# 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

 `1` ```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

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```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.