Is there any algorithm to compute the nth fibonacci number in sub linear time?
The matrix is multiplied n time because then only we can get the (n+1)th Fibonacci number as the element at the row and the column (0, 0) in the resultant matrix. If we apply the above method without using recursive matrix multiplication, then the Time Complexity: O(n) and Space Complexity: O(1) .
The time complexity of the Fibonacci Search Algorithm is O(logn) . The best-case time complexity is O(1) . It occurs when the element to be searched is the first element we compare.
To aid in stating properties of the Fibonacci sequence, we use the customary notation F0, F1, F2, F3, ÿ for the integers of the Fibonacci sequence. That is, F0 = 0, F1 = 1, F2 = F0 + F1 = 1, F3 = F1 + F2 = 2, F4 = F2 + F3 = 3, F5 = F3 + F4 = 5, F6 = F4 + F5 = 8, and so on.
In 1999, Divakar Viswanath showed that the growth rate of the random Fibonacci sequence is equal to 1.1319882487943... (sequence A078416 in the OEIS), a mathematical constant that was later named Viswanath's constant.
Following from Pillsy's reference to matrix exponentiation, such that for the matrix
M = [1 1] [1 0]
then
fib(n) = Mn1,2
Raising matrices to powers using repeated multiplication is not very efficient.
Two approaches to matrix exponentiation are divide and conquer which yields Mn in O(ln n) steps, or eigenvalue decomposition which is constant time, but may introduce errors due to limited floating point precision.
If you want an exact value greater than the precision of your floating point implementation, you have to use the O ( ln n ) approach based on this relation:
Mn = (Mn/2)2 if n even = M·Mn-1 if n is odd
The eigenvalue decomposition on M finds two matrices U and Λ such that Λ is diagonal and
M = U Λ U-1Mn = ( U Λ U-1) n = U Λ U-1U Λ U-1U Λ U-1 ... n times = U Λ Λ Λ ... U-1 = U Λ nU-1Raising a the diagonal matrix Λ to the nth power is a simple matter of raising each element in Λ to the nth, so this gives an O(1) method of raising M to the nth power. However, the values in Λ are not likely to be integers, so some error will occur.
Defining Λ for our 2x2 matrix as
Λ = [ λ1 0 ] = [ 0 λ2 ]
To find each λ, we solve
|M - λI| = 0
which gives
|M - λI| = -λ ( 1 - λ ) - 1 λ² - λ - 1 = 0
using the quadratic formula
λ = ( -b ± √ ( b² - 4ac ) ) / 2a = ( 1 ± √5 ) / 2 { λ1, λ2 } = { Φ, 1-Φ } where Φ = ( 1 + √5 ) / 2
If you've read Jason's answer, you can see where this is going to go.
Solving for the eigenvectors X1 and X2:
if X1 = [ X1,1, X1,2 ] M.X1 1 = λ1X1X1,1 + X1,2 = λ1X1,1X1,1 = λ1X1,2 => X1 = [ Φ, 1 ] X2 = [ 1-Φ, 1 ]
These vectors give U:
U = [ X1,1, X2,2 ] [ X1,1, X2,2 ] = [ Φ, 1-Φ ] [ 1, 1 ]
Inverting U using
A = [ a b ] [ c d ] => A-1 = ( 1 / |A| ) [ d -b ] [ -c a ]
so U-1 is given by
U-1 = ( 1 / ( Φ - ( 1 - Φ ) ) [ 1 Φ-1 ] [ -1 Φ ] U-1 = ( √5 )-1 [ 1 Φ-1 ] [ -1 Φ ]
Sanity check:
UΛU-1 = ( √5 )-1 [ Φ 1-Φ ] . [ Φ 0 ] . [ 1 Φ-1 ] [ 1 1 ] [ 0 1-Φ ] [ -1 Φ ] let Ψ = 1-Φ, the other eigenvalue as Φ is a root of λ²-λ-1=0 so -ΨΦ = Φ²-Φ = 1 and Ψ+Φ = 1 UΛU-1 = ( √5 )-1 [ Φ Ψ ] . [ Φ 0 ] . [ 1 -Ψ ] [ 1 1 ] [ 0 Ψ ] [ -1 Φ ] = ( √5 )-1 [ Φ Ψ ] . [ Φ -ΨΦ ] [ 1 1 ] [ -Ψ ΨΦ ] = ( √5 )-1 [ Φ Ψ ] . [ Φ 1 ] [ 1 1 ] [ -Ψ -1 ] = ( √5 )-1 [ Φ²-Ψ² Φ-Ψ ] [ Φ-Ψ 0 ] = [ Φ+Ψ 1 ] [ 1 0 ] = [ 1 1 ] [ 1 0 ] = M
So the sanity check holds.
Now we have everything we need to calculate Mn1,2:
Mn = UΛnU-1 = ( √5 )-1 [ Φ Ψ ] . [ Φn 0 ] . [ 1 -Ψ ] [ 1 1 ] [ 0 Ψn ] [ -1 Φ ] = ( √5 )-1 [ Φ Ψ ] . [ Φn -ΨΦn ] [ 1 1 ] [ -Ψn ΨnΦ ] = ( √5 )-1 [ Φ Ψ ] . [ Φn Φn-1 ] [ 1 1 ] [ -Ψn -Ψn-1 ] as ΨΦ = -1 = ( √5 )-1 [ Φn+1-Ψn+1 Φn-Ψn ] [ Φn-Ψn Φn-1-Ψn-1 ]
so
fib(n) = Mn1,2 = ( Φn - (1-Φ)n ) / √5
Which agrees with the formula given elsewhere.
You can derive it from a recurrance relation, but in engineering computing and simulation calculating the eigenvalues and eigenvectors of large matrices is an important activity, as it gives stability and harmonics of systems of equations, as well as allowing raising matrices to high powers efficiently.
The n
th Fibonacci number is given by
f(n) = Floor(phi^n / sqrt(5) + 1/2)
where
phi = (1 + sqrt(5)) / 2
Assuming that the primitive mathematical operations (+
, -
, *
and /
) are O(1)
you can use this result to compute the n
th Fibonacci number in O(log n)
time (O(log n)
because of the exponentiation in the formula).
In C#:
static double inverseSqrt5 = 1 / Math.Sqrt(5);
static double phi = (1 + Math.Sqrt(5)) / 2;
/* should use
const double inverseSqrt5 = 0.44721359549995793928183473374626
const double phi = 1.6180339887498948482045868343656
*/
static int Fibonacci(int n) {
return (int)Math.Floor(Math.Pow(phi, n) * inverseSqrt5 + 0.5);
}
If you want the exact number (which is a "bignum", rather than an int/float), then I'm afraid that
It's impossible!
As stated above, the formula for Fibonacci numbers is:
fib n = floor (phin/√5 + 1/2)
fib n ~= phin/√5
How many digits is fib n
?
numDigits (fib n) = log (fib n) = log (phin/√5) = log phin - log √5 = n * log phi - log √5
numDigits (fib n) = n * const + const
it's O(n)
Since the requested result is of O(n), it can't be calculated in less than O(n) time.
If you only want the lower digits of the answer, then it is possible to calculate in sub-linear time using the matrix exponentiation method.
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