Least Absolute Shrinkage and Selection Operator

Description

LASSO, or L1-regularized regression, is an optimization problem to solve

\textrm{min}_x ~ \frac{1}{2}\|Ax-b\|_2^2 + \lambda \|x\|_1

for sparsifying the coefficient vector x. The implementation is borrowed from Stephen Boyd's MATLAB code.

Usage

admm.lasso(
A,
b,
lambda = 1,
rho = 1,
alpha = 1,
abstol = 1e-04,
reltol = 0.01,
maxiter = 1000
)


Arguments

 A an (m\times n) regressor matrix b a length-m response vector lambda a regularization parameter rho an augmented Lagrangian parameter alpha an overrelaxation parameter in [1,2] abstol absolute tolerance stopping criterion reltol relative tolerance stopping criterion maxiter maximum number of iterations

Value

a named list containing

x

a length-n solution vector

history

dataframe recording iteration numerics. See the section for more details.

Iteration History

When you run the algorithm, output returns not only the solution, but also the iteration history recording following fields over iterates,

objval

object (cost) function value

r_norm

norm of primal residual

s_norm

norm of dual residual

eps_pri

feasibility tolerance for primal feasibility condition

eps_dual

feasibility tolerance for dual feasibility condition

In accordance with the paper, iteration stops when both r_norm and s_norm values become smaller than eps_pri and eps_dual, respectively.

References

Tibshirani R (1996). “Regression Shrinkage and Selection via the Lasso.” Journal of the Royal Statistical Society. Series B (Methodological), 58(1), 267–288. ISSN 00359246.

Examples


## generate sample data
m = 50
n = 100
p = 0.1   # percentange of non-zero elements

x0 = matrix(Matrix::rsparsematrix(n,1,p))
A  = matrix(rnorm(m*n),nrow=m)
for (i in 1:ncol(A)){
A[,i] = A[,i]/sqrt(sum(A[,i]*A[,i]))
}
b = A%*%x0 + sqrt(0.001)*matrix(rnorm(m))

## set regularization lambda value
lambda = 0.1*base::norm(t(A)%*%b, "F")

## run example
niter   = length(output$history$s_norm)
history = output$history ## report convergence plot opar <- par(no.readonly=TRUE) par(mfrow=c(1,3)) plot(1:niter, history$objval, "b", main="cost function")
plot(1:niter, history$r_norm, "b", main="primal residual") plot(1:niter, history$s_norm, "b", main="dual residual")