Look at the Wiki page for Softmax function (section "Smooth approximation of maximum"): https://en.wikipedia.org/wiki/Softmax_function

It is saying that the following is a smooth approximation to the softmax: $$ \mathcal{S}_{\alpha}\left(\left\{x_i\right\}_{i=1}^{n}\right) = \frac{\sum_{i=1}^{n}x_i e^{\alpha x_i}}{\sum_{i=1}^{n}e^{\alpha x_i}} $$

  • Is it an approximation to the Softmax?

    • If so, Softmax is already smooth; why do we create another smooth approximation?

    • If so, how do derive it from Softmax?

  • I don't see why this might be better than Softmax for gradien descent updates.

  • 1
    $\begingroup$ I am confused by this as well, but let me make a comment that might be useful. A smooth approximation of maximum that I am familiar with is $f(x,\alpha):=\alpha^{-1} \log\left(\sum_i e^{\alpha x_i}\right)$ which is always within an additive $(\log n)/\alpha$ from the maximum. The function in your question is $\partial f/\partial\alpha$. $\endgroup$ May 18, 2015 at 4:46
  • $\begingroup$ Out of curiosity, what problem are you solving? Are you sure you can't just use the max function? Many convex optimization algorithms can handle nondifferentiable objective functions. $\endgroup$
    – littleO
    Jul 18, 2015 at 5:13

1 Answer 1


This is a smooth approximation of maximum function:

$$ \max\{x_1,\dots, x_n\} $$

where $\alpha$ controls the "softness" of the maximum. The detailed explanation is available here: http://www.johndcook.com/blog/2010/01/13/soft-maximum/

Softmax is better then maximum, because it is smooth function, while $\max$ is not smooth and does not always have a gradient.

  • $\begingroup$ Could you perhaps sum up the detailed discussion in your answer? $\endgroup$ Sep 24, 2014 at 22:21
  • $\begingroup$ The sum up is the first sentence of my reply, in the explanation there are also visual examples given. $\endgroup$
    – coffee
    Sep 25, 2014 at 23:26
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    $\begingroup$ to be short, $\alpha \to +\infty$ makes softmax converge to max, and $\alpha \to -\infty$ makes softmax converge to min $\endgroup$
    – coffee
    Sep 26, 2014 at 2:53
  • $\begingroup$ The blog entry discusses a different function from the one in the question. $\endgroup$ May 18, 2015 at 4:42
  • $\begingroup$ yes, the function described there is a little different (it is numerically better I think, this is the one you've mentioned), but the idea is the same. Both are valid approximations. $\endgroup$
    – coffee
    May 21, 2015 at 8:10

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