libdlr: Efficient imaginary time calculations using the discrete Lehmann representation

TL;DR

libdlr is a software library that implements the discrete Lehmann representation for efficient and stable calculations of imaginary time Green's functions, facilitating integration into existing computational codes.

Contribution

It introduces libdlr, a library that implements the discrete Lehmann representation, enabling more efficient and stable imaginary time Green's function computations.

Findings

Provides a stable and efficient basis for Green's functions

Includes implementations in Fortran, C, Python, and Julia

Facilitates integration into existing computational workflows

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.