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

# CRS

*class* `qiskit.algorithms.optimizers.CRS(max_evals=1000)`

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

Bases: `NLoptOptimizer`

Controlled Random Search (CRS) with local mutation optimizer.

Controlled Random Search (CRS) with local mutation is part of the family of the CRS optimizers. The CRS optimizers start with a random population of points, and randomly evolve these points by heuristic rules. In the case of CRS with local mutation, the evolution is a randomized version of the [`NELDER_MEAD`](/docs/api/qiskit/0.46/qiskit.algorithms.optimizers.NELDER_MEAD "qiskit.algorithms.optimizers.NELDER_MEAD") local optimizer.

NLopt global optimizer, derivative-free. For further detail, please refer to [https://nlopt.readthedocs.io/en/latest/NLopt\_Algorithms/#controlled-random-search-crs-with-local-mutation](https://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/#controlled-random-search-crs-with-local-mutation)

**Parameters**

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

**Raises**

[**MissingOptionalLibraryError**](/docs/api/qiskit/0.46/exceptions#qiskit.exceptions.MissingOptionalLibraryError "qiskit.exceptions.MissingOptionalLibraryError") – NLopt library 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\_nlopt\_optimizer

`get_nlopt_optimizer()`

Return NLopt optimizer type

**Return type**

*NLoptOptimizerType*

### get\_support\_level

`get_support_level()`

return 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