Description Usage Arguments Details Value Author(s) See Also
Optimizes an l1-penalized loss using a coordinate wise descent algorithm.
1 2 3 |
f |
a |
gr |
a |
quad |
a |
p |
a |
beta |
a |
lambda |
a |
nlambda |
a |
lambda.min.ratio |
a |
penalty.factor |
a |
rho |
a |
c |
a |
reltol |
a |
trace |
a |
The function computes a matrix of optimal parameter values. Each column corresponds to a
value of the penalty parameter in lambda
. The estimates are computed in decreasing order
of the penalty parameters, and for each column the previous is used as a warm start.
The algorithm relies on iterative optimization of an l1-penalized quadratic approximation of the loss using a standard coordinate wise descent algorithm. A coordinate wise backtracking step is added to ensure that the algorithm takes descent steps.
This function relies on three auxiliary functions. The loss function f
, its gradient gr
and a third function, quad
, that computes the coefficient of the quadratic approximation
for the coordinate wise optimization.
The function returns a list with the vector of lambda values as the first entry and the estimated beta parameters as a matrix in the second entry. Each column in the matrix corresponds to one lambda value.
A list
of length 2. The first entry is the sequence lambda
, and the second, beta
, is the
matrix of parameter estimates. Each column in beta
corresponds to an entry in lambda
.
Niels Richard Hansen Niels.R.Hansen@math.ku.dk
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