CG

class CG(maxiter=20, disp=False, gtol=1e-05, tol=None, eps=1.4901161193847656e-08)

Conjugate Gradient optimizer.

CG is an algorithm for the numerical solution of systems of linear equations whose matrices are symmetric and positive-definite. It is an iterative algorithm in that it uses an initial guess to generate a sequence of improving approximate solutions for a problem, in which each approximation is derived from the previous ones. It is often used to solve unconstrained optimization problems, such as energy minimization.

Uses scipy.optimize.minimize CG. For further detail, please refer to https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html

Parameters

• maxiter (int) – Maximum number of iterations to perform.

• disp (bool) – Set to True to print convergence messages.

• gtol (float) – Gradient norm must be less than gtol before successful termination.

• tol (Optional[float]) – Tolerance for termination.

• eps (float) – If jac is approximated, use this value for the step size.

Attributes

CG.bounds_support_level	Returns bounds support level
CG.gradient_support_level	Returns gradient support level
CG.initial_point_support_level	Returns initial point support level
CG.is_bounds_ignored	Returns is bounds ignored
CG.is_bounds_required	Returns is bounds required
CG.is_bounds_supported	Returns is bounds supported
CG.is_gradient_ignored	Returns is gradient ignored
CG.is_gradient_required	Returns is gradient required
CG.is_gradient_supported	Returns is gradient supported
CG.is_initial_point_ignored	Returns is initial point ignored
CG.is_initial_point_required	Returns is initial point required
CG.is_initial_point_supported	Returns is initial point supported
CG.setting	Return setting

Methods

CG.get_support_level()	Return support level dictionary
CG.gradient_num_diff(x_center, f, epsilon[, …])	We compute the gradient with the numeric differentiation in the parallel way, around the point x_center.
CG.optimize(num_vars, objective_function[, …])	Perform optimization.
CG.print_options()	Print algorithm-specific options.
CG.set_max_evals_grouped(limit)	Set max evals grouped
CG.set_options(**kwargs)	Sets or updates values in the options dictionary.
CG.wrap_function(function, args)	Wrap the function to implicitly inject the args at the call of the function.