# Logarithmically concave function

In convex analysis, a non-negative function f : R$^n$ → R+ is logarithmically concave (or log-concave for short) if its domain is a convex set, and if it satisfies the inequality
    $f(\theta x+(1-\theta )y)\geq f(x)^{\theta }f(y)^{1-\theta }$
for all x,y ∈ dom f and 0 < θ < 1. If f is strictly positive, this is equivalent to saying that the logarithm of the function, log ∘ f, is concave; that is,
    $\log f(\theta x+(1-\theta )y)\geq \theta \log f(x)+(1-\theta )\log f(y)$
for all x,y ∈ dom f and 0 < θ < 1.
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
    $f(\theta x+(1-\theta )y)\leq f(x)^{\theta }f(y)^{1-\theta }$
for all x,y ∈ dom f and 0 < θ < 1.

## Properties

* A log-concave function is also quasi-concave. This follows from the fact that the logarithm is monotone implying that the superlevel sets of this function are convex.
* 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 f(x) = exp(−x$^2$/2) which is log-concave since log f(x) = −x$^2$/2 is a concave function of x. But f is not concave since the second derivative is positive for |x| > 1:
       $f''(x)=e^{-{\frac {x^{2}}{2}}}(x^{2}-1)\nleq 0$
* From above two points, concavity $\Rightarrow$ log-concavity $\Rightarrow$ quasiconcavity.
* A twice differentiable, nonnegative function with a convex domain is log-concave if and only if for all x satisfying f(x) > 0,
       $f(x)\nabla ^{2}f(x)\preceq \nabla f(x)\nabla f(x)^{T}$,
    i.e.
       $f(x)\nabla ^{2}f(x)-\nabla f(x)\nabla f(x)^{T}$ is
    negative semi-definite. For functions of one variable, this condition simplifies to
       $f(x)f''(x)\leq (f'(x))^{2}$

## Operations preserving log-concavity

* Products: The product of log-concave functions is also log-concave. Indeed, if f and g are log-concave functions, then log f and log g are concave by definition. Therefore
       $\log \,f(x)+\log \,g(x)=\log(f(x)g(x))$
    is concave, and hence also f g is log-concave.
* Marginals: if f(x,y) : R$^{n+m}$ → R is log-concave, then
       $g(x)=\int f(x,y)dy$
    is log-concave (see Prékopa–Leindler inequality).
* This implies that convolution preserves log-concavity, since h(x,y) = f(x-y) g(y) is log-concave if f and g are log-concave, and therefore
       $(f*g)(x)=\int f(x-y)g(y)dy=\int h(x,y)dy$
    is log-concave.

## Log-concave distributions

Log-concave distributions are necessary for a number of algorithms, e.g. adaptive rejection sampling. Every distribution with log-concave density is a maximum entropy probability distribution with specified mean μ and Deviation risk measure D. As it happens, many common probability distributions are log-concave. Some examples:
* the normal distribution and multivariate normal distributions,
* the exponential distribution,
* the uniform distribution over any convex set,
* the logistic distribution,
* the extreme value distribution,
* the Laplace distribution,
* the chi distribution,
* the hyperbolic secant distribution,
* the Wishart distribution, if n ≥ p + 1,
* the Dirichlet distribution, if all parameters are ≥ 1,
* the gamma distribution if the shape parameter is ≥ 1,
* the chi-square distribution if the number of degrees of freedom is ≥ 2,
* the beta distribution if both shape parameters are ≥ 1, and
* the Weibull distribution if the shape parameter is ≥ 1.
Note that all of the parameter restrictions have the same basic source: The exponent of non-negative quantity must be non-negative in order for the function to be log-concave.
The following distributions are non-log-concave for all parameters:
* the Student's t-distribution,
* the Cauchy distribution,
* the Pareto distribution,
* the log-normal distribution, and
* the F-distribution.
Note that the cumulative distribution function (CDF) of all log-concave distributions is also log-concave. However, some non-log-concave distributions also have log-concave CDF's:
* the log-normal distribution,
* the Pareto distribution,
* the Weibull distribution when the shape parameter < 1, and
* the gamma distribution when the shape parameter < 1.
The following are among the properties of log-concave distributions:
* If a density is log-concave, so is its cumulative distribution function (CDF).
* If a multivariate density is log-concave, so is the marginal density over any subset of variables.
* The sum of two independent log-concave random variables is log-concave. This follows from the fact that the convolution of two log-concave functions is log-concave.
* The product of two log-concave functions is log-concave. This means that joint densities formed by multiplying two probability densities (e.g. the normal-gamma distribution, which always has a shape parameter ≥ 1) will be log-concave. This property is heavily used in general-purpose Gibbs sampling programs such as BUGS and JAGS, which are thereby able to use adaptive rejection sampling over a wide variety of conditional distributions derived from the product of other distributions.
* If a density is log-concave, so is its survival function.
* If a density is log-concave, it has a monotone hazard rate (MHR), and is a regular distribution since the derivative of the logarithm of the survival function is the negative hazard rate, and by concavity is monotone i.e.
       ${\frac {d}{dx}}\log \left(1-F(x)\right)=-{\frac {f(x)}{1-F(x)}}$ which is decreasing as it is the derivative of a concave function.