APG_EN2: Accelerated Proximal Gradient on l1 regularized quadratic...
In accSDA: Accelerated Sparse Discriminant Analysis

APG_EN2

R Documentation

Accelerated Proximal Gradient on l1 regularized quadratic program

Applies accelerated proximal gradient algorithm 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

APG_EN2(A, d, x0, lam, alpha, maxits, tol, selector = rep(1, dim(x0)[1]))

`A`	p by p positive definite coefficient matrix `A = (\gamma Om + X^T X/n)` .
`d`	nx1 dimensional column vector.
`lam`	Regularization parameter for l1 penalty, must be greater than zero.
`alpha`	Step length.
`maxits`	Number of iterations to run
`tol`	Stopping tolerance for proximal gradient algorithm.
`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.

APG_EN2 returns an object of class "APG_EN2" including a list with the following named components