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Bases: `Optimizer`

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

Nota

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`).

Parâmetros
• 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.

• snapshot_dir (str | None) – 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` Minimize the scalar function. `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

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_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