206 lines
7.4 KiB
Plaintext
206 lines
7.4 KiB
Plaintext
---
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title: IMFIL
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description: API reference for qiskit.algorithms.optimizers.IMFIL
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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.IMFIL
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---
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# IMFIL
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<Class id="qiskit.algorithms.optimizers.IMFIL" isDedicatedPage={true} github="https://github.com/qiskit/qiskit/tree/stable/0.46/qiskit/algorithms/optimizers/imfil.py" signature="qiskit.algorithms.optimizers.IMFIL(maxiter=1000)" modifiers="class">
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Bases: [`Optimizer`](qiskit.algorithms.optimizers.Optimizer "qiskit.algorithms.optimizers.optimizer.Optimizer")
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IMplicit FILtering algorithm.
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Implicit filtering is a way to solve bound-constrained optimization problems for which derivatives are not available. In comparison to methods that use interpolation to reconstruct the function and its higher derivatives, implicit filtering builds upon coordinate search followed by interpolation to get an approximate gradient.
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Uses skquant.opt installed with pip install scikit-quant. For further detail, please refer to [https://github.com/scikit-quant/scikit-quant](https://github.com/scikit-quant/scikit-quant) and [https://qat4chem.lbl.gov/software](https://qat4chem.lbl.gov/software).
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**Parameters**
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**maxiter** ([*int*](https://docs.python.org/3/library/functions.html#int "(in Python v3.12)")) – Maximum number of function evaluations.
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**Raises**
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[**MissingOptionalLibraryError**](exceptions#qiskit.exceptions.MissingOptionalLibraryError "qiskit.exceptions.MissingOptionalLibraryError") – scikit-quant not installed
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## Attributes
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### bounds\_support\_level
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<Attribute id="qiskit.algorithms.optimizers.IMFIL.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.IMFIL.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.IMFIL.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.IMFIL.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.IMFIL.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.IMFIL.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.IMFIL.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.IMFIL.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.IMFIL.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.IMFIL.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.IMFIL.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.IMFIL.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.IMFIL.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.IMFIL.settings" />
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## Methods
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### get\_support\_level
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<Function id="qiskit.algorithms.optimizers.IMFIL.get_support_level" signature="get_support_level()">
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Returns support level dictionary.
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</Function>
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### gradient\_num\_diff
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<Function id="qiskit.algorithms.optimizers.IMFIL.gradient_num_diff" signature="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*](https://docs.python.org/3/library/functions.html#float "(in Python v3.12)")) – the epsilon used in the numeric differentiation.
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* **max\_evals\_grouped** ([*int*](https://docs.python.org/3/library/functions.html#int "(in Python v3.12)")) – 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.IMFIL.minimize" signature="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*](https://docs.python.org/3/library/functions.html#float "(in Python v3.12)")*]*) – 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*](https://docs.python.org/3/library/stdtypes.html#list "(in Python v3.12)")*\[*[*tuple*](https://docs.python.org/3/library/stdtypes.html#tuple "(in Python v3.12)")*\[*[*float*](https://docs.python.org/3/library/functions.html#float "(in Python v3.12)")*,* [*float*](https://docs.python.org/3/library/functions.html#float "(in Python v3.12)")*]] | 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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### print\_options
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<Function id="qiskit.algorithms.optimizers.IMFIL.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.algorithms.optimizers.IMFIL.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.algorithms.optimizers.IMFIL.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*](https://docs.python.org/3/library/stdtypes.html#dict "(in Python v3.12)")) – 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.IMFIL.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*](https://docs.python.org/3/library/stdtypes.html#tuple "(in Python v3.12)")) – 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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