davidkep committed Jul 24, 2019 1 2 3 4 5 6 % 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{  davidkep committed Aug 07, 2019 7 en_admm_options(max_it = 1000, eps = 1e-09, tau, sparse = FALSE,  davidkep committed Jul 24, 2019 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50  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}