Generalized mean

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 ${\displaystyle \textstyle {\sum _{i}w_{i}=1}}$ 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.

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 ${\displaystyle M_{-\infty }(x_{1},\dots ,x_{n})={\frac {1}{M_{\infty }(1/x_{1},\dots ,1/x_{n})}}.}$

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

Geometric proof without words that max (a,b) > quadratic mean or root mean square (QM) > arithmetic mean (AM) > geometric mean (GM) > harmonic mean (HM) > min (a,b) of two positive numbers a and b [6]

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.

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.

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.

• 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

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

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