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---
title: ADAM
description: API reference for qiskit.aqua.components.optimizers.ADAM
in_page_toc_min_heading_level: 1
python_api_type: class
python_api_name: qiskit.aqua.components.optimizers.ADAM
---
<span id="qiskit-aqua-components-optimizers-adam" />
# qiskit.aqua.components.optimizers.ADAM
<Class id="qiskit.aqua.components.optimizers.ADAM" isDedicatedPage={true} github="https://github.com/qiskit-community/qiskit-aqua/tree/stable/0.8/qiskit/aqua/components/optimizers/adam_amsgrad.py" signature="ADAM(maxiter=10000, tol=1e-06, lr=0.001, beta_1=0.9, beta_2=0.99, noise_factor=1e-08, eps=1e-10, amsgrad=False, snapshot_dir=None)" modifiers="class">
Adam and AMSGRAD optimizers.
Adam \[1] is a gradient-based optimization algorithm that is relies on adaptive estimates of lower-order moments. The algorithm requires little memory and is invariant to diagonal rescaling of the gradients. Furthermore, it is able to cope with non-stationary objective functions and noisy and/or sparse gradients.
AMSGRAD \[2] (a variant of Adam) uses a ‘long-term memory’ of past gradients and, thereby, improves convergence properties.
**References**
**\[1]: Kingma, Diederik & Ba, Jimmy (2014), Adam: A Method for Stochastic Optimization.**
[arXiv:1412.6980](https://arxiv.org/abs/1412.6980)
**\[2]: Sashank J. Reddi and Satyen Kale and Sanjiv Kumar (2018),**
On the Convergence of Adam and Beyond. [arXiv:1904.09237](https://arxiv.org/abs/1904.09237)
**Parameters**
* **maxiter** (`int`) – Maximum number of iterations
* **tol** (`float`) – Tolerance for termination
* **lr** (`float`) – Value >= 0, Learning rate.
* **beta\_1** (`float`) – Value in range 0 to 1, Generally close to 1.
* **beta\_2** (`float`) – Value in range 0 to 1, Generally close to 1.
* **noise\_factor** (`float`) – Value >= 0, Noise factor
* **eps** (`float`) – Value >=0, Epsilon to be used for finite differences if no analytic gradient method is given.
* **amsgrad** (`bool`) – True to use AMSGRAD, False if not
* **snapshot\_dir** (`Optional`\[`str`]) – If not None save the optimizer’s parameter after every step to the given directory
### \_\_init\_\_
<Function id="qiskit.aqua.components.optimizers.ADAM.__init__" signature="__init__(maxiter=10000, tol=1e-06, lr=0.001, beta_1=0.9, beta_2=0.99, noise_factor=1e-08, eps=1e-10, amsgrad=False, snapshot_dir=None)">
**Parameters**
* **maxiter** (`int`) – Maximum number of iterations
* **tol** (`float`) – Tolerance for termination
* **lr** (`float`) – Value >= 0, Learning rate.
* **beta\_1** (`float`) – Value in range 0 to 1, Generally close to 1.
* **beta\_2** (`float`) – Value in range 0 to 1, Generally close to 1.
* **noise\_factor** (`float`) – Value >= 0, Noise factor
* **eps** (`float`) – Value >=0, Epsilon to be used for finite differences if no analytic gradient method is given.
* **amsgrad** (`bool`) – True to use AMSGRAD, False if not
* **snapshot\_dir** (`Optional`\[`str`]) – If not None save the optimizer’s parameter after every step to the given directory
</Function>
## Methods
| | |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------- |
| [`__init__`](#qiskit.aqua.components.optimizers.ADAM.__init__ "qiskit.aqua.components.optimizers.ADAM.__init__")(\[maxiter, tol, lr, beta\_1, beta\_2, …]) | **type maxiter**`int` |
| [`get_support_level`](#qiskit.aqua.components.optimizers.ADAM.get_support_level "qiskit.aqua.components.optimizers.ADAM.get_support_level")() | Return support level dictionary |
| [`gradient_num_diff`](#qiskit.aqua.components.optimizers.ADAM.gradient_num_diff "qiskit.aqua.components.optimizers.ADAM.gradient_num_diff")(x\_center, f, epsilon\[, …]) | We compute the gradient with the numeric differentiation in the parallel way, around the point x\_center. |
| [`load_params`](#qiskit.aqua.components.optimizers.ADAM.load_params "qiskit.aqua.components.optimizers.ADAM.load_params")(load\_dir) | Load iteration parameters for a file called `adam_params.csv`. |
| [`minimize`](#qiskit.aqua.components.optimizers.ADAM.minimize "qiskit.aqua.components.optimizers.ADAM.minimize")(objective\_function, initial\_point, …) | Run the minimization. |
| [`optimize`](#qiskit.aqua.components.optimizers.ADAM.optimize "qiskit.aqua.components.optimizers.ADAM.optimize")(num\_vars, objective\_function\[, …]) | Perform optimization. |
| [`print_options`](#qiskit.aqua.components.optimizers.ADAM.print_options "qiskit.aqua.components.optimizers.ADAM.print_options")() | Print algorithm-specific options. |
| [`save_params`](#qiskit.aqua.components.optimizers.ADAM.save_params "qiskit.aqua.components.optimizers.ADAM.save_params")(snapshot\_dir) | Save the current iteration parameters to a file called `adam_params.csv`. |
| [`set_max_evals_grouped`](#qiskit.aqua.components.optimizers.ADAM.set_max_evals_grouped "qiskit.aqua.components.optimizers.ADAM.set_max_evals_grouped")(limit) | Set max evals grouped |
| [`set_options`](#qiskit.aqua.components.optimizers.ADAM.set_options "qiskit.aqua.components.optimizers.ADAM.set_options")(\*\*kwargs) | Sets or updates values in the options dictionary. |
| [`wrap_function`](#qiskit.aqua.components.optimizers.ADAM.wrap_function "qiskit.aqua.components.optimizers.ADAM.wrap_function")(function, args) | Wrap the function to implicitly inject the args at the call of the function. |
## Attributes
| | |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------- |
| [`bounds_support_level`](#qiskit.aqua.components.optimizers.ADAM.bounds_support_level "qiskit.aqua.components.optimizers.ADAM.bounds_support_level") | Returns bounds support level |
| [`gradient_support_level`](#qiskit.aqua.components.optimizers.ADAM.gradient_support_level "qiskit.aqua.components.optimizers.ADAM.gradient_support_level") | Returns gradient support level |
| [`initial_point_support_level`](#qiskit.aqua.components.optimizers.ADAM.initial_point_support_level "qiskit.aqua.components.optimizers.ADAM.initial_point_support_level") | Returns initial point support level |
| [`is_bounds_ignored`](#qiskit.aqua.components.optimizers.ADAM.is_bounds_ignored "qiskit.aqua.components.optimizers.ADAM.is_bounds_ignored") | Returns is bounds ignored |
| [`is_bounds_required`](#qiskit.aqua.components.optimizers.ADAM.is_bounds_required "qiskit.aqua.components.optimizers.ADAM.is_bounds_required") | Returns is bounds required |
| [`is_bounds_supported`](#qiskit.aqua.components.optimizers.ADAM.is_bounds_supported "qiskit.aqua.components.optimizers.ADAM.is_bounds_supported") | Returns is bounds supported |
| [`is_gradient_ignored`](#qiskit.aqua.components.optimizers.ADAM.is_gradient_ignored "qiskit.aqua.components.optimizers.ADAM.is_gradient_ignored") | Returns is gradient ignored |
| [`is_gradient_required`](#qiskit.aqua.components.optimizers.ADAM.is_gradient_required "qiskit.aqua.components.optimizers.ADAM.is_gradient_required") | Returns is gradient required |
| [`is_gradient_supported`](#qiskit.aqua.components.optimizers.ADAM.is_gradient_supported "qiskit.aqua.components.optimizers.ADAM.is_gradient_supported") | Returns is gradient supported |
| [`is_initial_point_ignored`](#qiskit.aqua.components.optimizers.ADAM.is_initial_point_ignored "qiskit.aqua.components.optimizers.ADAM.is_initial_point_ignored") | Returns is initial point ignored |
| [`is_initial_point_required`](#qiskit.aqua.components.optimizers.ADAM.is_initial_point_required "qiskit.aqua.components.optimizers.ADAM.is_initial_point_required") | Returns is initial point required |
| [`is_initial_point_supported`](#qiskit.aqua.components.optimizers.ADAM.is_initial_point_supported "qiskit.aqua.components.optimizers.ADAM.is_initial_point_supported") | Returns is initial point supported |
| [`setting`](#qiskit.aqua.components.optimizers.ADAM.setting "qiskit.aqua.components.optimizers.ADAM.setting") | Return setting |
### bounds\_support\_level
<Attribute id="qiskit.aqua.components.optimizers.ADAM.bounds_support_level">
Returns bounds support level
</Attribute>
### get\_support\_level
<Function id="qiskit.aqua.components.optimizers.ADAM.get_support_level" signature="get_support_level()">
Return support level dictionary
</Function>
### gradient\_num\_diff
<Function id="qiskit.aqua.components.optimizers.ADAM.gradient_num_diff" signature="gradient_num_diff(x_center, f, epsilon, max_evals_grouped=1)" 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*) – the epsilon used in the numeric differentiation.
* **max\_evals\_grouped** (*int*) – max evals grouped
**Returns**
the gradient computed
**Return type**
grad
</Function>
### gradient\_support\_level
<Attribute id="qiskit.aqua.components.optimizers.ADAM.gradient_support_level">
Returns gradient support level
</Attribute>
### initial\_point\_support\_level
<Attribute id="qiskit.aqua.components.optimizers.ADAM.initial_point_support_level">
Returns initial point support level
</Attribute>
### is\_bounds\_ignored
<Attribute id="qiskit.aqua.components.optimizers.ADAM.is_bounds_ignored">
Returns is bounds ignored
</Attribute>
### is\_bounds\_required
<Attribute id="qiskit.aqua.components.optimizers.ADAM.is_bounds_required">
Returns is bounds required
</Attribute>
### is\_bounds\_supported
<Attribute id="qiskit.aqua.components.optimizers.ADAM.is_bounds_supported">
Returns is bounds supported
</Attribute>
### is\_gradient\_ignored
<Attribute id="qiskit.aqua.components.optimizers.ADAM.is_gradient_ignored">
Returns is gradient ignored
</Attribute>
### is\_gradient\_required
<Attribute id="qiskit.aqua.components.optimizers.ADAM.is_gradient_required">
Returns is gradient required
</Attribute>
### is\_gradient\_supported
<Attribute id="qiskit.aqua.components.optimizers.ADAM.is_gradient_supported">
Returns is gradient supported
</Attribute>
### is\_initial\_point\_ignored
<Attribute id="qiskit.aqua.components.optimizers.ADAM.is_initial_point_ignored">
Returns is initial point ignored
</Attribute>
### is\_initial\_point\_required
<Attribute id="qiskit.aqua.components.optimizers.ADAM.is_initial_point_required">
Returns is initial point required
</Attribute>
### is\_initial\_point\_supported
<Attribute id="qiskit.aqua.components.optimizers.ADAM.is_initial_point_supported">
Returns is initial point supported
</Attribute>
### load\_params
<Function id="qiskit.aqua.components.optimizers.ADAM.load_params" signature="load_params(load_dir)">
Load iteration parameters for a file called `adam_params.csv`.
**Parameters**
**load\_dir** (`str`) – The directory containing `adam_params.csv`.
**Return type**
`None`
</Function>
### minimize
<Function id="qiskit.aqua.components.optimizers.ADAM.minimize" signature="minimize(objective_function, initial_point, gradient_function)">
Run the minimization.
**Parameters**
* **objective\_function** (`Callable`\[\[`ndarray`], `float`]) – A function handle to the objective function.
* **initial\_point** (`ndarray`) – The initial iteration point.
* **gradient\_function** (`Callable`\[\[`ndarray`], `float`]) – A function handle to the gradient of the objective function.
**Return type**
`Tuple`\[`ndarray`, `float`, `int`]
**Returns**
A tuple of (optimal parameters, optimal value, number of iterations).
</Function>
### optimize
<Function id="qiskit.aqua.components.optimizers.ADAM.optimize" signature="optimize(num_vars, objective_function, gradient_function=None, variable_bounds=None, initial_point=None)">
Perform optimization.
**Parameters**
* **num\_vars** (`int`) – Number of parameters to be optimized.
* **objective\_function** (`Callable`\[\[`ndarray`], `float`]) – Handle to a function that computes the objective function.
* **gradient\_function** (`Optional`\[`Callable`\[\[`ndarray`], `float`]]) – Handle to a function that computes the gradient of the objective function.
* **variable\_bounds** (`Optional`\[`List`\[`Tuple`\[`float`, `float`]]]) – deprecated
* **initial\_point** (`Optional`\[`ndarray`]) – The initial point for the optimization.
**Return type**
`Tuple`\[`ndarray`, `float`, `int`]
**Returns**
A tuple (point, value, nfev) where
> point: is a 1D numpy.ndarray\[float] containing the solution
>
> value: is a float with the objective function value
>
> nfev: is the number of objective function calls
</Function>
### print\_options
<Function id="qiskit.aqua.components.optimizers.ADAM.print_options" signature="print_options()">
Print algorithm-specific options.
</Function>
### save\_params
<Function id="qiskit.aqua.components.optimizers.ADAM.save_params" signature="save_params(snapshot_dir)">
Save the current iteration parameters to a file called `adam_params.csv`.
<Admonition title="Note" type="note">
The current parameters are appended to the file, if it exists already. The file is not overwritten.
</Admonition>
**Parameters**
**snapshot\_dir** (`str`) – The directory to store the file in.
**Return type**
`None`
</Function>
### set\_max\_evals\_grouped
<Function id="qiskit.aqua.components.optimizers.ADAM.set_max_evals_grouped" signature="set_max_evals_grouped(limit)">
Set max evals grouped
</Function>
### set\_options
<Function id="qiskit.aqua.components.optimizers.ADAM.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*) – options, given as name=value.
</Function>
### setting
<Attribute id="qiskit.aqua.components.optimizers.ADAM.setting">
Return setting
</Attribute>
### wrap\_function
<Function id="qiskit.aqua.components.optimizers.ADAM.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*) – the args to be injected
**Returns**
wrapper
**Return type**
function\_wrapper
</Function>
</Class>
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