libdlr: Efficient imaginary time calculations using the discrete Lehmann representation

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)
DOI: 10.1016/j.cpc.2022.108458 Publication Date: 2022-07-08T05:03:23Z
ABSTRACT
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.
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