skglm.penalties.SLOPE

class skglm.penalties.SLOPE(alphas)

Sorted L-One Penalized Estimation (SLOPE) penalty.

References

[1]

M. Bogdan, E. van den Berg, C. Sabatti, W. Su, E. Candes “SLOPE - Adaptive Variable Selection via Convex Optimization”, The Annals of Applied Statistics 9 (3): 1103-40 https://doi.org/10.1214/15-AOAS842

Attributes:

alphasarray, shape (n_features,)

Contain regularization levels for every feature. When alphas contain a single unique value, SLOPE is equivalent to the ``L1``penalty.

__init__(alphas)

Methods

__init__(alphas)
generalized_support(w)	Return a mask which is True for coefficients in the generalized support.
get_spec()	Specify the numba types of the class attributes.
is_penalized(n_features)	Return a binary mask with the penalized features.
params_to_dict()	Get the parameters to initialize an instance of the class.
prox_1d(value, stepsize, j)
prox_vec(x, stepsize)
subdiff_distance(w, grad, ws)
value(w)	Compute the value of SLOPE at w.