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Discrete Lehmann Representation (DLR)

Lehmann Representation

Any imaginary-time Green's function admits a spectral (Lehmann) representation:

\[ G(\tau) = -\int_{-\infty}^{\infty} K(\tau, \omega)\,\rho(\omega)\,d\omega, \qquad K(\tau, \omega) = \frac{e^{-\omega\tau}}{1 + \xi\, e^{-\beta\omega}} \]

where \(\rho(\omega)\) is the spectral density and \(\xi = -1\) (fermion) or \(+1\) (boson). When \(\rho\) is supported within \([-\omega_\mathrm{max}, \omega_\mathrm{max}]\), we define the dimensionless cutoff \(\Lambda \equiv \beta\omega_\mathrm{max}\).

DLR Approximation

The kernel \(K(\tau, \omega)\) is numerically low-rank. The DLR exploits this by approximating \(G(\tau)\) as a sum of \(r\) exponentials:

\[ G_\mathrm{DLR}(\tau) = \sum_{l=1}^{r} \widehat{g}_l\, K(\tau, \omega_l), \qquad r = O\!\bigl(\log(\Lambda)\log(1/\epsilon)\bigr) \]

The DLR frequencies \(\{\omega_l\}\) are selected via interpolative decomposition (pivoted QR) of \(K\) discretized on a composite Chebyshev grid. The same process yields \(r\) imaginary-time nodes \(\{\tau_k\}\) and \(r\) Matsubara frequency nodes \(\{i\nu_{n_k}\}\). The basis is universal: for given \(\Lambda\) and \(\epsilon\), the same \(\{\omega_l\}\) represent any Green's function within that cutoff.

The DLR coefficients \(\widehat{g}_l\) are recovered by solving an \(r \times r\) interpolation problem from samples at the DLR nodes. The expansion transforms analytically to Matsubara frequency:

\[ G(i\nu_n) = \sum_{l=1}^{r} \frac{\widehat{g}_l}{i\nu_n + \omega_l} \]

so that \(\tau \leftrightarrow i\nu_n\) transforms reduce to \(r \times r\) linear algebra instead of \(O(\beta)\)-sized FFTs.

QAssemble Implementation

The DLR class constructs separate fermionic and bosonic DLR objects via pydlr. Key attributes and methods:

Attribute / Method Description
tauF, tauB Fermionic / bosonic imaginary-time nodes
omega, nu Fermionic / bosonic Matsubara frequency nodes
FT2F, FF2T Fermionic \(\tau \leftrightarrow i\omega_n\) transforms
BT2F, BF2T Bosonic \(\tau \leftrightarrow i\nu_n\) transforms

References

  • J. Kaye, K. Chen & O. Parcollet, Phys. Rev. B 105, 235115 (2022). DOI
  • J. Kaye, K. Chen & H. U. Strand, Comput. Phys. Commun. 280, 108458 (2022). DOI