coop_lasso {scregclust} | R Documentation |
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
)
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
|
beta_0 |
Initial value for coefficients, allowing for warm start.
Can be set to NULL, which results in the initial |
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 |
Value
A list containing
beta |
The coefficients at convergence |
iterations |
Number of iterations |
References
Xu et al. (2017) Adaptive relaxed ADMM: Convergence theory and practical implementation. DOI 10.1109/CVPR.2017.765