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---
title: GSLS (v0.26)
description: API reference for qiskit.aqua.components.optimizers.GSLS in qiskit v0.26
in_page_toc_min_heading_level: 1
python_api_type: class
python_api_name: qiskit.aqua.components.optimizers.GSLS
---
<span id="qiskit-aqua-components-optimizers-gsls" />
# qiskit.aqua.components.optimizers.GSLS
<Class id="qiskit.aqua.components.optimizers.GSLS" isDedicatedPage={true} github="https://github.com/qiskit-community/qiskit-aqua/tree/stable/0.9/qiskit/aqua/components/optimizers/gsls.py" signature="GSLS(maxiter=10000, max_eval=10000, disp=False, sampling_radius=1e-06, sample_size_factor=1, initial_step_size=0.01, min_step_size=1e-10, step_size_multiplier=0.4, armijo_parameter=0.1, min_gradient_norm=1e-08, max_failed_rejection_sampling=50, max_iter=None)" modifiers="class">
Gaussian-smoothed Line Search.
An implementation of the line search algorithm described in [https://arxiv.org/pdf/1905.01332.pdf](https://arxiv.org/pdf/1905.01332.pdf), using gradient approximation based on Gaussian-smoothed samples on a sphere.
**Parameters**
* **maxiter** (`int`) – Maximum number of iterations.
* **max\_eval** (`int`) – Maximum number of evaluations.
* **disp** (`bool`) – Set to True to display convergence messages.
* **sampling\_radius** (`float`) – Sampling radius to determine gradient estimate.
* **sample\_size\_factor** (`int`) – The size of the sample set at each iteration is this number multiplied by the dimension of the problem, rounded to the nearest integer.
* **initial\_step\_size** (`float`) – Initial step size for the descent algorithm.
* **min\_step\_size** (`float`) – Minimum step size for the descent algorithm.
* **step\_size\_multiplier** (`float`) – Step size reduction after unsuccessful steps, in the interval (0, 1).
* **armijo\_parameter** (`float`) – Armijo parameter for sufficient decrease criterion, in the interval (0, 1).
* **min\_gradient\_norm** (`float`) – If the gradient norm is below this threshold, the algorithm stops.
* **max\_failed\_rejection\_sampling** (`int`) – Maximum number of attempts to sample points within bounds.
* **max\_iter** (`Optional`\[`int`]) – Deprecated, use maxiter.
### \_\_init\_\_
<Function id="qiskit.aqua.components.optimizers.GSLS.__init__" signature="__init__(maxiter=10000, max_eval=10000, disp=False, sampling_radius=1e-06, sample_size_factor=1, initial_step_size=0.01, min_step_size=1e-10, step_size_multiplier=0.4, armijo_parameter=0.1, min_gradient_norm=1e-08, max_failed_rejection_sampling=50, max_iter=None)">
**Parameters**
* **maxiter** (`int`) – Maximum number of iterations.
* **max\_eval** (`int`) – Maximum number of evaluations.
* **disp** (`bool`) – Set to True to display convergence messages.
* **sampling\_radius** (`float`) – Sampling radius to determine gradient estimate.
* **sample\_size\_factor** (`int`) – The size of the sample set at each iteration is this number multiplied by the dimension of the problem, rounded to the nearest integer.
* **initial\_step\_size** (`float`) – Initial step size for the descent algorithm.
* **min\_step\_size** (`float`) – Minimum step size for the descent algorithm.
* **step\_size\_multiplier** (`float`) – Step size reduction after unsuccessful steps, in the interval (0, 1).
* **armijo\_parameter** (`float`) – Armijo parameter for sufficient decrease criterion, in the interval (0, 1).
* **min\_gradient\_norm** (`float`) – If the gradient norm is below this threshold, the algorithm stops.
* **max\_failed\_rejection\_sampling** (`int`) – Maximum number of attempts to sample points within bounds.
* **max\_iter** (`Optional`\[`int`]) – Deprecated, use maxiter.
</Function>
## Methods
| | |
| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- |
| [`__init__`](#qiskit.aqua.components.optimizers.GSLS.__init__ "qiskit.aqua.components.optimizers.GSLS.__init__")(\[maxiter, max\_eval, disp, …]) | **type maxiter**`int` |
| [`get_support_level`](#qiskit.aqua.components.optimizers.GSLS.get_support_level "qiskit.aqua.components.optimizers.GSLS.get_support_level")() | Return support level dictionary. |
| [`gradient_approximation`](#qiskit.aqua.components.optimizers.GSLS.gradient_approximation "qiskit.aqua.components.optimizers.GSLS.gradient_approximation")(n, x, x\_value, …) | Construct gradient approximation from given sample. |
| [`gradient_num_diff`](#qiskit.aqua.components.optimizers.GSLS.gradient_num_diff "qiskit.aqua.components.optimizers.GSLS.gradient_num_diff")(x\_center, f, epsilon\[, …]) | We compute the gradient with the numeric differentiation in the parallel way, around the point x\_center. |
| [`ls_optimize`](#qiskit.aqua.components.optimizers.GSLS.ls_optimize "qiskit.aqua.components.optimizers.GSLS.ls_optimize")(n, obj\_fun, initial\_point, …) | Run the line search optimization. |
| [`optimize`](#qiskit.aqua.components.optimizers.GSLS.optimize "qiskit.aqua.components.optimizers.GSLS.optimize")(num\_vars, objective\_function\[, …]) | Perform optimization. |
| [`print_options`](#qiskit.aqua.components.optimizers.GSLS.print_options "qiskit.aqua.components.optimizers.GSLS.print_options")() | Print algorithm-specific options. |
| [`sample_points`](#qiskit.aqua.components.optimizers.GSLS.sample_points "qiskit.aqua.components.optimizers.GSLS.sample_points")(n, x, num\_points) | Sample `num_points` points around `x` on the `n`-sphere of specified radius. |
| [`sample_set`](#qiskit.aqua.components.optimizers.GSLS.sample_set "qiskit.aqua.components.optimizers.GSLS.sample_set")(n, x, var\_lb, var\_ub, num\_points) | Construct sample set of given size. |
| [`set_max_evals_grouped`](#qiskit.aqua.components.optimizers.GSLS.set_max_evals_grouped "qiskit.aqua.components.optimizers.GSLS.set_max_evals_grouped")(limit) | Set max evals grouped |
| [`set_options`](#qiskit.aqua.components.optimizers.GSLS.set_options "qiskit.aqua.components.optimizers.GSLS.set_options")(\*\*kwargs) | Sets or updates values in the options dictionary. |
| [`wrap_function`](#qiskit.aqua.components.optimizers.GSLS.wrap_function "qiskit.aqua.components.optimizers.GSLS.wrap_function")(function, args) | Wrap the function to implicitly inject the args at the call of the function. |
## Attributes
| | |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------- |
| [`bounds_support_level`](#qiskit.aqua.components.optimizers.GSLS.bounds_support_level "qiskit.aqua.components.optimizers.GSLS.bounds_support_level") | Returns bounds support level |
| [`gradient_support_level`](#qiskit.aqua.components.optimizers.GSLS.gradient_support_level "qiskit.aqua.components.optimizers.GSLS.gradient_support_level") | Returns gradient support level |
| [`initial_point_support_level`](#qiskit.aqua.components.optimizers.GSLS.initial_point_support_level "qiskit.aqua.components.optimizers.GSLS.initial_point_support_level") | Returns initial point support level |
| [`is_bounds_ignored`](#qiskit.aqua.components.optimizers.GSLS.is_bounds_ignored "qiskit.aqua.components.optimizers.GSLS.is_bounds_ignored") | Returns is bounds ignored |
| [`is_bounds_required`](#qiskit.aqua.components.optimizers.GSLS.is_bounds_required "qiskit.aqua.components.optimizers.GSLS.is_bounds_required") | Returns is bounds required |
| [`is_bounds_supported`](#qiskit.aqua.components.optimizers.GSLS.is_bounds_supported "qiskit.aqua.components.optimizers.GSLS.is_bounds_supported") | Returns is bounds supported |
| [`is_gradient_ignored`](#qiskit.aqua.components.optimizers.GSLS.is_gradient_ignored "qiskit.aqua.components.optimizers.GSLS.is_gradient_ignored") | Returns is gradient ignored |
| [`is_gradient_required`](#qiskit.aqua.components.optimizers.GSLS.is_gradient_required "qiskit.aqua.components.optimizers.GSLS.is_gradient_required") | Returns is gradient required |
| [`is_gradient_supported`](#qiskit.aqua.components.optimizers.GSLS.is_gradient_supported "qiskit.aqua.components.optimizers.GSLS.is_gradient_supported") | Returns is gradient supported |
| [`is_initial_point_ignored`](#qiskit.aqua.components.optimizers.GSLS.is_initial_point_ignored "qiskit.aqua.components.optimizers.GSLS.is_initial_point_ignored") | Returns is initial point ignored |
| [`is_initial_point_required`](#qiskit.aqua.components.optimizers.GSLS.is_initial_point_required "qiskit.aqua.components.optimizers.GSLS.is_initial_point_required") | Returns is initial point required |
| [`is_initial_point_supported`](#qiskit.aqua.components.optimizers.GSLS.is_initial_point_supported "qiskit.aqua.components.optimizers.GSLS.is_initial_point_supported") | Returns is initial point supported |
| [`setting`](#qiskit.aqua.components.optimizers.GSLS.setting "qiskit.aqua.components.optimizers.GSLS.setting") | Return setting |
### bounds\_support\_level
<Attribute id="qiskit.aqua.components.optimizers.GSLS.bounds_support_level">
Returns bounds support level
</Attribute>
### get\_support\_level
<Function id="qiskit.aqua.components.optimizers.GSLS.get_support_level" signature="get_support_level()">
Return support level dictionary.
**Return type**
`Dict`\[`str`, `int`]
**Returns**
A dictionary containing the support levels for different options.
</Function>
### gradient\_approximation
<Function id="qiskit.aqua.components.optimizers.GSLS.gradient_approximation" signature="gradient_approximation(n, x, x_value, directions, sample_set_x, sample_set_y)">
Construct gradient approximation from given sample.
**Parameters**
* **n** (`int`) – Dimension of the problem.
* **x** (`ndarray`) – Point around which the sample set was constructed.
* **x\_value** (`float`) – Objective function value at x.
* **directions** (`ndarray`) – Directions of the sample points wrt the central point x, as a 2D array.
* **sample\_set\_x** (`ndarray`) – x-coordinates of the sample set, one point per row, as a 2D array.
* **sample\_set\_y** (`ndarray`) – Objective function values of the points in sample\_set\_x, as a 1D array.
**Return type**
`ndarray`
**Returns**
Gradient approximation at x, as a 1D array.
</Function>
### gradient\_num\_diff
<Function id="qiskit.aqua.components.optimizers.GSLS.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.aqua.components.optimizers.GSLS.gradient_support_level">
Returns gradient support level
</Attribute>
### initial\_point\_support\_level
<Attribute id="qiskit.aqua.components.optimizers.GSLS.initial_point_support_level">
Returns initial point support level
</Attribute>
### is\_bounds\_ignored
<Attribute id="qiskit.aqua.components.optimizers.GSLS.is_bounds_ignored">
Returns is bounds ignored
</Attribute>
### is\_bounds\_required
<Attribute id="qiskit.aqua.components.optimizers.GSLS.is_bounds_required">
Returns is bounds required
</Attribute>
### is\_bounds\_supported
<Attribute id="qiskit.aqua.components.optimizers.GSLS.is_bounds_supported">
Returns is bounds supported
</Attribute>
### is\_gradient\_ignored
<Attribute id="qiskit.aqua.components.optimizers.GSLS.is_gradient_ignored">
Returns is gradient ignored
</Attribute>
### is\_gradient\_required
<Attribute id="qiskit.aqua.components.optimizers.GSLS.is_gradient_required">
Returns is gradient required
</Attribute>
### is\_gradient\_supported
<Attribute id="qiskit.aqua.components.optimizers.GSLS.is_gradient_supported">
Returns is gradient supported
</Attribute>
### is\_initial\_point\_ignored
<Attribute id="qiskit.aqua.components.optimizers.GSLS.is_initial_point_ignored">
Returns is initial point ignored
</Attribute>
### is\_initial\_point\_required
<Attribute id="qiskit.aqua.components.optimizers.GSLS.is_initial_point_required">
Returns is initial point required
</Attribute>
### is\_initial\_point\_supported
<Attribute id="qiskit.aqua.components.optimizers.GSLS.is_initial_point_supported">
Returns is initial point supported
</Attribute>
### ls\_optimize
<Function id="qiskit.aqua.components.optimizers.GSLS.ls_optimize" signature="ls_optimize(n, obj_fun, initial_point, var_lb, var_ub)">
Run the line search optimization.
**Parameters**
* **n** (`int`) – Dimension of the problem.
* **obj\_fun** (`Callable`) – Objective function.
* **initial\_point** (`ndarray`) – Initial point.
* **var\_lb** (`ndarray`) – Vector of lower bounds on the decision variables. Vector elements can be -np.inf if the corresponding variable is unbounded from below.
* **var\_ub** (`ndarray`) – Vector of upper bounds on the decision variables. Vector elements can be np.inf if the corresponding variable is unbounded from below.
**Return type**
`Tuple`\[`ndarray`, `float`, `int`, `float`]
**Returns**
Final iterate as a vector, corresponding objective function value, number of evaluations, and norm of the gradient estimate.
**Raises**
**ValueError** – If the number of dimensions mismatches the size of the initial point or the length of the lower or upper bound.
</Function>
### optimize
<Function id="qiskit.aqua.components.optimizers.GSLS.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.
**Return type**
`Tuple`\[`ndarray`, `float`, `int`]
**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.aqua.components.optimizers.GSLS.print_options" signature="print_options()">
Print algorithm-specific options.
</Function>
### sample\_points
<Function id="qiskit.aqua.components.optimizers.GSLS.sample_points" signature="sample_points(n, x, num_points)">
Sample `num_points` points around `x` on the `n`-sphere of specified radius.
The radius of the sphere is `self._options['sampling_radius']`.
**Parameters**
* **n** (`int`) – Dimension of the problem.
* **x** (`ndarray`) – Point around which the sample set is constructed.
* **num\_points** (`int`) – Number of points in the sample set.
**Return type**
`Tuple`\[`ndarray`, `ndarray`]
**Returns**
A tuple containing the sampling points and the directions.
</Function>
### sample\_set
<Function id="qiskit.aqua.components.optimizers.GSLS.sample_set" signature="sample_set(n, x, var_lb, var_ub, num_points)">
Construct sample set of given size.
**Parameters**
* **n** (`int`) – Dimension of the problem.
* **x** (`ndarray`) – Point around which the sample set is constructed.
* **var\_lb** (`ndarray`) – Vector of lower bounds on the decision variables. Vector elements can be -np.inf if the corresponding variable is unbounded from below.
* **var\_ub** (`ndarray`) – Vector of lower bounds on the decision variables. Vector elements can be np.inf if the corresponding variable is unbounded from above.
* **num\_points** (`int`) – Number of points in the sample set.
**Return type**
`Tuple`\[`ndarray`, `ndarray`]
**Returns**
Matrices of (unit-norm) sample directions and sample points, one per row. Both matrices are 2D arrays of floats.
**Raises**
**RuntimeError** – If not enough samples could be generated within the bounds.
</Function>
### set\_max\_evals\_grouped
<Function id="qiskit.aqua.components.optimizers.GSLS.set_max_evals_grouped" signature="set_max_evals_grouped(limit)">
Set max evals grouped
</Function>
### set\_options
<Function id="qiskit.aqua.components.optimizers.GSLS.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.aqua.components.optimizers.GSLS.setting">
Return setting
</Attribute>
### wrap\_function
<Function id="qiskit.aqua.components.optimizers.GSLS.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>
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