Write a function to calculate the nth Fibonacci Sequence is a common interview question and often the solution is something like

int Fib(int n)

{

if(n < 1) return 1;

return Fib(n-1) + Fib(n-2);

}

The next question is to ask **for n=100, how many items will be on the stack**. The answer is not 100 but actually horrible! It is closer to 2^100.

take the first call – we start a stack on Fib(99) and one on Fib(98). There is nothing to allow Fib(99) to *borrow *the result of Fib(98). So one step is two stack items to recurse. Each subsequent call changes one stack item into 2 items. For example

- 2 –> call [Fib(1), Fib(0)]
- 3 –> calls [ Fib(2)->[Fib(1), Fib(0)], Fib(1) –> Fib(0) ]
- 4 –> calls [ Fib(3)->[[[ Fib(2)->[Fib(1), Fib(0)], Fib(1) –> Fib(0) ]], Fib(2)->[Fib(1), Fib(0)], Fib(1) –> Fib(0) ]

Missing this issue is very often seen with by-rote developers (who are excellent for some tasks).

A better solution is to cache the values as each one is computed – effectively creating a lookup table. You are trading stack space for memory space.

Placing constraints on memory and stack space may force the developer to do some actual thinking. A solution that conforms to this is shown below

private static long Fibonacci(int n) {

long a = 0L;

long b = 1L;

for (int i = 31; i >= 0; i—) //31 is arbitrary, see below

{

long d = a * (b * 2 - a);

long e = a * a + b * b;

a = d;

b = e;

if ((((uint)n >> i) & 1) != 0) {

long c = a + b;

a = b;

b = c;

}

}

return a;

}

The output of the above shows what is happening and suggests that the ”31” taking the log base 2 of N can likely be done to improve efficiency

for 32:

for 65

for 129

What is the difference in performance for the naive vs the latter?

I actually did not wait until the naive solution finished… I aborted at **4 minutes**

The new improved version was 85 ms, over a 3000 fold improvement.

# Take Away

This question:

- Identify if a person knows what recursion is and can code it.
- Identify if he understands what the consequence of recursion is and how it will be executed(i.e. think about what the code does)
- Most recursion questions are atomic (i.e. factorial) and not composite (recursion that is not simple)
- Is able to do analysis of a simple mathematical issue and generate a performing solution.