admm.bp {ADMM} | R Documentation |
For an underdetermined system, Basis Pursuit aims to find a sparse solution that solves
\textrm{min}_x ~ \|x\|_1 \quad \textrm{s.t} \quad Ax=b
which is a relaxed version of strict non-zero support finding problem. The implementation is borrowed from Stephen Boyd's MATLAB code.
admm.bp(
A,
b,
xinit = NA,
rho = 1,
alpha = 1,
abstol = 1e-04,
reltol = 0.01,
maxiter = 1000
)
A |
an |
b |
a length- |
xinit |
a length- |
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 |
a named list containing
a length-n
solution vector
dataframe recording iteration numerics. See the section for more details.
When you run the algorithm, output returns not only the solution, but also the iteration history recording following fields over iterates,
object (cost) function value
norm of primal residual
norm of dual residual
feasibility tolerance for primal feasibility condition
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.
## generate sample data
n = 30
m = 10
A = matrix(rnorm(n*m), nrow=m) # design matrix
x = c(stats::rnorm(3),rep(0,n-3)) # coefficient
x = base::sample(x)
b = as.vector(A%*%x) # response
## run example
output = admm.bp(A, b)
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")
par(opar)