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)
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
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__
__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
Methods
__init__([maxiter, tol, lr, beta_1, beta_2, …]) | type maxiterint |
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 |
bounds_support_level
Returns bounds support level
get_support_level
get_support_level()
Return support level dictionary
gradient_num_diff
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
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
load_params
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
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).
optimize
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
print_options
print_options()
Print algorithm-specific options.
save_params
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
set_max_evals_grouped(limit)
Set max evals grouped
set_options
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.
setting
Return setting
wrap_function
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