# What does #iter mean in libsvm when using RBF as a kernel?

I have a dataset with 60,000 training examples. I tried the following command under windows and with libsvm:

svm-train.exe -t 2 -g 0.07 -c 1 images.train


and it started giving multiple outputs for example:

.....*..*
optimization finished, #iter = 7090
nu = 0.117216
obj = -667.069565, rho = -0.055205
nSV = 4702, nBSV = 26
....*..*
optimization finished, #iter = 6485
nu = 0.100224
obj = -565.615150, rho = -0.198917
nSV = 4342, nBSV = 29
..*.*
optimization finished, #iter = 3671
nu = 0.027754
obj = -176.698407, rho = -0.838783
nSV = 2560, nBSV = 52
....*..*
optimization finished, #iter = 6192
nu = 0.104976
obj = -604.912230, rho = -0.284038
nSV = 4109, nBSV = 96
......^C


I stopped it because I didn't understand what was going on. I searched online in the libsvm website however I did not find any explanation on why I get multiple outputs and what these outputs mean exactly.

I tried running the heart_scale and I only got one output:

svm-train.exe -t 2 -g 0.07 -c 1 heart_scale
*
optimization finished, #iter = 134
nu = 0.433785
obj = -101.855060, rho = 0.426412
nSV = 130, nBSV = 107
Total nSV = 130


so does the amount of training samples have anything to do with this and if so what's the exact correlation? thanks