# Posts Tagged brute-force

### Brute-force packing

Because I’m a huge music fan, I own quite a few CDs; rather more than I’d readily admit to! A few years ago, I started ripping my collection to FLAC, a lossless audio format, and backing everything up to DVD-R. In order to be efficient, I want to get as many albums as I can on each DVD-R disc, minimising the space wasted — this is known as a Packing Problem.

Now, I’d love to be able to present you with a highly-optimised algorithm that I wrote, but that’s not what I did: I brute-forced it. Processor cycles are very cheap, and if it’s going to be orders of magnitude quicker to iterate a few million times than it will be to research a whole new area of maths, then iterating it’ll be. My original code was a VB app (so I could drag folders onto a GUI), but here’s a similar version of the code in R:

``````set.seed(1234);
containerSize <- 4500; # roughly DVD size in MB
itemSize <- c(1641,1498,1747,751,1090,164,1602,1020,1126,553); # album sizes in MB
cat(sprintf("No. containers needed (no partitioning): %5.2f\n", sum(itemSize) / containerSize));

Z <- 1000; # Number of iterations

# To keep track of the best partition
best.remainder <- 1.0;
best.partition <- NULL;

for(i in 1:Z) {

working <- sample(itemSize); # randomly re-order our list of sizes
partition <- list();
k <- 1;
# Using the order as per 'working', partition the items
# such that the container size isn't exceeded:
while (length(working) > 0) {
this.partition.indexes <- which( cumsum(working) <= containerSize );
partition[[k]] <- working[this.partition.indexes];
working <- working[-(this.partition.indexes)];
k <- k+1;
}
npm1 <- length(partition) - 1; # Number of partitions minus 1
partition.totals <- unlist(lapply(partition, sum));
remainder <- (sum(rep(containerSize, npm1) - partition.totals[1:npm1]))
/ (npm1 * containerSize);

if (remainder < best.remainder) {
best.remainder <- remainder;
best.partition <- partition;
totals.str <- paste("(", paste(partition.totals, collapse=","), ")", sep="");
partition.str <- paste(unlist(lapply(partition,
function(x) paste("(",paste(x,collapse=","),")",sep=""))),collapse=",")
cat(sprintf("i = %3d, rem. = %5.2f%%; totals = %s; partition = %s\n", i,
remainder * 100.0), totals.str, partition.str));
}

} # end for loop
``````

This code (1000 iterations) runs in the blink of an eye:

``````i =   1, rem. = 19.00%; totals = (3772,3518,3902);
partition = (1498,164,1090,1020),(1126,751,1641),(1602,553,1747)
i =   2, rem. = 13.56%; totals = (4439,3341,3412);
partition = (1602,1090,1747),(553,1498,1126,164),(1641,1020,751)
i =   4, rem. = 13.18%; totals = (3963,3851,3378);
partition = (1090,1747,1126),(751,1498,1602),(1641,553,164,1020)
i =   6, rem. =  4.78%; totals = (4303,4267,2622);
partition = (1641,1747,164,751),(553,1126,1498,1090),(1020,1602)
i =  13, rem. =  4.04%; totals = (4301,4335,2556);
partition = (1020,1126,553,1602),(1747,1498,1090),(751,1641,164)
i =  23, rem. =  0.26%; totals = (4478,4499,2215);
partition = (1090,1641,1747),(1020,1126,1602,751),(1498,553,164)
i = 524, rem. =  0.02%; totals = (4499,4499,2194);
partition = (1126,1602,751,1020),(1747,1498,1090,164),(553,1641)
``````

The figure rem. is the percentage of space wasted on the discs that could be full — clearly, not all the discs can be 100% full. So in this case, I knew I was going to be burning three DVD-Rs, but there’s only 1 MB of unused space on each of the first two discs; for the third, I can either find some other files to backup, or keep those two albums to burn later — which is what I usually do; saving even more space, by repeatedly putting off burning the least-full disc.