Young's inequality for products can be used to prove Hölder's inequality. It is also widely used to estimate the norm of nonlinear terms in PDE theory, since it allows one to estimate a product of two terms by a sum of the same terms raised to a power and scaled.
Standard version for conjugate Hölder exponents
The standard form of the inequality is the following, which can be used to prove Hölder's inequality.
Since A graph on the -plane is thus also a graph From sketching a visual representation of the integrals of the area between this curve and the axes, and the area in the rectangle bounded by the lines and the fact that is always increasing for increasing and vice versa, we can see that upper bounds the area of the rectangle below the curve (with equality when ) and upper bounds the area of the rectangle above the curve (with equality when ). Thus, with equality when (or equivalently, ). Young's inequality follows from evaluating the integrals. (See below for a generalization.)
The claim is certainly true if or so henceforth assume that and
Put and
Because the logarithm function is concave,
with the equality holding if and only if
Young's inequality follows by exponentiating.
Yet another proof is to first prove it with an then apply the resulting inequality to . The proof below illustrates also why Hölder conjugate exponent is the only possible parameter that makes Young's inequality hold for all non-negative values. The details follow:
Proof
Let and . The inequality
holds if and only if (and hence ). This can be shown by convexity arguments or by simply minimizing the single-variable function.
To prove full Young's inequality, clearly we assume that and . Now, we apply the inequality above to to obtain:
It is easy to see that choosing and multiplying both sides by yields Young's inequality.
Young's inequality may equivalently be written as
Where this is just the concavity of the logarithm function.
Equality holds if and only if or
This also follows from the weighted AM-GM inequality.
Generalizations
Theorem[4] — Suppose and
If and are such that then
Using and replacing with and with results in the inequality:
which is useful for proving Hölder's inequality.
Define a real-valued function on the positive real numbers by
for every and then calculate its minimum.
Theorem — If with then
Equality holds if and only if all the s with non-zero s are equal.
Elementary case
An elementary case of Young's inequality is the inequality with exponent
which also gives rise to the so-called Young's inequality with (valid for every ), sometimes called the Peter–Paul inequality.
[5] This name refers to the fact that tighter control of the second term is achieved at the cost of losing some control of the first term – one must "rob Peter to pay Paul"
Proof: Young's inequality with exponent is the special case However, it has a more elementary proof.
Start by observing that the square of every real number is zero or positive. Therefore, for every pair of real numbers and we can write:
Work out the square of the right hand side:
Add to both sides:
Divide both sides by 2 and we have Young's inequality with exponent
Young's inequality with follows by substituting and as below into Young's inequality with exponent
Matricial generalization
T. Ando proved a generalization of Young's inequality for complex matrices ordered
by Loewner ordering.[6] It states that for any pair of complex matrices of order there exists a unitary matrix such that
where denotes the conjugate transpose of the matrix and
Standard version for increasing functions
For the standard version[7][8] of the inequality,
let denote a real-valued, continuous and strictly increasing function on with and Let denote the inverse function of Then, for all and
with equality if and only if
With and this reduces to standard version for conjugate Hölder exponents.
For details and generalizations we refer to the paper of Mitroi & Niculescu.[9]
Generalization using Fenchel–Legendre transforms
By denoting the convex conjugate of a real function by we obtain
This follows immediately from the definition of the convex conjugate. For a convex function this also follows from the Legendre transformation.
More generally, if is defined on a real vector space and its convex conjugate is denoted by (and is defined on the dual space), then
where is the dual pairing.
Examples
The convex conjugate of is with such that and thus Young's inequality for conjugate Hölder exponents mentioned above is a special case.
^T. Ando (1995). "Matrix Young Inequalities". In Huijsmans, C. B.; Kaashoek, M. A.; Luxemburg, W. A. J.; et al. (eds.). Operator Theory in Function Spaces and Banach Lattices. Springer. pp. 33–38. ISBN978-3-0348-9076-2.