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joss/paper.bib

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@@ -72,6 +72,16 @@ @misc{kaye23_diagrams
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primaryClass={cond-mat.str-el}
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}
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@misc{kiese24,
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title={Discrete Lehmann representation of three-point functions},
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author={Dominik Kiese and Hugo U. R. Strand and Kun Chen and Nils Wentzell
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and Olivier Parcollet and Jason Kaye},
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year={2024},
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eprint={2405.06716},
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archivePrefix={arXiv},
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primaryClass={physics.comp-ph}
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}
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@article{sheng23,
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title = {Low-rank {G}reen's function representations applied to dynamical mean-field theory},
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author = {Sheng, Nan and Hampel, Alexander and Beck, Sophie and Parcollet, Olivier and Wentzell, Nils and Kaye, Jason and Chen, Kun},

joss/paper.md

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@@ -58,12 +58,12 @@ exponential functions [@kaye22_dlr]. The number of basis functions required in b
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The DLR's use of an explicit basis of simple functions makes many common operations,
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including interpolation, integration, Fourier transform, and convolution, simple and
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highly efficient. This has led to a variety of recent algorithmic advances, including the
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reduction of the size of the Matsubara frequency mesh in dynamical mean-field theory
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calculations [@sheng23], stabilizing the calculation of the single-particle self-energy via the Dyson
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equation [@labollita23], improving the efficiency of the imaginary time discretization in the
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mixing Green's function of the Keldysh formalism [@kaye23_eqdyson; @blommel24], and accelerating the
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evaluation of imaginary time Feynman diagrams [@kaye23_diagrams]. It has also
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highly efficient. This has led to a variety of recent algorithmic advances: compact Matsubara frequency meshes in dynamical mean-field theory
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calculations [@sheng23], a stable method to calculate the single-particle self-energy via the Dyson
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equation [@labollita23], an improved discretization of the
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mixing Green's function in the Keldysh formalism [@kaye23_eqdyson; @blommel24], a fast
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algorithm to evaluate imaginary time Feynman diagrams [@kaye23_diagrams], and compact
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representations of three-point correlation functions [@kiese24]. It has also
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yielded immediate applications in computational physics, for example in low-temperature
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studies of superconductivity [@cai22; @hou24; @tanjaroonly23]. The DLR can be straightforwardly
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integrated into existing algorithms and codes, often yielding significant

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