Generalized mean on arvau

Wikipedia / Generalized mean

In mathematics, generalized means (or power mean or Hölder mean from Otto Hölder) are a family of functions for aggregating sets of numbers. These include as special cases the Pythagorean means (arithmetic, geometric, and harmonic means).

Definition

If is a non-zero real number are positive real numbers, then the generalized mean or power mean with exponent of these positive real numbers is

(See p-norm). For we set it equal to the geometric mean (which is the limit of means with exponents approaching zero, as proved below):

Furthermore, for a sequence of positive weights we define the weighted power mean as and when , it is equal to the weighted geometric mean:

The unweighted means correspond to setting all .

Special cases

corresponds to a well known mean.

Properties

be a sequence of positive real numbers, then the following properties hold:

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is a permutation operator.
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.

Generalized mean inequality

In general, if and the two means are equal if and only if .

The inequality is true for real values of and , as well as positive and negative infinity values.

It follows from the fact that, for all real which can be proved using Jensen's inequality.

In particular, for in , the generalized mean inequality implies the Pythagorean means inequality as well as the inequality of arithmetic and geometric means.

Proof of the weighted inequality

We will prove the weighted power mean inequality. For the purpose of the proof we will assume the following without loss of generality

The proof for unweighted power means can be easily obtained by substituting .

Equivalence of inequalities between means of opposite signs

Suppose an average between power means with exponents and

We get the inequality for means with exponents and , and we can use the same reasoning backwards, thus proving the inequalities to be equivalent, which will be used in some of the later proofs.

Geometric mean

For any

The proof follows from Jensen's inequality, making use of the fact the logarithm

By applying the exponential function

Taking -th powers of the

Thus, we are done for the inequality with positive ; the case for negatives is identical but for the swapped signs in the last step:

Of course, taking each side to the power of a negative number swaps the direction of the inequality.

Inequality between any two power means

We are to prove that for any if is negative, and

The proof for positive and is as follows: Define the following function: . is a power function, so it does have a second derivative which is strictly positive within the domain of , since , so we know is convex.

after raising both side to the power of (an increasing function, since is positive) we get the inequality which was to be proven:

Using the previously shown equivalence we can prove the inequality for negative and by replacing them with and , respectively.

Generalized f-mean

The power mean could be generalized further to the generalized f-mean:

This covers the geometric mean without using a limit with . The power mean is obtained for . Properties of these means are studied in de Carvalho (2016).

Applications

Signal processing

A power mean serves a non-linear moving average which is shifted towards small signal values for small and emphasizes big signal values for big . Given an efficient implementation of a moving arithmetic mean called smooth one can implement a moving power mean according to the following Haskell code.

For big it can serve as an envelope detector on a rectified signal.
For small it can serve as a baseline detector on a mass spectrum.