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---
title: CG
description: API reference for qiskit.algorithms.optimizers.CG
in_page_toc_min_heading_level: 1
python_api_type: class
python_api_name: qiskit.algorithms.optimizers.CG
---
# CG
<Class id="qiskit.algorithms.optimizers.CG" isDedicatedPage={true} github="https://github.com/qiskit/qiskit/tree/stable/0.45/qiskit/algorithms/optimizers/cg.py" signature="qiskit.algorithms.optimizers.CG(maxiter=20, disp=False, gtol=1e-05, tol=None, eps=1.4901161193847656e-08, options=None, max_evals_grouped=1, **kwargs)" modifiers="class">
Bases: [`SciPyOptimizer`](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 "(in Python v3.12)")) Maximum number of iterations to perform.
* **disp** ([*bool*](https://docs.python.org/3/library/functions.html#bool "(in Python v3.12)")) Set to True to print convergence messages.
* **gtol** ([*float*](https://docs.python.org/3/library/functions.html#float "(in Python v3.12)")) Gradient norm must be less than gtol before successful termination.
* **tol** ([*float*](https://docs.python.org/3/library/functions.html#float "(in Python v3.12)") *| None*) Tolerance for termination.
* **eps** ([*float*](https://docs.python.org/3/library/functions.html#float "(in Python v3.12)")) If jac is approximated, use this value for the step size.
* **options** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict "(in Python v3.12)") *| None*) A dictionary of solver options.
* **max\_evals\_grouped** ([*int*](https://docs.python.org/3/library/functions.html#int "(in Python v3.12)")) Max number of default gradient evaluations performed simultaneously.
* **kwargs** additional kwargs for scipy.optimize.minimize.
## Attributes
### bounds\_support\_level
<Attribute id="qiskit.algorithms.optimizers.CG.bounds_support_level">
Returns bounds support level
</Attribute>
### gradient\_support\_level
<Attribute id="qiskit.algorithms.optimizers.CG.gradient_support_level">
Returns gradient support level
</Attribute>
### initial\_point\_support\_level
<Attribute id="qiskit.algorithms.optimizers.CG.initial_point_support_level">
Returns initial point support level
</Attribute>
### is\_bounds\_ignored
<Attribute id="qiskit.algorithms.optimizers.CG.is_bounds_ignored">
Returns is bounds ignored
</Attribute>
### is\_bounds\_required
<Attribute id="qiskit.algorithms.optimizers.CG.is_bounds_required">
Returns is bounds required
</Attribute>
### is\_bounds\_supported
<Attribute id="qiskit.algorithms.optimizers.CG.is_bounds_supported">
Returns is bounds supported
</Attribute>
### is\_gradient\_ignored
<Attribute id="qiskit.algorithms.optimizers.CG.is_gradient_ignored">
Returns is gradient ignored
</Attribute>
### is\_gradient\_required
<Attribute id="qiskit.algorithms.optimizers.CG.is_gradient_required">
Returns is gradient required
</Attribute>
### is\_gradient\_supported
<Attribute id="qiskit.algorithms.optimizers.CG.is_gradient_supported">
Returns is gradient supported
</Attribute>
### is\_initial\_point\_ignored
<Attribute id="qiskit.algorithms.optimizers.CG.is_initial_point_ignored">
Returns is initial point ignored
</Attribute>
### is\_initial\_point\_required
<Attribute id="qiskit.algorithms.optimizers.CG.is_initial_point_required">
Returns is initial point required
</Attribute>
### is\_initial\_point\_supported
<Attribute id="qiskit.algorithms.optimizers.CG.is_initial_point_supported">
Returns is initial point supported
</Attribute>
### setting
<Attribute id="qiskit.algorithms.optimizers.CG.setting">
Return setting
</Attribute>
### settings
<Attribute id="qiskit.algorithms.optimizers.CG.settings" />
## Methods
### get\_support\_level
<Function id="qiskit.algorithms.optimizers.CG.get_support_level" signature="get_support_level()">
Return support level dictionary
</Function>
### gradient\_num\_diff
<Function id="qiskit.algorithms.optimizers.CG.gradient_num_diff" signature="gradient_num_diff(x_center, f, epsilon, max_evals_grouped=None)" modifiers="static">
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 "(in Python v3.12)")) the epsilon used in the numeric differentiation.
* **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).
**Returns**
the gradient computed
**Return type**
grad
</Function>
### minimize
<Function id="qiskit.algorithms.optimizers.CG.minimize" signature="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 "(in Python v3.12)")*]*) 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 "(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.
**Returns**
The result of the optimization, containing e.g. the result as attribute `x`.
**Return type**
[OptimizerResult](qiskit.algorithms.optimizers.OptimizerResult "qiskit.algorithms.optimizers.OptimizerResult")
</Function>
### print\_options
<Function id="qiskit.algorithms.optimizers.CG.print_options" signature="print_options()">
Print algorithm-specific options.
</Function>
### set\_max\_evals\_grouped
<Function id="qiskit.algorithms.optimizers.CG.set_max_evals_grouped" signature="set_max_evals_grouped(limit)">
Set max evals grouped
</Function>
### set\_options
<Function id="qiskit.algorithms.optimizers.CG.set_options" signature="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 "(in Python v3.12)")) options, given as name=value.
</Function>
### wrap\_function
<Function id="qiskit.algorithms.optimizers.CG.wrap_function" signature="wrap_function(function, args)" modifiers="static">
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 "(in Python v3.12)")) the args to be injected
**Returns**
wrapper
**Return type**
function\_wrapper
</Function>
</Class>