Quantics Tensor Cross Interpolation for High-Resolution, Parsimonious Representations of Multivariate Functions in Physics and Beyond
ORAL
Abstract
Multivariate functions of continuous variables arise in countless branches of science. Numerical computations with such functions typically involve a compromise between two contrary desiderata: accurate resolution of the functional dependence, versus parsimonious memory usage. Recently, two promising strategies have emerged for satisfying both requirements:
(i) The quantics representation, which expresses functions as multi-index tensors, with each index representing one bit of a binary encoding of one of the variables; and
(ii) tensor cross interpolation (TCI), which, if applicable, yields parsimonious interpolations for multi-index tensors.
Here, we present a strategy, quantics TCI (QTCI), which combines the advantages of both schemes. We illustrate its potential with an application from condensed matter physics: the computation of Brillouin zone integrals.
A ready-for-use QTCI toolbox will be published as open source library in the near future.
(i) The quantics representation, which expresses functions as multi-index tensors, with each index representing one bit of a binary encoding of one of the variables; and
(ii) tensor cross interpolation (TCI), which, if applicable, yields parsimonious interpolations for multi-index tensors.
Here, we present a strategy, quantics TCI (QTCI), which combines the advantages of both schemes. We illustrate its potential with an application from condensed matter physics: the computation of Brillouin zone integrals.
A ready-for-use QTCI toolbox will be published as open source library in the near future.
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Publication: M. K. Ritter, Y. Núñez Fernández, M. Wallerberger, J. von Delft, H. Shinaoka, and X. Waintal, Quantics Tensor Cross Interpolation for High-Resolution, Parsimonious Representations of Multivariate Functions in Physics and Beyond, arXiv:2303.11819.
Yuriel Núñez Fernández, Marc K. Ritter, Matthieu Jeannin, Jheng-Wei Li, Thomas Kloss, Olivier Parcollet, Jan von Delft, Hiroshi Shinaoka and Xavier Waintal: Learning low rank tensor train representations: new algorithms and the xfac library, to be submitted to SciPost Physics Codebases.
Presenters
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Marc K Ritter
- Ludwig-Maximilians-Universitaet (LMU-Munich)