ADMM_EN_SMW

Diagonal of $A^{-1}$ term in SMW formula, where A is an n by n
positive definite coefficient matrix.

Ainv

Upper triangular matrix in Cholesky decomposition of $I + UA^{-1}V$.

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

Augmented Lagrangian penalty parameter, must be greater than zero.

maxits

Vector of stopping tolerances, first value is absolute, second is relative tolerance.

Logical controlling display of intermediate statistics.

quiet

alpha

Applies Alternating Direction Method of Multipliers to the l1-regularized quadratic program
$$f(\mathbf{x}) + g(\mathbf{x}) = \frac{1}{2}\mathbf{x}^TA\mathbf{x} - d^T\mathbf{x} + \lambda |\mathbf{x}|_1$$

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.

ADMM_EN_SMW: ADMM on l1 regularized quadratic program

Description

Usage

Arguments

Value

Details

See Also