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Smooth maximum : ウィキペディア英語版
Smooth maximum

In mathematics, a smooth maximum of an indexed family ''x''1, ..., ''x''''n'' of numbers is a differentiable approximation to the maximum function
: \ \mapsto \max\, \,
and the concept of smooth minimum is similarly defined.
For large positive values of the parameter \alpha > 0, the following formulation is one smooth, differentiable approximation of the maximum function. For negative values of the parameter that are large in absolute value, it approximates the minimum.
:
\mathcal_\alpha (\_^n) = \frac}}

\mathcal_\alpha has the following properties:
#\mathcal_\alpha\to \max as \alpha\to\infty
#\mathcal_0 is the average of its inputs
#\mathcal_\alpha\to \min as \alpha\to -\infty
The gradient of \mathcal_ is closely related to softmax and is given by
:
\nabla_\mathcal_\alpha (\_^n) = \frac^n e^} (+ \alpha(x_i - \mathcal_\alpha (\_^n)) ).

This makes the softmax function useful for optimization techniques that use gradient descent.
Another formulation is:
:
g(x_1, \ldots, x_n) = \log( \exp(x_1) + \ldots + \exp(x_n) )

== Smooth minimum ==


抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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