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Logarithmically concave function
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Logarithmically concave function : ウィキペディア英語版
Logarithmically concave function
In convex analysis, a non-negative function is logarithmically concave (or log-concave for short) if its domain is a convex set, and if it satisfies the inequality
:
f(\theta x + (1 - \theta) y) \geq f(x)^ f(y)^

for all and . If is strictly positive, this is equivalent to saying that the logarithm of the function, , is concave; that is,
:
\log f(\theta x + (1 - \theta) y) \geq \theta \log f(x) + (1-\theta) \log f(y)

for all and .
Examples of log-concave functions are the 0-1 indicator functions of convex sets (which requires the more flexible definition), and the Gaussian function.
Similarly, a function is log-convex if it satisfies the reverse inequality
:
f(\theta x + (1 - \theta) y) \leq f(x)^ f(y)^

for all and .
==Properties==

* A positive log-concave function is also quasi-concave.
* Every concave function that is nonnegative on its domain is log-concave. However, the reverse does not necessarily hold. An example is the Gaussian function  =  which is log-concave since  =  is a concave function of . But is not concave since the second derivative is positive for || > 1:
::f''(x)=e^} (x^2-1) \nleq 0
* A twice differentiable, nonnegative function with a convex domain is log-concave if and only if for all satisfying ,
::f(x)\nabla^2f(x) \preceq \nabla f(x)\nabla f(x)^T,〔Stephen Boyd and Lieven Vandenberghe, (Convex Optimization ) (PDF) p.105〕
:i.e.
::f(x)\nabla^2f(x) - \nabla f(x)\nabla f(x)^T is
:negative semi-definite. For functions of one variable, this condition simplifies to
::f(x)f''(x) \leq (f'(x))^2

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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