Solver

class cthyb.Solver(beta, gf_struct, n_iw=1025, n_tau=10001, n_l=30)

Bases: pytriqs.applications.impurity_solvers.cthyb.SolverCore

__init__(beta, gf_struct, n_iw=1025, n_tau=10001, n_l=30)

Initialise the solver.

Parameters:

beta : scalar

Inverse temperature.

gf_struct : dict{str:list}

Structure of the Green’s functions. It must be a dictionary, which maps the name of each block of the Green’s function as a string to a list of integer indices. For example: {'up': [1,2,3], 'down', [1,2,3]}.

n_iw : integer, optional

Number of Matsubara frequencies used for the Green’s functions.

n_tau : integer, optional

Number of imaginary time points used for the Green’s functions.

n_l : integer, optional

Number of legendre polynomials to use in accumulations of the Green’s functions.

solve(**params_kw)

Solve the impurity problem. If measure_g_tau (default = True), G_iw and Sigma_iw will be calculated and their tails fitted. In addition to the solver parameters, parameters to control the tail fitting can be provided.

Parameters:

params_kw : dict {‘param’:value} that is passed to the core solver.

Two required parameters are

• h_int (Operator object): the local Hamiltonian of the impurity problem to be solved,
• n_cycles (int): number of measurements to be made.

perform_post_proc : boolean, optional, default = True

Should G_iw and Sigma_iw be calculated?

perform_tail_fit : boolean, optional, default = False

Should the tails of Sigma_iw and G_iw be fitted?

fit_max_moment : integer, optional, default = 3

Highest moment to fit in the tail of Sigma_iw.

fit_known_moments : dict{str:TailGf object}, optional, default = {‘block_name’: TailGf(dim1, dim2, max_moment, order_min)}

Known moments of Sigma_iw, given as a TailGf object.

fit_min_n : integer, optional, default = int(0.8 * self.n_iw)

Index of iw from which to start fitting.

fit_max_n : integer, optional, default = n_iw

Index of iw to fit until.

Delta_tau

\(\Delta(\tau)\) in imaginary time.

G0_iw

\(G_0(i\omega)\) in imaginary frequencies.

G_l

Accumulated \(G_l\) in Legendre polynomials representation.

G_tau

Accumulated \(G(\tau)\) in imaginary time.

atomic_gf

Atomic \(G(\tau)\) in imaginary time.

average_sign

Monte Carlo average sign.

density_matrix

Accumulated density matrix.

h_loc

The local Hamiltonian of the problem: \(H_{loc}\) used in the last call to solve().

h_loc_diagonalization

Diagonalization of \(H_{loc}\).

last_solve_parameters

Set of parameters used in the last call to solve().

performance_analysis

Histograms related to the performance analysis.

perturbation_order

Histograms of the perturbation order for each block.

perturbation_order_total

Histogram of the total perturbation order.

solve_status

Status of the solve() on exit.