In mathematics, a concave function is one for which the function value at any convex combination of elements in the domain is greater than or equal to that convex combination of those domain elements. Equivalently, a concave function is any function for which the hypograph is convex. The class of concave functions is in a sense the opposite of the class of convex functions. A concave function is also synonymously called concave downwards, concave down, convex upwards, convex cap, or upper convex.
Definition
A real-valued function <math>f</math> on an interval (or, more generally, a convex set in vector space) is said to be concave if, for any <math>x</math> and <math>y</math> in the interval and for any <math>\alpha \in [0,1]</math>,
:<math>f((1-\alpha )x+\alpha y)\geq (1-\alpha ) f(x)+\alpha f(y)</math>
A function is called strictly concave if
:<math>f((1-\alpha )x+\alpha y) > (1-\alpha ) f(x)+\alpha f(y)</math>
for any <math>\alpha \in (0,1)</math> and <math>x \neq y</math>.
For a function <math>f: \mathbb{R} \to \mathbb{R}</math>, this second definition merely states that for every <math>z</math> strictly between <math>x</math> and <math>y</math>, the point <math>(z, f(z))</math> on the graph of <math>f</math> is above the straight line joining the points <math>(x, f(x))</math> and <math>(y, f(y))</math>.
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A function <math>f</math> is quasiconcave if the upper contour sets of the function <math>S(a)=\{x: f(x)\geq a\}</math> are convex sets.
Properties
class=skin-invert-image|thumb|A cubic function is concave (left half) when its first derivative (red) is monotonically decreasing i.e. its second derivative (orange) is negative, and convex (right half) when its first derivative is monotonically increasing i.e. its second derivative is positive
Functions of a single variable
- A differentiable function is (strictly) concave on an interval if and only if its derivative function is (strictly) monotonically decreasing on that interval, that is, a concave function has a non-increasing (decreasing) slope.
- Points where concavity changes (between concave and convex) are inflection points.
- If is twice-differentiable, then is concave if and only if is non-positive (or, informally, if the "acceleration" is non-positive). If is negative then is strictly concave, but the converse is not true, as shown by .
- If is concave and differentiable, then it is bounded above by its first-order Taylor approximation:
Applications
- Rays bending in the computation of radiowave attenuation in the atmosphere involve concave functions.
- In expected utility theory for choice under uncertainty, cardinal utility functions of risk averse decision makers are concave.
- In microeconomic theory, production functions are usually assumed to be concave over some or all of their domains, resulting in diminishing returns to input factors.
- In thermodynamics and information theory, entropy is a concave function. In the case of thermodynamic entropy, without phase transition, entropy as a function of extensive variables is strictly concave. If the system can undergo phase transition, and if it is allowed to split into two subsystems of different phase (phase separation, e.g. boiling), the entropy-maximal parameters of the subsystems will result in a combined entropy precisely on the straight line between the two phases. This means that the "effective entropy" of a system with phase transition is the convex envelope of entropy without phase separation; therefore, the entropy of a system including phase separation will be non-strictly concave.
See also
- Concave polygon
- Jensen's inequality
- Logarithmically concave function
- Quasiconcave function
- Concavification