ADMM_EN_SMW | R Documentation |
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
ADMM_EN_SMW(Ainv, V, R, d, x0, lam, mu, maxits, tol, quiet, selector)
Ainv |
Diagonal of |
V |
Matrix from SMW formula. |
R |
Upper triangular matrix in Cholesky decomposition of |
d |
nx1 dimensional column vector. |
lam |
Regularization parameter for l1 penalty, must be greater than zero. |
mu |
Augmented Lagrangian penalty parameter, must be greater than zero. |
maxits |
Number of iterations to run |
tol |
Vector of stopping tolerances, first value is absolute, second is relative tolerance. |
quiet |
Logical controlling display of intermediate statistics. |
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.
ADMM_EN_SMW
returns an object of class
"ADMM_EN_SMW
" including a list
with the following named components
call
The matched call.
x
Found solution.
y
Dual solution.
z
Slack variables.
k
Number of iterations used.
Used by: SDAD
and the SDADcv
cross-validation version.
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