APG_EN2: Accelerated Proximal Gradient on l1 regularized quadratic...

In accSDA: Accelerated Sparse Discriminant Analysis

Accelerated Proximal Gradient on l1 regularized quadratic program

Description

Applies accelerated proximal gradient algorithm to the l1-regularized quadratic program

f(x) + g(x) = 0.5*x^T*A*x - d^T*x + lambda*|x|_l1

Usage

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

Arguments

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.

Details

This function is used by other functions and should only be called explicitly for debugging purposes.

Value

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

call

The matched call.

x

Found solution.

k

Number of iterations used.

See Also

Used by: SDAAP and the SDAAPcv cross-validation version.

accSDA documentation built on Sept. 5, 2022, 5:05 p.m.