positive.golazo: This is a wrapper for the positive GOLAZO problem.

In pzwiernik/golazo: Regularized likelihood for Gaussian graphical models under a general asymmetric lasso-type penalty

Description

The function simply running golazo() with L_ij=0 and U_ij=rho.

Usage

positive.golazo(S, rho, tol = 1e-07, diagonal.pen = FALSE, verbose = TRUE)

Arguments

S	Positive semidefinite matrix. This will be typically the sample covariance matrix but it can be somethink different in the dual likelihood computation or when the data follow the non-paranormal distribution.
rho	the penalty on the positive entries of K (can be Inf).
tol	The convergence tolerance (default tol=1e-7). The algorithm termininnates when teh dual gap (guaranteed to be nonnegative) is less than tol.
diagonal.pen	if FALSE (default) the diagonal of K is not penalized.
verbose	if TRUE (default) the output will be printed.

Value

K the optimal value of the concentration matrix

Sig the optimal value of the covariance matrix

it the number of iterations

Examples

data(ability.cov)
S <- ability.cov$cov
R <- stats::cov2cor(S)
d <- nrow(R)
res <- positive.golazo(R,rho=0.1)
Khat <- res$K
print(Khat)

pzwiernik/golazo documentation built on Aug. 13, 2020, 4:15 p.m.