MLE for bimodal distribution

Hello everyone,

I'm trying to use mle from package stats4 to fit a bi/multi-modal
distribution to some data, but I have some problems with it.
Here's what I'm doing (for a bimodal distribution):

# Build some fake binormally distributed data, the procedure fails also with
real data, so the problem isn't here
data = c(rnorm(1000, 3, 0.5), rnorm(500, 5, 0.3))
# Just to check it's bimodal
plot(density(data))
f = function(m, s, m2, s2, w)
{
-log( sum(w*dnorm(data, mean=m, sd=s)) + sum((1-w)*dnorm(data, mean=m2,
sd=s2)) )
}

res = mle(f, start=list("m"=3, "s"=0.5, "m2"=5, "s2"=0.35, "w"=0.6))

This gives an error:
Error in optim(start, f, method = method, hessian = TRUE, ...) : 
  non-finite finite-difference value [2]
In addition: There were 50 or more warnings (use warnings() to see the first
50)
And the warnings are about dnorm producing NaNs

So, my questions are:
1) What does "non-finite finite-difference value" mean? My guess would be
that an Inf was passed somewhere where a finite number was expected...? I'm
not sure how optim works though...
2) Is the log likelihood function I wrote correct? Can I use the same type
of function for 3 modes?