APG_EN2

p by p positive definite coefficient matrix
$$A = (\gamma Om + X^T X/n)$$.

Regularization parameter for l1 penalty, must be greater than zero.

alpha

maxits

Stopping tolerance for proximal gradient algorithm.

Applies accelerated proximal gradient algorithm to the l1-regularized quadratic program
$$f(\mathbf{x}) + g(\mathbf{x}) = \frac{1}{2}\mathbf{x}^TA\mathbf{x} - d^T\mathbf{x} + \lambda |\mathbf{x}|_1$$

internal

Implementation of sparse linear discriminant analysis, which is a supervised
classification method for multiple classes. Various novel optimization approaches to
this problem are implemented including alternating direction method of multipliers (ADMM),
proximal gradient (PG) and accelerated proximal gradient (APG) (See Atkins et al. <arXiv:1705.07194>).
Functions for performing cross validation are also supplied along with basic prediction
and plotting functions.
Sparse zero variance discriminant analysis (SZVD) is also included in the package
(See Ames and Hong, <arXiv:1401.5492>). See the github wiki for a more extended description.

APG_EN2: Accelerated Proximal Gradient on l1 regularized quadratic program

Description

Usage

Arguments

Value

Details

See Also