Why is the complexity of computing the Fibonacci series 2^n and not n^2?

Question

I am trying to find complexity of Fibonacci series using a recursion tree and concluded height of tree = O(n) worst case, cost of each level = cn,

Answer 1

t(n)=t(n-1)+t(n-2) which can be solved through tree method:

                                  t(n-1)  +  t(n-2)        2^1=2
                                   |         |  
                            t(n-2)+t(n-3)  t(n-3)+t(n-4)   2^2=4  
                                .               .          2^3=8
                                .               .           .
                                .               .           .

similarly for the last level . . 2^n
it will make total time complexity=>2+4+8+.....2^n after solving the above gp we will get time complexity as O(2^n)

Answer 2

I cannot resist the temptation of connecting a linear time iterative algorithm for Fib to the exponential time recursive one: if one reads Jon Bentley's wonderful little book on "Writing Efficient Algorithms" I believe it is a simple case of "caching": whenever Fib(k) is calculated, store it in array FibCached[k]. Whenever Fib(j) is called, first check if it is cached in FibCached[j]; if yes, return the value; if not use recursion. (Look at the tree of calls now ...)

Answer 3

The complexity of a naive recursive fibonacci is indeed 2ⁿ.

T(n) = T(n-1) + T(n-2) = T(n-2) + T(n-3) + T(n-3) + T(n-4) = 
= T(n-3) + T(n-4) + T(n-4) + T(n-5) + T(n-4) + T(n-5) + T(n-5) + T(n-6) = ...

In each step you call T twice, thus will provide eventual asymptotic barrier of:
T(n) = 2⋅2⋅...⋅2 = 2ⁿ

bonus: The best theoretical implementation to fibonacci is actually a close formula, using the golden ratio:

Fib(n) = (φⁿ – (–φ)⁻ⁿ)/sqrt(5) [where φ is the golden ratio]

(However, it suffers from precision errors in real life due to floating point arithmetics, which are not exact)