libdlr: Efficient imaginary time calculations using the discrete Lehmann representation

Jason Kaye

doi:10.1016/j.cpc.2022.108458

2022-11-01

Jason Kaye *

陈昆 (Kun Chen)

Hugo U. R. Strand *

DOI：10.1016/j.cpc.2022.108458

原文链接

查看原文

摘要

En 中文

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. (c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Keyword:

Many -body quantum physics

Imaginary time Green?s functions

Low rank compression