Multiscale interpolative construction of quantized tensor trains

Fuente: arXiv
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Auteur principal: Lindsey, Michael
Format: Preprint
Publié: 2023
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author Lindsey, Michael
author_facet Lindsey, Michael
contents Quantized tensor trains (QTTs) have recently emerged as a framework for the numerical discretization of continuous functions, with the potential for widespread applications in numerical analysis. However, the theory of QTT approximation is not fully understood. In this work, we advance this theory from the point of view of multiscale polynomial interpolation. This perspective clarifies why QTT ranks decay with increasing depth, quantitatively controls QTT rank in terms of smoothness of the target function, and explains why certain functions with sharp features and poor quantitative smoothness can still be well approximated by QTTs. The perspective also motivates new practical and efficient algorithms for the construction of QTTs from function evaluations on multiresolution grids.
format Preprint
id arxiv_https___arxiv_org_abs_2311_12554
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Multiscale interpolative construction of quantized tensor trains
Lindsey, Michael
Numerical Analysis
Quantized tensor trains (QTTs) have recently emerged as a framework for the numerical discretization of continuous functions, with the potential for widespread applications in numerical analysis. However, the theory of QTT approximation is not fully understood. In this work, we advance this theory from the point of view of multiscale polynomial interpolation. This perspective clarifies why QTT ranks decay with increasing depth, quantitatively controls QTT rank in terms of smoothness of the target function, and explains why certain functions with sharp features and poor quantitative smoothness can still be well approximated by QTTs. The perspective also motivates new practical and efficient algorithms for the construction of QTTs from function evaluations on multiresolution grids.
title Multiscale interpolative construction of quantized tensor trains
topic Numerical Analysis
url https://arxiv.org/abs/2311.12554