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Main Author: Nakayama, Katsumasa
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2401.06389
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author Nakayama, Katsumasa
author_facet Nakayama, Katsumasa
contents We study the tensor renormalization group (TRG) in the dimension larger than two as the Higher-order TRG (HOTRG) with the randomized SVD method. The randomized SVD and the detailed discussion on the low order tensor representation, we can calculate the HOTRG with the reduced computational cost. We also represent our method by using the cost function, and the details of the cost function for the isometry determine the precision, stability, and calculation time. In our study, we show calculation order improvement using randomized SVD. We also propose that the internal line respect for any TRG method improves the calculation without changing the order of the computational cost.
format Preprint
id arxiv_https___arxiv_org_abs_2401_06389
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Application of the projective truncation and randomized singular value decomposition to a higher dimension
Nakayama, Katsumasa
High Energy Physics - Lattice
We study the tensor renormalization group (TRG) in the dimension larger than two as the Higher-order TRG (HOTRG) with the randomized SVD method. The randomized SVD and the detailed discussion on the low order tensor representation, we can calculate the HOTRG with the reduced computational cost. We also represent our method by using the cost function, and the details of the cost function for the isometry determine the precision, stability, and calculation time. In our study, we show calculation order improvement using randomized SVD. We also propose that the internal line respect for any TRG method improves the calculation without changing the order of the computational cost.
title Application of the projective truncation and randomized singular value decomposition to a higher dimension
topic High Energy Physics - Lattice
url https://arxiv.org/abs/2401.06389