## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")

## -----------------------------------------------------------------------------
library(qap)
set.seed(2024)

## -----------------------------------------------------------------------------
instances <- c("tai30a", "tai50a", "tai80a", "sko100a")
reps <- 3

run <- function(p, method, ...) {
  time <- system.time(a <- qap(p$A, p$B, method = method, ...))[["elapsed"]]
  c(obj = unname(attr(a, "obj")), time = time)
}

res <- do.call(rbind, lapply(instances, function(f) {
  p <- read_qaplib(system.file("qaplib", paste0(f, ".dat"), package = "qap"))
  sa <- run(p, "SA", rep = reps)
  ro <- run(p, "RoTS", rep = reps)
  data.frame(
    instance = f,
    n = nrow(p$A),
    opt = p$opt,
    SA_gap = (sa[["obj"]] - p$opt) / p$opt * 100,
    RoTS_gap = (ro[["obj"]] - p$opt) / p$opt * 100,
    SA_time = sa[["time"]],
    RoTS_time = ro[["time"]]
  )
}))

## -----------------------------------------------------------------------------
knitr::kable(res, digits = 3, row.names = FALSE,
  col.names = c("Instance", "n", "Optimum", "SA gap (%)", "RoTS gap (%)",
                "SA time (s)", "RoTS time (s)"))

## ----fig.width = 7, fig.height = 3.5, fig.alt = "Solution gap and run time of both solvers by instance size."----
op <- par(mfrow = c(1, 2), mar = c(4, 4, 2, 1))
matplot(res$n, res[, c("SA_gap", "RoTS_gap")], type = "b", pch = 1:2,
        lty = 1, col = 1:2, xlab = "n", ylab = "gap above optimum (%)",
        main = "Solution quality")
legend("topleft", legend = c("SA", "RoTS"), pch = 1:2, col = 1:2, lty = 1)
matplot(res$n, res[, c("SA_time", "RoTS_time")], type = "b", pch = 1:2,
        lty = 1, col = 1:2, log = "y", xlab = "n", ylab = "seconds",
        main = "Run time")
par(op)

## ----results = "asis", echo = FALSE-------------------------------------------
print(citation("qap"), style = "text")