266 lines
16 KiB
Plaintext
266 lines
16 KiB
Plaintext
---
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title: SPSA (v0.26)
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description: API reference for qiskit.aqua.components.optimizers.SPSA 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.aqua.components.optimizers.SPSA
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---
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<span id="qiskit-aqua-components-optimizers-spsa" />
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# qiskit.aqua.components.optimizers.SPSA
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<Class id="qiskit.aqua.components.optimizers.SPSA" isDedicatedPage={true} github="https://github.com/qiskit-community/qiskit-aqua/tree/stable/0.9/qiskit/aqua/components/optimizers/spsa.py" signature="SPSA(maxiter=1000, save_steps=1, last_avg=1, c0=0.6283185307179586, c1=0.1, c2=0.602, c3=0.101, c4=0, skip_calibration=False, max_trials=None)" modifiers="class">
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Simultaneous Perturbation Stochastic Approximation (SPSA) optimizer.
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SPSA is an algorithmic method for optimizing systems with multiple unknown parameters. As an optimization method, it is appropriately suited to large-scale population models, adaptive modeling, and simulation optimization.
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<Admonition title="See also" type="note">
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Many examples are presented at the [SPSA Web site](http://www.jhuapl.edu/SPSA).
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</Admonition>
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SPSA is a descent method capable of finding global minima, sharing this property with other methods as simulated annealing. Its main feature is the gradient approximation, which requires only two measurements of the objective function, regardless of the dimension of the optimization problem.
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<Admonition title="Note" type="note">
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SPSA can be used in the presence of noise, and it is therefore indicated in situations involving measurement uncertainty on a quantum computation when finding a minimum. If you are executing a variational algorithm using a Quantum ASseMbly Language (QASM) simulator or a real device, SPSA would be the most recommended choice among the optimizers provided here.
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</Admonition>
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The optimization process includes a calibration phase, which requires additional functional evaluations.
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For further details, please refer to [https://arxiv.org/pdf/1704.05018v2.pdf#section\*.11](https://arxiv.org/pdf/1704.05018v2.pdf#section*.11) (Supplementary information Section IV.)
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**Parameters**
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* **maxiter** (`int`) – Maximum number of iterations to perform.
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* **save\_steps** (`int`) – Save intermediate info every save\_steps step. It has a min. value of 1.
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* **last\_avg** (`int`) – Averaged parameters over the last\_avg iterations. If last\_avg = 1, only the last iteration is considered. It has a min. value of 1.
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* **c0** (`float`) – The initial a. Step size to update parameters.
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* **c1** (`float`) – The initial c. The step size used to approximate gradient.
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* **c2** (`float`) – The alpha in the paper, and it is used to adjust a (c0) at each iteration.
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* **c3** (`float`) – The gamma in the paper, and it is used to adjust c (c1) at each iteration.
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* **c4** (`float`) – The parameter used to control a as well.
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* **skip\_calibration** (`bool`) – Skip calibration and use provided c(s) as is.
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* **max\_trials** (`Optional`\[`int`]) – Deprecated, use maxiter.
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### \_\_init\_\_
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<Function id="qiskit.aqua.components.optimizers.SPSA.__init__" signature="__init__(maxiter=1000, save_steps=1, last_avg=1, c0=0.6283185307179586, c1=0.1, c2=0.602, c3=0.101, c4=0, skip_calibration=False, max_trials=None)">
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**Parameters**
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* **maxiter** (`int`) – Maximum number of iterations to perform.
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* **save\_steps** (`int`) – Save intermediate info every save\_steps step. It has a min. value of 1.
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* **last\_avg** (`int`) – Averaged parameters over the last\_avg iterations. If last\_avg = 1, only the last iteration is considered. It has a min. value of 1.
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* **c0** (`float`) – The initial a. Step size to update parameters.
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* **c1** (`float`) – The initial c. The step size used to approximate gradient.
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* **c2** (`float`) – The alpha in the paper, and it is used to adjust a (c0) at each iteration.
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* **c3** (`float`) – The gamma in the paper, and it is used to adjust c (c1) at each iteration.
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* **c4** (`float`) – The parameter used to control a as well.
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* **skip\_calibration** (`bool`) – Skip calibration and use provided c(s) as is.
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* **max\_trials** (`Optional`\[`int`]) – Deprecated, use maxiter.
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</Function>
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## Methods
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| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------- |
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| [`__init__`](#qiskit.aqua.components.optimizers.SPSA.__init__ "qiskit.aqua.components.optimizers.SPSA.__init__")(\[maxiter, save\_steps, last\_avg, …]) | **type maxiter**`int` |
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| [`get_support_level`](#qiskit.aqua.components.optimizers.SPSA.get_support_level "qiskit.aqua.components.optimizers.SPSA.get_support_level")() | return support level dictionary |
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| [`gradient_num_diff`](#qiskit.aqua.components.optimizers.SPSA.gradient_num_diff "qiskit.aqua.components.optimizers.SPSA.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.SPSA.optimize "qiskit.aqua.components.optimizers.SPSA.optimize")(num\_vars, objective\_function\[, …]) | Perform optimization. |
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| [`print_options`](#qiskit.aqua.components.optimizers.SPSA.print_options "qiskit.aqua.components.optimizers.SPSA.print_options")() | Print algorithm-specific options. |
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| [`set_max_evals_grouped`](#qiskit.aqua.components.optimizers.SPSA.set_max_evals_grouped "qiskit.aqua.components.optimizers.SPSA.set_max_evals_grouped")(limit) | Set max evals grouped |
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| [`set_options`](#qiskit.aqua.components.optimizers.SPSA.set_options "qiskit.aqua.components.optimizers.SPSA.set_options")(\*\*kwargs) | Sets or updates values in the options dictionary. |
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| [`wrap_function`](#qiskit.aqua.components.optimizers.SPSA.wrap_function "qiskit.aqua.components.optimizers.SPSA.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.SPSA.bounds_support_level "qiskit.aqua.components.optimizers.SPSA.bounds_support_level") | Returns bounds support level |
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| [`gradient_support_level`](#qiskit.aqua.components.optimizers.SPSA.gradient_support_level "qiskit.aqua.components.optimizers.SPSA.gradient_support_level") | Returns gradient support level |
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| [`initial_point_support_level`](#qiskit.aqua.components.optimizers.SPSA.initial_point_support_level "qiskit.aqua.components.optimizers.SPSA.initial_point_support_level") | Returns initial point support level |
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| [`is_bounds_ignored`](#qiskit.aqua.components.optimizers.SPSA.is_bounds_ignored "qiskit.aqua.components.optimizers.SPSA.is_bounds_ignored") | Returns is bounds ignored |
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| [`is_bounds_required`](#qiskit.aqua.components.optimizers.SPSA.is_bounds_required "qiskit.aqua.components.optimizers.SPSA.is_bounds_required") | Returns is bounds required |
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| [`is_bounds_supported`](#qiskit.aqua.components.optimizers.SPSA.is_bounds_supported "qiskit.aqua.components.optimizers.SPSA.is_bounds_supported") | Returns is bounds supported |
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| [`is_gradient_ignored`](#qiskit.aqua.components.optimizers.SPSA.is_gradient_ignored "qiskit.aqua.components.optimizers.SPSA.is_gradient_ignored") | Returns is gradient ignored |
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| [`is_gradient_required`](#qiskit.aqua.components.optimizers.SPSA.is_gradient_required "qiskit.aqua.components.optimizers.SPSA.is_gradient_required") | Returns is gradient required |
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| [`is_gradient_supported`](#qiskit.aqua.components.optimizers.SPSA.is_gradient_supported "qiskit.aqua.components.optimizers.SPSA.is_gradient_supported") | Returns is gradient supported |
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| [`is_initial_point_ignored`](#qiskit.aqua.components.optimizers.SPSA.is_initial_point_ignored "qiskit.aqua.components.optimizers.SPSA.is_initial_point_ignored") | Returns is initial point ignored |
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| [`is_initial_point_required`](#qiskit.aqua.components.optimizers.SPSA.is_initial_point_required "qiskit.aqua.components.optimizers.SPSA.is_initial_point_required") | Returns is initial point required |
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| [`is_initial_point_supported`](#qiskit.aqua.components.optimizers.SPSA.is_initial_point_supported "qiskit.aqua.components.optimizers.SPSA.is_initial_point_supported") | Returns is initial point supported |
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| [`setting`](#qiskit.aqua.components.optimizers.SPSA.setting "qiskit.aqua.components.optimizers.SPSA.setting") | Return setting |
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### bounds\_support\_level
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<Attribute id="qiskit.aqua.components.optimizers.SPSA.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.SPSA.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.SPSA.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.SPSA.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.SPSA.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.SPSA.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.SPSA.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.SPSA.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.SPSA.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.SPSA.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.SPSA.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.SPSA.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.SPSA.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.SPSA.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.SPSA.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.SPSA.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.SPSA.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.SPSA.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.SPSA.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.SPSA.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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