TY - JOUR
T1 - sparse-ir
T2 - Optimal compression and sparse sampling of many-body propagators
AU - Wallerberger, Markus
AU - Badr, Samuel
AU - Hoshino, Shintaro
AU - Huber, Sebastian
AU - Kakizawa, Fumiya
AU - Koretsune, Takashi
AU - Nagai, Yuki
AU - Nogaki, Kosuke
AU - Nomoto, Takuya
AU - Mori, Hitoshi
AU - Otsuki, Junya
AU - Ozaki, Soshun
AU - Plaikner, Thomas
AU - Sakurai, Rihito
AU - Vogel, Constanze
AU - Witt, Niklas
AU - Yoshimi, Kazuyoshi
AU - Shinaoka, Hiroshi
N1 - Funding Information:
MW was supported by the FWF, Austria through project P30997 . NW acknowledges funding by the Cluster of Excellence EXC 2056 of the DFG, Germany – project ID 390715994 , and support by the DFG research unit QUAST FOR5249, Germany (project DFG WE 5342/8-1 ). RS, FK and HS were supported by JST, PRESTO, Japan Grant No. JPMJPR2012 . HS was supported by JSPS, Japan KAKENHI Grants No. 21H01041 and No. 21H01003 . SH was supported by No. JP21K03459. TK was supported by JSPS, Japan KAKENHI Grants No. 21H01003 , 21H04437 , and 22K03447 . SO was supported by JSPS, Japan KAKENHI Grants No. 18H01162 and JSPS through the Program for Leading Graduate Schools (MERIT), Japan . KN was supported by JSPS, Japan Grants No. JP21J23007 .
Publisher Copyright:
© 2022 The Author(s)
PY - 2023/2
Y1 - 2023/2
N2 - We introduce sparse-ir, a collection of libraries to efficiently handle imaginary-time propagators, a central object in finite-temperature quantum many-body calculations. We leverage two concepts: firstly, the intermediate representation (IR), an optimal compression of the propagator with robust a priori error estimates, and secondly, sparse sampling, near-optimal grids in imaginary time and imaginary frequency from which the propagator can be reconstructed and on which diagrammatic equations can be solved. IR and sparse sampling are packaged into stand-alone, easy-to-use Python, Julia and Fortran libraries, which can readily be included into existing software. We also include an extensive set of sample codes showcasing the library for typical many-body and ab initio methods.
AB - We introduce sparse-ir, a collection of libraries to efficiently handle imaginary-time propagators, a central object in finite-temperature quantum many-body calculations. We leverage two concepts: firstly, the intermediate representation (IR), an optimal compression of the propagator with robust a priori error estimates, and secondly, sparse sampling, near-optimal grids in imaginary time and imaginary frequency from which the propagator can be reconstructed and on which diagrammatic equations can be solved. IR and sparse sampling are packaged into stand-alone, easy-to-use Python, Julia and Fortran libraries, which can readily be included into existing software. We also include an extensive set of sample codes showcasing the library for typical many-body and ab initio methods.
KW - Fortran
KW - Intermediate representation
KW - Julia
KW - Python
KW - Sparse sampling
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U2 - 10.1016/j.softx.2022.101266
DO - 10.1016/j.softx.2022.101266
M3 - Article
AN - SCOPUS:85143490206
SN - 2352-7110
VL - 21
JO - SoftwareX
JF - SoftwareX
M1 - 101266
ER -