237 lines
7.3 KiB
Plaintext
237 lines
7.3 KiB
Plaintext
---
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title: ESCH (v0.31)
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description: API reference for qiskit.algorithms.optimizers.ESCH in qiskit v0.31
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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.ESCH
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---
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# ESCH
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<Class id="qiskit.algorithms.optimizers.ESCH" isDedicatedPage={true} github="https://github.com/qiskit/qiskit/tree/stable/0.18/qiskit/algorithms/optimizers/nlopts/esch.py" signature="ESCH(max_evals=1000)" modifiers="class">
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Bases: `qiskit.algorithms.optimizers.nlopts.nloptimizer.NLoptOptimizer`
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ESCH evolutionary optimizer.
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ESCH is an evolutionary algorithm for global optimization that supports bound constraints only. Specifically, it does not support nonlinear constraints.
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NLopt global optimizer, derivative-free. For further detail, please refer to
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[http://nlopt.readthedocs.io/en/latest/NLopt\_Algorithms/#esch-evolutionary-algorithm](http://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/#esch-evolutionary-algorithm)
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**Parameters**
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**max\_evals** (`int`) – Maximum allowed number of function evaluations.
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**Raises**
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[**MissingOptionalLibraryError**](qiskit.aqua.MissingOptionalLibraryError "qiskit.aqua.MissingOptionalLibraryError") – NLopt library not installed.
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## Methods
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<span id="qiskit-algorithms-optimizers-esch-get-nlopt-optimizer" />
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### get\_nlopt\_optimizer
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<Function id="qiskit.algorithms.optimizers.ESCH.get_nlopt_optimizer" signature="ESCH.get_nlopt_optimizer()">
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Return NLopt optimizer type
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**Return type**
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`NLoptOptimizerType`
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</Function>
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<span id="qiskit-algorithms-optimizers-esch-get-support-level" />
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### get\_support\_level
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<Function id="qiskit.algorithms.optimizers.ESCH.get_support_level" signature="ESCH.get_support_level()">
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return support level dictionary
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</Function>
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<span id="qiskit-algorithms-optimizers-esch-gradient-num-diff" />
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### gradient\_num\_diff
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<Function id="qiskit.algorithms.optimizers.ESCH.gradient_num_diff" signature="ESCH.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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<span id="qiskit-algorithms-optimizers-esch-optimize" />
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### optimize
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<Function id="qiskit.algorithms.optimizers.ESCH.optimize" signature="ESCH.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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<span id="qiskit-algorithms-optimizers-esch-print-options" />
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### print\_options
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<Function id="qiskit.algorithms.optimizers.ESCH.print_options" signature="ESCH.print_options()">
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Print algorithm-specific options.
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</Function>
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<span id="qiskit-algorithms-optimizers-esch-set-max-evals-grouped" />
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### set\_max\_evals\_grouped
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<Function id="qiskit.algorithms.optimizers.ESCH.set_max_evals_grouped" signature="ESCH.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-esch-set-options" />
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### set\_options
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<Function id="qiskit.algorithms.optimizers.ESCH.set_options" signature="ESCH.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-esch-wrap-function" />
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### wrap\_function
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<Function id="qiskit.algorithms.optimizers.ESCH.wrap_function" signature="ESCH.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.ESCH.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.ESCH.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.ESCH.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.ESCH.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.ESCH.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.ESCH.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.ESCH.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.ESCH.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.ESCH.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.ESCH.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.ESCH.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.ESCH.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.ESCH.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.ESCH.settings" />
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</Class>
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