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libdlr: Efficient imaginary time calculations using the discrete Lehmann representation
Center for Computational Mathematics, Flatiron Institute, New York NY, USA; Center for Computational Quantum Physics, Flatiron Institute, New York NY, USA.
Center for Computational Quantum Physics, Flatiron Institute, New York NY, USA.
2022 (English)In: Computer Physics Communications, ISSN 0010-4655, E-ISSN 1879-2944, Vol. 280, article id 108458Article in journal (Refereed) Published
Abstract [en]

We introduce libdlr, a library implementing the recently introduced discrete Lehmann representation (DLR) of imaginary time Green's functions. The DLR basis consists of a collection of exponentials chosen by the interpolative decomposition to ensure stable and efficient recovery of Green's functions from imaginary time or Matsubara frequency samples. The library provides subroutines to build the DLR basis and grids, and to carry out various standard operations. The simplicity of the DLR makes it straightforward to incorporate into existing codes as a replacement for less efficient representations of imaginary time Green's functions, and libdlr is intended to facilitate this process. libdlr is written in Fortran, provides a C header interface, and contains a Python module pydlr. We also introduce a stand-alone Julia implementation, Lehmann.jl. Program summary Program Title: libdlr CPC Library link to program files: https://doi .org /10 .17632 /56z594pzsj .1 Developer's repository link: https://github .com /jasonkaye /libdlr Licensing provisions: Apache-2.0 Programming language: Fortran, C, Python, Julia Nature of problem: Discretization and compression of functions (Green's functions and self-energies) with an imaginary time variable. Solution method: Explicit basis functions and discretization points obtained by low rank compression of the analytical continuation kernel.

Place, publisher, year, edition, pages
Elsevier, 2022. Vol. 280, article id 108458
Keywords [en]
Many-body quantum physics, Imaginary time Green?s functions, Low rank compression
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-101783DOI: 10.1016/j.cpc.2022.108458ISI: 000863312100003Scopus ID: 2-s2.0-85135708204OAI: oai:DiVA.org:oru-101783DiVA, id: diva2:1704159
Note

Funding agency:

European Research Council (ERC) 854843-FASTCORR

Available from: 2022-10-17 Created: 2022-10-17 Last updated: 2022-10-17Bibliographically approved

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Strand, Hugo U. R.

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