Generalized mean

In mathematics, generalized means (or power mean, or Hölder mean)[1] are a family of functions for aggregating sets of numbers, that include as special cases the Pythagorean means (arithmetic, geometric, and harmonic means).

Definition

If p is a non-zero real number, and ${\displaystyle x_{1},\dots ,x_{n}}$ are positive real numbers, then the generalized mean or power mean with exponent p of these positive real numbers is:[2]

${\displaystyle M_{p}(x_{1},\dots ,x_{n})=\left({\frac {1}{n}}\sum _{i=1}^{n}x_{i}^{p}\right)^{\frac {1}{p}}.}$

(See p-norm). For p = 0 we set it equal to the geometric mean (which is the limit of means with exponents approaching zero, as proved below):

${\displaystyle M_{0}(x_{1},\dots ,x_{n})={\sqrt[{n}]{\prod _{i=1}^{n}x_{i}}}}$

Furthermore, for a sequence of positive weights w_i with sum ${\displaystyle \sum w_{i}=1}$ we define the weighted power mean as:

${\displaystyle {\begin{aligned}M_{p}(x_{1},\dots ,x_{n})&=\left(\sum _{i=1}^{n}w_{i}x_{i}^{p}\right)^{\frac {1}{p}}\\M_{0}(x_{1},\dots ,x_{n})&=\prod _{i=1}^{n}x_{i}^{w_{i}}\end{aligned}}}$

The unweighted means correspond to setting all w_i = 1/n.

Special cases

A visual depiction of some of the specified cases for n = 2 with a = x₁ = M_∞ and b = x₂ = M_−∞:

  harmonic mean, H = M₋₁(a, b),

  geometric mean, G = M₀(a, b)

  arithmetic mean, A = M₁(a, b)

  quadratic mean, Q = M₂(a, b)

A few particular values of ${\displaystyle p}$ yield special cases with their own names:[3]

${\displaystyle M_{-\infty }(x_{1},\dots ,x_{n})=\lim _{p\to -\infty }M_{p}(x_{1},\dots ,x_{n})=\min\{x_{1},\dots ,x_{n}\}}$	minimum
${\displaystyle M_{-1}(x_{1},\dots ,x_{n})={\frac {n}{{\frac {1}{x_{1}}}+\dots +{\frac {1}{x_{n}}}}}}$	harmonic mean
${\displaystyle M_{0}(x_{1},\dots ,x_{n})=\lim _{p\to 0}M_{p}(x_{1},\dots ,x_{n})={\sqrt[{n}]{x_{1}\cdot \dots \cdot x_{n}}}}$	geometric mean
${\displaystyle M_{1}(x_{1},\dots ,x_{n})={\frac {x_{1}+\dots +x_{n}}{n}}}$	arithmetic mean
${\displaystyle M_{2}(x_{1},\dots ,x_{n})={\sqrt {\frac {x_{1}^{2}+\dots +x_{n}^{2}}{n}}}}$	root mean square or quadratic mean[4][5]
${\displaystyle M_{3}(x_{1},\dots ,x_{n})={\sqrt[{3}]{\frac {x_{1}^{3}+\dots +x_{n}^{3}}{n}}}}$	cubic mean
${\displaystyle M_{+\infty }(x_{1},\dots ,x_{n})=\lim _{p\to \infty }M_{p}(x_{1},\dots ,x_{n})=\max\{x_{1},\dots ,x_{n}\}}$	maximum

Proof of ${\displaystyle \textstyle \lim _{p\to 0}M_{p}=M_{0}}$ (geometric mean)

We can rewrite the definition of Mp using the exponential function ${\displaystyle M_{p}(x_{1},\dots ,x_{n})=\exp {\left(\ln {\left[\left(\sum _{i=1}^{n}w_{i}x_{i}^{p}\right)^{1/p}\right]}\right)}=\exp {\left({\frac {\ln {\left(\sum _{i=1}^{n}w_{i}x_{i}^{p}\right)}}{p}}\right)}}$ In the limit p → 0, we can apply L'Hôpital's rule to the argument of the exponential function. Differentiating the numerator and denominator with respect to p, we have ${\displaystyle \lim _{p\to 0}{\frac {\ln {\left(\sum _{i=1}^{n}w_{i}x_{i}^{p}\right)}}{p}}=\lim _{p\to 0}{\frac {\frac {\sum _{i=1}^{n}w_{i}x_{i}^{p}\ln {x_{i}}}{\sum _{j=1}^{n}w_{j}x_{j}^{p}}}{1}}=\lim _{p\to 0}{\frac {\sum _{i=1}^{n}w_{i}x_{i}^{p}\ln {x_{i}}}{\sum _{j=1}^{n}w_{j}x_{j}^{p}}}=\sum _{i=1}^{n}{\frac {w_{i}\ln {x_{i}}}{\lim _{p\to 0}\sum _{j=1}^{n}w_{j}\left({\frac {x_{j}}{x_{i}}}\right)^{p}}}=\sum _{i=1}^{n}w_{i}\ln {x_{i}}=\ln {\left(\prod _{i=1}^{n}x_{i}^{w_{i}}\right)}}$ By the continuity of the exponential function, we can substitute back into the above relation to obtain ${\displaystyle \lim _{p\to 0}M_{p}(x_{1},\dots ,x_{n})=\exp {\left(\ln {\left(\prod _{i=1}^{n}x_{i}^{w_{i}}\right)}\right)}=\prod _{i=1}^{n}x_{i}^{w_{i}}=M_{0}(x_{1},\dots ,x_{n})}$ as desired.[2]

Proof of ${\displaystyle \textstyle \lim _{p\to \infty }M_{p}=M_{\infty }}$ and ${\displaystyle \textstyle \lim _{p\to -\infty }M_{p}=M_{-\infty }}$

Assume (possibly after relabeling and combining terms together) that ${\displaystyle x_{1}\geq \dots \geq x_{n}}$. Then ${\displaystyle \lim _{p\to \infty }M_{p}(x_{1},\dots ,x_{n})=\lim _{p\to \infty }\left(\sum _{i=1}^{n}w_{i}x_{i}^{p}\right)^{1/p}=x_{1}\lim _{p\to \infty }\left(\sum _{i=1}^{n}w_{i}\left({\frac {x_{i}}{x_{1}}}\right)^{p}\right)^{1/p}=x_{1}=M_{\infty }(x_{1},\dots ,x_{n}).}$ The formula for ${\displaystyle M_{-\infty }}$ follows from ${\displaystyle M_{-\infty }(x_{1},\dots ,x_{n})={\frac {1}{M_{\infty }(1/x_{1},\dots ,1/x_{n})}}.}$

Properties

Let ${\displaystyle x_{1},\dots ,x_{n}}$ be a sequence of positive real numbers and ${\displaystyle P}$ a permutation operator, then the following properties hold:[1]

1. ${\displaystyle \min(x_{1},\dots ,x_{n})\leq M_{p}(x_{1},\dots ,x_{n})\leq \max(x_{1},\dots ,x_{n})}$.

   Each generalized mean always lies between the smallest and largest of the x values.

2. ${\displaystyle M_{p}(x_{1},\dots ,x_{n})=M_{p}(P(x_{1},\dots ,x_{n}))}$.

   Each generalized mean is a symmetric function of its arguments; permuting the arguments of a generalized mean does not change its value.

3. ${\displaystyle M_{p}(bx_{1},\dots ,bx_{n})=b\cdot M_{p}(x_{1},\dots ,x_{n})}$.

   Like most means, the generalized mean is a homogeneous function of its arguments x₁, ..., x_n. That is, if b is a positive real number, then the generalized mean with exponent p of the numbers ${\displaystyle b\cdot x_{1},\dots ,b\cdot x_{n}}$ is equal to b times the generalized mean of the numbers x₁, …, x_n.

4. ${\displaystyle M_{p}(x_{1},\dots ,x_{n\cdot k})=M_{p}\left[M_{p}(x_{1},\dots ,x_{k}),M_{p}(x_{k+1},\dots ,x_{2\cdot k}),\dots ,M_{p}(x_{(n-1)\cdot k+1},\dots ,x_{n\cdot k})\right]}$.

   Like the quasi-arithmetic means, the computation of the mean can be split into computations of equal sized sub-blocks. This enables use of a divide and conquer algorithm to calculate the means, when desirable.

Generalized mean inequality

In general,

if p < q, then ${\displaystyle M_{p}(x_{1},\dots ,x_{n})\leq M_{q}(x_{1},\dots ,x_{n})}$

and the two means are equal if and only if x₁ = x₂ = ... = x_n.

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

It follows from the fact that, for all real p,

${\displaystyle {\frac {\partial }{\partial p}}M_{p}(x_{1},\dots ,x_{n})\geq 0}$

which can be proved using Jensen's inequality.

In particular, for p in {−1, 0, 1}, the generalized mean inequality implies the Pythagorean means inequality as well as the inequality of arithmetic and geometric means.

Proof of power means inequality

We will prove weighted power means inequality, for the purpose of the proof we will assume the following without loss of generality:

${\displaystyle {\begin{aligned}w_{i}\in [0,1]\\\sum _{i=1}^{n}w_{i}=1\end{aligned}}}$

Proof for unweighted power means is easily obtained by substituting w_i = 1/n.

Equivalence of inequalities between means of opposite signs

Suppose an average between power means with exponents p and q holds:

${\displaystyle {\sqrt[{p}]{\sum _{i=1}^{n}w_{i}x_{i}^{p}}}\geq {\sqrt[{q}]{\sum _{i=1}^{n}w_{i}x_{i}^{q}}}}$

applying this, then:

${\displaystyle {\sqrt[{p}]{\sum _{i=1}^{n}{\frac {w_{i}}{x_{i}^{p}}}}}\geq {\sqrt[{q}]{\sum _{i=1}^{n}{\frac {w_{i}}{x_{i}^{q}}}}}}$

We raise both sides to the power of −1 (strictly decreasing function in positive reals):

${\displaystyle {\sqrt[{-p}]{\sum _{i=1}^{n}w_{i}x_{i}^{-p}}}={\sqrt[{p}]{\frac {1}{\sum _{i=1}^{n}w_{i}{\frac {1}{x_{i}^{p}}}}}}\leq {\sqrt[{q}]{\frac {1}{\sum _{i=1}^{n}w_{i}{\frac {1}{x_{i}^{q}}}}}}={\sqrt[{-q}]{\sum _{i=1}^{n}w_{i}x_{i}^{-q}}}}$

We get the inequality for means with exponents −p and −q, 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 q > 0 and non-negative weights summing to 1, the following inequality holds:

${\displaystyle {\sqrt[{-q}]{\sum _{i=1}^{n}w_{i}x_{i}^{-q}}}\leq \prod _{i=1}^{n}x_{i}^{w_{i}}\leq {\sqrt[{q}]{\sum _{i=1}^{n}w_{i}x_{i}^{q}}}.}$

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

${\displaystyle \log \prod _{i=1}^{n}x_{i}^{w_{i}}=\sum _{i=1}^{n}w_{i}\log x_{i}\leq \log \sum _{i=1}^{n}w_{i}x_{i}.}$

By applying the exponential function to both sides and observing that as a strictly increasing function it preserves the sign of the inequality, we get

${\displaystyle \prod _{i=1}^{n}x_{i}^{w_{i}}\leq \sum _{i=1}^{n}w_{i}x_{i}.}$

Taking qth powers of the x_i, we are done for the inequality with positive q; the case for negatives is identical.

Inequality between any two power means

We are to prove that for any p < q the following inequality holds:

${\displaystyle {\sqrt[{p}]{\sum _{i=1}^{n}w_{i}x_{i}^{p}}}\leq {\sqrt[{q}]{\sum _{i=1}^{n}w_{i}x_{i}^{q}}}}$

if p is negative, and q is positive, the inequality is equivalent to the one proved above:

${\displaystyle {\sqrt[{p}]{\sum _{i=1}^{n}w_{i}x_{i}^{p}}}\leq \prod _{i=1}^{n}x_{i}^{w_{i}}\leq {\sqrt[{q}]{\sum _{i=1}^{n}w_{i}x_{i}^{q}}}}$

The proof for positive p and q is as follows: Define the following function: f : R₊ → R₊ ${\displaystyle f(x)=x^{\frac {q}{p}}}$. f is a power function, so it does have a second derivative:

${\displaystyle f''(x)=\left({\frac {q}{p}}\right)\left({\frac {q}{p}}-1\right)x^{{\frac {q}{p}}-2}}$

which is strictly positive within the domain of f, since q > p, so we know f is convex.

Using this, and the Jensen's inequality we get:

${\displaystyle {\begin{aligned}f\left(\sum _{i=1}^{n}w_{i}x_{i}^{p}\right)&\leq \sum _{i=1}^{n}w_{i}f(x_{i}^{p})\\[3pt]{\sqrt[{\frac {p}{q}}]{\sum _{i=1}^{n}w_{i}x_{i}^{p}}}&\leq \sum _{i=1}^{n}w_{i}x_{i}^{q}\end{aligned}}}$

after raising both side to the power of 1/q (an increasing function, since 1/q is positive) we get the inequality which was to be proven:

${\displaystyle {\sqrt[{p}]{\sum _{i=1}^{n}w_{i}x_{i}^{p}}}\leq {\sqrt[{q}]{\sum _{i=1}^{n}w_{i}x_{i}^{q}}}}$

Using the previously shown equivalence we can prove the inequality for negative p and q by substituting them with, respectively, −q and −p, QED.

Generalized f-mean

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

${\displaystyle M_{f}(x_{1},\dots ,x_{n})=f^{-1}\left({{\frac {1}{n}}\cdot \sum _{i=1}^{n}{f(x_{i})}}\right)}$

This covers the geometric mean without using a limit with f(x) = log(x). The power mean is obtained for f(x) = x^p.

Applications

Signal processing

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

 powerSmooth :: Floating a => ([a] -> [a]) -> a -> [a] -> [a]
 powerSmooth smooth p = map (** recip p) . smooth . map (**p)

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

See also

• Arithmetic-geometric mean
• Average
• Heronian mean
• Inequality of arithmetic and geometric means
• Lehmer mean – also a mean related to powers
• Root mean square
• Minkowski distance

Notes

1. Sýkora, Stanislav (2009). Mathematical means and averages: basic properties. 3. Stan’s Library: Castano Primo, Italy. doi:10.3247/SL3Math09.001.
2. P. S. Bullen: Handbook of Means and Their Inequalities. Dordrecht, Netherlands: Kluwer, 2003, pp. 175-177
3. Weisstein, Eric W. "Power Mean". MathWorld. (retrieved 2019-08-17)
4. Thompson, Sylvanus P. (1965). Calculus Made Easy. Macmillan International Higher Education. p. 185. ISBN 9781349004874. Retrieved 5 July 2020.
5. Jones, Alan R. (2018). Probability, Statistics and Other Frightening Stuff. Routledge. p. 48. ISBN 9781351661386. Retrieved 5 July 2020.

References and further reading

• P. S. Bullen: Handbook of Means and Their Inequalities. Dordrecht, Netherlands: Kluwer, 2003, chapter III (The Power Means), pp. 175-265

External links

• Power mean at MathWorld
• Examples of Generalized Mean
• A proof of the Generalized Mean on PlanetMath