qiskit.algorithms.optimizers.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]¶

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

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__(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]¶

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

`__init__`([maxiter, tol, lr, beta_1, beta_2, …])	type maxiter `int`
`get_support_level`()	Return support level dictionary
`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`(load_dir)	Load iteration parameters for a file called `adam_params.csv`.
`minimize`(objective_function, initial_point, …)	Run the minimization.
`optimize`(num_vars, objective_function[, …])	Perform optimization.
`print_options`()	Print algorithm-specific options.
`save_params`(snapshot_dir)	Save the current iteration parameters to a file called `adam_params.csv`.
`set_max_evals_grouped`(limit)	Set max evals grouped
`set_options`(**kwargs)	Sets or updates values in the options dictionary.
`wrap_function`(function, args)	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

property bounds_support_level¶: Returns bounds support level

get_support_level()[source]¶: Return support level dictionary

static gradient_num_diff(x_center, f, epsilon, max_evals_grouped=1)¶

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

property gradient_support_level¶: Returns gradient support level

property initial_point_support_level¶: Returns initial point support level

property is_bounds_ignored¶: Returns is bounds ignored

property is_bounds_required¶: Returns is bounds required

property is_bounds_supported¶: Returns is bounds supported

property is_gradient_ignored¶: Returns is gradient ignored

property is_gradient_required¶: Returns is gradient required

property is_gradient_supported¶: Returns is gradient supported

property is_initial_point_ignored¶: Returns is initial point ignored

property is_initial_point_required¶: Returns is initial point required

property is_initial_point_supported¶: Returns is initial point supported

load_params(load_dir)[source]¶

Load iteration parameters for a file called adam_params.csv.

Parameters: load_dir (str) – The directory containing adam_params.csv.
Return type: None

minimize(objective_function, initial_point, gradient_function)[source]¶

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).

optimize(num_vars, objective_function, gradient_function=None, variable_bounds=None, initial_point=None)[source]¶

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

print_options()¶: Print algorithm-specific options.

save_params(snapshot_dir)[source]¶

Save the current iteration parameters to a file called adam_params.csv.

Note

The current parameters are appended to the file, if it exists already. The file is not overwritten.

Parameters: snapshot_dir (str) – The directory to store the file in.
Return type: None

set_max_evals_grouped(limit)¶: Set max evals grouped

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.

property setting¶: Return setting

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) – the args to be injected

Returns

wrapper

Return type

function_wrapper