adelie (version 1.0.7)

gaussian_cov: Solves group elastic net via covariance method.

Description

Solves group elastic net via covariance method.

Usage

gaussian_cov(
  A,
  v,
  constraints = NULL,
  groups = NULL,
  alpha = 1,
  penalty = NULL,
  lmda_path = NULL,
  max_iters = as.integer(1e+05),
  tol = 1e-07,
  rdev_tol = 0.001,
  newton_tol = 1e-12,
  newton_max_iters = 1000,
  n_threads = 1,
  early_exit = TRUE,
  screen_rule = "pivot",
  min_ratio = 0.01,
  lmda_path_size = 100,
  max_screen_size = NULL,
  max_active_size = NULL,
  pivot_subset_ratio = 0.1,
  pivot_subset_min = 1,
  pivot_slack_ratio = 1.25,
  check_state = FALSE,
  progress_bar = FALSE,
  warm_start = NULL
)

Value

State of the solver.

Arguments

A

Positive semi-definite matrix.

v

Linear term.

constraints

Constraints.

groups

Groups.

alpha

Elastic net parameter.

penalty

Penalty factor.

lmda_path

The regularization path.

max_iters

Maximum number of coordinate descents.

tol

Coordinate descent convergence tolerance.

rdev_tol

Relative percent deviance explained tolerance.

newton_tol

Convergence tolerance for the BCD update.

newton_max_iters

Maximum number of iterations for the BCD update.

n_threads

Number of threads.

early_exit

TRUE if the function should exit early.

screen_rule

Screen rule (currently the only value is the default "pivot".

min_ratio

Ratio between largest and smallest regularization parameter, default is 0.01.

lmda_path_size

Number of regularization steps in the path, default is 100.

max_screen_size

Maximum number of screen groups, default is NULL for no maximum.

max_active_size

Maximum number of active groups, default is NULL for no maximum.

pivot_subset_ratio

Subset ratio of pivot rule, default is 0.1.

pivot_subset_min

Minimum subset of pivot rule, default is 1.

pivot_slack_ratio

Slack ratio of pivot rule, default is 1.25.

check_state

Check state, default is FALSE.

progress_bar

Progress bar, default is FALSE.

warm_start

Warm start, default is NULL (no warm start).

Examples

set.seed(0)
n <- 100
p <- 200
X <- matrix(rnorm(n * p), n, p)
y <- X[,1] * rnorm(1) + rnorm(n)
A <- t(X) %*% X / n
v <- t(X) %*% y / n
state <- gaussian_cov(A, v)