# Logarithmically concave function

In convex analysis, a non-negative function f : RnR+ is logarithmically concave (or log-concave for short) if its domain is a convex set, and if it satisfies the inequality

${\displaystyle f(\theta x+(1-\theta )y)\geq f(x)^{\theta }f(y)^{1-\theta }}$

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,

${\displaystyle \log f(\theta x+(1-\theta )y)\geq \theta \log f(x)+(1-\theta )\log f(y)}$

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

${\displaystyle f(\theta x+(1-\theta )y)\leq f(x)^{\theta }f(y)^{1-\theta }}$

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.[1]
• 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(−x2/2) which is log-concave since log f(x) = x2/2 is a concave function of x. But f is not concave since the second derivative is positive for |x| > 1:
${\displaystyle f''(x)=e^{-{\frac {x^{2}}{2}}}(x^{2}-1)\nleq 0}$
• From above two points, concavity ${\displaystyle \Rightarrow }$ log-concavity ${\displaystyle \Rightarrow }$ quasiconcavity.
• A twice differentiable, nonnegative function with a convex domain is log-concave if and only if for all x satisfying f(x) > 0,
${\displaystyle f(x)\nabla ^{2}f(x)\preceq \nabla f(x)\nabla f(x)^{T}}$,[1]
i.e.
${\displaystyle f(x)\nabla ^{2}f(x)-\nabla f(x)\nabla f(x)^{T}}$ is
negative semi-definite. For functions of one variable, this condition simplifies to
${\displaystyle f(x)f''(x)\leq (f'(x))^{2}}$

## 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
${\displaystyle \log \,f(x)+\log \,g(x)=\log(f(x)g(x))}$
is concave, and hence also f g is log-concave.
• Marginals: if f(x,y) : Rn+m → R is log-concave, then
${\displaystyle g(x)=\int f(x,y)dy}$
is log-concave (see Prékopa–Leindler inequality).
• This implies that convolution preserves log-concavity, since h(x,y) = f(x-yg(y) is log-concave if f and g are log-concave, and therefore
${\displaystyle (f*g)(x)=\int f(x-y)g(y)dy=\int h(x,y)dy}$
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.[2] As it happens, many common probability distributions are log-concave. Some examples:[3]

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:

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 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.

## Notes

1. ^ a b Stephen Boyd and Lieven Vandenberghe, Convex Optimization (PDF) Section 3.5
2. ^ Grechuk, B., Molyboha, A., Zabarankin, M. (2009) Maximum Entropy Principle with General Deviation Measures, Mathematics of Operations Research 34(2), 445--467, 2009.
3. ^ See Mark Bagnoli and Ted Bergstrom (1989), "Log-Concave Probability and Its Applications", University of Michigan.[1]
4. ^ a b Prékopa, András (1971). "Logarithmic concave measures with application to stochastic programming". Acta Scientiarum Mathematicarum. 32: 301–316.

## References

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• Dharmadhikari, Sudhakar; Joag-Dev, Kumar (1988). Unimodality, convexity, and applications. Probability and Mathematical Statistics. Boston, MA: Academic Press, Inc. pp. xiv+278. ISBN 0-12-214690-5. MR 0954608.
• Pfanzagl, Johann; with the assistance of R. Hamböker (1994). Parametric Statistical Theory. Walter de Gruyter. ISBN 3-11-013863-8. MR 1291393.
• Pečarić, Josip E.; Proschan, Frank; Tong, Y. L. (1992). Convex functions, partial orderings, and statistical applications. Mathematics in Science and Engineering. 187. Boston, MA: Academic Press, Inc. pp. xiv+467 pp. ISBN 0-12-549250-2. MR 1162312.