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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)[fuente]

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

Parámetros
  • 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

gradient_support_level

Returns gradient 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_gradient_ignored

Returns is gradient ignored

is_gradient_required

Returns is gradient required

is_gradient_supported

Returns is gradient 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
Tipo del valor devuelto

Dict[str, Any]