WuYang¶
-
class
n2v.WuYang[source]¶ Bases:
objectPerforms Optimization as in: 10.1063/1.1535422 - Qin Wu + Weitao Yang
lambda_rgl: {None, float}. If float, lambda-regularization is added with lambda=lambda_rgl.
Attributes Summary
Methods Summary
find_regularization_constant_wy(opt_max_iter)Finding regularization constant lambda.
gradient_wy(v)Calculates gradient wrt target density Equation (11) of main reference
hessian_wy(v)Calculates gradient wrt target density Equation (13) of main reference
Lagrangian to be minimized wrt external potential Equation (5) of main reference
wuyang(opt_max_iter[, reg, tol, gtol, …])Calls scipy minimizer to minimize lagrangian.
Attributes Documentation
-
lambda_reg= None¶
-
regul_norm= None¶
Methods Documentation
-
find_regularization_constant_wy(opt_max_iter, opt_method='trust-krylov', gtol=0.001, tol=None, opt=None, lambda_list=None)[source]¶ Finding regularization constant lambda.
- Note: it is recommend to set a specific convergence criteria by opt or tol,
in order to control the same convergence for different lambda value.
After the calculation is done, one can plot the returns to select a good lambda.
- opt_max_iter: int
maximum iteration
- opt_method: string default: “trust-krylov”
opt_methods available in scipy.optimize.minimize
- tol: float
Tolerance for termination. See scipy.optimize.minimize for details.
- gtol: float
gtol for scipy.optimize.minimize: the gradient norm for convergence
- opt: dictionary, optional
- if given:
scipy.optimize.minimize(method=opt_method, options=opt).
Notice that opt has lower priorities than opt_max_iter and gtol.
- lambda_list: np.ndarray, optional
A array of lambda to search; otherwise, it will be 10 ** np.linspace(-1, -7, 7).
- lambda_list: np.ndarray
A array of lambda searched.
- P_list: np.ndarray
The value defined by [Bulat, Heaton-Burgess, Cohen, Yang 2007] eqn (21). Corresponding to lambda in lambda_list.
- Ts_list: np.ndarray
The Ts value for each lambda.
-