221 lines
7.3 KiB
Plaintext
221 lines
7.3 KiB
Plaintext
---
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title: COBYLA
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description: API reference for qiskit.algorithms.optimizers.COBYLA
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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.COBYLA
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---
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# COBYLA
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<Class id="qiskit.algorithms.optimizers.COBYLA" isDedicatedPage={true} github="https://github.com/qiskit/qiskit/tree/stable/0.24/qiskit/algorithms/optimizers/cobyla.py" signature="COBYLA(maxiter=1000, disp=False, rhobeg=1.0, tol=None, options=None, **kwargs)" modifiers="class">
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Bases: [`SciPyOptimizer`](qiskit.algorithms.optimizers.SciPyOptimizer "qiskit.algorithms.optimizers.scipy_optimizer.SciPyOptimizer")
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Constrained Optimization By Linear Approximation optimizer.
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COBYLA is a numerical optimization method for constrained problems where the derivative of the objective function is not known.
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Uses scipy.optimize.minimize COBYLA. For further detail, please refer to [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 evaluations.
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* **disp** (*bool*) – Set to True to print convergence messages.
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* **rhobeg** (*float*) – Reasonable initial changes to the variables.
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* **tol** (*float | None*) – Final accuracy in the optimization (not precisely guaranteed). This is a lower bound on the size of the trust region.
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* **options** (*dict | None*) – A dictionary of solver options.
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* **kwargs** – additional kwargs for scipy.optimize.minimize.
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## Methods
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<span id="qiskit-algorithms-optimizers-cobyla-get-support-level" />
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### get\_support\_level
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<Function id="qiskit.algorithms.optimizers.COBYLA.get_support_level" signature="COBYLA.get_support_level()">
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Return support level dictionary
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</Function>
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<span id="qiskit-algorithms-optimizers-cobyla-gradient-num-diff" />
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### gradient\_num\_diff
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<Function id="qiskit.algorithms.optimizers.COBYLA.gradient_num_diff" signature="COBYLA.gradient_num_diff(x_center, f, epsilon, max_evals_grouped=None)" 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, defaults to 1 (i.e. no batching).
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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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<span id="qiskit-algorithms-optimizers-cobyla-minimize" />
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### minimize
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<Function id="qiskit.algorithms.optimizers.COBYLA.minimize" signature="COBYLA.minimize(fun, x0, jac=None, bounds=None)">
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Minimize the scalar function.
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**Parameters**
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* **fun** (*Callable\[\[POINT], float]*) – The scalar function to minimize.
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* **x0** (*POINT*) – The initial point for the minimization.
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* **jac** (*Callable\[\[POINT], POINT] | None*) – The gradient of the scalar function `fun`.
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* **bounds** (*list\[tuple\[float, float]] | None*) – Bounds for the variables of `fun`. This argument might be ignored if the optimizer does not support bounds.
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**Returns**
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The result of the optimization, containing e.g. the result as attribute `x`.
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**Return type**
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[OptimizerResult](qiskit.algorithms.optimizers.OptimizerResult "qiskit.algorithms.optimizers.OptimizerResult")
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</Function>
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<span id="qiskit-algorithms-optimizers-cobyla-print-options" />
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### print\_options
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<Function id="qiskit.algorithms.optimizers.COBYLA.print_options" signature="COBYLA.print_options()">
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Print algorithm-specific options.
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</Function>
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<span id="qiskit-algorithms-optimizers-cobyla-set-max-evals-grouped" />
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### set\_max\_evals\_grouped
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<Function id="qiskit.algorithms.optimizers.COBYLA.set_max_evals_grouped" signature="COBYLA.set_max_evals_grouped(limit)">
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Set max evals grouped
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</Function>
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<span id="qiskit-algorithms-optimizers-cobyla-set-options" />
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### set\_options
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<Function id="qiskit.algorithms.optimizers.COBYLA.set_options" signature="COBYLA.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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<span id="qiskit-algorithms-optimizers-cobyla-wrap-function" />
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### wrap\_function
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<Function id="qiskit.algorithms.optimizers.COBYLA.wrap_function" signature="COBYLA.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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## Attributes
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### bounds\_support\_level
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<Attribute id="qiskit.algorithms.optimizers.COBYLA.bounds_support_level">
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Returns bounds support level
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</Attribute>
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### gradient\_support\_level
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<Attribute id="qiskit.algorithms.optimizers.COBYLA.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.COBYLA.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.COBYLA.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.COBYLA.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.COBYLA.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.COBYLA.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.COBYLA.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.COBYLA.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.COBYLA.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.COBYLA.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.COBYLA.is_initial_point_supported">
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Returns is initial point supported
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</Attribute>
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### setting
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<Attribute id="qiskit.algorithms.optimizers.COBYLA.setting">
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Return setting
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</Attribute>
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### settings
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<Attribute id="qiskit.algorithms.optimizers.COBYLA.settings" />
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</Class>
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