Revision

**1d34d9c07e34cf0306445d49f7206994f5a79537**authored by James Scott on**19 July 2016, 08:40:22 UTC**, committed by James Scott on**19 July 2016, 08:40:22 UTC****1 parent**a7ab0a6

ADMMutils.R

```
library(limSolve)
ilogit = function(x) 1/{1+exp(-x)}
makeD1 = function(d) {
# Construct the first-difference matrix
D1 = diag(1,d-1)
D1 = rbind(D1,0)
D1 = cbind(0,D1)
diag(D1) = -1
D1 = D1[-nrow(D1),]
D1
}
Dcrossx = function(x) {
# efficient compute cross-product D^T x,
# where D is the first-difference matrix
n = length(x) + 1
out = rep(0, n)
out[1] = -x[1]
out[2:(n-1)] = rev(diff(rev(x)))
out[n] = x[n-1]
out
}
softthresh = function(x, lambda) {
return(sign(x)*pmax(0, abs(x) - lambda))
}
fit_1dfusedlasso_ADMM = function(y, lambda, weights=NULL, initial_values = NULL, iter_max = 10000, rel_tol = 1e-4, alpha=1.0) {
# Fits the 1D weighted fused lasso by ADMM
n = length(y)
m = n-1
if(missing(weights)) {
weights = rep(1, n)
}
# Step-size parameter
a = lambda
# the D matrix is the first-difference operator
# K is the matrix (W + a D^T D)
# where W is the diagonal matrix of weights.
# We use a tridiagonal representation of K
Kd = c(a, rep(2*a, n-2), a) + weights # diagonal entries
Kl = rep(-a, n-1) # below the diagonal
Ku = rep(-a, n-1) # above the diagonal
# print("K:")
# print(length(Kd))
# print(length(Kl))
# print(length(Ku))
# Initialize primal and dual variables
if(missing(initial_values)) {
x = rep(mean(y), n)
z = rep(0,m)
u = rep(0,m)
} else {
x = initial_values$x
z = initial_values$z
u = initial_values$u
u = pmin(lambda, pmax(-lambda, u)) # ensure feasibility of starting point
}
primal_trace = NULL
dual_trace = NULL
converged = FALSE
counter = 0
while(!converged & counter < iter_max) {
# Update x
out = Dcrossx(a*z - u)
x = Solve.tridiag(Kl, Kd, Ku, weights*y + out)
Dx = diff(x)
# Update z: over-relaxation?
Dx_hat = alpha*Dx + (1-alpha)*z
z_new = softthresh(Dx_hat + u/a, lambda/a)
dual_residual = a * Dcrossx(z_new - z)
z = z_new
primal_residual = Dx - z
# Update u
u = u + a*primal_residual
# Check convergence
primal_resnorm = sqrt(mean(primal_residual^2))
dual_resnorm = sqrt(mean(dual_residual^2))
primal_trace = c(primal_trace, primal_resnorm)
dual_trace = c(dual_trace, dual_resnorm)
if(dual_resnorm < rel_tol && primal_resnorm < rel_tol) {
converged=TRUE
}
counter = counter+1
# Update step-size parameter based on norm of primal and dual residuals
if(primal_resnorm > 10*dual_resnorm) {
a = 2*a
Kd = c(a, rep(2*a, n-2), a) + weights # diagonal entries
Kl = rep(-a, n-1) # below the diagonal
Ku = rep(-a, n-1) # above the diagonal
} else if(dual_resnorm > 10*primal_resnorm) {
a = a/2
Kd = c(a, rep(2*a, n-2), a) + weights # diagonal entries
Kl = rep(-a, n-1) # below the diagonal
Ku = rep(-a, n-1) # above the diagonal
}
}
dof = sum(Dx > rel_tol) + 1
AIC = sum((y-x)^2) + 2*dof
list(x=x, z=z, u=u, dof=dof, AIC=AIC)
}
```

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