different results form summarization by loop and sum or rowMeans function

Hi,

I found different results calculating the rowMeans by the function 
rowMeans() and a simple for-loop. The differences are very low. But 
after this calculation I will start some optimization algorithms (BFGS 
or CG) and there I get huge differences (from the small changes in the 
beginning or start values, I changed nothing else in the code).
How I can avoid these differences between sum-loops and sum-functions?

Attached a small testcode using data form Bioconductor.

Best
Markus

library(affy)
data(affybatch.example)
mat <- exprs(affybatch.example)[1:100,1:3]
mat <- exp(1)*mat
mat <- asinh(mat)

rowM1<- rowMeans(mat)

t=rep(0,100) # Vektor mit 0en
for(i in 1:100){
   for(j in 1:3)
       t[i] <- t[i] + mat[i,j]
}
rowM2 <- t/3

m1 <- mat - rowM1
m2 <- mat -rowM2

print(m1-m2)

sessionInfo()
R version 2.7.1 (2008-06-23)
i386-pc-mingw32

locale:
LC_COLLATE=German_Germany.1252;LC_CTYPE=German_Germany.1252;LC_MONETARY=German_Germany.1252;LC_NUMERIC=C;LC_TIME=German_Germany.1252

attached base packages:
[1] tools     stats     graphics  grDevices utils     datasets  methods 
[8] base    

other attached packages:
[1] affy_1.18.2          preprocessCore_1.2.0 affyio_1.8.0       
[4] Biobase_2.0.1