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In mathematics, the '''Lehmer mean''' of a [[tuple]] <math>x</math> of positive [[real number]]s, named after [[Derrick Henry Lehmer]],<ref>P. S. Bullen. ''Handbook of means and their inequalities''. Springer, 1987.</ref> is defined as: | |||
:<math>L_p(x) = \frac{\sum_{k=1}^n x_k^p}{\sum_{k=1}^n x_k^{p-1}}.</math> | |||
The '''weighted Lehmer mean''' with respect to a tuple <math>w</math> of positive weights is defined as: | |||
:<math>L_{p,w}(x) = \frac{\sum_{k=1}^n w_k\cdot x_k^p}{\sum_{k=1}^n w_k\cdot x_k^{p-1}}.</math> | |||
The Lehmer mean is an alternative to [[power mean]]s | |||
for [[Interpolation|interpolating]] between [[minimum]] and [[maximum]] via [[arithmetic mean]] and [[harmonic mean]]. | |||
== Properties == | |||
The derivative of <math>p \mapsto L_p(x)</math> is non-negative | |||
:<math> | |||
\frac{\partial}{\partial p} L_p(x) = | |||
\frac | |||
{\sum_{j=1}^{n}\sum_{k=j+1}^{n} | |||
(x_j-x_k)\cdot(\ln x_j - \ln x_k)\cdot(x_j\cdot x_k)^{p-1}} | |||
{\left(\sum_{k=1}^{n} x_k^{p-1}\right)^2}, | |||
</math> | |||
thus this function is monotonic and the inequality | |||
:<math>p\le q \Rightarrow L_p(x) \le L_q(x)</math> | |||
holds. | |||
==Special cases== | |||
*<math>\lim_{p\to-\infty} L_p(x)</math> is the [[minimum]] of the elements of <math>x</math>. | |||
*<math>L_0(x)</math> is the [[harmonic mean]]. | |||
*<math>L_\frac{1}{2}\left((x_0,x_1)\right)</math> is the [[geometric mean]] of the two values <math>x_0</math> and <math>x_1</math>. | |||
*<math>L_1(x)</math> is the [[arithmetic mean]]. | |||
*<math>L_2(x)</math> is the [[contraharmonic mean]]. | |||
*<math>\lim_{p\to\infty} L_p(x)</math> is the [[maximum]] of the elements of <math>x</math>. | |||
:Sketch of a proof: [[Without loss of generality]] let <math>x_1,\dots,x_k</math> be the values which equal the maximum. Then <math>L_p(x)=x_1\cdot\frac{k+\left(\frac{x_{k+1}}{x_1}\right)^p+\cdots+\left(\frac{x_{n}}{x_1}\right)^p}{k+\left(\frac{x_{k+1}}{x_1}\right)^{p-1}+\cdots+\left(\frac{x_{n}}{x_1}\right)^{p-1}}</math> | |||
== Applications == | |||
===Signal processing=== | |||
Like a [[power mean]], | |||
a Lehmer mean serves a non-linear [[moving average]] which is shifted towards small signal values for small <math>p</math> and emphasizes big signal values for big <math>p</math>. Given an efficient implementation of a [[lowpass|moving arithmetic mean]] called <tt>smooth</tt> you can implement a moving Lehmer mean | |||
according to the following [[Haskell (programming language)|Haskell]] code. | |||
<source lang="haskell"> | |||
lehmerSmooth :: Floating a => ([a] -> [a]) -> a -> [a] -> [a] | |||
lehmerSmooth smooth p xs = zipWith (/) | |||
(smooth (map (**p) xs)) | |||
(smooth (map (**(p-1)) xs)) | |||
</source> | |||
* For big <math>p</math> it can serve an [[envelope detector]] on a [[rectifier|rectified]] signal. | |||
* For small <math>p</math> it can serve an [[Baseline (spectrometry)|baseline detector]] on a [[mass spectrum]]. | |||
==See also== | |||
*[[mean]] | |||
*[[power mean]] | |||
==Notes== | |||
{{reflist}} | |||
==External links== | |||
*[http://mathworld.wolfram.com/LehmerMean.html Lehmer Mean at MathWorld] | |||
[[Category:Means]] | |||
[[Category:Articles with example Haskell code]] |
Latest revision as of 01:29, 21 November 2013
In mathematics, the Lehmer mean of a tuple of positive real numbers, named after Derrick Henry Lehmer,[1] is defined as:
The weighted Lehmer mean with respect to a tuple of positive weights is defined as:
The Lehmer mean is an alternative to power means for interpolating between minimum and maximum via arithmetic mean and harmonic mean.
Properties
The derivative of is non-negative
thus this function is monotonic and the inequality
holds.
Special cases
- is the minimum of the elements of .
- is the harmonic mean.
- is the geometric mean of the two values and .
- is the arithmetic mean.
- is the contraharmonic mean.
- is the maximum of the elements of .
- Sketch of a proof: Without loss of generality let be the values which equal the maximum. Then
Applications
Signal processing
Like a power mean, a Lehmer 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 you can implement a moving Lehmer mean according to the following Haskell code.
lehmerSmooth :: Floating a => ([a] -> [a]) -> a -> [a] -> [a]
lehmerSmooth smooth p xs = zipWith (/)
(smooth (map (**p) xs))
(smooth (map (**(p-1)) xs))
- For big it can serve an envelope detector on a rectified signal.
- For small it can serve an baseline detector on a mass spectrum.
See also
Notes
43 year old Petroleum Engineer Harry from Deep River, usually spends time with hobbies and interests like renting movies, property developers in singapore new condominium and vehicle racing. Constantly enjoys going to destinations like Camino Real de Tierra Adentro.
External links
- ↑ P. S. Bullen. Handbook of means and their inequalities. Springer, 1987.