---
title: IMFIL (v0.46)
description: API reference for qiskit.algorithms.optimizers.IMFIL in qiskit v0.46
source: https://eu-de.quantum.cloud.ibm.com/docs/en/api/qiskit/0.46/qiskit.algorithms.optimizers.IMFIL
---

# IMFIL

*class* `qiskit.algorithms.optimizers.IMFIL(maxiter=1000)`

[GitHub](https://github.com/qiskit/qiskit/tree/stable/0.46/qiskit/algorithms/optimizers/imfil.py)

Bases: [`Optimizer`](/docs/api/qiskit/0.46/qiskit.algorithms.optimizers.Optimizer "qiskit.algorithms.optimizers.optimizer.Optimizer")

IMplicit FILtering algorithm.

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.

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).

**Parameters**

**maxiter** ([*int*](https://docs.python.org/3/library/functions.html#int)) – Maximum number of function evaluations.

**Raises**

[**MissingOptionalLibraryError**](/docs/api/qiskit/0.46/exceptions#qiskit.exceptions.MissingOptionalLibraryError "qiskit.exceptions.MissingOptionalLibraryError") – scikit-quant not installed

## Attributes

### bounds\_support\_level

Returns bounds support level

### gradient\_support\_level

Returns gradient support level

### initial\_point\_support\_level

Returns initial point support level

### is\_bounds\_ignored

Returns is bounds ignored

### is\_bounds\_required

Returns is bounds required

### is\_bounds\_supported

Returns is bounds supported

### is\_gradient\_ignored

Returns is gradient ignored

### is\_gradient\_required

Returns is gradient required

### is\_gradient\_supported

Returns is gradient supported

### is\_initial\_point\_ignored

Returns is initial point ignored

### is\_initial\_point\_required

Returns is initial point required

### is\_initial\_point\_supported

Returns is initial point supported

### setting

Return setting

### settings

## Methods

### get\_support\_level

`get_support_level()`

Returns support level dictionary.

### gradient\_num\_diff

*static* `gradient_num_diff(x_center, f, epsilon, max_evals_grouped=None)`

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*](https://docs.python.org/3/library/functions.html#float)) – the epsilon used in the numeric differentiation.
- **max\_evals\_grouped** ([*int*](https://docs.python.org/3/library/functions.html#int)) – max evals grouped, defaults to 1 (i.e. no batching).

**Returns**

the gradient computed

**Return type**

grad

### minimize

`minimize(fun, x0, jac=None, bounds=None)`

Minimize the scalar function.

**Parameters**

- **fun** (*Callable\[\[POINT],* [*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The scalar function to minimize.
- **x0** (*POINT*) – The initial point for the minimization.
- **jac** (*Callable\[\[POINT], POINT] | None*) – The gradient of the scalar function `fun`.
- **bounds** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*tuple*](https://docs.python.org/3/library/stdtypes.html#tuple)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*,* [*float*](https://docs.python.org/3/library/functions.html#float)*]] | None*) – Bounds for the variables of `fun`. This argument might be ignored if the optimizer does not support bounds.

**Returns**

The result of the optimization, containing e.g. the result as attribute `x`.

**Return type**

[OptimizerResult](/docs/api/qiskit/0.46/qiskit.algorithms.optimizers.OptimizerResult "qiskit.algorithms.optimizers.OptimizerResult")

### print\_options

`print_options()`

Print algorithm-specific options.

### set\_max\_evals\_grouped

`set_max_evals_grouped(limit)`

Set max evals grouped

### set\_options

`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*](https://docs.python.org/3/library/stdtypes.html#dict)) – options, given as name=value.

### wrap\_function

*static* `wrap_function(function, args)`

Wrap the function to implicitly inject the args at the call of the function.

**Parameters**

- **function** (*func*) – the target function
- **args** ([*tuple*](https://docs.python.org/3/library/stdtypes.html#tuple)) – the args to be injected

**Returns**

wrapper

**Return type**

function\_wrapper