pyspmac

usage

Python 3.8 or higher is required.

# install pyspmac
python3 -m pip install .

# run a sample
cd sample/multiple
spmac input.toml

The names of parameters are case-insensitive.

output: str
- Output directory name (default: output)
num_flavor: int
- Number of flavors (orbitals)
filein_g: str
- Filename storing G(τ)
- When num_flavor > 1, "{filein_g}.{a}{b}" are read for G{ab}, where a,b = 0,1,...,num_flavor-1
column: int
- Index of column storing G(τ) (0-origin)
beta: float
- Inverse temperature β
max_omega: float
- Upper bound of ω
min_omega: float
- Lower bound of ω
num_omega: int
- Number of ωs
nonnegative: bool
- Impose non-negativity (single flavor) or semi-positive definiteness (multiple flavor) (default:true)
sumrule: bool
- Impose sum-rule (default: true)
min_sv: float
- Cutoff in singular value (default: 1e-10)
max_iteration: int
- Maximum number of iterations of ADMM (default: 1000)
optimize: bool
- Optimize λ by the elbow method (default: false)
max_loglambda: float
- Maximum value of log10(λ)
- If optimize
min_loglambda: float
- Minimum value of log10(λ)
- If optimize
loglambda: float
- log10(λ)
- If not optimize

Run pytest at the root directory:

python3 -m pytest

Sparse Modeling Analytic Continuation
- Junya Otsuki, Masayuki Ohzeki, Hiroshi Shinaoka, and Kazuyoshi Yoshimi, "Sparse modeling approach to analytical continuation of imaginary-time quantum Monte Carlo data", Phys. Rev. E 95, 061302(R).
SpM AC + Pade approximation
- Yuichi Motoyama, Kazuyoshi Yoshimi, and Junya Otsuki, "Robust analytic continuation combining the advantages of the sparse modeling approach and the Padé approximation", Phys. Rev. B 105, 035139.
SpM AC for multi-orbital data
- Yuichi Motoyama, Hiroshi Shinaoka, Junya Otsuki, and Kazuyoshi Yoshimi, "Robust analytic continuation using sparse modeling approach imposed by semi-positive definiteness for multi-orbital systems", arXiv:2409.01509.

PySpMAC is distributed under the Mozilla Public License 2.0