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Added multiple zero finding methods to sumk.calc_mu

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alberto-carta 2023-01-31 20:46:08 +01:00 committed by Alexander Hampel
parent 03aa19b90d
commit 27bdb61136
2 changed files with 68 additions and 8 deletions
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1
.gitignore vendored
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@ -1,2 +1,3 @@
compile_commands.json compile_commands.json
doc/cpp2rst_generated doc/cpp2rst_generated
build/

75
python/triqs_dft_tools/sumk_dft.py
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@ -36,7 +36,7 @@ from .block_structure import BlockStructure
from itertools import product from itertools import product
from warnings import warn from warnings import warn
from scipy import compress from scipy import compress
from scipy.optimize import minimize from scipy.optimize import minimize, newton
class SumkDFT(object): class SumkDFT(object):
@ -1945,7 +1945,7 @@ class SumkDFT(object):
""" """
self.chemical_potential = mu self.chemical_potential = mu
def calc_mu(self, precision=0.01, broadening=None, delta=0.5, max_loops=100): def calc_mu(self, precision=0.01, broadening=None, delta=0.5, max_loops=100, method="dichotomy"):
r""" r"""
Searches for the chemical potential that gives the DFT total charge. Searches for the chemical potential that gives the DFT total charge.
A simple bisection method is used. A simple bisection method is used.
@ -1960,6 +1960,12 @@ class SumkDFT(object):
Only relevant for real-frequency GF. Only relevant for real-frequency GF.
max_loops : int, optional max_loops : int, optional
Number of dichotomy loops maximally performed. Number of dichotomy loops maximally performed.
max_loops : string, optional
Type of optimization used:
* dichotomy: usual bisection algorithm from the TRIQS library
* newton: newton method, faster convergence but more unstable
* preconditioned_newton: uses a dichotomy adjustement with low tolerence to initialize the newton algorithm to improve stability
Returns Returns
------- -------
@ -1968,14 +1974,67 @@ class SumkDFT(object):
within specified precision. within specified precision.
""" """
def F(mu): return self.total_density(mu=mu, broadening=broadening).real def F_bisection(mu): return self.total_density(mu=mu, broadening=broadening).real
density = self.density_required - self.charge_below density = self.density_required - self.charge_below
def F_newton(mu):
self.chemical_potential = dichotomy.dichotomy(function=F, mpi.report("Trying out mu = {}".format(str(mu)))
x_init=self.chemical_potential, y_value=density, calc_dens = self.total_density(mu=mu, broadening=broadening).real - density
precision_on_y=precision, delta_x=delta, max_loops=max_loops, mpi.report("Delta to target density = {}".format(str(calc_dens)))
x_name="Chemical Potential", y_name="Total Density", return calc_dens
verbosity=3)[0]
#check for lowercase matching for the method variable
match method.lower():
case "dichotomy":
mpi.report("SUMK calc_mu: Using dichtomy adjustment to find chemical potential")
self.chemical_potential = dichotomy.dichotomy(function=F_bisection,
x_init=self.chemical_potential, y_value=density,
precision_on_y=precision, delta_x=delta, max_loops=max_loops,
x_name="Chemical Potential", y_name="Total Density",
verbosity=3)[0]
case 'newton':
mpi.report("SUMK calc_mu: Using newton method to find chemical potential")
self.chemical_potential = newton(func=F_newton,
x0=self.chemical_potential,
tol=precision, maxiter=max_loops,
)
case 'preconditioned-newton':
mpi.report("SUMK calc_mu: preconditioning newton with low tolerance dichtomy")
mu_guess_0 = dichotomy.dichotomy(function=F_bisection,
x_init=self.chemical_potential, y_value=density,
precision_on_y=0.2, delta_x=delta, max_loops=max_loops,
x_name="Chemical Potential", y_name="Total Density",
verbosity=3)[0]
mu_guess_1 = dichotomy.dichotomy(function=F_bisection,
x_init=mu_guess_0, y_value=density,
precision_on_y=0.05, delta_x=delta, max_loops=max_loops,
x_name="Chemical Potential", y_name="Total Density",
verbosity=3)[0]
mu_guess_1 = np.round(mu_guess_1, 4)+0.01 # rounding off second guess in case it is numerically too similar to guess 0
mpi.report(f"SUMK calc_mu: Chemical potential guesses are: {mu_guess_0} and {mu_guess_1}")
mpi.report("SUMK calc_mu: Refining guesses with newton method to find chemical potential")
self.chemical_potential = newton(func=F_newton,
x0=mu_guess_0,
x1=mu_guess_1,
tol=precision, maxiter=max_loops,
)
case _:
raise ValueError(
f"SUMK calc_mu: The method selected: {method}, is not implemented",
"""
Please check for typos or select one of the following:
* dichotomy: usual bisection algorithm from the TRIQS library
* newton: newton method, faster convergence but more unstable
* preconditioned_newton: uses a dichotomy adjustement with low tolerence to initialize the newton algorithm to improve stability
"""
)
return self.chemical_potential return self.chemical_potential