scregclust (version 0.2.0)

coop_lasso: ADMM algorithm for solving the group-penalized least squares problem

Description

Implements estimation of the coop-lasso problem.

Usage

coop_lasso(
  y,
  x,
  lambda,
  weights,
  beta_0 = NULL,
  rho_0 = 0.2,
  alpha_0 = 1.5,
  n_update = 2L,
  eps_corr = 0.2,
  max_iter = 1000L,
  eps_rel = 1e-08,
  eps_abs = 1e-12,
  verbose = FALSE
)

Value

A list containing

beta

The coefficients at convergence

iterations

Number of iterations

Arguments

y

Target (n x m)

x

Design matrix (n x p)

lambda

Penalization parameter

weights

A specific weight for each group (typically this is sqrt(group size)).

beta_0

Initial value for coefficients, allowing for warm start. Can be set to NULL, which results in the initial beta being a zero matrix.

rho_0

Initial ADMM step-size

alpha_0

Initial ADMM relaxation parameter

n_update

Number of steps in-between updates of the step-size/adaptation parameters

eps_corr

Lower bound for the correlation in the step-size update steps

max_iter

Maximum number of iterations

eps_rel

Relative tolerance for convergence check

eps_abs

Absolute tolerance for convergence check

verbose

Whether or not information about the optimization process should be printed to the terminal

References

Xu et al. (2017) Adaptive relaxed ADMM: Convergence theory and practical implementation. DOI 10.1109/CVPR.2017.765