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# TNC¶

class TNC(maxiter=100, disp=False, accuracy=0, ftol=-1, xtol=-1, gtol=-1, tol=None, eps=1e-08, options=None, max_evals_grouped=1, **kwargs)[source]

Bases : `SciPyOptimizer`

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

Paramètres
• 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.

• options (`Optional`[`dict`]) – A dictionary of solver options.

• max_evals_grouped (`int`) – Max number of default gradient evaluations performed simultaneously.

• kwargs – additional kwargs for scipy.optimize.minimize.

Methods

 `get_support_level` Return support level dictionary `gradient_num_diff` We compute the gradient with the numeric differentiation in the parallel way, around the point x_center. `minimize` Minimize the scalar function. `print_options` Print algorithm-specific options. `set_max_evals_grouped` Set max evals grouped `set_options` Sets or updates values in the options dictionary. `wrap_function` Wrap the function to implicitly inject the args at the call of the function.

Attributes

bounds_support_level

Returns bounds support level

initial_point_support_level

Returns initial point support level

is_bounds_ignored

Returns is bounds ignored

is_bounds_required

Returns is bounds required

is_bounds_supported

Returns is bounds supported

is_initial_point_ignored

Returns is initial point ignored

is_initial_point_required

Returns is initial point required

is_initial_point_supported

Returns is initial point supported

setting

Return setting

settings
Type renvoyé

`Dict`[`str`, `Any`]