341 lines
13 KiB
Plaintext
341 lines
13 KiB
Plaintext
---
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title: GSLS
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description: API reference for qiskit.algorithms.optimizers.GSLS
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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.algorithms.optimizers.GSLS
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---
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# GSLS
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<Class id="qiskit.algorithms.optimizers.GSLS" isDedicatedPage={true} github="https://github.com/qiskit/qiskit/tree/stable/0.24/qiskit/algorithms/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)" modifiers="class">
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Bases: [`Optimizer`](qiskit.algorithms.optimizers.Optimizer "qiskit.algorithms.optimizers.optimizer.Optimizer")
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Gaussian-smoothed Line Search.
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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.
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<Admonition title="Note" type="note">
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This component has some function that is normally random. If you want to reproduce behavior then you should set the random number generator seed in the algorithm\_globals (`qiskit.utils.algorithm_globals.random_seed = seed`).
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</Admonition>
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**Parameters**
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* **maxiter** (*int*) – Maximum number of iterations.
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* **max\_eval** (*int*) – Maximum number of evaluations.
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* **disp** (*bool*) – Set to True to display convergence messages.
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* **sampling\_radius** (*float*) – Sampling radius to determine gradient estimate.
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* **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.
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* **initial\_step\_size** (*float*) – Initial step size for the descent algorithm.
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* **min\_step\_size** (*float*) – Minimum step size for the descent algorithm.
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* **step\_size\_multiplier** (*float*) – Step size reduction after unsuccessful steps, in the interval (0, 1).
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* **armijo\_parameter** (*float*) – Armijo parameter for sufficient decrease criterion, in the interval (0, 1).
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* **min\_gradient\_norm** (*float*) – If the gradient norm is below this threshold, the algorithm stops.
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* **max\_failed\_rejection\_sampling** (*int*) – Maximum number of attempts to sample points within bounds.
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## Methods
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<span id="qiskit-algorithms-optimizers-gsls-get-support-level" />
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### get\_support\_level
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<Function id="qiskit.algorithms.optimizers.GSLS.get_support_level" signature="GSLS.get_support_level()">
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Return support level dictionary.
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**Returns**
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A dictionary containing the support levels for different options.
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**Return type**
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dict\[str, int]
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</Function>
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<span id="qiskit-algorithms-optimizers-gsls-gradient-approximation" />
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### gradient\_approximation
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<Function id="qiskit.algorithms.optimizers.GSLS.gradient_approximation" signature="GSLS.gradient_approximation(n, x, x_value, directions, sample_set_x, sample_set_y)">
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Construct gradient approximation from given sample.
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**Parameters**
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* **n** (*int*) – Dimension of the problem.
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* **x** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray "(in NumPy v1.25)")) – Point around which the sample set was constructed.
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* **x\_value** (*float*) – Objective function value at x.
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* **directions** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray "(in NumPy v1.25)")) – Directions of the sample points wrt the central point x, as a 2D array.
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* **sample\_set\_x** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray "(in NumPy v1.25)")) – x-coordinates of the sample set, one point per row, as a 2D array.
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* **sample\_set\_y** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray "(in NumPy v1.25)")) – Objective function values of the points in sample\_set\_x, as a 1D array.
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**Returns**
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Gradient approximation at x, as a 1D array.
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**Return type**
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[*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray "(in NumPy v1.25)")
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</Function>
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<span id="qiskit-algorithms-optimizers-gsls-gradient-num-diff" />
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### gradient\_num\_diff
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<Function id="qiskit.algorithms.optimizers.GSLS.gradient_num_diff" signature="GSLS.gradient_num_diff(x_center, f, epsilon, max_evals_grouped=None)" 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, defaults to 1 (i.e. no batching).
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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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<span id="qiskit-algorithms-optimizers-gsls-ls-optimize" />
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### ls\_optimize
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<Function id="qiskit.algorithms.optimizers.GSLS.ls_optimize" signature="GSLS.ls_optimize(n, obj_fun, initial_point, var_lb, var_ub)">
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Run the line search optimization.
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**Parameters**
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* **n** (*int*) – Dimension of the problem.
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* **obj\_fun** (*Callable\[\[POINT], float]*) – Objective function.
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* **initial\_point** (*np.ndarray*) – Initial point.
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* **var\_lb** (*np.ndarray*) – Vector of lower bounds on the decision variables. Vector elements can be -np.inf if the corresponding variable is unbounded from below.
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* **var\_ub** (*np.ndarray*) – Vector of upper bounds on the decision variables. Vector elements can be np.inf if the corresponding variable is unbounded from below.
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**Returns**
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Final iterate as a vector, corresponding objective function value, number of evaluations, and norm of the gradient estimate.
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**Raises**
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**ValueError** – If the number of dimensions mismatches the size of the initial point or the length of the lower or upper bound.
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**Return type**
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tuple\[np.ndarray, float, int, float]
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</Function>
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<span id="qiskit-algorithms-optimizers-gsls-minimize" />
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### minimize
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<Function id="qiskit.algorithms.optimizers.GSLS.minimize" signature="GSLS.minimize(fun, x0, jac=None, bounds=None)">
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Minimize the scalar function.
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**Parameters**
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* **fun** (*Callable\[\[POINT], float]*) – The scalar function to minimize.
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* **x0** (*POINT*) – The initial point for the minimization.
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* **jac** (*Callable\[\[POINT], POINT] | None*) – The gradient of the scalar function `fun`.
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* **bounds** (*list\[tuple\[float, float]] | None*) – Bounds for the variables of `fun`. This argument might be ignored if the optimizer does not support bounds.
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**Returns**
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The result of the optimization, containing e.g. the result as attribute `x`.
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**Return type**
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[OptimizerResult](qiskit.algorithms.optimizers.OptimizerResult "qiskit.algorithms.optimizers.OptimizerResult")
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</Function>
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<span id="qiskit-algorithms-optimizers-gsls-print-options" />
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### print\_options
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<Function id="qiskit.algorithms.optimizers.GSLS.print_options" signature="GSLS.print_options()">
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Print algorithm-specific options.
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</Function>
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<span id="qiskit-algorithms-optimizers-gsls-sample-points" />
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### sample\_points
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<Function id="qiskit.algorithms.optimizers.GSLS.sample_points" signature="GSLS.sample_points(n, x, num_points)">
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Sample `num_points` points around `x` on the `n`-sphere of specified radius.
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The radius of the sphere is `self._options['sampling_radius']`.
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**Parameters**
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* **n** (*int*) – Dimension of the problem.
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* **x** (*np.ndarray*) – Point around which the sample set is constructed.
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* **num\_points** (*int*) – Number of points in the sample set.
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**Returns**
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A tuple containing the sampling points and the directions.
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**Return type**
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tuple\[np.ndarray, np.ndarray]
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</Function>
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<span id="qiskit-algorithms-optimizers-gsls-sample-set" />
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### sample\_set
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<Function id="qiskit.algorithms.optimizers.GSLS.sample_set" signature="GSLS.sample_set(n, x, var_lb, var_ub, num_points)">
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Construct sample set of given size.
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**Parameters**
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* **n** (*int*) – Dimension of the problem.
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* **x** (*np.ndarray*) – Point around which the sample set is constructed.
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* **var\_lb** (*np.ndarray*) – Vector of lower bounds on the decision variables. Vector elements can be -np.inf if the corresponding variable is unbounded from below.
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* **var\_ub** (*np.ndarray*) – Vector of lower bounds on the decision variables. Vector elements can be np.inf if the corresponding variable is unbounded from above.
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* **num\_points** (*int*) – Number of points in the sample set.
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**Returns**
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Matrices of (unit-norm) sample directions and sample points, one per row. Both matrices are 2D arrays of floats.
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**Raises**
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**RuntimeError** – If not enough samples could be generated within the bounds.
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**Return type**
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tuple\[np.ndarray, np.ndarray]
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</Function>
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<span id="qiskit-algorithms-optimizers-gsls-set-max-evals-grouped" />
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### set\_max\_evals\_grouped
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<Function id="qiskit.algorithms.optimizers.GSLS.set_max_evals_grouped" signature="GSLS.set_max_evals_grouped(limit)">
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Set max evals grouped
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</Function>
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<span id="qiskit-algorithms-optimizers-gsls-set-options" />
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### set\_options
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<Function id="qiskit.algorithms.optimizers.GSLS.set_options" signature="GSLS.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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<span id="qiskit-algorithms-optimizers-gsls-wrap-function" />
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### wrap\_function
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<Function id="qiskit.algorithms.optimizers.GSLS.wrap_function" signature="GSLS.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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## Attributes
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### bounds\_support\_level
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<Attribute id="qiskit.algorithms.optimizers.GSLS.bounds_support_level">
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Returns bounds support level
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</Attribute>
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### gradient\_support\_level
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<Attribute id="qiskit.algorithms.optimizers.GSLS.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.algorithms.optimizers.GSLS.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.algorithms.optimizers.GSLS.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.algorithms.optimizers.GSLS.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.algorithms.optimizers.GSLS.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.algorithms.optimizers.GSLS.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.algorithms.optimizers.GSLS.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.algorithms.optimizers.GSLS.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.algorithms.optimizers.GSLS.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.algorithms.optimizers.GSLS.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.algorithms.optimizers.GSLS.is_initial_point_supported">
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Returns is initial point supported
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</Attribute>
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### setting
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<Attribute id="qiskit.algorithms.optimizers.GSLS.setting">
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Return setting
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</Attribute>
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### settings
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<Attribute id="qiskit.algorithms.optimizers.GSLS.settings" />
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</Class>
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