# TNC

`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)`

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 (opens in a new tab)

Parameters

• maxiter (int (opens in a new tab)) – Maximum number of function evaluation.
• disp (bool (opens in a new tab)) – Set to True to print convergence messages.
• accuracy (float (opens in a new tab)) – Relative precision for finite difference calculations. If <= machine_precision, set to sqrt(machine_precision). Defaults to 0.
• ftol (float (opens in a new tab)) – 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 (opens in a new tab)) – 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 (opens in a new tab)) – 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 (opens in a new tab) | None) – Tolerance for termination.
• eps (float (opens in a new tab)) – Step size used for numerical approximation of the Jacobian.
• options (dict (opens in a new tab) | None) – A dictionary of solver options.
• max_evals_grouped (int (opens in a new tab)) – Max number of default gradient evaluations performed simultaneously.
• kwargs – additional kwargs for scipy.optimize.minimize.

## 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

Return setting

## Methods

### get_support_level

`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) – point around which we compute the gradient
• f (func) – the function of which the gradient is to be computed.
• epsilon (float (opens in a new tab)) – the epsilon used in the numeric differentiation.
• max_evals_grouped (int (opens in a new tab)) – max evals grouped, defaults to 1 (i.e. no batching).

Returns

Return type

### minimize

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

Minimize the scalar function.

Parameters

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

`set_max_evals_grouped(limit)`

Set max evals grouped

### set_options

`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 (opens in a new tab)) – options, given as name=value.

### wrap_function

`static wrap_function(function, args)`

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

Parameters

Returns

wrapper

Return type

function_wrapper