Arithmetic–geometric mean

In mathematics, the arithmetic–geometric mean (AGM) of two positive real numbers x and y is defined as follows:

First, compute the arithmetic and geometric means of x and y, calling them a₁ and g₁ respectively (the latter is the principal square root of the product xy):

$\begin{align} a_1 &= \tfrac12(x + y)\\ g_1 &= \sqrt{xy} \end{align}$

Then, use iteration, with a₁ taking the place of x and g₁ taking the place of y. In this way, two sequences (a_n) and (g_n) are defined:

$\begin{align} a_{n+1} &= \tfrac12(a_n + g_n)\\ g_{n+1} &= \sqrt{a_n g_n} \end{align}$

These two sequences converge to the same number, which is the arithmetic–geometric mean of x and y; it is denoted by M(x, y), or sometimes by agm(x, y).

This can be used for algorithmic purposes as in the AGM method, which makes it possible to construct fast algorithms for calculating exponential and trigonometric functions, as well as some mathematical constants, in particular, to quickly $\pi$.

Example

To find the arithmetic–geometric mean of a₀ = 24 and g₀ = 6, first calculate their arithmetic and geometric means, thus:

$\begin{align} a_1 &= \tfrac12(24 + 6) = 15\\ g_1 &= \sqrt{24 \times 6} = 12 \end{align}$

and then iterate as follows:

$\begin{align} a_2 &= \tfrac12(15 + 12) = 13.5\\ g_2 &= \sqrt{15 \times 12} = 13.41640786500\dots\\ \dots \end{align}$

The first five iterations give the following values:

n	an	gn
0	24	6
1	15	12
2	13.5	13.416407864998738178455042…
3	13.458203932499369089227521…	13.458139030990984877207090…
4	13.458171481745176983217305…	13.458171481706053858316334…
5	13.458171481725615420766820…	13.458171481725615420766806…

As can be seen, the number of digits in agreement (underlined) approximately doubles with each iteration. The arithmetic–geometric mean of 24 and 6 is the common limit of these two sequences, which is approximately 13.4581714817256154207668131569743992430538388544.^[1]

History

The first algorithm based on this sequence pair appeared in the works of Lagrange. Its properties were further analyzed by Gauss.^[2]

Properties

The geometric mean of two positive numbers is never bigger than the arithmetic mean (see inequality of arithmetic and geometric means); as a consequence, (g_n) is an increasing sequence, (a_n) is a decreasing sequence, and g_n ≤ M(x, y) ≤ a_n. These are strict inequalities if x ≠ y.

M(x, y) is thus a number between the geometric and arithmetic mean of x and y; it is also between x and y.

If r ≥ 0, then M(rx,ry) = r M(x,y).

There is an integral-form expression for M(x,y):

$\begin{align} M(x,y) &= \frac\pi2\bigg/\int_0^\frac{\pi}{2}\frac{d\theta}{\sqrt{x^2\cos^2\theta+y^2\sin^2\theta}}\\ &=\frac{\pi}{4} \cdot \frac{x + y}{K\left( \frac{x - y}{x + y} \right)} \end{align}$

where K(k) is the complete elliptic integral of the first kind:

$K(k) = \int_0^\frac{\pi}{2}\frac{d\theta}{\sqrt{1 - k^2\sin^2(\theta)}}$

Indeed, since the arithmetic–geometric process converges so quickly, it provides an efficient way to compute elliptic integrals via this formula. In engineering, it is used for instance in elliptic filter design.^[3]

Related concepts

The reciprocal of the arithmetic–geometric mean of 1 and the square root of 2 is called Gauss's constant, after Carl Friedrich Gauss.

$\frac{1}{M(1, \sqrt{2})} = G = 0.8346268\dots$

The geometric–harmonic mean can be calculated by an analogous method, using sequences of geometric and harmonic means. The arithmetic–harmonic mean can be similarly defined, but takes the same value as the geometric mean.

The arithmetic–geometric mean can be used to compute logarithms and complete elliptic integrals of the first kind. A modified arithmetic–geometric mean (MAGM) can be used to efficiently compute complete elliptic integrals of the second kind.^[4] Both AGM and MAGM turn out being special cases of the generalized arithmetic–geometric mean (GAGM).^[5]

Proof of existence

From inequality of arithmetic and geometric means we can conclude that:

$g_n \leqslant a_n$

and thus

$g_{n + 1} = \sqrt{g_n \cdot a_n} \geqslant \sqrt{g_n \cdot g_n} = g_n$

that is, the sequence g_n is nondecreasing.

Furthermore, it is easy to see that it is also bounded above by the larger of x and y (which follows from the fact that both the arithmetic and geometric means of two numbers lie between them). Thus, by the monotone convergence theorem, the sequence is convergent, so there exists a g such that:

$\lim_{n\to \infty}g_n = g$

However, we can also see that:

$a_n = \frac{g_{n + 1}^2}{g_n}$

and so:

$\lim_{n\to \infty}a_n = \lim_{n\to \infty}\frac{g_{n + 1}^2}{g_{n}} = \frac{g^2}{g} = g$

Q.E.D.

Proof of the integral-form expression

This proof is given by Gauss.^[2] Let

$I(x,y) = \int_0^{\pi/2}\frac{d\theta}{\sqrt{x^2\cos^2\theta+y^2\sin^2\theta}},$

Changing the variable of integration to $\theta'$, where

$\sin\theta = \frac{2x\sin\theta'}{(x+y)+(x-y)\sin^2\theta'},$

gives

$\begin{align} I(x,y) &= \int_0^{\pi/2}\frac{d\theta'}{\sqrt{\bigl(\frac12(x+y)\bigr)^2\cos^2\theta'+\bigl(\sqrt{xy}\bigr)^2\sin^2\theta'}}\\ &= I\bigl(\tfrac12(x+y),\sqrt{xy}\bigr). \end{align}$

Thus, we have

$\begin{align} I(x,y) &= I(a_1, g_1) = I(a_2, g_2) = \cdots\\ &= I\bigl(M(x,y),M(x,y)\bigr) = \pi/\bigr(2M(x,y)\bigl). \end{align}$

The last equality comes from observing that $I(z,z) = \pi/(2z)$.

Finally, we obtain the desired result

$M(x,y) = \pi/\bigl(2 I(x,y) \bigr).$

AGM method

Gauss noticed^[6][7] that the sequences

$\begin{align} a_0 & & b_0 \\ a_1 & = \frac{a_0+b_0}{2}, & b_1 & = \sqrt{a_0 b_0} \\ a_2 & = \frac{a_1+b_1}{2}, & b_2 & = \sqrt{a_1 b_1} \\ & {}\ \ \vdots & & {}\ \ \vdots \\ a_{N+1} & = \frac{a_N + b_N}{2}, & b_{N+1} & = \sqrt{a_N b_N} \end{align}$

as

$N\to +\infty, \,$

have the same limit:

$\lim_{N\to\infty}a_N = \lim_{N\to\infty}b_N = M(a,b), \,$

the arithmetic–geometric mean.

It is possible to use this fact to construct fast algorithms for calculating elementary transcendental functions and some classical constants, in particular, the constant π.

Applications

The number π

For example, according to the Gauss–Salamin formula:^[8]

$\pi = \frac{4 \left( M(1; \frac{1}{\sqrt{2}}) \right)^2} {\displaystyle 1 - \sum_{j=1}^\infty 2^{j+1} c_j^2} ,$

where

$c_j = \frac 12\left(a_{j-1}-b_{j-1}\right)$

which can be computed without loss of precision using

$c_j = \frac{c_{j-1}^2}{4a_j}.$

Complete elliptic integral K(α)

At the same time, if we take

$a_0 = 1, \quad b_0 = \cos\alpha,$

then

$\lim_{N\to\infty}a_N = \frac{\pi}{2K(\alpha)},$

where K(α) is a complete elliptic integral of the first kind

$K(\alpha) = \int_0^{\pi/2}(1 - \alpha \sin^2\theta)^{-1/2} \, d\theta .$

Other applications

Using this property of the AGM along with the ascending transformations of Landen,^[9] Richard Brent^[10] suggested the first AGM algorithms for the fast evaluation of elementary transcendental functions (e^x, cos x, sin x). Subsequently, many authors went on to study the use of the AGM algorithms, see, for example, the book Pi and the AGM by Jonathan and Peter Borwein.^[11]

See also

• Generalized mean
• Inequality of arithmetic and geometric means
• Gauss–Legendre algorithm

External links

• Arithmetic-Geometric Mean Calculator
• Proof of convergence rate in PlanetMath

References

• Adlaj, Semjon (September 2012). "An eloquent formula for the perimeter of an ellipse" (PDF). Notices of the AMS 59 (8): 1094–1099. doi:10.1090/noti879.
• Jonathan Borwein, Peter Borwein, Pi and the AGM. A study in analytic number theory and computational complexity. Reprint of the 1987 original. Canadian Mathematical Society Series of Monographs and Advanced Texts, 4. A Wiley-Interscience Publication. John Wiley & Sons, Inc., New York, 1998. xvi+414 pp. ISBN 0-471-31515-X MR 1641658
• Zoltán Daróczy, Zsolt Páles, Gauss-composition of means and the solution of the Matkowski–Suto problem. Publ. Math. Debrecen 61/1-2 (2002), 157–218.
• M. Hazewinkel (2001), "Arithmetic–geometric mean process", in Hazewinkel, Michiel, Encyclopedia of Mathematics, Springer, ISBN 978-1-55608-010-4
• Weisstein, Eric W., "Arithmetic–Geometric mean", MathWorld.

1. agm(24, 6) at WolframAlpha
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3. Hercules G. Dimopoulos (2011). Analog Electronic Filters: Theory, Design and Synthesis. Springer. pp. 147–155. ISBN 978-94-007-2189-0.
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