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)
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.
[2]: Sashank J. Reddi and Satyen Kale and Sanjiv Kumar (2018),
On the Convergence of Adam and Beyond. arXiv:1904.09237
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
ADAM.get_support_level()
Return support level dictionary
gradient_num_diff
static ADAM.gradient_num_diff(x_center, f, epsilon, max_evals_grouped=None)
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, defaults to 1 (i.e. no batching).
Returns
the gradient computed
Return type
grad
load_params
ADAM.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
minimize
ADAM.minimize(fun, x0, jac=None, bounds=None, objective_function=None, initial_point=None, gradient_function=None)
Minimize the scalar function.
Parameters
- fun (
Callable[[Union[float,ndarray]],float]) – The scalar function to minimize. - x0 (
Union[float,ndarray]) – The initial point for the minimization. - jac (
Optional[Callable[[Union[float,ndarray]],Union[float,ndarray]]]) – The gradient of the scalar functionfun. - bounds (
Optional[List[Tuple[float,float]]]) – Bounds for the variables offun. This argument might be ignored if the optimizer does not support bounds. - objective_function (
Optional[Callable[[ndarray],float]]) – DEPRECATED. A function handle to the objective function. - initial_point (
Optional[ndarray]) – DEPRECATED. The initial iteration point. - gradient_function (
Optional[Callable[[ndarray],float]]) – DEPRECATED. A function handle to the gradient of the objective function.
Return type
Returns
The result of the optimization, containing e.g. the result as attribute x.
print_options
ADAM.print_options()
Print algorithm-specific options.
save_params
ADAM.save_params(snapshot_dir)
Save the current iteration parameters to a file called adam_params.csv.
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
ADAM.set_max_evals_grouped(limit)
Set max evals grouped
set_options
ADAM.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.
wrap_function
static ADAM.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
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]