Experimental observation on a low-rank tensor model for eigenvalue problems
Peking University（北京大学）
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
