Hölder's inequality

In mathematical analysis, Hölder's inequality, named after Otto Hölder, is a fundamental inequality between integrals and an indispensable tool for the study of spaces.
The numbers and above are said to be Hölder conjugates of each other. The special case gives a form of the Cauchy–Schwarz inequality. Hölder's inequality holds even if is infinite, the right-hand side also being infinite in that case. Conversely, if is in and is in, then the pointwise product is in.
Hölder's inequality is used to prove the Minkowski inequality, which is the triangle inequality in the space, and also to establish that is the dual space of for .
Hölder's inequality was first found by Leonard James Rogers, and discovered independently by.

Remarks

Conventions

The brief statement of Hölder's inequality uses some conventions.

In the definition of Hölder conjugates, means zero.
If , then and stand for the expressions
If, then stands for the essential supremum of, similarly for.
The notation with is a slight abuse, because in general it is only a norm of if is finite and is considered as equivalence class of -almost everywhere equal functions. If and, then the notation is adequate.
On the right-hand side of Hölder's inequality, 0 × ∞ as well as ∞ × 0 means 0. Multiplying with ∞ gives ∞.
Estimates for integrable products

As above, let and denote measurable real- or complex-valued functions defined on. If is finite, then the pointwise products of with and its complex conjugate function are -integrable, the estimate
and the similar one for hold, and Hölder's inequality can be applied to the right-hand side. In particular, if and are in the Hilbert space, then Hölder's inequality for implies
where the angle brackets refer to the inner product of. This is also called Cauchy–Schwarz inequality, but requires for its statement that and are finite to make sure that the inner product of and is well defined. We may recover the original inequality by using the functions and in place of and.

Generalization for probability measures

If is a probability space, then just need to satisfy, rather than being Hölder conjugates. A combination of Hölder's inequality and Jensen's inequality implies that
for all measurable real- or complex-valued functions and on .

Notable special cases

For the following cases assume that and are in the open interval with.

Counting measure

For the -dimensional Euclidean space, when the set is with the counting measure, we have
If with the counting measure, then we get Hölder's inequality for sequence spaces:

Lebesgue measure

If is a measurable subset of with the Lebesgue measure, and and are measurable real- or complex-valued functions on , then Hölder inequality is

Probability measure

For the probability space let denote the expectation operator. For real- or complex-valued random variables and on Hölder's inequality reads
Let and define Then is the Hölder conjugate of Applying Hölder's inequality to the random variables and we obtain
In particular, if the ^th absolute moment is finite, then the ^th absolute moment is finite, too.

Product measure

For two σ-finite measure spaces and define the product measure space by
where is the Cartesian product of and, the arises as product σ-algebra of and, and denotes the product measure of and. Then Tonelli's theorem allows us to rewrite Hölder's inequality using iterated integrals: If and are real- or complex-valued functions on the Cartesian product , then
This can be generalized to more than two measure spaces.

Vector-valued functions

Let denote a measure space and suppose that and are -measurable functions on, taking values in the -dimensional real- or complex Euclidean space. By taking the product with the counting measure on, we can rewrite the above product measure version of Hölder's inequality in the form
If the two integrals on the right-hand side are finite, then equality holds if and only if there exist real numbers, not both of them zero, such that
for -almost all in.
This finite-dimensional version generalizes to functions and taking values in a normed space which could be for example a sequence space or an inner product space.

Proof of Hölder's inequality

There are several proofs of Hölder's inequality; the main idea in the following is Young's inequality for products.
If, then is zero -almost everywhere, and the product is zero -almost everywhere, hence the left-hand side of Hölder's inequality is zero. The same is true if. Therefore, we may assume and in the following.
If or, then the right-hand side of Hölder's inequality is infinite. Therefore, we may assume that and are in.
If and, then almost everywhere and Hölder's inequality follows from the monotonicity of the Lebesgue integral. Similarly for and. Therefore, we may also assume .
Dividing and by and, respectively, we can assume that
We now use Young's inequality for products, which states that
for all nonnegative and, where equality is achieved if and only if. Hence
Integrating both sides gives
which proves the claim.
Under the assumptions and, equality holds if and only if almost everywhere. More generally, if and are in, then Hölder's inequality becomes an equality if and only if there exist real numbers, namely
such that
The case corresponds to in. The case corresponds to in.

Alternate proof using Jensen's inequality

Extremal equality

Statement

Assume that and let denote the Hölder conjugate. Then, for every,
where max indicates that there actually is a maximizing the right-hand side. When and if each set in the with contains a subset with , then

Proof of the extremal equality

Remarks and examples

The equality for fails whenever there exists a set of infinite measure in the -field with that has no subset that satisfies: Then the indicator function satisfies but every has to be -almost everywhere constant on because it is -measurable, and this constant has to be zero, because is -integrable. Therefore, the above supremum for the indicator function is zero and the extremal equality fails.
For the supremum is in general not attained. As an example, let and the counting measure. Define:
Applications
The extremal equality is one of the ways for proving the triangle inequality for all and in, see Minkowski inequality.
Hölder's inequality implies that every defines a bounded linear functional on by the formula
Generalization of Hölder's inequality

Assume that and such that
. Then, for all measurable real- or complex-valued functions defined on,
.
In particular,
Note: For, contrary to the notation, is in general not a norm, because it doesn't satisfy the triangle inequality.

Proof of the generalization

Interpolation

Let and let denote weights with. Define as the weighted harmonic mean, i.e.,
Given measurable real- or complex-valued functions on, then the above generalization of Hölder's inequality gives
In particular, taking gives
Specifying further and, in the case, we obtain the interpolation result
for and
An application of Hölder gives Lyapunov's inequality: If
then
and in particular
Both Littlewood and Lyapunov imply that if then for all

Reverse Hölder inequality

Assume that and that the measure space satisfies. Then, for all measurable real- or complex-valued functions and on such that for all,
If
then the reverse Hölder inequality is an equality if and only if
Note: The expressions:
are not norms, they are just compact notations for

Proof of the reverse Hölder inequality

Conditional Hölder inequality

Let be a probability space, a, and Hölder conjugates, meaning that. Then, for all real- or complex-valued random variables and on ,
Remarks:

If a non-negative random variable has infinite expected value, then its conditional expectation is defined by
On the right-hand side of the conditional Hölder inequality, 0 times ∞ as well as ∞ times 0 means 0. Multiplying with ∞ gives ∞.

Proof of the conditional Hölder inequality