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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/control_options.R
\name{en_admm_options}
\alias{en_admm_options}
\title{Options for the ADMM Elastic Net Algorithm}
\usage{
7
en_admm_options(max_it = 1000, eps = 1e-09, tau, sparse = FALSE,
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  admm_type = c("auto", "linearized", "var-stepsize"),
  tau_lower_mult = 0.01, tau_adjustment_lower = 0.98,
  tau_adjustment_upper = 0.999)
}
\arguments{
\item{max_it}{maximum number of iterations.}

\item{eps}{numerical tolerance to check for convergence.}

\item{tau}{step size for the algorithm if using the \code{linearized} version
and the largest step size if using the \code{var-stepsize} version.}

\item{sparse}{use sparse coefficients.}

\item{admm_type}{what type of the ADMM algorithm to use. If \code{linearized},
uses a linearized version of ADMM which has runtime $O()$
and converges linearly.
If \code{var-stepsize}, uses a variable step-size ADMM
algorithm which converges quadratically for "true" EN
penalties (i.e., \eqn{alpha < 1}) but has runtime $O()$.
If \code{auto} (the default), chooses the type based on the
penalty and the problem size.}

\item{tau_adjustment_lower}{(smallest) multiplicative factor for the
adjustment of the step size
\code{tau = tau_adjustment * tau}
(only for the \code{var-stepsize} version).}

\item{tau_adjustment_upper}{(largest) multiplicative factor for the
adjustment of the step size
\code{tau = tau_adjustment * tau}
(only for the \code{var-stepsize} version).}
}
\value{
options for the ADMM EN algorithm.
}
\description{
Options for the ADMM Elastic Net Algorithm
}
\seealso{
Other EN algorithms: \code{\link{en_dal_options}}
}
\concept{EN algorithms}