# Mathematical methods for economic theory

Martin J. Osborne

## 3.3 Concave and convex functions of many variables

### Convex sets

To extend the notions of concavity and convexity to functions of many variables we first define the notion of a convex set.

Definition
A set S of n-vectors is convex if
(1−λ)x + λx' ∈ S whenever x ∈ S, x' ∈ S, and λ ∈ [0,1].
We call (1 − λ)x + λx' a convex combination of x and x'.

For example, the two-dimensional set in the first of the next two figures is convex, because every line segment joining a pair of points in the set lies entirely in the set. The set in the second figure is not convex, because the line segment joining the points x and x' does not lie entirely in the set.

The following property of convex sets (which you are asked to prove in an exercise) is sometimes useful.

Proposition
The intersection of convex sets is convex.
Note that the union of convex sets is not necessarily convex.

### Concave and convex functions

Let f be a function of many variables, defined on a convex set S. We say that f is concave if the line segment joining any two points on the graph of f is never above the graph; f is convex if the line segment joining any two points on the graph is never below the graph. (That is, the definitions are the same as the definitions for functions of a single variable.)

More precisely, we can make the following definition (which is again essentially the same as the corresponding definition for a function of a single variable). Note that only functions defined on convex sets are covered by the definition.

Definition
Let f be a function of many variables defined on the convex set S. Then f is
• concave on the set S if for all x ∈ S, all x' ∈ S, and all λ ∈ (0,1) we have
 f((1−λ)x + λx') ≥ (1−λ)f(x) + λf(x')
• convex on the set S if for all x ∈ S, all x' ∈ S, and all λ ∈ (0,1) we have
 f((1−λ)x + λx') ≤ (1−λ)f(x) + λf(x').
As for a function of a single variable, a strictly concave function satisfies the definition for concavity with a strict inequality (> rather than ≥) for all x ≠ x', and a strictly convex function satisfies the definition for concavity with a strict inequality (< rather than ≤) for all x ≠ x'.
Definition
Let f be a function of many variables defined on the convex set S. Then f is
• strictly concave on the set S if for all x ∈ S, all x' ∈ S with x' ≠ x, and all λ ∈ (0,1) we have
 f((1−λ)x + λx') > (1−λ)f(x) + λf(x')
• strictly convex on the set S if for all x ∈ S, all x' ∈ S with x' ≠ x, and all λ ∈ (0,1) we have
 f((1−λ)x + λx') < (1−λ)f(x) + λf(x').
Example
Let f be a linear function, defined by f(x) = a1x1 + ... + anxn = a·x on a convex set, where ai is a constant for each i. Then f is both concave and convex:
 f((1 − λ)x + λx') = a·[(1−λ)x + λx'] for all x, x', and λ ∈ [0, 1] = (1−λ)a·x + λa·x' for all x, x', and λ ∈ [0, 1] = (1−λ)f(x) + λf(x') for all x, x', and λ ∈ [0, 1].
Example
Suppose the function g of a single variable is concave on [a,b], and the function f of two variables is defined by f(x,y) = g(x) on [ab] × [cd]. Is f concave?

First note that the domain of f is a convex set, so the definition of concavity can apply.

The functions g and f are illustrated in the following figures. (The axes for g are shown in perspective, like those for f, to make the relation between the two figures clear. If we were plotting only g, we would view it straight on, so that the x-axis would be horizontal. Note that every cross-section of the graph of f parallel to the x-axis is the graph of the function g.)

From the graph of f (the roof of a horizontal tunnel), you can see that it is concave. The following argument is precise.

 f((1−λ)(x, y) + λ(x', y')) = f((1−λ)x + λx', (1−λ)y + λy') = g((1−λ)x + λx') ≥ (1−λ)g(x) + λg(x') = (1−λ)f(x, y) + λf(x', y')
so f is concave.
Example
Let f and g be defined as in the previous example. Assume now that g is strictly concave. Is f strictly concave?

The strict concavity of f implies that

 f((1−λ)(x, y) + λ(x', y')) > (1−λ)f(x, y) + λf(x', y')
for all x ≠ x'. But to show that f is strictly concave we need to show that the inequality is strict whenever (x, y) ≠ (x', y')—in particular, for cases in which x = x' and y ≠ y'. In such a case, we have
 f((1−λ)(x, y) + λ(x', y')) = f(x, (1−λ)y + λy') = g(x) = (1−λ)f(x, y) + λf(x, y').
Thus f is not strictly concave. You can see the lack of strict concavity in the figure (in the previous example): if you take two (xy) pairs with the same value of x, the line joining them lies everywhere on the surface of the function, never below it.

### Characterizations of concave and convex functions

Having seen many examples of concave functions, you should find it plausible that a function is concave if and only if the set of points under its graph—the set shaded pink in the following figure—is convex. The result is stated precisely in the following proposition.

Proposition
A function f of many variables defined on the convex set S is
• concave if and only if the set of points on or below its graph is convex:
{(xy): x ∈ S and y ≤ f(x)} is convex
• convex if and only if the set of points on or above its graph is convex:
{(xy): x ∈ S and y ≥ f(x)} is convex.
Proof
Let L = {(xy): x ∈ S and y ≤ f(x)}.

First suppose f is concave and let (xy) ∈ L and (x', y') ∈ L. Then x ∈ S, x' ∈ S, y ≤ f(x) and y' ≤ f(x'). The last two inequalities imply that

 (1 − λ)y + λy' ≤ (1 − λ)f(x) + λf(x') for any λ ∈ [0, 1].
Now, because S is convex, (1 − λ)x + λx' ∈ S, so that (1 − λ)x + λx' is in the domain of f, and because f is concave,
(1 − λ)f(x) + λf(x') ≤ f((1 − λ)x + λx').
Thus
(1 − λ)y + λy' ≤ f((1 − λ)x + λx'),
and hence ((1 − λ)x + λx', (1 − λ)y + λy') = (1 − λ)(xy) + λ(x', y') ∈ L, establishing that L is convex.

Conversely, suppose L is convex. Let x ∈ S and x' ∈ S. Then (xf(x)) ∈ L and (x', f(x')) ∈ L, so by the convexity of L, (1 − λ)(xf(x)) + λ(x', f(x')) = ((1 − λ)x + λx', (1 − λ)f(x) + λf(x')) ∈ L for any λ ∈ [0, 1]. Thus (1 − λ)f(x) + λf(x') ≤ f((1 − λ)x + λx'), establishing that f is concave.

The argument for a convex function is symmetric.

Another characterization of a concave function is the following generalization of Jensen's inequality for functions of a single variable.
Proposition (Jensen's inequality)
A function f of many variables defined on the convex set S is concave if and only if for all n ≥ 2
 f(λ1x1 + ... + λnxn) ≥ λ1f(x1) + ... + λnf(xn)
for all x1 ∈ S, ..., xn ∈ S and all λ1 ≥ 0, ..., λn ≥ 0 with ∑n
i=1
λi = 1.

The function f of many variables defined on the convex set S is convex if and only if for all n ≥ 2

 f(λ1x1 + ... + λnxn) ≤ λ1f(x1) + ... + λnf(xn)
for all x1 ∈ S, ..., xn ∈ S and all λ1 ≥ 0, ..., λn ≥ 0 with ∑n
i=1
λi = 1.
Proof
Here is the proof for concavity; the proof for convexity is analogous.

If the inequality is satisfied for all n, it is satisfied in particular for n = 2, so that f is concave directly from the definition of a concave function.

Now suppose that f is concave. Then the definition of a concave function implies directly that the inequality is satisfied for n = 2. To show that it is satisfied for all n ≥ 3 I argue by induction. Let m ≥ 2 and suppose that the inequality is satisfied for all n ≤ m. I show that it is satisfied for n = m + 1. Take any x1 ∈ S, ..., xm+1 ∈ S and λ1 ≥ 0, ..., λm+1 ≥ 0 with ∑m+1
i=1
λi = 1. If λ1 = 1 then λ2 = ... = λm+1 = 0, so that the inequality is trivially satisfied. If λ1 < 1 then

 f(∑m+1i=1λixi) = f(λ1x1 + (1 − λ1)∑m+1i=2(λi/(1−λ1))xi) ≥ λ1f(x1) + (1 − λ1)f(∑m+1i=2(λi/(1−λ1))xi)
by taking x = x1, x' = ∑m+1
i=2
i/(1−λ1))xi, and λ = 1 − λ1 in the definition of a concave function. Now, because the inequality is satisfied for n = m we have
 f(∑m+1i=2(λi/(1−λ1))xi) ≥ ∑m+1i=2(λi/(1−λ1))f(xi),
so that
 f(∑m+1i=1λixi) ≥ λ1f(x1) + (1 − λ1)∑m+1i=2(λi/(1−λ1))f(xi) = ∑m+1i=1λif(xi),
completing the argument.

### Differentiable concave and convex functions

A previous result shows that the graph of a concave function of a single variable lies everywhere on or below all of its tangents. The generalization of this result to concave functions of many variables says that the graph of such a function lies everywhere on or below all of its tangent planes. As for a function of a single variable, a symmetric result holds for convex functions. Like the result for functions of a single variable, it is used to show that stationary points are global maximizers of concave functions and global minimizers of convex functions.
Proposition
The differentiable function f of n variables defined on a convex set S is concave on S if and only if
 f(x) − f(x*) ≤ ∑ni=1f'i(x*)·(xi − x*i) for all x ∈ S and x* ∈ S
and is convex on S if and only if
 f(x) − f(x*) ≥ ∑ni=1f'i(x*)·(xi − x*i) for all x ∈ S and x* ∈ S.
Source
One proof follows the lines of the proof of the analogous result for a function of a single variable. For details, see Sydsæter and Hammond (1995), Theorem 17.7 (p. 629). (Note that by this result their assumption that the function has continuous partial derivatives is equivalent to the assumption that the function is continuously differentiable, and by Corollary 25.5.1 (p. 246) in Rockafellar (1970), a differentiable convex function, and hence a differentiable concave function, is continuously differentiable.) Simon and Blume (1994), Theorem 21.3 (p. 511), give a different proof.

### Twice-differentiable concave and convex functions

A twice-differentiable function of a single variable is concave if and only if its second derivative is nonpositive everywhere.

To determine whether a twice-differentiable function of many variables is concave or convex, we need to examine all its second partial derivatives. We call the matrix of all the second partial derivatives the Hessian of the function.

Definition
Let f be a twice-differentiable function of n variables. The Hessian of f at x is
H(x) =
 f"11(x) f"12(x) ... f"1n(x) f"21(x) f"22(x) ... f"2n(x) ... ... ... ... f"n1(x) f"n2(x) ... f"nn(x)
.
Note that by Young's theorem, the Hessian of any function for which all second partial derivatives are continuous is symmetric for all values of the argument of the function.

We can determine the concavity/convexity of a function by determining whether the Hessian is negative or positive semidefinite, as follows.

Proposition
Let f be a function of many variables with continuous partial derivatives and cross partial derivatives on the convex open set S and denote the Hessian of f at the point x by H(x). Then
Source
For a proof, see Sydsæter and Hammond (1995), Theorems 17.13 (p. 640) and 17.14 (p. 641).
Note that the result does not claim that if f is strictly concave then H(x) is negative definite for all x ∈ S. Indeed, consider the function f of a single variable defined by f(x) = −x4. This function is strictly concave, but the 1 × 1 matrix H(0) is not negative definite (its single component is 0).

Thus if you want to determine whether a function is strictly concave or strictly convex, you should first check the Hessian. If the Hessian is negative definite for all values of x then the function is strictly concave, and if the Hessian is positive definite for all values of x then the function is strictly convex. If the Hessian is not negative semidefinite for all values of x then the function is not concave, and hence of course is not strictly concave. Similarly, if the Hessian is not positive semidefinite the function is not convex. If the Hessian is not negative definite for all values of x but is negative semidefinite for all values of x, the function may or may not be strictly concave; you need to use the basic definition of strict concavity to determine whether it is strictly concave or not. Similarly, if the Hessian is not positive definite for all values of x but is positive semidefinite for all values of x, the function may or may not be strictly convex.

Example
Consider the function f(xy) = 2x − y − x2 + 2xy − y2 defined on the set of all pairs of numbers. Its Hessian is
 −2 2 2 −2
which is negative semidefinite. (In this case the Hessian does not depend on (xy); in general it does.) Thus f is concave.
Example
Consider the function f(x1x2x3) = x2
1
+ 2x2
2
+ 3x2
3
+ 2x1x2 + 2x1x3 defined on the set of all triples of numbers. Its first partials are
 f'1(x1, x2, x3) = 2x1 + 2x2 + 2x3 f'2(x1, x2, x3) = 4x2 + 2x1 f'3(x1, x2, x3) = 6x3 + 2x1.
So its Hessian is
 f"11 f"12 f"13 f"21 f"22 f"23 f"31 f"32 f"33
=
 2 2 2 2 4 0 2 0 6
.
The leading principal minors of the Hessian are 2 > 0, 4 > 0, and 8 > 0. So the Hessian is positive definite, and f is strictly convex.
In these two examples, the Hessian of f is independent of its argument, because f is a quadratic. In the next example, the Hessian of the function does not have this property.
Example
Consider the Cobb-Douglas function, defined by f(KL) = AKaLb on the set of pairs (KL) with K ≥ 0 and L ≥ 0. Assume that A > 0. The Hessian of this function is
 a(a−1)AKa−2Lb abAKa−1Lb−1 abAKa−1Lb−1 b(b−1)AKaLb−2
.
Thus for f to be concave we need a(a−1)AKa−2Lb ≤ 0, b(b−1)AKaLb−2 ≤ 0, and abA2K2a−2L2b−2(1 − (a + b)) ≥ 0 for all K ≥ 0 and L ≥ 0. Thus f is concave if and only if a ≥ 0, b ≥ 0, and a + b ≤ 1 (so that a ≤ 1 and b ≤ 1), and is strictly concave if a > 0, b > 0, and a + b < 1 (so that a < 1 and b < 1).
If we have a function that is a sum of functions that we know are concave, or is a concave increasing function of a concave function, the following result is useful. The proof of the first part is an exercise, and the proof of the second part is symmetric with the proof of the first part. The last two parts generalize a previous result to functions of many variables. The proofs of these parts are the same as the proofs for functions of a single variable.
Proposition
Let f and g be functions of many variables.
• If f and g are concave and a ≥ 0 and b ≥ 0 then the function h defined by h(x) = af(x) + bg(x) for all x is concave.
• If f and g are convex and a ≥ 0 and b ≥ 0 then the function h defined by h(x) = af(x) + bg(x) for all x is convex.
Let U be a function of many variables and let g be a function of a single variable.
• If U is concave and g is nondecreasing and concave then the function f defined by f(x) = g(U(x)) for all x is concave.
• If U is convex and g is nondecreasing and convex then the function f defined by f(x) = g(U(x)) for all x is convex.
Example
A firm produces the output f(x) from the vector x of inputs, which costs it c(x). The function f is concave and the function c is convex. The firm sells its output at a fixed price p > 0. Its profit when it uses the input vector x is
π(x) = pf(x) − c(x).
That is, π is the sum of two functions, pf and −c. The function −c is concave because c is convex, so by the proposition π is concave.