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

# CG

*class* `qiskit.algorithms.optimizers.CG(maxiter=20, disp=False, gtol=1e-05, tol=None, eps=1.4901161193847656e-08, options=None, max_evals_grouped=1, **kwargs)`

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

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

Conjugate Gradient optimizer.

CG is an algorithm for the numerical solution of systems of linear equations whose matrices are symmetric and positive-definite. It is an *iterative algorithm* in that it uses an initial guess to generate a sequence of improving approximate solutions for a problem, in which each approximation is derived from the previous ones. It is often used to solve unconstrained optimization problems, such as energy minimization.

Uses scipy.optimize.minimize CG. For further detail, please refer to [https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html)

**Parameters**

- **maxiter** ([*int*](https://docs.python.org/3/library/functions.html#int)) – Maximum number of iterations to perform.
- **disp** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) – Set to True to print convergence messages.
- **gtol** ([*float*](https://docs.python.org/3/library/functions.html#float)) – Gradient norm must be less than gtol before successful termination.
- **tol** ([*float*](https://docs.python.org/3/library/functions.html#float) *| None*) – Tolerance for termination.
- **eps** ([*float*](https://docs.python.org/3/library/functions.html#float)) – If jac is approximated, use this value for the step size.
- **options** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict) *| None*) – A dictionary of solver options.
- **max\_evals\_grouped** ([*int*](https://docs.python.org/3/library/functions.html#int)) – Max number of default gradient evaluations performed simultaneously.
- **kwargs** – additional kwargs for scipy.optimize.minimize.

## 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()`

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