# TNC#

class qiskit.algorithms.optimizers.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

Parameters:
• maxiter (int) â€“ Maximum number of function evaluation.

• disp (bool) â€“ Set to True to print convergence messages.

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

• ftol (float) â€“ 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) â€“ 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) â€“ 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 (float | None) â€“ Tolerance for termination.

• eps (float) â€“ Step size used for numerical approximation of the Jacobian.

• options (dict | None) â€“ A dictionary of solver options.

• max_evals_grouped (int) â€“ Max number of default gradient evaluations performed simultaneously.

• kwargs â€“ additional kwargs for scipy.optimize.minimize.

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#

Methods

get_support_level()#

Return support level dictionary

static gradient_num_diff(x_center, f, epsilon, max_evals_grouped=None)#

We compute the gradient with the numeric differentiation in the parallel way, around the point x_center.

Parameters:
• x_center (ndarray) â€“ point around which we compute the gradient

• f (func) â€“ the function of which the gradient is to be computed.

• epsilon (float) â€“ the epsilon used in the numeric differentiation.

• max_evals_grouped (int) â€“ max evals grouped, defaults to 1 (i.e. no batching).

Returns:

the gradient computed

Return type:

grad

minimize(fun, x0, jac=None, bounds=None)#

Minimize the scalar function.

Parameters:
• fun (Callable[[POINT], float]) â€“ The scalar function to minimize.

• x0 (POINT) â€“ The initial point for the minimization.

• jac (Callable[[POINT], POINT] | None) â€“ The gradient of the scalar function `fun`.

• bounds (list[tuple[float, float]] | None) â€“ Bounds for the variables of `fun`. This argument might be ignored if the optimizer does not support bounds.

Returns:

The result of the optimization, containing e.g. the result as attribute `x`.

Return type:

OptimizerResult

print_options()#

Print algorithm-specific options.

set_max_evals_grouped(limit)#

Set max evals grouped

set_options(**kwargs)#

Sets or updates values in the options dictionary.

The options dictionary may be used internally by a given optimizer to pass additional optional values for the underlying optimizer/optimization function used. The options dictionary may be initially populated with a set of key/values when the given optimizer is constructed.

Parameters:

kwargs (dict) â€“ options, given as name=value.

static wrap_function(function, args)#

Wrap the function to implicitly inject the args at the call of the function.

Parameters:
• function (func) â€“ the target function

• args (tuple) â€“ the args to be injected

Returns:

wrapper

Return type:

function_wrapper