minimizeFunction: Minimize the objective function of an unsmoothed or smoothed...

In smoothedLasso: A Framework to Smooth L1 Penalized Regression Operators using Nesterov Smoothing

Description

Minimize the objective function of an unsmoothed or smoothed regression operator with respect to betavector using BFGS.

Usage

minimizeFunction(p, obj, objgrad)

Arguments

p	The dimension of the unknown parameters (regression coefficients).
obj	The objective function of the regression operator as a function of betavector.
objgrad	The gradient function of the regression operator as a function of betavector.

Value

The estimator betavector (minimizer) of the regression operator.

References

Hahn, G., Lutz, S., Laha, N., and Lange, C. (2020). A framework to efficiently smooth L1 penalties for linear regression. bioRxiv:2020.09.17.301788.

Examples

library(smoothedLasso)
n <- 100
p <- 500
betavector <- runif(p)
X <- matrix(runif(n*p),nrow=n,ncol=p)
y <- X %*% betavector
lambda <- 1
temp <- standardLasso(X,y,lambda)
obj <- function(z) objFunctionSmooth(z,temp$u,temp$v,temp$w,mu=0.1)
objgrad <- function(z) objFunctionSmoothGradient(z,temp$w,temp$du,temp$dv,temp$dw,mu=0.1)
print(minimizeFunction(p,obj,objgrad))

smoothedLasso documentation built on March 21, 2021, 9:07 a.m.