beta_update_net: Updates beta coefficients.
In LassoNet: 3CoSE Algorithm

Description Usage Arguments Details Value Author(s) References Examples

This function updates β for given penalty parameters.

1	beta.update.net(x,y,beta,lambda1,lambda2,M1,n.iter,iscpp,tol)

`x`	input data matrix of size n \times p; n - number of observations; p - number of covariates
`y`	response vector or size n \times 1
`beta`	initial value for β; default - zero vector of size n \times 1
`lambda1`	lasso penalty parameter
`lambda2`	network penalty parameter
`M1`	penalty matrix
`n.iter`	maximum number of iterations for β step; default - 1e5
`iscpp`	binary choice for using cpp function in coordinate updates; 1 - use C++ (default), 0 - use R
`tol`	convergence tolerance level; default - 1e-6

Updates the coefficient vector β given the data and penalty parameters λ1 and λ2. Convergence criterion is defined as ∑_{i=1}^p |β_{i,j} - β_{i,j-1}| ≤q to.

`beta`	updated β vector
`convergence`	binary variable; 1 - yes
`steps`	number of steps until convergence

Maintainer: Jonas Striaukas <jonas.striaukas@gmail.com>

Weber, M., Striaukas, J., Schumacher, M., Binder, H. "Network-Constrained Covariate Coefficient and Connection Sign Estimation" (2018) <doi:10.2139/ssrn.3211163>

p<-200
n<-100
beta.0=array(1,c(p,1))
x<-matrix(rnorm(n*p),n,p)
y<-rnorm(n,mean=0,sd=1)
lambda1<-1
lambda2<-1
M1<-diag(p)
updates<-beta.update.net(x, y, beta.0, lambda1, lambda2, M1)