libdlr: Efficient imaginary time calculations using the discrete Lehmann representation

• DOI: 10.1016/j.cpc.2022.108458
• Publication Date: 2022-07-08T05:03:23Z
• Authors: Jason Kaye; Kun Chen; Hugo U.R. Strand
• Keywords: G.4; FOS: Computer and information sciences; J.2; Strongly Correlated Electrons (cond-mat.str-el); FOS: Physical sciences; G.1.2; 81-04, 65D15; Numerical Analysis (math.NA); Computational Physics (physics.comp-ph); 01 natural sciences; Condensed Matter - Strongly Correlated Electrons; 0103 physical sciences; FOS: Mathematics; G.4; J.2; G.1.2; Computer Science - Mathematical Software; Mathematics - Numerical Analysis; Physics - Computational Physics; Mathematical Software (cs.MS)

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 Matsbuara 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.