prox_ENbt: Accelerated Proximal Gradient on l1 regularized quadratic...
In gumeo/accSDA: Accelerated Sparse Discriminant Analysis

prox_ENbt

R Documentation

Accelerated Proximal Gradient on l1 regularized quadratic program with backtracking

Applies accelerated proximal gradient (with backtracking) 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

prox_ENbt(A, Xt, Om, gamma, d, x0, lam, L, eta, maxits, tol)

`A`	p by p positive definite coefficient matrix `A = (\gamma Om + X^T X/n)` .
`Xt`	Same as X above, we need it to make calculations faster.
`Om`	Same reason as for the above parameter.
`gamma`	l2 regularizing parameter.
`d`	nx1 dimensional column vector.
`lam`	Regularization parameter for l1 penalty, must be greater than zero.
`L`	Initial value of backtracking Lipshitz constant.
`eta`	Backtracking scaling parameter.
`maxits`	Number of iterations to run
`tol`	Stopping tolerance for proximal gradient algorithm.

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

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