Convex functions play an important role in many areas of mathematics. They are especially important in the study of optimization problems where they are distinguished by a number of convenient properties. For instance, a strictly convex function on an open set has no more than one minimum. Even in infinite-dimensional spaces, under suitable additional hypotheses, convex functions continue to satisfy such properties and as a result, they are the most well-understood functionals in the calculus of variations. In probability theory, a convex function applied to the expected value of a random variable is always bounded above by the expected value of the convex function of the random variable. This result, known as Jensen's inequality, can be used to deduce inequalities such as the arithmetic–geometric mean inequality and Hölder's inequality.
Visualizing a convex function and Jensen's Inequality
Let be a convex subset of a real vector space and let be a function.
Then is called convex if and only if any of the following equivalent conditions hold:
- For all and all :
The right hand side represents the straight line between and in the graph of as a function of increasing from to or decreasing from to sweeps this line. Similarly, the argument of the function in the left hand side represents the straight line between and in or the -axis of the graph of So, this condition requires that the straight line between any pair of points on the curve of to be above or just meets the graph.
- For all and all such that :
The difference of this second condition with respect to the first condition above is that this condition does not include the intersection points (e.g., and ) between the straight line passing through a pair of points on the curve of (the straight line is represented by the right hand side of this condition) and the curve of the first condition includes the intersection points as it becomes or at or or In fact, the intersection points do not need to be considered in a condition of convex using because and are always true (so not useful to be a part of a condition).
The second statement characterizing convex functions that are valued in the real line is also the statement used to define convex functions that are valued in the extended real number line where such a function is allowed to (but is not required to) take as a value. The first statement is not used because it permits to take or as a value, in which case, if or respectively, then would be undefined (because the multiplications and are undefined). The sum is also undefined so a convex extended real-valued function is typically only allowed to take exactly one of and as a value.
The second statement can also be modified to get the definition of strict convexity, where the latter is obtained by replacing with the strict inequality
Explicitly, the map is called strictly convex if and only if for all real and all such that :
A strictly convex function is a function that the straight line between any pair of points on the curve is above the curve except for the intersection points between the straight line and the curve.
The function is said to be concave (resp. strictly concave) if ( multiplied by -1) is convex (resp. strictly convex ).
The term convex is often referred to as convex down or concave upward, and the term concave is often referred as concave down or convex upward. If the term "convex" is used without an "up" or "down" keyword, then it refers strictly to a cup shaped graph . As an example, Jensen's inequality refers to an inequality involving a convex or convex-(up), function.
Many properties of convex functions have the same simple formulation for functions of many variable as for functions of one variable. See below the properties for the case of many variables, as some of them are not listed for functions of one variable.
Functions of one variable
- Suppose is a function of one real variable defined on an interval, and let
(note that is the slope of the purple line in the above drawing; the function R is symmetric in means that R does not change by exchanging and ). is convex if and only if is monotonically non-decreasing in for every fixed (or vice versa). This characterization of convexity is quite useful to prove the following results.
- A convex function of one real variable defined on some open interval C is continuous on admits left and right derivatives, and these are monotonically non-decreasing. As a consequence, is differentiable at all but at most countably many points, the set on which is not differentiable can however still be dense. If is closed, then may fail to be continuous at the endpoints of (an example is shown in the examples section).
- A differentiable function of one variable is convex on an interval if and only if its derivative is monotonically non-decreasing on that interval. If a function is differentiable and convex then it is also continuously differentiable.
- A differentiable function of one variable is convex on an interval if and only if its graph lies above all of its tangents:: 69
for all x and y in the interval.
- A twice differentiable function of one variable is convex on an interval if and only if its second derivative is non-negative there; this gives a practical test for convexity. Visually, a twice differentiable convex function "curves up", without any bends the other way (inflection points). If its second derivative is positive at all points then the function is strictly convex, but the converse does not hold. For example, the second derivative of is , which is zero for but is strictly convex.
- This property and the above property in terms of "...its derivative is monotonically non-decreasing..." are not equal since if is non-negative on an interval then is monotonically non-decreasing on while its converse is not true, for example, is monotonically non-decreasing on while its derivative is not defined at some points on .
- If is a convex function of one real variable, and , then is superadditive on the positive reals, that is for positive real numbers and .
Since is convex, by using one of the convex function definitions above and letting it follows that for all real
From this it follows that
- A function is midpoint convex on an interval if for all
This condition is only slightly weaker than convexity. For example, a real-valued Lebesgue measurable function that is midpoint-convex is convex: this is a theorem of Sierpinski. In particular, a continuous function that is midpoint convex will be convex.
Functions of several variables
- A function valued in the extended real numbers is convex if and only if its epigraph
is a convex set.
- A differentiable function defined on a convex domain is convex if and only if holds for all in the domain.
- A twice differentiable function of several variables is convex on a convex set if and only if its Hessian matrix of second partial derivatives is positive semidefinite on the interior of the convex set.
- For a convex function the sublevel sets and with are convex sets. A function that satisfies this property is called a quasiconvex function and may fail to be a convex function.
- Consequently, the set of global minimisers of a convex function is a convex set: - convex.
- Any local minimum of a convex function is also a global minimum. A strictly convex function will have at most one global minimum.
- Jensen's inequality applies to every convex function . If is a random variable taking values in the domain of then where E denotes the mathematical expectation. Indeed, convex functions are exactly those that satisfies the hypothesis of Jensen's inequality.
- A first-order homogeneous function of two positive variables and (that is, a function satisfying for all positive real ) that is convex in one variable must be convex in the other variable.
Strongly convex functions
The concept of strong convexity extends and parametrizes the notion of strict convexity. A strongly convex function is also strictly convex, but not vice versa.
A differentiable function is called strongly convex with parameter m > 0 if the following inequality holds for all points x, y in its domain:
or, more generally,
where is any norm. Some authors, such as  refer to functions satisfying this inequality as elliptic functions.
An equivalent condition is the following:
It is not necessary for a function to be differentiable in order to be strongly convex. A third definition for a strongly convex function, with parameter m, is that, for all x, y in the domain and
Notice that this definition approaches the definition for strict convexity as m → 0, and is identical to the definition of a convex function when m = 0. Despite this, functions exist that are strictly convex but are not strongly convex for any m > 0 (see example below).
If the function is twice continuously differentiable, then it is strongly convex with parameter m if and only if for all x in the domain, where I is the identity and is the Hessian matrix, and the inequality means that is positive semi-definite. This is equivalent to requiring that the minimum eigenvalue of be at least m for all x. If the domain is just the real line, then is just the second derivative so the condition becomes . If m = 0, then this means the Hessian is positive semidefinite (or if the domain is the real line, it means that ), which implies the function is convex, and perhaps strictly convex, but not strongly convex.
Assuming still that the function is twice continuously differentiable, one can show that the lower bound of implies that it is strongly convex. Using Taylor's Theorem there exists
by the assumption about the eigenvalues, and hence we recover the second strong convexity equation above.
A function is strongly convex with parameter m if and only if the function
The distinction between convex, strictly convex, and strongly convex can be subtle at first glance. If is twice continuously differentiable and the domain is the real line, then we can characterize it as follows:
- convex if and only if for all x.
- strictly convex if for all x (note: this is sufficient, but not necessary).
- strongly convex if and only if for all x.
For example, let be strictly convex, and suppose there is a sequence of points such that . Even though , the function is not strongly convex because will become arbitrarily small.
A twice continuously differentiable function on a compact domain that satisfies for all is strongly convex. The proof of this statement follows from the extreme value theorem, which states that a continuous function on a compact set has a maximum and minimum.
Strongly convex functions are in general easier to work with than convex or strictly convex functions, since they are a smaller class. Like strictly convex functions, strongly convex functions have unique minima on compact sets.
Uniformly convex functions
A uniformly convex function, with modulus , is a function that, for all x, y in the domain and t ∈ [0, 1], satisfies
where is a function that is non-negative and vanishes only at 0. This is a generalization of the concept of strongly convex function; by taking we recover the definition of strong convexity.