263 lines
12 KiB
Plaintext
263 lines
12 KiB
Plaintext
---
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title: ADAM
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description: API reference for qiskit.algorithms.optimizers.ADAM
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in_page_toc_min_heading_level: 1
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python_api_type: class
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python_api_name: qiskit.algorithms.optimizers.ADAM
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---
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# ADAM
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<Class id="qiskit.algorithms.optimizers.ADAM" isDedicatedPage={true} github="https://github.com/qiskit/qiskit/tree/stable/0.45/qiskit/algorithms/optimizers/adam_amsgrad.py" signature="qiskit.algorithms.optimizers.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">
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Bases: [`Optimizer`](qiskit.algorithms.optimizers.Optimizer "qiskit.algorithms.optimizers.optimizer.Optimizer")
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Adam and AMSGRAD optimizers.
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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.
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AMSGRAD \[2] (a variant of Adam) uses a ‘long-term memory’ of past gradients and, thereby, improves convergence properties.
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**References**
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**\[1]: Kingma, Diederik & Ba, Jimmy (2014), Adam: A Method for Stochastic Optimization.**
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[arXiv:1412.6980](https://arxiv.org/abs/1412.6980)
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**\[2]: Sashank J. Reddi and Satyen Kale and Sanjiv Kumar (2018),**
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On the Convergence of Adam and Beyond. [arXiv:1904.09237](https://arxiv.org/abs/1904.09237)
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<Admonition title="Note" type="note">
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This component has some function that is normally random. If you want to reproduce behavior then you should set the random number generator seed in the algorithm\_globals (`qiskit.utils.algorithm_globals.random_seed = seed`).
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</Admonition>
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**Parameters**
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* **maxiter** ([*int*](https://docs.python.org/3/library/functions.html#int "(in Python v3.12)")) – Maximum number of iterations
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* **tol** ([*float*](https://docs.python.org/3/library/functions.html#float "(in Python v3.12)")) – Tolerance for termination
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* **lr** ([*float*](https://docs.python.org/3/library/functions.html#float "(in Python v3.12)")) – Value >= 0, Learning rate.
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* **beta\_1** ([*float*](https://docs.python.org/3/library/functions.html#float "(in Python v3.12)")) – Value in range 0 to 1, Generally close to 1.
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* **beta\_2** ([*float*](https://docs.python.org/3/library/functions.html#float "(in Python v3.12)")) – Value in range 0 to 1, Generally close to 1.
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* **noise\_factor** ([*float*](https://docs.python.org/3/library/functions.html#float "(in Python v3.12)")) – Value >= 0, Noise factor
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* **eps** ([*float*](https://docs.python.org/3/library/functions.html#float "(in Python v3.12)")) – Value >=0, Epsilon to be used for finite differences if no analytic gradient method is given.
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* **amsgrad** ([*bool*](https://docs.python.org/3/library/functions.html#bool "(in Python v3.12)")) – True to use AMSGRAD, False if not
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* **snapshot\_dir** ([*str*](https://docs.python.org/3/library/stdtypes.html#str "(in Python v3.12)") *| None*) – If not None save the optimizer’s parameter after every step to the given directory
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## Attributes
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### bounds\_support\_level
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<Attribute id="qiskit.algorithms.optimizers.ADAM.bounds_support_level">
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Returns bounds support level
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</Attribute>
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### gradient\_support\_level
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<Attribute id="qiskit.algorithms.optimizers.ADAM.gradient_support_level">
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Returns gradient support level
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</Attribute>
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### initial\_point\_support\_level
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<Attribute id="qiskit.algorithms.optimizers.ADAM.initial_point_support_level">
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Returns initial point support level
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</Attribute>
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### is\_bounds\_ignored
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<Attribute id="qiskit.algorithms.optimizers.ADAM.is_bounds_ignored">
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Returns is bounds ignored
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</Attribute>
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### is\_bounds\_required
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<Attribute id="qiskit.algorithms.optimizers.ADAM.is_bounds_required">
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Returns is bounds required
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</Attribute>
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### is\_bounds\_supported
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<Attribute id="qiskit.algorithms.optimizers.ADAM.is_bounds_supported">
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Returns is bounds supported
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</Attribute>
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### is\_gradient\_ignored
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<Attribute id="qiskit.algorithms.optimizers.ADAM.is_gradient_ignored">
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Returns is gradient ignored
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</Attribute>
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### is\_gradient\_required
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<Attribute id="qiskit.algorithms.optimizers.ADAM.is_gradient_required">
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Returns is gradient required
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</Attribute>
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### is\_gradient\_supported
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<Attribute id="qiskit.algorithms.optimizers.ADAM.is_gradient_supported">
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Returns is gradient supported
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</Attribute>
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### is\_initial\_point\_ignored
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<Attribute id="qiskit.algorithms.optimizers.ADAM.is_initial_point_ignored">
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Returns is initial point ignored
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</Attribute>
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### is\_initial\_point\_required
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<Attribute id="qiskit.algorithms.optimizers.ADAM.is_initial_point_required">
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Returns is initial point required
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</Attribute>
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### is\_initial\_point\_supported
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<Attribute id="qiskit.algorithms.optimizers.ADAM.is_initial_point_supported">
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Returns is initial point supported
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</Attribute>
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### setting
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<Attribute id="qiskit.algorithms.optimizers.ADAM.setting">
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Return setting
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</Attribute>
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### settings
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<Attribute id="qiskit.algorithms.optimizers.ADAM.settings" />
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## Methods
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### get\_support\_level
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<Function id="qiskit.algorithms.optimizers.ADAM.get_support_level" signature="get_support_level()">
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Return support level dictionary
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</Function>
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### gradient\_num\_diff
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<Function id="qiskit.algorithms.optimizers.ADAM.gradient_num_diff" signature="gradient_num_diff(x_center, f, epsilon, max_evals_grouped=None)" modifiers="static">
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We compute the gradient with the numeric differentiation in the parallel way, around the point x\_center.
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**Parameters**
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* **x\_center** (*ndarray*) – point around which we compute the gradient
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* **f** (*func*) – the function of which the gradient is to be computed.
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* **epsilon** ([*float*](https://docs.python.org/3/library/functions.html#float "(in Python v3.12)")) – the epsilon used in the numeric differentiation.
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* **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).
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**Returns**
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the gradient computed
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**Return type**
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grad
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</Function>
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### load\_params
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<Function id="qiskit.algorithms.optimizers.ADAM.load_params" signature="load_params(load_dir)">
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Load iteration parameters for a file called `adam_params.csv`.
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**Parameters**
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**load\_dir** ([*str*](https://docs.python.org/3/library/stdtypes.html#str "(in Python v3.12)")) – The directory containing `adam_params.csv`.
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</Function>
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### minimize
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<Function id="qiskit.algorithms.optimizers.ADAM.minimize" signature="minimize(fun, x0, jac=None, bounds=None, objective_function=None, initial_point=None, gradient_function=None)">
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Minimize the scalar function.
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<Admonition title="Deprecated since version 0.19.0" type="danger">
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`qiskit.algorithms.optimizers.adam_amsgrad.ADAM.minimize()`’s argument `gradient_function` is deprecated as of qiskit-terra 0.19.0. It will be removed no earlier than 3 months after the release date. Instead, use the argument `jac`, which behaves identically.
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</Admonition>
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<Admonition title="Deprecated since version 0.19.0" type="danger">
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`qiskit.algorithms.optimizers.adam_amsgrad.ADAM.minimize()`’s argument `initial_point` is deprecated as of qiskit-terra 0.19.0. It will be removed no earlier than 3 months after the release date. Instead, use the argument `fun`, which behaves identically.
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</Admonition>
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<Admonition title="Deprecated since version 0.19.0" type="danger">
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`qiskit.algorithms.optimizers.adam_amsgrad.ADAM.minimize()`’s argument `objective_function` is deprecated as of qiskit-terra 0.19.0. It will be removed no earlier than 3 months after the release date. Instead, use the argument `fun`, which behaves identically.
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</Admonition>
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**Parameters**
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* **fun** (*Callable\[\[POINT],* [*float*](https://docs.python.org/3/library/functions.html#float "(in Python v3.12)")*]*) – The scalar function to minimize.
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* **x0** (*POINT*) – The initial point for the minimization.
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* **jac** (*Callable\[\[POINT], POINT] | None*) – The gradient of the scalar function `fun`.
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* **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.
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* **objective\_function** (*Callable\[\[np.ndarray],* [*float*](https://docs.python.org/3/library/functions.html#float "(in Python v3.12)")*] | None*) – DEPRECATED. A function handle to the objective function.
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* **initial\_point** (*np.ndarray | None*) – DEPRECATED. The initial iteration point.
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* **gradient\_function** (*Callable\[\[np.ndarray],* [*float*](https://docs.python.org/3/library/functions.html#float "(in Python v3.12)")*] | None*) – DEPRECATED. A function handle to the gradient of the objective function.
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**Returns**
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The result of the optimization, containing e.g. the result as attribute `x`.
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**Return type**
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[OptimizerResult](qiskit.algorithms.optimizers.OptimizerResult "qiskit.algorithms.optimizers.OptimizerResult")
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</Function>
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### print\_options
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<Function id="qiskit.algorithms.optimizers.ADAM.print_options" signature="print_options()">
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Print algorithm-specific options.
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</Function>
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### save\_params
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<Function id="qiskit.algorithms.optimizers.ADAM.save_params" signature="save_params(snapshot_dir)">
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Save the current iteration parameters to a file called `adam_params.csv`.
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<Admonition title="Note" type="note">
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The current parameters are appended to the file, if it exists already. The file is not overwritten.
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</Admonition>
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**Parameters**
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**snapshot\_dir** ([*str*](https://docs.python.org/3/library/stdtypes.html#str "(in Python v3.12)")) – The directory to store the file in.
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</Function>
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### set\_max\_evals\_grouped
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<Function id="qiskit.algorithms.optimizers.ADAM.set_max_evals_grouped" signature="set_max_evals_grouped(limit)">
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Set max evals grouped
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</Function>
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### set\_options
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<Function id="qiskit.algorithms.optimizers.ADAM.set_options" signature="set_options(**kwargs)">
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Sets or updates values in the options dictionary.
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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.
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**Parameters**
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**kwargs** ([*dict*](https://docs.python.org/3/library/stdtypes.html#dict "(in Python v3.12)")) – options, given as name=value.
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</Function>
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### wrap\_function
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<Function id="qiskit.algorithms.optimizers.ADAM.wrap_function" signature="wrap_function(function, args)" modifiers="static">
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Wrap the function to implicitly inject the args at the call of the function.
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**Parameters**
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* **function** (*func*) – the target function
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* **args** ([*tuple*](https://docs.python.org/3/library/stdtypes.html#tuple "(in Python v3.12)")) – the args to be injected
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**Returns**
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wrapper
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**Return type**
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function\_wrapper
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</Function>
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</Class>
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