sparse_ho.optimizers.GradientDescent

class sparse_ho.optimizers.GradientDescent(n_outer=100, step_size=None, p_grad_norm=1, verbose=False, tol=1e-05, tol_decrease=None, t_max=10000)

Gradient descent for the outer problem. This gradient descent scheme uses a (heuristic) adaptive stepsize:

log_alphak = log_alphak - p_grad_norm * grad_outer / norm(grad_outer)

Parameters
n_outer: int, optional (default=100).

number of maximum updates of alpha.

step_size: float

stepsize of the gradient descent

p_grad_norm: float

Coefficient multiplying grad_outer / norm(grad_outer) in the gradient descent.

verbose: bool, optional (default=False)

Indicates whether information about hyperparameter optimization process is printed or not.

tolfloat, optional (default=1e-5)

Tolerance for the inner optimization solver.

tol_decrease: bool

To use or not a tolerance decrease strategy in the gradient descent.

t_max: float, optional (default=10000)

Maximum running time threshold in seconds.

__init__(n_outer=100, step_size=None, p_grad_norm=1, verbose=False, tol=1e-05, tol_decrease=None, t_max=10000)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([n_outer, step_size, p_grad_norm, …])

Initialize self.