ADMM_EN2: ADMM on l1 regularized quadratic program
In gumeo/accSDA: Accelerated Sparse Discriminant Analysis

ADMM_EN2

R Documentation

ADMM on l1 regularized quadratic program

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_EN2(R, d, x0, lam, mu, maxits, tol, quiet, selector = rep(1, dim(x)[1]))

`R`	Upper triangular matrix in Chol decomp `\mu I + A = R^T R`.
`d`	nx1 dimensional column vector.
`lam`	Regularization parameter for l1 penalty, must be greater than zero.
`mu`	Augmented Lagrangian penalty parameter, must be greater than zero.
`maxits`	Number of iterations to run
`tol`	Vector of stopping tolerances, first value is absolute, second is relative tolerance.
`quiet`	Logical controlling display of intermediate statistics.
`selector`	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.

ADMM_EN2 returns an object of class "ADMM_EN2" including a list with the following named components