ADAM¶

class 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)[source]¶

Bases: qiskit.algorithms.optimizers.optimizer.Optimizer

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
[2]: Sashank J. Reddi and Satyen Kale and Sanjiv Kumar (2018),: On the Convergence of Adam and Beyond. arXiv:1904.09237

Note

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).

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

Methods

`get_support_level`	Return support level dictionary
`gradient_num_diff`	We compute the gradient with the numeric differentiation in the parallel way, around the point x_center.
`load_params`	Load iteration parameters for a file called `adam_params.csv`.
`minimize`	Run the minimization.
`optimize`	Perform optimization.
`print_options`	Print algorithm-specific options.
`save_params`	Save the current iteration parameters to a file called `adam_params.csv`.
`set_max_evals_grouped`	Set max evals grouped
`set_options`	Sets or updates values in the options dictionary.
`wrap_function`	Wrap the function to implicitly inject the args at the call of the function.

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¶

Return type: Dict[str, Any]