title: Experimental observation on a low-rank tensor model for eigenvalue problems
• Authors: Jun Hu and Pengzhan Jin
• Abstract summary: We utilize a low-rank tensor model (LTM) as a function approxor, combined with the gradient descent method, to solve evalue problems.
• Score: 3.378115152915894
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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