Question

This is (the greatest part of) the cost function for a genetic optimization, so it needs to be really fast. Right now it’s painfully slow even for toy problem sizes. I’m pretty sure there are faster ways to do many of the operations in this code, but I’m not that good at tuning R code. Can anyone suggest anything?

Fb, Ft, and Fi are scalar constants. tpat is a large, constant 2D matrix. jpat is a smaller matrix which is being optimized. nrow(tpat) == nrow(jpat) and ncol(tpat) %% ncol(jpat) == 0 are invariants. All entries in tpat and jpat are real numbers in [0,1].

# Toy jamming model for genetic optimization.
#
# A jamming pattern is a vector of real numbers \in [0,1], interpreted
# as a matrix with subcarrier frequency bands in the rows and time
# slots in the columns.  Every time slot, the jammer transmits
# Gaussian noise centered on the subcarrier frequency (todo: should
# that be Gaussian baseband noise *modulated* onto the subcarrier
# frequency?) with intensity equal to the number in the appropriate
# matrix cell (0 = off, 1 = maximum power).
#
# A transmission pattern is similar, but there are many more time
# slots; the jamming pattern is repeated horizontally to cover the
# complete transmission pattern.  (todo: implement jamming duty cycles.)
#
# The transmitter is required to transmit complete packets of some
# fixed length equal to several time slots, and it uses a fixed
# intensity Itr < 1 for each packet (we assume that the jammer is in
# between the transmitter and receiver, so its effective power at the
# receiver is higher).

Itr <- 0.75;
Fb  <- 0.1;
Ft  <- 0.1;
Fi  <- 0.5;

Nb  <- 100;
Sj  <- 30;
St  <- Sj * 20;

# success metric
pkt.matrix <- function(tpat) {
  # Find all the packets in tpat.  A packet is a contiguous sequence
  # of timeslots during which the transmitter was active on at least
  # one frequency band.  Returns a logical matrix with
  # nrow=(number of packets), ncol=(total timeslots), in which each
  # row will select one packet from the original matrix.
  runs <- rle(ifelse(apply(tpat, 2, sum) > 0, TRUE, FALSE));
  pkt  <- matrix(FALSE, nrow=sum(runs$values == TRUE),
                 ncol=sum(runs$lengths));
  i <- 1
  j <- 1
  for (r in 1:length(runs$lengths)) {
    if (runs$values[r]) {
      pkt[i, j:(runs$lengths[r]+j-1)] <- TRUE;
      i <- i + 1;
    }
    j <- j + runs$lengths[r];
  }
  return(pkt);
}

success.metric <- function(jpat, tpat) {
  if (ncol(tpat) %% ncol(jpat)) error("non-conformable arrays");
  if (ncol(tpat) > ncol(jpat))
    # there must be a better way to do this...
    jpat <- do.call(cbind, rep(alist(jpat), ncol(tpat)/ncol(jpat)));

  pktm <- pkt.matrix(tpat);
  pkts <- nrow(pktm);
  jammed <- 0;
  for (i in 1:pkts) {
    pkt <- tpat[,pktm[i,]];
    jam <- jpat[,pktm[i,]];

    # jamming on a channel not being used by the transmitter at the time
    # is totally ineffective
    jam[pkt==0] <- 0;

    # at least Ft of the time slots used by `pkt` must have had at least
    # one channel jammed
    if (sum(apply(jam, 2, sum) > 0) < Ft * ncol(pkt)) next;

    # at least Fb of the time slots used by `pkt` must have been jammed
    # at least once
    if (sum(apply(jam, 1, sum) > 0) < Fb * nrow(pkt)) next;

    # the total intensity produced by the jammer must be at least Fi the
    # total intensity produced by the source
    if (sum(jam) < Fi * sum(pkt)) next;

    jammed <- jammed + 1;
  }
  return((pkts - jammed) * 100 / pkts);
}

# some `tpat` examples; `jpat` is generated by genoud()
## saturation transmission: on for 19, off for 1
sat.base <- c(rep(Itr, 19), 0);
### single constant subcarrier
sat.scs <- matrix(0, nrow=Nb, ncol=St);
sat.scs[Nb/2,] <- sat.base;

### FHSS with an incredibly foolish hopping pattern
sat.fhss <- matrix(0, nrow=Nb, ncol=St);
# razzum frazzum 1-based arrays
sat.fhss[((col(sat.fhss) - 1) %% nrow(sat.fhss)) + 1 == 
         row(sat.fhss)] <- sat.base;

Answer 1

Lots of loops, lots of sweeping along arrays, very few statistical functions… I’d rewrite it in C.

Keep your slow R version for checking, and rewrite this in C. Make sure your R and C give the same values for test data sets.

Oh, but first profile everything to make sure its this bit that is slow – it certainly looks like a prime candidate.