Applies Alternating Direction Method of Multipliers to the l1-regularized quadratic program $$f(\mathbf{x}) + g(\mathbf{x}) = \frac{1}{2}\mathbf{x}^TA\mathbf{x} - d^T\mathbf{x} + \lambda |\mathbf{x}|_1$$
ADMM_EN_SMW(Ainv, V, R, d, x0, lam, mu, maxits, tol, quiet, selector)ADMM_EN_SMW returns an object of class "ADMM_EN_SMW" including a list
with the following named components
callThe matched call.
xFound solution.
yDual solution.
zSlack variables.
kNumber of iterations used.
Diagonal of \(A^{-1}\) term in SMW formula, where A is an n by n positive definite coefficient matrix.
Matrix from SMW formula.
Upper triangular matrix in Cholesky decomposition of \(I + UA^{-1}V\).
nx1 dimensional column vector.
Regularization parameter for l1 penalty, must be greater than zero.
Augmented Lagrangian penalty parameter, must be greater than zero.
Number of iterations to run
Vector of stopping tolerances, first value is absolute, second is relative tolerance.
Logical controlling display of intermediate statistics.
Vector to choose which parameters in the discriminant vector will be used to calculate the regularization terms. The size of the vector must be *p* the number of predictors. The default value is a vector of all ones. This is currently only used for ordinal classification.
This function is used by other functions and should only be called explicitly for debugging purposes.
Used by: SDAD and the SDADcv cross-validation version.