Feigenbaum constants

List of numbers Irrational and suspected irrational numbers $γ$ $ζ$ (3) √2 √3 √5 $φ$ $ρ$ $δ$ _$S$ $e$ $π$ $δ$

Feigenbaum constant δ expresses the limit of the ratio of distances between consecutive bifurcation diagram on L_i / L_{i + 1}

In mathematics, specifically bifurcation theory, the Feigenbaum constants are two mathematical constants which both express ratios in a bifurcation diagram for a non-linear map. They are named after the mathematician Mitchell Feigenbaum.

History

Feigenbaum originally related the first constant to the period-doubling bifurcations in the logistic map, but also showed it to hold for all one-dimensional maps with a single quadratic maximum. As a consequence of this generality, every chaotic system that corresponds to this description will bifurcate at the same rate. It was discovered in 1978.^[1]

The first constant

The first Feigenbaum constant is the limiting ratio of each bifurcation interval to the next between every period doubling, of a one-parameter map

x_{i+1}=f(x_{i})

where $f (x)$ is a function parameterized by the bifurcation parameter $a$ .

It is given by the limit:^[2]

\delta =\lim _{n\rightarrow \infty }{\dfrac {a_{n-1}-a_{n-2}}{a_{n}-a_{n-1}}}=4.669\,201\,609\,\cdots

where $a n$ are discrete values of $a$ at the $n$ th period doubling.

According to (sequence A006890 in the OEIS), this number to 30 decimal places is: $δ$ = 4.669201609102990671853203821578….

Illustration

Non-linear maps

To see how this number arises, consider the real one-parameter map:

f(x)=a-x^{2}.

Here $a$ is the bifurcation parameter, $x$ is the variable. The values of $a$ for which the period doubles (e.g. the largest value for $a$ with no period 2 orbit, or the largest $a$ with no period 4 orbit),are $a 1$ , $a 2$ etc. These are tabulated below:^[3]

$n$	Period	Bifurcation parameter ( $a n$ )	Ratio $a n -1 - a n -2 / a n - a n -1$
1	2	0.75	—
2	4	1.25	—
3	8	7000136809889999999♠1.3680989	4.2337
4	16	7000139404620000000♠1.3940462	4.5515
5	32	7000139963120000000♠1.3996312	4.6458
6	64	7000140082860000000♠1.4008286	4.6639
7	128	7000140108530000000♠1.4010853	4.6682
8	256	7000140114020000000♠1.4011402	4.6689

The ratio in the last column converges to the first Feigenbaum constant. The same number arises for the logistic map

f(x)=ax(1-x)

with real parameter $a$ and variable . Tabulating the bifurcation values again:^[4]

$n$	Period	Bifurcation parameter ( $a n$ )	Ratio $a n -1 - a n -2 / a n - a n -1$
1	2	3	—
2	4	7000344948970000000♠3.4494897	—
3	8	7000354409030000000♠3.5440903	4.7514
4	16	7000356440730000000♠3.5644073	4.6562
5	32	7000356875940000000♠3.5687594	4.6683
6	64	7000356969160000000♠3.5696916	4.6686
7	128	7000356989130000000♠3.5698913	4.6692
8	256	7000356993400000000♠3.5699340	4.6694

Fractals

Self similarity in the Mandelbrot set shown by zooming in on a round feature while panning in the negative-

x

direction. The display center pans from (−1, 0) to (−1.31, 0) while the view magnifies from 0.5 × 0.5 to 0.12 × 0.12 to approximate the Feigenbaum ratio.

In the case of the Mandelbrot set for complex quadratic polynomial

f(z)=z^{2}+c

the Feigenbaum constant is the ratio between the diameters of successive circles on the real axis in the complex plane (see animation on the right).

$n$	Period = $2 n$	Bifurcation parameter ( $c n$ )	Ratio $={\dfrac {c_{n-1}-c_{n-2}}{c_{n}-c_{n-1}}}$
1	2	3000250000000000000♠−0.75	—
2	4	2999875000000000000♠−1.25	—
3	8	2999863190110000000♠−1.3680989	4.2337
4	16	2999860595380000000♠−1.3940462	4.5515
5	32	2999860036880000000♠−1.3996312	4.6458
6	64	2999859917130000000♠−1.4008287	4.6639
7	128	2999859891470000000♠−1.4010853	4.6682
8	256	2999859885980000000♠−1.4011402	4.6689
9	512	2999859884801797100♠−1.401151982029
10	1024	2999859884549776300♠−1.401154502237
$\infty$		2999859884481099999♠−1.4011551890…

Bifurcation parameter is a root point of period = $2 n$ component. This series converges to the Feigenbaum point $c$ = −1.401155. The ratio in the last column converges to the first Feigenbaum constant.

Other maps also reproduce this ratio, in this sense the Feigenbaum constant in bifurcation theory is analogous to $π$ in geometry and $e$ in calculus.

The second constant

The second Feigenbaum constant (sequence A006891 in the OEIS),

α

= 2.502907875095892822283902873218…,

is the ratio between the width of a tine and the width of one of its two subtines (except the tine closest to the fold). A negative sign is applied to $α$ when the ratio between the lower subtine and the width of the tine is measured.^[5]

These numbers apply to a large class of dynamical systems (for example, dripping faucets to population growth).^[5]

Properties

Both numbers are believed to be transcendental, although they have not been proven to be so.^[6]

The first proof of the universality of the Feigenbaum constants carried out by Lanford^[7] (with a small correction by Eckmann and Wittwer,^[8]) was computer assisted. Over the years, non-numerical methods were discovered for different parts of the proof aiding Lyubich in producing the first complete non-numerical proof.^[9]

Approximations

Though there is no known closed form equation or infinite series that can exactly calculate either constant, there are closed form approximations for several digits. One of the most accurate, up to six digits, is (sequence A094078 in the OEIS)

\pi +\tan ^{-1}(e^{\pi })

which is accurate up to 4.669202. Two closely related expressions that accurately estimate both $δ$ and $α$ to three decimal places are respectively^[10]

{\frac {2\varphi }{\ln 2}}\approx 4.669\,,\qquad {\frac {2\varphi +1}{\ln 2+1}}\approx 2.502

where $φ$ is the golden ratio and $ln 2$ is the natural logarithm of 2.

Notes

↑ Chaos: An Introduction to Dynamical Systems, K.T. Alligood, T.D. Sauer, J.A. Yorke, Textbooks in mathematical sciences ,Springer, 1996, ISBN 978-0-38794-677-1
↑ Non-Linear Ordinary Differential Equations: Introduction for Scientists and Engineers (4th Edition), D.W. Jordan, P. Smith, Oxford University Press, 2007, ISBN 978-0-19-920825-8
↑ Alligood, p. 503.
↑ Alligood, p. 504.
1 2 Nonlinear Dynamics and Chaos, Steven H. Strogatz, Studies in Nonlinearity ,Perseus Books Publishing, 1994, ISBN 978-0-7382-0453-6
↑ Briggs, Keith (1997). feigenbaum scaling in discrete dynamical systems (PDF). Annals of Mathematics (Thesis).
↑ Lanford III, Oscar (1982). "A computer-assisted proof of the Feigenbaum conjectures". Bull. Amer. Math. Soc. 6 (3): 427–434. doi:10.1090/S0273-0979-1982-15008-X.
↑ Eckmann, J. P.; Wittwer, P. (1987). "A complete proof of the Feigenbaum conjectures". Journal of Statistical Physics. 46 (3–4): 455. Bibcode:1987JSP....46..455E. doi:10.1007/BF01013368.
↑ Lyubich, Mikhail (1999). "Feigenbaum-Coullet-Tresser universality and Milnor's Hairiness Conjecture". Annals of Mathematics. 149 (2): 319–420. doi:10.2307/120968.
↑ Smith, Reginald (2013). "Period doubling, information entropy, and estimates for Feigenbaum's constants". International Journal of Bifurcation and Chaos. 23 (11): 1350190. arXiv:1307.5251. Bibcode:2013IJBC...2350190S. doi:10.1142/S0218127413501903.

References

Alligood, Kathleen T., Tim D. Sauer, James A. Yorke, Chaos: An Introduction to Dynamical Systems, Textbooks in mathematical sciences Springer, 1996, ISBN 978-0-38794-677-1
Briggs, Keith (July 1991). "A Precise Calculation of the Feigenbaum Constants" (PDF). Mathematics of Computation. American Mathematical Society. 57 (195): 435–439. Bibcode:1991MaCom..57..435B. doi:10.1090/S0025-5718-1991-1079009-6.
Briggs, Keith (1997). Feigenbaum scaling in discrete dynamical systems (PDF) (PhD thesis). University of Melbourne.
Broadhurst, David (22 March 1999). "Feigenbaum constants to 1018 decimal places".

Weisstein, Eric W. "Feigenbaum Constant". MathWorld.

External links

Feigenbaum Constant – from Wolfram MathWorld
(A006890)& (A006891) from oeis.org
(A006890)& (A094078) from oeis.org
Feigenbaum constant – PlanetMath
Moriarty, Philip; Bowley, Roger (2009). " $δ$ – Feigenbaum Constant". Sixty Symbols. Brady Haran for the University of Nottingham.

Chaos theory

Chaos theory	Anosov diffeomorphism Bifurcation theory Butterfly effect Chaos theory in organizational development Complexity Control of chaos Dynamical system Edge of chaos Fractal Predictability Quantum chaos Santa Fe Institute Synchronization of chaos Unintended consequences

Chaotic maps (list)	Arnold tongue Arnold's cat map Baker's map Complex quadratic map Complex squaring map Coupled map lattice Double pendulum Double scroll attractor Duffing equation Duffing map Dyadic transformation Dynamical billiards outer Exponential map Gauss map Gingerbreadman map Hénon map Horseshoe map Ikeda map Interval exchange map Kaplan–Yorke map Logistic map Lorenz system Multiscroll attractor Rabinovich–Fabrikant equations Rössler attractor Standard map Swinging Atwood's machine Tent map Tinkerbell map Van der Pol oscillator Zaslavskii map

Chaos systems	Bouncing ball dynamics Chua's circuit Economic bubble Tilt-A-Whirl

Chaos theorists	Michael Berry Leon O. Chua Mitchell Feigenbaum Martin Gutzwiller Brosl Hasslacher Michel Hénon Edward Norton Lorenz Aleksandr Lyapunov Benoît Mandelbrot Henri Poincaré Otto Rössler David Ruelle Oleksandr Mykolaiovych Sharkovsky Floris Takens James A. Yorke

Irrational numbers

Prime ( $ρ$ ) Euler–Mascheroni ( $γ$ ) Logarithm of 2 Twelfth root of two (¹²√2) Apéry's ( $ζ (3)$ ) Sophomore's dream Plastic ( $ρ$ ) Square root of 2 (√2) Erdős–Borwein ( $E$ ) Golden ratio ( $φ$ ) Square root of 3 (√3) Square root of 5 (√5) Silver ratio ( $δ S$ ) Second Feigenbaum ( $α$ ) Euler's ( $e$ ) Pi ( $π$ ) First Feigenbaum ( $δ$ )

Schizophrenic Transcendental Trigonometric

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