APG_EN2bt | R Documentation |
Applies accelerated proximal gradient algorithm (with backtracking) to the l1-regularized quadratic program
f(x) + g(x) = 0.5*x^T*A*x - d^T*x + lambda*|x|_l1
APG_EN2bt( A, Xt, Om, gamma, d, x0, lam, L, eta, maxits, tol, selector = rep(1, dim(x0)[1]) )
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 vlaue of the backtracking Lipshitz constant. |
eta |
Backtracking scaling parameter. |
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. |
This function is used by other functions and should only be called explicitly for debugging purposes.
APG_EN2bt
returns an object of class
"APG_EN2bt
" including a list
with the following named components
call
The matched call.
x
Found solution.
k
Number of iterations used.
Used by: SDAAP
and the SDAAPcv
cross-validation version.
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