TNC

class TNC(maxiter=100, disp=False, accuracy=0, ftol=- 1, xtol=- 1, gtol=- 1, tol=None, eps=1e-08)

Truncated Newton (TNC) optimizer.

TNC uses a truncated Newton algorithm to minimize a function with variables subject to bounds. This algorithm uses gradient information; it is also called Newton Conjugate-Gradient. It differs from the CG method as it wraps a C implementation and allows each variable to be given upper and lower bounds.

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

Parameters

• maxiter (int) – Maximum number of function evaluation.

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

• accuracy (float) – Relative precision for finite difference calculations. If <= machine_precision, set to sqrt(machine_precision). Defaults to 0.

• ftol (float) – Precision goal for the value of f in the stopping criterion. If ftol < 0.0, ftol is set to 0.0 defaults to -1.

• xtol (float) – Precision goal for the value of x in the stopping criterion (after applying x scaling factors). If xtol < 0.0, xtol is set to sqrt(machine_precision). Defaults to -1.

• gtol (float) – Precision goal for the value of the projected gradient in the stopping criterion (after applying x scaling factors). If gtol < 0.0, gtol is set to 1e-2 * sqrt(accuracy). Setting it to 0.0 is not recommended. Defaults to -1.

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

• eps (float) – Step size used for numerical approximation of the Jacobian.

Attributes

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

Methods

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