Real projective plane: Difference between revisions
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= | In mathematics, the '''random Fibonacci sequence''' is a stochastic analogue of the [[Fibonacci sequence]] defined by the [[recurrence relation]] ''f''<sub>''n''</sub> = ''f''<sub>''n''−1</sub> ± ''f''<sub>''n''−2</sub>, where the signs + or − are chosen [[Bernoulli distribution|at random]] with equal probability 1/2, [[Independence (probability theory)|independently]] for different ''n''. By a theorem of [[Harry Kesten]] and [[Hillel Furstenberg]], random recurrent sequences of this kind grow at a certain [[exponential growth|exponential rate]], but it is difficult to compute the rate explicitly. In 1999, [[Divakar Viswanath]] showed that the growth rate of the random Fibonacci sequence is equal to 1.1319882487943…, a [[mathematical constant]] that was later named Viswanath's constant.<ref>{{cite doi|10.1090/S0025-5718-99-01145-X}}</ref><ref>{{cite doi|10.1023/A:1014702122205}}</ref><ref>{{cite doi|10.1016/j.jnt.2006.01.002}} {{arxiv|math.NT/0510159}}</ref> | ||
== Description == | |||
The random Fibonacci sequence is an integer random sequence {''f''<sub>''n''</sub>}, where ''f''<sub>1</sub> = ''f''<sub>2</sub> = 1 and the subsequent terms are determined from the random recurrence relation | |||
= | :<math> | ||
f_n = \begin{cases} | |||
f_{n-1}+f_{n-2}, & \text{ with probability 1/2}; \\ | |||
f_{n-1}-f_{n-2}, & \text{ with probability 1/2}. | |||
\end{cases} | |||
</math> | |||
A run of the random Fibonacci sequence starts with 1,1 and the value of the each subsequent term is determined by a [[fair coin]] toss: given two consecutive elements of the sequence, the next element is either their sum or their difference with probability 1/2, independently of all the choices made previously. If in the random Fibonacci sequence the plus sign is chosen at each step, the corresponding run is the [[Fibonacci sequence]] {''F''<sub>''n''</sub>}, | |||
: <math> 1,1,2,3,5,8,13,21,34,55,\ldots. </math> | |||
If the signs alternate in minus-plus-plus-minus-plus-plus-... pattern, the result is the sequence | |||
: <math> 1,1,0,1,1,0,1,1,0,1,\ldots.</math> | |||
However, such patterns occur with vanishing probability in a random experiment. In a typical run, the terms will not follow a predictable pattern: | |||
: <math> 1, 1, 2, 3, 1, -2, -3, -5, -2, -3, \ldots | |||
\text{ for the signs } +, +, -, -, -, +, -, -, \ldots.</math> | |||
Similarly to the deterministic case, the random Fibonacci sequence may be profitably described via matrices: | |||
:<math>{f_{n-1} \choose f_{n}} = \begin{pmatrix} 0 & 1 \\ \pm 1 & 1 \end{pmatrix} {f_{n-2} \choose f_{n-1}},</math> | |||
where the signs are chosen independently for different ''n'' with equal probabilities for + or −. Thus | |||
:<math>{f_{n-1} \choose f_{n}} = M_{n}M_{n-1}\ldots M_3{f_{1} \choose f_{2}},</math> | |||
where {''M''<sub>''k''</sub>} is a sequence of [[Independent and identically-distributed random variables|independent identically distributed random matrices]] taking values ''A'' or ''B'' with probability 1/2: | |||
: <math> A=\begin{pmatrix} 0 & 1 \\ 1 & 1 \end{pmatrix}, \quad | |||
B=\begin{pmatrix} 0 & 1 \\ -1 & 1 \end{pmatrix}. </math> | |||
== Growth rate == | |||
[[Johannes Kepler]] discovered that as ''n'' increases, the ratio of the successive terms of the Fibonacci sequence {''F''<sub>''n''</sub>} approaches the [[golden ratio]] <math>\varphi=(1+\sqrt{5})/2,</math> which is approximately 1.61803. In 1765, [[Leonhard Euler]] published an explicit formula, known today as the [[Binet formula]], | |||
:<math> F_n = {{\varphi^n-(-1/\varphi)^{n}} \over {\sqrt 5}}. </math> | |||
It demonstrates that the Fibonacci numbers grow at an exponential rate equal to the golden ratio ''φ''. | |||
In 1960, [[Hillel Furstenberg]] and [[Harry Kesten]] showed that for a general class of random [[matrix (math)|matrix]] products, the [[matrix norm|norm]] grows as ''λ''<sup>''n''</sup>, where ''n'' is the number of factors. Their results apply to a broad class of random sequence generating processes that includes the random Fibonacci sequence. As a consequence, the ''n''th root of |''f''<sub>''n''</sub>| converges to a constant value ''[[almost surely]]'', or with probability one: | |||
:<math> \sqrt[n]{|f_n|} \to 1.13198824\dots \text{ as } n \to \infty. </math> | |||
An explicit expression for this constant was found by Divakar Viswanath in 1999. It uses Furstenberg's formula for the [[Lyapunov exponent]] of a random matrix product and integration over a certain [[fractal|fractal measure]] on the [[Stern–Brocot tree]]. Moreover, Viswanath computed the numerical value above using [[floating point]] arithmetics validated by an analysis of the [[rounding error]]. | |||
==Related work== | |||
The [[Embree–Trefethen constant]] describes the qualitative behavior of the random sequence with the recurrence relation | |||
: <math> f_n=f_{n-1}\pm \beta f_{n-2}</math> | |||
for different values of β.<ref>{{cite doi|10.1098/rspa.1999.0412|noedit}}</ref> | |||
==References== | |||
{{reflist}} | |||
==External links== | |||
* [http://sciencenews.org/sn_arc99/6_12_99/bob1.htm A brief explanation] | |||
* {{MathWorld|urlname=RandomFibonacciSequence|title=Random Fibonacci Sequence}} | |||
* {{SloanesRef|sequencenumber=A078416}} | |||
[[Category:Fibonacci numbers]] | |||
[[Category:Mathematical constants]] | |||
[[Category:Number theory]] | |||
Revision as of 11:30, 10 January 2014
In mathematics, the random Fibonacci sequence is a stochastic analogue of the Fibonacci sequence defined by the recurrence relation fn = fn−1 ± fn−2, where the signs + or − are chosen at random with equal probability 1/2, independently for different n. By a theorem of Harry Kesten and Hillel Furstenberg, random recurrent sequences of this kind grow at a certain exponential rate, but it is difficult to compute the rate explicitly. In 1999, Divakar Viswanath showed that the growth rate of the random Fibonacci sequence is equal to 1.1319882487943…, a mathematical constant that was later named Viswanath's constant.[1][2][3]
Description
The random Fibonacci sequence is an integer random sequence {fn}, where f1 = f2 = 1 and the subsequent terms are determined from the random recurrence relation
A run of the random Fibonacci sequence starts with 1,1 and the value of the each subsequent term is determined by a fair coin toss: given two consecutive elements of the sequence, the next element is either their sum or their difference with probability 1/2, independently of all the choices made previously. If in the random Fibonacci sequence the plus sign is chosen at each step, the corresponding run is the Fibonacci sequence {Fn},
If the signs alternate in minus-plus-plus-minus-plus-plus-... pattern, the result is the sequence
However, such patterns occur with vanishing probability in a random experiment. In a typical run, the terms will not follow a predictable pattern:
Similarly to the deterministic case, the random Fibonacci sequence may be profitably described via matrices:
where the signs are chosen independently for different n with equal probabilities for + or −. Thus
where {Mk} is a sequence of independent identically distributed random matrices taking values A or B with probability 1/2:
Growth rate
Johannes Kepler discovered that as n increases, the ratio of the successive terms of the Fibonacci sequence {Fn} approaches the golden ratio which is approximately 1.61803. In 1765, Leonhard Euler published an explicit formula, known today as the Binet formula,
It demonstrates that the Fibonacci numbers grow at an exponential rate equal to the golden ratio φ.
In 1960, Hillel Furstenberg and Harry Kesten showed that for a general class of random matrix products, the norm grows as λn, where n is the number of factors. Their results apply to a broad class of random sequence generating processes that includes the random Fibonacci sequence. As a consequence, the nth root of |fn| converges to a constant value almost surely, or with probability one:
An explicit expression for this constant was found by Divakar Viswanath in 1999. It uses Furstenberg's formula for the Lyapunov exponent of a random matrix product and integration over a certain fractal measure on the Stern–Brocot tree. Moreover, Viswanath computed the numerical value above using floating point arithmetics validated by an analysis of the rounding error.
Related work
The Embree–Trefethen constant describes the qualitative behavior of the random sequence with the recurrence relation
for different values of β.[4]
References
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External links
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