Libdlr: Efficient imaginary time calculations using the discrete Lehmann representation

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 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 summaryProgram Title: libdlrCPC Library link to program files:https://doi.org/10.17632/56z594pzsj.1Developer's repository link:https://github.com/jasonkaye/libdlrLicensing provisions: Apache-2.0Programming language: Fortran, C, Python, JuliaNature 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.