216 lines
7.0 KiB
Plaintext
216 lines
7.0 KiB
Plaintext
---
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title: DIRECT_L
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description: API reference for qiskit.algorithms.optimizers.DIRECT_L
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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.DIRECT_L
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---
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# DIRECT\_L
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<Class id="qiskit.algorithms.optimizers.DIRECT_L" isDedicatedPage={true} github="https://github.com/qiskit/qiskit/tree/stable/0.22/qiskit/algorithms/optimizers/nlopts/direct_l.py" signature="DIRECT_L(max_evals=1000)" modifiers="class">
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Bases: `qiskit.algorithms.optimizers.nlopts.nloptimizer.NLoptOptimizer`
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DIviding RECTangles Locally-biased optimizer.
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DIviding RECTangles (DIRECT) is a deterministic-search algorithms based on systematic division of the search domain into increasingly smaller hyper-rectangles. The DIRECT-L version is a “locally biased” variant of DIRECT that makes the algorithm more biased towards local search, so that it is more efficient for functions with few local minima.
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NLopt global optimizer, derivative-free. For further detail, please refer to [http://nlopt.readthedocs.io/en/latest/NLopt\_Algorithms/#direct-and-direct-l](http://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/#direct-and-direct-l)
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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** – NLopt library not installed.
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## Methods
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### get\_nlopt\_optimizer
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<Function id="qiskit.algorithms.optimizers.DIRECT_L.get_nlopt_optimizer" signature="DIRECT_L.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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### get\_support\_level
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<Function id="qiskit.algorithms.optimizers.DIRECT_L.get_support_level" signature="DIRECT_L.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.algorithms.optimizers.DIRECT_L.gradient_num_diff" signature="DIRECT_L.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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### minimize
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<Function id="qiskit.algorithms.optimizers.DIRECT_L.minimize" signature="DIRECT_L.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`\[\[`Union`\[`float`, `ndarray`]], `float`]) – The scalar function to minimize.
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* **x0** (`Union`\[`float`, `ndarray`]) – The initial point for the minimization.
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* **jac** (`Optional`\[`Callable`\[\[`Union`\[`float`, `ndarray`]], `Union`\[`float`, `ndarray`]]]) – The gradient of the scalar function `fun`.
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* **bounds** (`Optional`\[`List`\[`Tuple`\[`float`, `float`]]]) – Bounds for the variables of `fun`. This argument might be ignored if the optimizer does not support bounds.
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**Return type**
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[`OptimizerResult`](qiskit.algorithms.optimizers.OptimizerResult "qiskit.algorithms.optimizers.optimizer.OptimizerResult")
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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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</Function>
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### print\_options
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<Function id="qiskit.algorithms.optimizers.DIRECT_L.print_options" signature="DIRECT_L.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.algorithms.optimizers.DIRECT_L.set_max_evals_grouped" signature="DIRECT_L.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.algorithms.optimizers.DIRECT_L.set_options" signature="DIRECT_L.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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### wrap\_function
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<Function id="qiskit.algorithms.optimizers.DIRECT_L.wrap_function" signature="DIRECT_L.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.DIRECT_L.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.DIRECT_L.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.DIRECT_L.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.DIRECT_L.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.DIRECT_L.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.DIRECT_L.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.DIRECT_L.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.DIRECT_L.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.DIRECT_L.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.DIRECT_L.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.DIRECT_L.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.DIRECT_L.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.DIRECT_L.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.DIRECT_L.settings" />
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</Class>
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