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## ----echo=F-------------------------------------------------------------------
### get knitr just the way we like it
knitr::opts_chunk$set(
message = FALSE,
warning = FALSE,
error = FALSE,
tidy = FALSE,
cache = FALSE
)
## -----------------------------------------------------------------------------
library(ECOSolveR)
library(Matrix)
set.seed(182391)
n <- 1000L
m <- 10L
density <- 0.01
c <- c(rep(0.0, n), rep(1.0, n))
## -----------------------------------------------------------------------------
sprandn <- function(nrow, ncol, density) {
items <- ceiling(nrow * ncol * density)
matrix(c(rnorm(items),
rep(0, nrow * ncol - items)),
nrow = nrow)
}
## -----------------------------------------------------------------------------
A <- sprandn(m, n, density)
Atilde <- Matrix(cbind(A, matrix(rep(0.0, m * n), nrow = m)), sparse = TRUE)
b <- rnorm(m)
I <- diag(n)
G <- rbind(cbind(I, -I),
cbind(-I, -I))
G <- as(G, "dgCMatrix")
h <- rep(0.0, 2L * n)
dims <- list(l = 2L * n, q = NULL, e = 0L)
## -----------------------------------------------------------------------------
## Solve the problem
z <- ECOS_csolve(c = c, G = G, h = h, dims = dims, A = Atilde, b = b)
## -----------------------------------------------------------------------------
names(z)
z$infostring
## -----------------------------------------------------------------------------
x <- z$x[1:n]
u <- z$x[(n+1):(2*n)]
nnzx = sum(abs(x) > 1e-8)
sprintf("x reconstructed with %d non-zero entries", nnzx / length(x) * 100)
## -----------------------------------------------------------------------------
## Set up workspace once
ws <- ECOS_setup(c = c, G = G, h = h, dims = dims, A = Atilde, b = b)
## Sweep a parameter: scale h from 0 to 1
alphas <- seq(0, 1, length.out = 11)
pcosts <- numeric(length(alphas))
for (i in seq_along(alphas)) {
ECOS_update(ws, h = h + alphas[i])
res <- ECOS_solve(ws)
pcosts[i] <- res$summary[["pcost"]]
}
ECOS_cleanup(ws)
## Show results
data.frame(alpha = alphas, pcost = round(pcosts, 4))
|
