scnc
/
qiskit-documentation
mirror of https://github.com/Qiskit/documentation.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity
qiskit-documentation/docs/api/qiskit/0.26/qiskit.algorithms.optimizer...

242 lines
13 KiB
Plaintext
Raw Permalink Blame History

This file contains invisible Unicode characters

This file contains invisible Unicode characters that are indistinguishable to humans but may be processed differently by a computer. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

---
title: COBYLA (v0.26)
description: API reference for qiskit.algorithms.optimizers.COBYLA in qiskit v0.26
in_page_toc_min_heading_level: 1
python_api_type: class
python_api_name: qiskit.algorithms.optimizers.COBYLA
---
<span id="qiskit-algorithms-optimizers-cobyla" />
# qiskit.algorithms.optimizers.COBYLA
<Class id="qiskit.algorithms.optimizers.COBYLA" isDedicatedPage={true} github="https://github.com/qiskit/qiskit/tree/stable/0.17/qiskit/algorithms/optimizers/cobyla.py" signature="COBYLA(maxiter=1000, disp=False, rhobeg=1.0, tol=None)" modifiers="class">
Constrained Optimization By Linear Approximation optimizer.
COBYLA is a numerical optimization method for constrained problems where the derivative of the objective function is not known.
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)
**Parameters**
* **maxiter** (`int`) – Maximum number of function evaluations.
* **disp** (`bool`) – Set to True to print convergence messages.
* **rhobeg** (`float`) – Reasonable initial changes to the variables.
* **tol** (`Optional`\[`float`]) – Final accuracy in the optimization (not precisely guaranteed). This is a lower bound on the size of the trust region.
### \_\_init\_\_
<Function id="qiskit.algorithms.optimizers.COBYLA.__init__" signature="__init__(maxiter=1000, disp=False, rhobeg=1.0, tol=None)">
**Parameters**
* **maxiter** (`int`) – Maximum number of function evaluations.
* **disp** (`bool`) – Set to True to print convergence messages.
* **rhobeg** (`float`) – Reasonable initial changes to the variables.
* **tol** (`Optional`\[`float`]) – Final accuracy in the optimization (not precisely guaranteed). This is a lower bound on the size of the trust region.
</Function>
## Methods
| | |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------- |
| [`__init__`](#qiskit.algorithms.optimizers.COBYLA.__init__ "qiskit.algorithms.optimizers.COBYLA.__init__")(\[maxiter, disp, rhobeg, tol]) | **type maxiter**`int` |
| [`get_support_level`](#qiskit.algorithms.optimizers.COBYLA.get_support_level "qiskit.algorithms.optimizers.COBYLA.get_support_level")() | Return support level dictionary |
| [`gradient_num_diff`](#qiskit.algorithms.optimizers.COBYLA.gradient_num_diff "qiskit.algorithms.optimizers.COBYLA.gradient_num_diff")(x\_center, f, epsilon\[, …]) | We compute the gradient with the numeric differentiation in the parallel way, around the point x\_center. |
| [`optimize`](#qiskit.algorithms.optimizers.COBYLA.optimize "qiskit.algorithms.optimizers.COBYLA.optimize")(num\_vars, objective\_function\[, …]) | Perform optimization. |
| [`print_options`](#qiskit.algorithms.optimizers.COBYLA.print_options "qiskit.algorithms.optimizers.COBYLA.print_options")() | Print algorithm-specific options. |
| [`set_max_evals_grouped`](#qiskit.algorithms.optimizers.COBYLA.set_max_evals_grouped "qiskit.algorithms.optimizers.COBYLA.set_max_evals_grouped")(limit) | Set max evals grouped |
| [`set_options`](#qiskit.algorithms.optimizers.COBYLA.set_options "qiskit.algorithms.optimizers.COBYLA.set_options")(\*\*kwargs) | Sets or updates values in the options dictionary. |
| [`wrap_function`](#qiskit.algorithms.optimizers.COBYLA.wrap_function "qiskit.algorithms.optimizers.COBYLA.wrap_function")(function, args) | Wrap the function to implicitly inject the args at the call of the function. |
## Attributes
| | |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------- |
| [`bounds_support_level`](#qiskit.algorithms.optimizers.COBYLA.bounds_support_level "qiskit.algorithms.optimizers.COBYLA.bounds_support_level") | Returns bounds support level |
| [`gradient_support_level`](#qiskit.algorithms.optimizers.COBYLA.gradient_support_level "qiskit.algorithms.optimizers.COBYLA.gradient_support_level") | Returns gradient support level |
| [`initial_point_support_level`](#qiskit.algorithms.optimizers.COBYLA.initial_point_support_level "qiskit.algorithms.optimizers.COBYLA.initial_point_support_level") | Returns initial point support level |
| [`is_bounds_ignored`](#qiskit.algorithms.optimizers.COBYLA.is_bounds_ignored "qiskit.algorithms.optimizers.COBYLA.is_bounds_ignored") | Returns is bounds ignored |
| [`is_bounds_required`](#qiskit.algorithms.optimizers.COBYLA.is_bounds_required "qiskit.algorithms.optimizers.COBYLA.is_bounds_required") | Returns is bounds required |
| [`is_bounds_supported`](#qiskit.algorithms.optimizers.COBYLA.is_bounds_supported "qiskit.algorithms.optimizers.COBYLA.is_bounds_supported") | Returns is bounds supported |
| [`is_gradient_ignored`](#qiskit.algorithms.optimizers.COBYLA.is_gradient_ignored "qiskit.algorithms.optimizers.COBYLA.is_gradient_ignored") | Returns is gradient ignored |
| [`is_gradient_required`](#qiskit.algorithms.optimizers.COBYLA.is_gradient_required "qiskit.algorithms.optimizers.COBYLA.is_gradient_required") | Returns is gradient required |
| [`is_gradient_supported`](#qiskit.algorithms.optimizers.COBYLA.is_gradient_supported "qiskit.algorithms.optimizers.COBYLA.is_gradient_supported") | Returns is gradient supported |
| [`is_initial_point_ignored`](#qiskit.algorithms.optimizers.COBYLA.is_initial_point_ignored "qiskit.algorithms.optimizers.COBYLA.is_initial_point_ignored") | Returns is initial point ignored |
| [`is_initial_point_required`](#qiskit.algorithms.optimizers.COBYLA.is_initial_point_required "qiskit.algorithms.optimizers.COBYLA.is_initial_point_required") | Returns is initial point required |
| [`is_initial_point_supported`](#qiskit.algorithms.optimizers.COBYLA.is_initial_point_supported "qiskit.algorithms.optimizers.COBYLA.is_initial_point_supported") | Returns is initial point supported |
| [`setting`](#qiskit.algorithms.optimizers.COBYLA.setting "qiskit.algorithms.optimizers.COBYLA.setting") | Return setting |
### bounds\_support\_level
<Attribute id="qiskit.algorithms.optimizers.COBYLA.bounds_support_level">
Returns bounds support level
</Attribute>
### get\_support\_level
<Function id="qiskit.algorithms.optimizers.COBYLA.get_support_level" signature="get_support_level()">
Return support level dictionary
</Function>
### gradient\_num\_diff
<Function id="qiskit.algorithms.optimizers.COBYLA.gradient_num_diff" signature="gradient_num_diff(x_center, f, epsilon, max_evals_grouped=1)" modifiers="static">
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*) – the epsilon used in the numeric differentiation.
* **max\_evals\_grouped** (*int*) – max evals grouped
**Returns**
the gradient computed
**Return type**
grad
</Function>
### gradient\_support\_level
<Attribute id="qiskit.algorithms.optimizers.COBYLA.gradient_support_level">
Returns gradient support level
</Attribute>
### initial\_point\_support\_level
<Attribute id="qiskit.algorithms.optimizers.COBYLA.initial_point_support_level">
Returns initial point support level
</Attribute>
### is\_bounds\_ignored
<Attribute id="qiskit.algorithms.optimizers.COBYLA.is_bounds_ignored">
Returns is bounds ignored
</Attribute>
### is\_bounds\_required
<Attribute id="qiskit.algorithms.optimizers.COBYLA.is_bounds_required">
Returns is bounds required
</Attribute>
### is\_bounds\_supported
<Attribute id="qiskit.algorithms.optimizers.COBYLA.is_bounds_supported">
Returns is bounds supported
</Attribute>
### is\_gradient\_ignored
<Attribute id="qiskit.algorithms.optimizers.COBYLA.is_gradient_ignored">
Returns is gradient ignored
</Attribute>
### is\_gradient\_required
<Attribute id="qiskit.algorithms.optimizers.COBYLA.is_gradient_required">
Returns is gradient required
</Attribute>
### is\_gradient\_supported
<Attribute id="qiskit.algorithms.optimizers.COBYLA.is_gradient_supported">
Returns is gradient supported
</Attribute>
### is\_initial\_point\_ignored
<Attribute id="qiskit.algorithms.optimizers.COBYLA.is_initial_point_ignored">
Returns is initial point ignored
</Attribute>
### is\_initial\_point\_required
<Attribute id="qiskit.algorithms.optimizers.COBYLA.is_initial_point_required">
Returns is initial point required
</Attribute>
### is\_initial\_point\_supported
<Attribute id="qiskit.algorithms.optimizers.COBYLA.is_initial_point_supported">
Returns is initial point supported
</Attribute>
### optimize
<Function id="qiskit.algorithms.optimizers.COBYLA.optimize" signature="optimize(num_vars, objective_function, gradient_function=None, variable_bounds=None, initial_point=None)">
Perform optimization.
**Parameters**
* **num\_vars** (*int*) – Number of parameters to be optimized.
* **objective\_function** (*callable*) – A function that computes the objective function.
* **gradient\_function** (*callable*) – A function that computes the gradient of the objective function, or None if not available.
* **variable\_bounds** (*list\[(float, float)]*) – List of variable bounds, given as pairs (lower, upper). None means unbounded.
* **initial\_point** (*numpy.ndarray\[float]*) – Initial point.
**Returns**
**point, value, nfev**
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
**Raises**
**ValueError** – invalid input
</Function>
### print\_options
<Function id="qiskit.algorithms.optimizers.COBYLA.print_options" signature="print_options()">
Print algorithm-specific options.
</Function>
### set\_max\_evals\_grouped
<Function id="qiskit.algorithms.optimizers.COBYLA.set_max_evals_grouped" signature="set_max_evals_grouped(limit)">
Set max evals grouped
</Function>
### set\_options
<Function id="qiskit.algorithms.optimizers.COBYLA.set_options" signature="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*) – options, given as name=value.
</Function>
### setting
<Attribute id="qiskit.algorithms.optimizers.COBYLA.setting">
Return setting
</Attribute>
### wrap\_function
<Function id="qiskit.algorithms.optimizers.COBYLA.wrap_function" signature="wrap_function(function, args)" modifiers="static">
Wrap the function to implicitly inject the args at the call of the function.
**Parameters**
* **function** (*func*) – the target function
* **args** (*tuple*) – the args to be injected
**Returns**
wrapper
**Return type**
function\_wrapper
</Function>
</Class>
Reference in New Issue View Git Blame Copy Permalink