242 lines
15 KiB
Plaintext
242 lines
15 KiB
Plaintext
---
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title: P_BFGS
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description: API reference for qiskit.aqua.components.optimizers.P_BFGS
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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.aqua.components.optimizers.P_BFGS
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---
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<span id="qiskit-aqua-components-optimizers-p-bfgs" />
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# qiskit.aqua.components.optimizers.P\_BFGS
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<Class id="qiskit.aqua.components.optimizers.P_BFGS" isDedicatedPage={true} github="https://github.com/qiskit-community/qiskit-aqua/tree/stable/0.8/qiskit/aqua/components/optimizers/p_bfgs.py" signature="P_BFGS(maxfun=1000, factr=10, iprint=- 1, max_processes=None)" modifiers="class">
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Parallelized Limited-memory BFGS optimizer.
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P-BFGS is a parallelized version of [`L_BFGS_B`](qiskit.aqua.components.optimizers.L_BFGS_B "qiskit.aqua.components.optimizers.L_BFGS_B") with which it shares the same parameters. P-BFGS can be useful when the target hardware is a quantum simulator running on a classical machine. This allows the multiple processes to use simulation to potentially reach a minimum faster. The parallelization may also help the optimizer avoid getting stuck at local optima.
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Uses scipy.optimize.fmin\_l\_bfgs\_b. For further detail, please refer to [https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fmin\_l\_bfgs\_b.html](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fmin_l_bfgs_b.html)
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**Parameters**
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* **maxfun** (`int`) – Maximum number of function evaluations.
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* **factr** (`float`) – The iteration stops when (f^k - f^\{k+1})/max\{|f^k|, |f^\{k+1}|,1} \<= factr \* eps, where eps is the machine precision, which is automatically generated by the code. Typical values for factr are: 1e12 for low accuracy; 1e7 for moderate accuracy; 10.0 for extremely high accuracy. See Notes for relationship to ftol, which is exposed (instead of factr) by the scipy.optimize.minimize interface to L-BFGS-B.
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* **iprint** (`int`) – Controls the frequency of output. iprint \< 0 means no output; iprint = 0 print only one line at the last iteration; 0 \< iprint \< 99 print also f and |proj g| every iprint iterations; iprint = 99 print details of every iteration except n-vectors; iprint = 100 print also the changes of active set and final x; iprint > 100 print details of every iteration including x and g.
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* **max\_processes** (`Optional`\[`int`]) – maximum number of processes allowed, has a min. value of 1 if not None.
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### \_\_init\_\_
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<Function id="qiskit.aqua.components.optimizers.P_BFGS.__init__" signature="__init__(maxfun=1000, factr=10, iprint=- 1, max_processes=None)">
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**Parameters**
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* **maxfun** (`int`) – Maximum number of function evaluations.
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* **factr** (`float`) – The iteration stops when (f^k - f^\{k+1})/max\{|f^k|, |f^\{k+1}|,1} \<= factr \* eps, where eps is the machine precision, which is automatically generated by the code. Typical values for factr are: 1e12 for low accuracy; 1e7 for moderate accuracy; 10.0 for extremely high accuracy. See Notes for relationship to ftol, which is exposed (instead of factr) by the scipy.optimize.minimize interface to L-BFGS-B.
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* **iprint** (`int`) – Controls the frequency of output. iprint \< 0 means no output; iprint = 0 print only one line at the last iteration; 0 \< iprint \< 99 print also f and |proj g| every iprint iterations; iprint = 99 print details of every iteration except n-vectors; iprint = 100 print also the changes of active set and final x; iprint > 100 print details of every iteration including x and g.
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* **max\_processes** (`Optional`\[`int`]) – maximum number of processes allowed, has a min. value of 1 if not None.
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</Function>
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## Methods
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| ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- |
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| [`__init__`](#qiskit.aqua.components.optimizers.P_BFGS.__init__ "qiskit.aqua.components.optimizers.P_BFGS.__init__")(\[maxfun, factr, iprint, max\_processes]) | **type maxfun**`int` |
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| [`get_support_level`](#qiskit.aqua.components.optimizers.P_BFGS.get_support_level "qiskit.aqua.components.optimizers.P_BFGS.get_support_level")() | return support level dictionary |
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| [`gradient_num_diff`](#qiskit.aqua.components.optimizers.P_BFGS.gradient_num_diff "qiskit.aqua.components.optimizers.P_BFGS.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.aqua.components.optimizers.P_BFGS.optimize "qiskit.aqua.components.optimizers.P_BFGS.optimize")(num\_vars, objective\_function\[, …]) | Perform optimization. |
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| [`print_options`](#qiskit.aqua.components.optimizers.P_BFGS.print_options "qiskit.aqua.components.optimizers.P_BFGS.print_options")() | Print algorithm-specific options. |
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| [`set_max_evals_grouped`](#qiskit.aqua.components.optimizers.P_BFGS.set_max_evals_grouped "qiskit.aqua.components.optimizers.P_BFGS.set_max_evals_grouped")(limit) | Set max evals grouped |
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| [`set_options`](#qiskit.aqua.components.optimizers.P_BFGS.set_options "qiskit.aqua.components.optimizers.P_BFGS.set_options")(\*\*kwargs) | Sets or updates values in the options dictionary. |
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| [`wrap_function`](#qiskit.aqua.components.optimizers.P_BFGS.wrap_function "qiskit.aqua.components.optimizers.P_BFGS.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.aqua.components.optimizers.P_BFGS.bounds_support_level "qiskit.aqua.components.optimizers.P_BFGS.bounds_support_level") | Returns bounds support level |
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| [`gradient_support_level`](#qiskit.aqua.components.optimizers.P_BFGS.gradient_support_level "qiskit.aqua.components.optimizers.P_BFGS.gradient_support_level") | Returns gradient support level |
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| [`initial_point_support_level`](#qiskit.aqua.components.optimizers.P_BFGS.initial_point_support_level "qiskit.aqua.components.optimizers.P_BFGS.initial_point_support_level") | Returns initial point support level |
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| [`is_bounds_ignored`](#qiskit.aqua.components.optimizers.P_BFGS.is_bounds_ignored "qiskit.aqua.components.optimizers.P_BFGS.is_bounds_ignored") | Returns is bounds ignored |
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| [`is_bounds_required`](#qiskit.aqua.components.optimizers.P_BFGS.is_bounds_required "qiskit.aqua.components.optimizers.P_BFGS.is_bounds_required") | Returns is bounds required |
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| [`is_bounds_supported`](#qiskit.aqua.components.optimizers.P_BFGS.is_bounds_supported "qiskit.aqua.components.optimizers.P_BFGS.is_bounds_supported") | Returns is bounds supported |
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| [`is_gradient_ignored`](#qiskit.aqua.components.optimizers.P_BFGS.is_gradient_ignored "qiskit.aqua.components.optimizers.P_BFGS.is_gradient_ignored") | Returns is gradient ignored |
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| [`is_gradient_required`](#qiskit.aqua.components.optimizers.P_BFGS.is_gradient_required "qiskit.aqua.components.optimizers.P_BFGS.is_gradient_required") | Returns is gradient required |
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| [`is_gradient_supported`](#qiskit.aqua.components.optimizers.P_BFGS.is_gradient_supported "qiskit.aqua.components.optimizers.P_BFGS.is_gradient_supported") | Returns is gradient supported |
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| [`is_initial_point_ignored`](#qiskit.aqua.components.optimizers.P_BFGS.is_initial_point_ignored "qiskit.aqua.components.optimizers.P_BFGS.is_initial_point_ignored") | Returns is initial point ignored |
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| [`is_initial_point_required`](#qiskit.aqua.components.optimizers.P_BFGS.is_initial_point_required "qiskit.aqua.components.optimizers.P_BFGS.is_initial_point_required") | Returns is initial point required |
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| [`is_initial_point_supported`](#qiskit.aqua.components.optimizers.P_BFGS.is_initial_point_supported "qiskit.aqua.components.optimizers.P_BFGS.is_initial_point_supported") | Returns is initial point supported |
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| [`setting`](#qiskit.aqua.components.optimizers.P_BFGS.setting "qiskit.aqua.components.optimizers.P_BFGS.setting") | Return setting |
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### bounds\_support\_level
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<Attribute id="qiskit.aqua.components.optimizers.P_BFGS.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.aqua.components.optimizers.P_BFGS.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.aqua.components.optimizers.P_BFGS.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.aqua.components.optimizers.P_BFGS.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.aqua.components.optimizers.P_BFGS.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.aqua.components.optimizers.P_BFGS.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.aqua.components.optimizers.P_BFGS.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.aqua.components.optimizers.P_BFGS.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.aqua.components.optimizers.P_BFGS.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.aqua.components.optimizers.P_BFGS.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.aqua.components.optimizers.P_BFGS.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.aqua.components.optimizers.P_BFGS.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.aqua.components.optimizers.P_BFGS.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.aqua.components.optimizers.P_BFGS.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.aqua.components.optimizers.P_BFGS.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.aqua.components.optimizers.P_BFGS.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.aqua.components.optimizers.P_BFGS.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.aqua.components.optimizers.P_BFGS.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.aqua.components.optimizers.P_BFGS.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.aqua.components.optimizers.P_BFGS.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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