250 lines
14 KiB
Plaintext
250 lines
14 KiB
Plaintext
---
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title: TNC (v0.26)
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description: API reference for qiskit.algorithms.optimizers.TNC in qiskit v0.26
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in_page_toc_min_heading_level: 1
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python_api_type: class
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python_api_name: qiskit.algorithms.optimizers.TNC
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---
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<span id="qiskit-algorithms-optimizers-tnc" />
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# qiskit.algorithms.optimizers.TNC
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<Class id="qiskit.algorithms.optimizers.TNC" isDedicatedPage={true} github="https://github.com/qiskit/qiskit/tree/stable/0.17/qiskit/algorithms/optimizers/tnc.py" signature="TNC(maxiter=100, disp=False, accuracy=0, ftol=- 1, xtol=- 1, gtol=- 1, tol=None, eps=1e-08)" modifiers="class">
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Truncated Newton (TNC) optimizer.
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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`](qiskit.algorithms.optimizers.CG "qiskit.algorithms.optimizers.CG") method as it wraps a C implementation and allows each variable to be given upper and lower bounds.
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Uses scipy.optimize.minimize TNC For further detail, please refer to See [https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html)
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**Parameters**
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* **maxiter** (`int`) – Maximum number of function evaluation.
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* **disp** (`bool`) – Set to True to print convergence messages.
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* **accuracy** (`float`) – Relative precision for finite difference calculations. If \<= machine\_precision, set to sqrt(machine\_precision). Defaults to 0.
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* **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.
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* **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.
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* **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.
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* **tol** (`Optional`\[`float`]) – Tolerance for termination.
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* **eps** (`float`) – Step size used for numerical approximation of the Jacobian.
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### \_\_init\_\_
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<Function id="qiskit.algorithms.optimizers.TNC.__init__" signature="__init__(maxiter=100, disp=False, accuracy=0, ftol=- 1, xtol=- 1, gtol=- 1, tol=None, eps=1e-08)">
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**Parameters**
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* **maxiter** (`int`) – Maximum number of function evaluation.
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* **disp** (`bool`) – Set to True to print convergence messages.
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* **accuracy** (`float`) – Relative precision for finite difference calculations. If \<= machine\_precision, set to sqrt(machine\_precision). Defaults to 0.
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* **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.
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* **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.
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* **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.
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* **tol** (`Optional`\[`float`]) – Tolerance for termination.
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* **eps** (`float`) – Step size used for numerical approximation of the Jacobian.
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</Function>
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## Methods
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| ------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------- |
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| [`__init__`](#qiskit.algorithms.optimizers.TNC.__init__ "qiskit.algorithms.optimizers.TNC.__init__")(\[maxiter, disp, accuracy, ftol, …]) | **type maxiter**`int` |
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| [`get_support_level`](#qiskit.algorithms.optimizers.TNC.get_support_level "qiskit.algorithms.optimizers.TNC.get_support_level")() | return support level dictionary |
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| [`gradient_num_diff`](#qiskit.algorithms.optimizers.TNC.gradient_num_diff "qiskit.algorithms.optimizers.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. |
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| [`optimize`](#qiskit.algorithms.optimizers.TNC.optimize "qiskit.algorithms.optimizers.TNC.optimize")(num\_vars, objective\_function\[, …]) | Perform optimization. |
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| [`print_options`](#qiskit.algorithms.optimizers.TNC.print_options "qiskit.algorithms.optimizers.TNC.print_options")() | Print algorithm-specific options. |
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| [`set_max_evals_grouped`](#qiskit.algorithms.optimizers.TNC.set_max_evals_grouped "qiskit.algorithms.optimizers.TNC.set_max_evals_grouped")(limit) | Set max evals grouped |
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| [`set_options`](#qiskit.algorithms.optimizers.TNC.set_options "qiskit.algorithms.optimizers.TNC.set_options")(\*\*kwargs) | Sets or updates values in the options dictionary. |
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| [`wrap_function`](#qiskit.algorithms.optimizers.TNC.wrap_function "qiskit.algorithms.optimizers.TNC.wrap_function")(function, args) | Wrap the function to implicitly inject the args at the call of the function. |
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## Attributes
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| ------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------- |
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| [`bounds_support_level`](#qiskit.algorithms.optimizers.TNC.bounds_support_level "qiskit.algorithms.optimizers.TNC.bounds_support_level") | Returns bounds support level |
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| [`gradient_support_level`](#qiskit.algorithms.optimizers.TNC.gradient_support_level "qiskit.algorithms.optimizers.TNC.gradient_support_level") | Returns gradient support level |
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| [`initial_point_support_level`](#qiskit.algorithms.optimizers.TNC.initial_point_support_level "qiskit.algorithms.optimizers.TNC.initial_point_support_level") | Returns initial point support level |
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| [`is_bounds_ignored`](#qiskit.algorithms.optimizers.TNC.is_bounds_ignored "qiskit.algorithms.optimizers.TNC.is_bounds_ignored") | Returns is bounds ignored |
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| [`is_bounds_required`](#qiskit.algorithms.optimizers.TNC.is_bounds_required "qiskit.algorithms.optimizers.TNC.is_bounds_required") | Returns is bounds required |
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| [`is_bounds_supported`](#qiskit.algorithms.optimizers.TNC.is_bounds_supported "qiskit.algorithms.optimizers.TNC.is_bounds_supported") | Returns is bounds supported |
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| [`is_gradient_ignored`](#qiskit.algorithms.optimizers.TNC.is_gradient_ignored "qiskit.algorithms.optimizers.TNC.is_gradient_ignored") | Returns is gradient ignored |
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| [`is_gradient_required`](#qiskit.algorithms.optimizers.TNC.is_gradient_required "qiskit.algorithms.optimizers.TNC.is_gradient_required") | Returns is gradient required |
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| [`is_gradient_supported`](#qiskit.algorithms.optimizers.TNC.is_gradient_supported "qiskit.algorithms.optimizers.TNC.is_gradient_supported") | Returns is gradient supported |
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| [`is_initial_point_ignored`](#qiskit.algorithms.optimizers.TNC.is_initial_point_ignored "qiskit.algorithms.optimizers.TNC.is_initial_point_ignored") | Returns is initial point ignored |
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| [`is_initial_point_required`](#qiskit.algorithms.optimizers.TNC.is_initial_point_required "qiskit.algorithms.optimizers.TNC.is_initial_point_required") | Returns is initial point required |
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| [`is_initial_point_supported`](#qiskit.algorithms.optimizers.TNC.is_initial_point_supported "qiskit.algorithms.optimizers.TNC.is_initial_point_supported") | Returns is initial point supported |
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| [`setting`](#qiskit.algorithms.optimizers.TNC.setting "qiskit.algorithms.optimizers.TNC.setting") | Return setting |
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### bounds\_support\_level
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<Attribute id="qiskit.algorithms.optimizers.TNC.bounds_support_level">
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Returns bounds support level
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</Attribute>
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### get\_support\_level
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<Function id="qiskit.algorithms.optimizers.TNC.get_support_level" signature="get_support_level()">
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return support level dictionary
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</Function>
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### gradient\_num\_diff
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<Function id="qiskit.algorithms.optimizers.TNC.gradient_num_diff" signature="gradient_num_diff(x_center, f, epsilon, max_evals_grouped=1)" modifiers="static">
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We compute the gradient with the numeric differentiation in the parallel way, around the point x\_center.
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**Parameters**
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* **x\_center** (*ndarray*) – point around which we compute the gradient
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* **f** (*func*) – the function of which the gradient is to be computed.
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* **epsilon** (*float*) – the epsilon used in the numeric differentiation.
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* **max\_evals\_grouped** (*int*) – max evals grouped
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**Returns**
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the gradient computed
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**Return type**
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grad
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</Function>
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### gradient\_support\_level
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<Attribute id="qiskit.algorithms.optimizers.TNC.gradient_support_level">
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Returns gradient support level
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</Attribute>
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### initial\_point\_support\_level
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<Attribute id="qiskit.algorithms.optimizers.TNC.initial_point_support_level">
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Returns initial point support level
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</Attribute>
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### is\_bounds\_ignored
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<Attribute id="qiskit.algorithms.optimizers.TNC.is_bounds_ignored">
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Returns is bounds ignored
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</Attribute>
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### is\_bounds\_required
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<Attribute id="qiskit.algorithms.optimizers.TNC.is_bounds_required">
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Returns is bounds required
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</Attribute>
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### is\_bounds\_supported
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<Attribute id="qiskit.algorithms.optimizers.TNC.is_bounds_supported">
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Returns is bounds supported
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</Attribute>
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### is\_gradient\_ignored
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<Attribute id="qiskit.algorithms.optimizers.TNC.is_gradient_ignored">
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Returns is gradient ignored
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</Attribute>
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### is\_gradient\_required
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<Attribute id="qiskit.algorithms.optimizers.TNC.is_gradient_required">
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Returns is gradient required
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</Attribute>
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### is\_gradient\_supported
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<Attribute id="qiskit.algorithms.optimizers.TNC.is_gradient_supported">
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Returns is gradient supported
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</Attribute>
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### is\_initial\_point\_ignored
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<Attribute id="qiskit.algorithms.optimizers.TNC.is_initial_point_ignored">
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Returns is initial point ignored
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</Attribute>
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### is\_initial\_point\_required
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<Attribute id="qiskit.algorithms.optimizers.TNC.is_initial_point_required">
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Returns is initial point required
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</Attribute>
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### is\_initial\_point\_supported
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<Attribute id="qiskit.algorithms.optimizers.TNC.is_initial_point_supported">
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Returns is initial point supported
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</Attribute>
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### optimize
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<Function id="qiskit.algorithms.optimizers.TNC.optimize" signature="optimize(num_vars, objective_function, gradient_function=None, variable_bounds=None, initial_point=None)">
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Perform optimization.
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**Parameters**
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* **num\_vars** (*int*) – Number of parameters to be optimized.
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* **objective\_function** (*callable*) – A function that computes the objective function.
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* **gradient\_function** (*callable*) – A function that computes the gradient of the objective function, or None if not available.
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* **variable\_bounds** (*list\[(float, float)]*) – List of variable bounds, given as pairs (lower, upper). None means unbounded.
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* **initial\_point** (*numpy.ndarray\[float]*) – Initial point.
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**Returns**
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**point, value, nfev**
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point: is a 1D numpy.ndarray\[float] containing the solution value: is a float with the objective function value nfev: number of objective function calls made if available or None
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**Raises**
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**ValueError** – invalid input
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</Function>
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### print\_options
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<Function id="qiskit.algorithms.optimizers.TNC.print_options" signature="print_options()">
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Print algorithm-specific options.
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</Function>
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### set\_max\_evals\_grouped
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<Function id="qiskit.algorithms.optimizers.TNC.set_max_evals_grouped" signature="set_max_evals_grouped(limit)">
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Set max evals grouped
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</Function>
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### set\_options
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<Function id="qiskit.algorithms.optimizers.TNC.set_options" signature="set_options(**kwargs)">
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Sets or updates values in the options dictionary.
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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.
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**Parameters**
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**kwargs** (*dict*) – options, given as name=value.
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</Function>
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### setting
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<Attribute id="qiskit.algorithms.optimizers.TNC.setting">
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Return setting
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</Attribute>
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### wrap\_function
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<Function id="qiskit.algorithms.optimizers.TNC.wrap_function" signature="wrap_function(function, args)" modifiers="static">
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Wrap the function to implicitly inject the args at the call of the function.
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**Parameters**
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* **function** (*func*) – the target function
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* **args** (*tuple*) – the args to be injected
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**Returns**
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wrapper
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**Return type**
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function\_wrapper
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</Function>
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</Class>
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