Softmax overflow AND underflow I am trying to use the typical Multinomial model:
$y_{i} \sim Multinomial(N, \theta_{1}, ...,\theta_{p})$
where each theta is defined as:
$\theta_{i}=\frac{e^{V_{i} }}{\sum_{j} e^{V_{j}}}$
I have an issue with the overflow of the exponentials. I know a solution is given here Softmax overflow which basically suggests to subtract the maximum $V$ from both the power of numerator and the denominator but then that also creates the underflow problem in my case and many thetas will become 0! Is there any better solution to correctly calculate the probabilities (thetas) without facing overflow and underflow?
Here is my data and what really happens when I want to use softmax and the logSumExp approach:
library(dplyr)
library(matrixStats)

df <- data.frame(id         = seq(1,11,1),
                 real_probs = c(0.009589089, 0.007534532, 0.001860207, 0.05084681, 
                                0.1630076, 0.04244464, 0.007857292, 0.3566669, 
                                0.1793573, 0.1698827, 0.01095301),
                 V          = c(2653.254, 2364.478, 521.0566, 14615.73, 
                                46912.33, 12705.54, 1837.695, 102773.2, 
                                51674.49, 48950.77, 3129.556))

df_new <- df %>% 
  mutate(total_V = sum(V),
         pred_probs_without_softmax = V/total_V,
         exp_V = exp(V), # To show the Overflow problem
         exp_new_V = exp(V - max(V)), # To show the Underflow problem
         logsumex = logSumExp(V),
         pred_probs_with_softmax = exp(V - logsumex))


This question seems to me like it is built on a false premise of how precision methods in floating-point arithmetic work.  If any $\theta_i$ value is positive, but is below the smallest number that can be represented in your floating-point arithmetic (at whatever bit-size you are using), then it is going to get rounded down to zero.  That is an unavoidable aspect of using floating-point representation of numbers directly on the value of interest.
Now, by taking the sofmax transformation, your goal is to be able to compute the values of $V_i$ without overflow or underflow.  You can also compute the values of $\log \theta_i$ using the fact that:
$$\log \theta_i = V_i - \text{logsumexp}(V_1,...,V_n).$$
Of course, even if you do this, any value of $\theta_i$ that is below the smallest floating-point number in your system is still going to get rounded to zero or one due to the limited precision.  But who cares?  If you can compute $\log \theta_i$ without overflow or underflow, that should give you what you need for any subsequent computations.  And if your ultimate goal is to get a non-zero value for $\theta_i$ then you are going to have to decide how many bits you are willing to use to represent it.
Now, if you really want to get an accurate value for $\theta_i$, another way you can do this is to try to represent it as being "squeezed" between two rational numbers represented using arbitrary-precision arithmetic.  Given sufficient computing power, that will allow you to compute the number down to an arbitrarily small interval with exactly known bounds, so you can avoid underflow.

Your data: In relation to your posted data, your values for V in your data-frame df do not match the probability values you put in, and it is not clear where they come from.  If you want to find the values that correspond to these probabilities you can use the softmaxinv function in the utilities package.
#Load library
library(utilities)
softmaxinv(df$real_probs)
[1] -0.1329884 -0.3741176 -1.7729265  1.5352031  2.7001825  1.3545863 -0.3321723  3.4831880  2.7957656  2.7414939

As to the predicted probabilities you are getting with your V values, those are correct (to within a tiny margin of error that is far below the minimum FLOP).  Your eighth value in V is over $50,000$ more than the nearest value, so the probability is over $\exp(50,000)$ times as big as the nearest value.  Unsurprisingly, this large probability is being rounded to one and all other values to zero.
