# An integer formula for Fibonacci numbers

This code, somewhat surprisingly, generates Fibonacci numbers.

``    def fib(n):         return (4 << n*(3+n)) // ((4 << 2*n) - (2 << n) - 1) & ((2 << n) - 1)``

In this blog post, I’ll explain where it comes from and how it works.

Before getting to explaining, I’ll give a whirlwind background overview of Fibonacci numbers and how to compute them. If you’re already a maths whiz, you can skip most of the introduction, quickly skim the section “Generating functions” and then read “An integer formula”.

## Overview

The Fibonacci numbers are a well-known sequence of numbers:

The

th number in the sequence is defined to be the sum of the previous two, or formally by this recurrence relation:

I’ve chosen to start the sequence at index 0 rather than the more usual 1.

### Computing Fibonacci numbers

There’s a few different reasonably well-known ways of computing the sequence. The obvious recursive implementation is slow:

``    def fib_recursive(n):         if n < 2: return 1         return fib_recursive(n - 1) + fib_recursive(n - 2)``

An iterative implementation works in

operations:

``    def fib_iter(n):         a, b = 1, 1         for _ in xrange(n):             a, b = a + b, a         return b``

And a slightly less well-known matrix power implementation works in

operations.

``    def fib_matpow(n):         m = numpy.matrix('1 1 ; 1 0') ** n         return m.item(0)``

The last method works by considering the `a` and `b` in `fib_iter` as sequences, and noting that

From which follows

and so if

then

(noting that unlike Python, matrix indexes are usually 1-based).

It’s

based on the assumption that numpy’s matrix power does something like exponentation by squaring.

Another method is to find a closed form for the solution of the recurrence relation. This leads to the real-valued formula:

whereand. The practical flaw in this method is that it requires arbitrary precision real-valued arithmetic, but it works for small

.

``    def fib_phi(n):         phi = (1 + math.sqrt(5)) / 2.0         psi = (1 - math.sqrt(5)) / 2.0         return int((phi ** (n+1) - psi ** (n+1)) / math.sqrt(5))``

### Generating Functions

A generating function for an arbitrary sequence

is the infinite sum. In the specific case of the Fibonacci numbers, that means. In words, it’s an infinite power series, with the coefficient ofbeing the

th Fibonacci number.

Now,

Multiplying by

and summing over all

, we get:

If we let

to be the generating function of, which is defined to be

then this equation can be simplified:

and simplifying,

We can solve this equation for

to get

It’s surprising that we’ve managed to find a small and simple formula which captures all of the Fibonacci numbers, but it’s not yet obvious how we can use it. We’ll get to that in the next section.

A technical aside is that we’re going to want to evaluate

at some values of, and we’d like the power series to converge. We know the Fibonacci numbers grow likeand that geometric seriesconverge if, so we know that if

then the power series converges.

### An integer formula

Now we’re ready to start understanding the Python code.

To get the intuition behind the formula, we’ll evaluate the generating function

at

.

Interestingly, we can see some Fibonacci numbers in this decimal expansion:

. That seems magical and surprising, but it’s because

.

In this example, the Fibonacci numbers are spaced out at multiples of

, which means once they start getting bigger that 1000 they’ll start interfering with their neighbours. We can see that starting at 988 in the computation of

above: the correct Fibonacci number is 987, but there’s a 1 overflowed from the next number in the sequence causing an off-by-one error. This breaks the pattern from then on.

But, for any value of

, we can arrange for the negative power of 10 to be large enough that overflows don’t disturb theth Fibonacci. For now, we’ll just assume that there’s somewhich makes

sufficient, and we’ll come back to picking it later.

Also, since we’d like to use integer maths (because it’s easier to code), let’s multiply by

, which also puts the

th Fibonacci number just to the left of the decimal point, and simplify the equation.

If we take this result modulo

, we’ll get theth Fibonacci number (again, assuming we’ve picked

large enough).

Before proceeding, let’s switch to base 2 rather than base 10, which changes nothing but will make it easier to program.

Now all that’s left is to pick a value of

large enough so that. We know that the Fibonacci numbers grow like, and, so

is safe.

So! Putting that together:

If we use left-shift notation that’s available in python, where

then we can write this as:

Observing that

can be expressed as the bitwise and ( `&` ) of

, we reconstruct our original Python program:

``    def fib(n):         return (4 << n*(3+n)) // ((4 << 2*n) - (2 << n) - 1) & ((2 << n) - 1)``

Although it’s curious to find a non-iterative, closed-form solution, this isn’t a practical method at all. We’re doing integer arithmetic with integers of size

bits, and in fact, before performing the final bit-wise and, we’ve got an integer that is the first

Fibonacci numbers concatenated together!