Low-rank matrix decompositions for ab initio nuclear structure

Tichai, A.; Arthuis, P.; Hebeler, K.; Heinz, M.; Hoppe, J.; Schwenk, A.

doi:10.1016/j.physletb.2021.136623

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Low-rank matrix decompositions for ab initio nuclear structure

MPS-Authors

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Tichai, A.
Division Prof. Dr. Klaus Blaum, MPI for Nuclear Physics, Max Planck Society;

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Schwenk, A.
Division Prof. Dr. Klaus Blaum, MPI for Nuclear Physics, Max Planck Society;

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https://www.sciencedirect.com/science/article/pii/S0370269321005633/pdfft?md5=2ad961d4644454f7ec3003a935453188&pid=1-s2.0-S0370269321005633-main.pdf
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引用

Tichai, A., Arthuis, P., Hebeler, K., Heinz, M., Hoppe, J., & Schwenk, A. (2021). Low-rank matrix decompositions for ab initio nuclear structure. Physics Letters B, 821:. doi:10.1016/j.physletb.2021.136623.

引用: https://hdl.handle.net/21.11116/0000-0009-50FE-F

要旨

The extension of ab initio quantum many-body theory to higher accuracy and
larger systems is intrinsically limited by the handling of large data objects
in form of wave-function expansions and/or many-body operators. In this work we
present matrix factorization techniques as a systematically improvable and
robust tool to significantly reduce the computational cost in many-body
applications at the price of introducing a moderate decomposition error. We
demonstrate the power of this approach for the nuclear two-body systems, for
many-body perturbation theory calculations of symmetric nuclear matter, and for
non-perturbative in-medium similarity renormalization group simulations of
finite nuclei. Establishing low-rank expansions of chiral nuclear interactions
offers possibilities to reformulate many-body methods in ways that take
advantage of tensor factorization strategies.