# Different ways to calculate fibonacci numbers

NOTE: All code below are written in a dialect of Scheme called Racket.

## Naive approach

Problem: exponential complexity, even can’t calculate (fibonacci 1000).

## Improved approach

This version is not only faster, but also takes constant space, as the tail recursion can be optimized by compiler.

Problem: if (fibonacci 1000) is called twice, it performs the same computation twice!

## Memoization

Idea: Use a table to remember computation resutls.

## Even faster

Idea: A faster approach to compute $$a^{10}$$ is to compute $$(a \times (a^2)^2)^2$$, which only requires 4 steps of computation. If the power n is large, it can reduce the complexity logarithmically.

In the fibonacci-iter above, the operation a’ = b and b’ = a + b is performed n times, which is equivalent to multiply the matrix $$\bigl(\begin{smallmatrix} 0 & 1 \\ 1 & 1\end{smallmatrix}\bigr)$$ n times. This fact allows us to perform the same optimization as in computing exponentiation.

You can verify that $$\bigl(\begin{smallmatrix} 0 \\ 1\end{smallmatrix}\bigr) \times \bigl(\begin{smallmatrix} 0 & 1 \\ 1 & 1\end{smallmatrix}\bigr)^n$$ computes the n-th and (n+1)-th fibonacci numbers. The multiplier vector $$\bigl(\begin{smallmatrix} 0 \\ 1\end{smallmatrix}\bigr)$$ just selects the element c and d from the result matrix $$\bigl(\begin{smallmatrix} a & b \\ c & d\end{smallmatrix}\bigr)$$, so it can be omitted, as whenever we know the matrix, we know the result.

Another useful fact is that in the result matrix $$\bigl(\begin{smallmatrix} a & b \\ c & d\end{smallmatrix}\bigr)$$ we have b = c and d = a + b. So the matrix can be written as $$\bigl(\begin{smallmatrix} a & b \\ b & a + b\end{smallmatrix}\bigr)$$.

Also note that according to matrix multiplication, following formula holds:

$$\bigl(\begin{smallmatrix} a & b \\ b & a + b\end{smallmatrix}\bigr)^2 = \bigl(\begin{smallmatrix} a' & b' \\ b' & a' + b'\end{smallmatrix}\bigr)$$ where $$a' = a^2 + b^2$$ and $$b' = 2ab + b^2$$.

From the mathematical facts above, the implementation comes naturally.

The version above works, but we can do better to use tail recursion.

The improved version utilizes another mathematical fact:

$$\bigl(\begin{smallmatrix} a & b \\ b & a + b\end{smallmatrix}\bigr) \times \bigl(\begin{smallmatrix} p & q \\ q & p + q\end{smallmatrix}\bigr) = \bigl(\begin{smallmatrix} a' & b' \\ b' & a' + b'\end{smallmatrix}\bigr)$$ where $$a' = ap + bq$$ and $$b' = aq + bq + bp$$.

It’s also helpful to know that for any integer n and matrix X:

$$X^n = X^{b_0} \times (X^2)^{b_1} \times (X^4)^{b_2} \times (X^8)^{b_3} \cdots \times (X^{2^t})^{b_t}$$ where $$b_i$$ is either 0 or 1.

The formula above means that $$b_t \cdots b_2b_1b_0$$ is the binary representation of n. The improved version uses p and q to accumulate the result of the first i terms, a and b remembers $$X^{2^i}$$. If n can be divided by 2 for i times, it means the i-th position of n(i.e. $$b_i$$) is 0, then i can be increased, accumulator unchanged. Otherwise, it means the i-th position of n(i.e. $$b_i$$) is 1, then the accumulator p and q is updated, and i will be increased in the next recursion call as we’ve changed the last bit in $$b_t \cdots b_{i+1}b_i$$ to 0 through n - 1. The process stops if n is equal to zero, which means for every i bigger than its current value, $$b_i$$ is zero.

The complexity of this fast method is $$\Theta(log(n))$$, compared to $$\Theta(n)$$ of the second approach above.

## Reference

• SCIP section 1.2 and 3.3