210 lines
8.7 KiB
Plaintext
210 lines
8.7 KiB
Plaintext
---
|
||
title: P_BFGS
|
||
description: API reference for qiskit.algorithms.optimizers.P_BFGS
|
||
in_page_toc_min_heading_level: 1
|
||
python_api_type: class
|
||
python_api_name: qiskit.algorithms.optimizers.P_BFGS
|
||
---
|
||
|
||
<span id="p-bfgs" />
|
||
|
||
# P\_BFGS
|
||
|
||
<Class id="qiskit.algorithms.optimizers.P_BFGS" isDedicatedPage={true} github="https://github.com/qiskit/qiskit/tree/stable/0.25/qiskit/algorithms/optimizers/p_bfgs.py" signature="qiskit.algorithms.optimizers.P_BFGS(maxfun=1000, ftol=2.220446049250313e-15, iprint=-1, max_processes=None, options=None, max_evals_grouped=1, **kwargs)" modifiers="class">
|
||
Bases: [`SciPyOptimizer`](qiskit.algorithms.optimizers.SciPyOptimizer "qiskit.algorithms.optimizers.scipy_optimizer.SciPyOptimizer")
|
||
|
||
Parallelized Limited-memory BFGS optimizer.
|
||
|
||
P-BFGS is a parallelized version of [`L_BFGS_B`](qiskit.algorithms.optimizers.L_BFGS_B "qiskit.algorithms.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.
|
||
|
||
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)
|
||
|
||
**Parameters**
|
||
|
||
* **maxfun** ([*int*](https://docs.python.org/3/library/functions.html#int "(in Python v3.12)")) – Maximum number of function evaluations.
|
||
* **ftol** (*SupportsFloat*) – The iteration stops when (f^k - f^\{k+1})/max\{|f^k|,|f^\{k+1}|,1} \<= ftol.
|
||
* **iprint** ([*int*](https://docs.python.org/3/library/functions.html#int "(in Python v3.12)")) – 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.
|
||
* **max\_processes** ([*int*](https://docs.python.org/3/library/functions.html#int "(in Python v3.12)") *| None*) – maximum number of processes allowed, has a min. value of 1 if not None.
|
||
* **options** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict "(in Python v3.12)") *| None*) – A dictionary of solver options.
|
||
* **max\_evals\_grouped** ([*int*](https://docs.python.org/3/library/functions.html#int "(in Python v3.12)")) – Max number of default gradient evaluations performed simultaneously.
|
||
* **kwargs** – additional kwargs for scipy.optimize.minimize.
|
||
|
||
## Attributes
|
||
|
||
### bounds\_support\_level
|
||
|
||
<Attribute id="qiskit.algorithms.optimizers.P_BFGS.bounds_support_level">
|
||
Returns bounds support level
|
||
</Attribute>
|
||
|
||
### gradient\_support\_level
|
||
|
||
<Attribute id="qiskit.algorithms.optimizers.P_BFGS.gradient_support_level">
|
||
Returns gradient support level
|
||
</Attribute>
|
||
|
||
### initial\_point\_support\_level
|
||
|
||
<Attribute id="qiskit.algorithms.optimizers.P_BFGS.initial_point_support_level">
|
||
Returns initial point support level
|
||
</Attribute>
|
||
|
||
### is\_bounds\_ignored
|
||
|
||
<Attribute id="qiskit.algorithms.optimizers.P_BFGS.is_bounds_ignored">
|
||
Returns is bounds ignored
|
||
</Attribute>
|
||
|
||
### is\_bounds\_required
|
||
|
||
<Attribute id="qiskit.algorithms.optimizers.P_BFGS.is_bounds_required">
|
||
Returns is bounds required
|
||
</Attribute>
|
||
|
||
### is\_bounds\_supported
|
||
|
||
<Attribute id="qiskit.algorithms.optimizers.P_BFGS.is_bounds_supported">
|
||
Returns is bounds supported
|
||
</Attribute>
|
||
|
||
### is\_gradient\_ignored
|
||
|
||
<Attribute id="qiskit.algorithms.optimizers.P_BFGS.is_gradient_ignored">
|
||
Returns is gradient ignored
|
||
</Attribute>
|
||
|
||
### is\_gradient\_required
|
||
|
||
<Attribute id="qiskit.algorithms.optimizers.P_BFGS.is_gradient_required">
|
||
Returns is gradient required
|
||
</Attribute>
|
||
|
||
### is\_gradient\_supported
|
||
|
||
<Attribute id="qiskit.algorithms.optimizers.P_BFGS.is_gradient_supported">
|
||
Returns is gradient supported
|
||
</Attribute>
|
||
|
||
### is\_initial\_point\_ignored
|
||
|
||
<Attribute id="qiskit.algorithms.optimizers.P_BFGS.is_initial_point_ignored">
|
||
Returns is initial point ignored
|
||
</Attribute>
|
||
|
||
### is\_initial\_point\_required
|
||
|
||
<Attribute id="qiskit.algorithms.optimizers.P_BFGS.is_initial_point_required">
|
||
Returns is initial point required
|
||
</Attribute>
|
||
|
||
### is\_initial\_point\_supported
|
||
|
||
<Attribute id="qiskit.algorithms.optimizers.P_BFGS.is_initial_point_supported">
|
||
Returns is initial point supported
|
||
</Attribute>
|
||
|
||
### setting
|
||
|
||
<Attribute id="qiskit.algorithms.optimizers.P_BFGS.setting">
|
||
Return setting
|
||
</Attribute>
|
||
|
||
### settings
|
||
|
||
<Attribute id="qiskit.algorithms.optimizers.P_BFGS.settings" />
|
||
|
||
## Methods
|
||
|
||
### get\_support\_level
|
||
|
||
<Function id="qiskit.algorithms.optimizers.P_BFGS.get_support_level" signature="get_support_level()">
|
||
Return support level dictionary
|
||
</Function>
|
||
|
||
### gradient\_num\_diff
|
||
|
||
<Function id="qiskit.algorithms.optimizers.P_BFGS.gradient_num_diff" signature="gradient_num_diff(x_center, f, epsilon, max_evals_grouped=None)" 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*](https://docs.python.org/3/library/functions.html#float "(in Python v3.12)")) – the epsilon used in the numeric differentiation.
|
||
* **max\_evals\_grouped** ([*int*](https://docs.python.org/3/library/functions.html#int "(in Python v3.12)")) – max evals grouped, defaults to 1 (i.e. no batching).
|
||
|
||
**Returns**
|
||
|
||
the gradient computed
|
||
|
||
**Return type**
|
||
|
||
grad
|
||
</Function>
|
||
|
||
### minimize
|
||
|
||
<Function id="qiskit.algorithms.optimizers.P_BFGS.minimize" signature="minimize(fun, x0, jac=None, bounds=None)">
|
||
Minimize the scalar function.
|
||
|
||
**Parameters**
|
||
|
||
* **fun** (*Callable\[\[POINT],* [*float*](https://docs.python.org/3/library/functions.html#float "(in Python v3.12)")*]*) – The scalar function to minimize.
|
||
* **x0** (*POINT*) – The initial point for the minimization.
|
||
* **jac** (*Callable\[\[POINT], POINT] | None*) – The gradient of the scalar function `fun`.
|
||
* **bounds** ([*list*](https://docs.python.org/3/library/stdtypes.html#list "(in Python v3.12)")*\[*[*tuple*](https://docs.python.org/3/library/stdtypes.html#tuple "(in Python v3.12)")*\[*[*float*](https://docs.python.org/3/library/functions.html#float "(in Python v3.12)")*,* [*float*](https://docs.python.org/3/library/functions.html#float "(in Python v3.12)")*]] | None*) – Bounds for the variables of `fun`. This argument might be ignored if the optimizer does not support bounds.
|
||
|
||
**Returns**
|
||
|
||
The result of the optimization, containing e.g. the result as attribute `x`.
|
||
|
||
**Return type**
|
||
|
||
[OptimizerResult](qiskit.algorithms.optimizers.OptimizerResult "qiskit.algorithms.optimizers.OptimizerResult")
|
||
</Function>
|
||
|
||
### print\_options
|
||
|
||
<Function id="qiskit.algorithms.optimizers.P_BFGS.print_options" signature="print_options()">
|
||
Print algorithm-specific options.
|
||
</Function>
|
||
|
||
### set\_max\_evals\_grouped
|
||
|
||
<Function id="qiskit.algorithms.optimizers.P_BFGS.set_max_evals_grouped" signature="set_max_evals_grouped(limit)">
|
||
Set max evals grouped
|
||
</Function>
|
||
|
||
### set\_options
|
||
|
||
<Function id="qiskit.algorithms.optimizers.P_BFGS.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*](https://docs.python.org/3/library/stdtypes.html#dict "(in Python v3.12)")) – options, given as name=value.
|
||
</Function>
|
||
|
||
### wrap\_function
|
||
|
||
<Function id="qiskit.algorithms.optimizers.P_BFGS.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*](https://docs.python.org/3/library/stdtypes.html#tuple "(in Python v3.12)")) – the args to be injected
|
||
|
||
**Returns**
|
||
|
||
wrapper
|
||
|
||
**Return type**
|
||
|
||
function\_wrapper
|
||
</Function>
|
||
</Class>
|
||
|