ho lee

Greenhorn

Posts: 1

posted 3 years ago

Greetings,

Well first to help yourself out, you should really indent your code so that a) you can better understand it, and 2) to make it easier for other to help.

Now to your question……

Get a pad of paper and a pencil.

Now looking only on fib not the code in main.

Start with this….

So there you go, the magic of the recursive call, i.e.: the nominal cases.

fib(4) and above are for you.

-steve

Well first to help yourself out, you should really indent your code so that a) you can better understand it, and 2) to make it easier for other to help.

Now to your question……

Get a pad of paper and a pencil.

Now looking only on fib not the code in main.

Start with this….

So there you go, the magic of the recursive call, i.e.: the nominal cases.

fib(4) and above are for you.

-steve

Campbell Ritchie

Marshal

Posts: 56529

172

posted 3 years ago

Welcome to the Ranch

What have they told you about that recursive Fibonacci program? You realise that to work out fib(4) you call fib(2) and fib(3) and fib(3) calls fib(2), so you end up calling fib(2) twice. If you try fib(5), you call fib(3) twice and fib(2) thrice. It is a classic example of what can go wrong with recursion if you design it badly.

You can design Fibonacci programs where the number of recursive calls is proportional to the argument (so fib(12) would need 2 more calls than fib(10), but they are slightly more complicated. That is linear complexity

Kaldewaij shows a version of Fibonacci numbers where the number of calls is approximately proportional to the logarithm of

What you have there runs in exponential complexity, which you can verify by adding a counter to your fib method:

What have they told you about that recursive Fibonacci program? You realise that to work out fib(4) you call fib(2) and fib(3) and fib(3) calls fib(2), so you end up calling fib(2) twice. If you try fib(5), you call fib(3) twice and fib(2) thrice. It is a classic example of what can go wrong with recursion if you design it badly.

You can design Fibonacci programs where the number of recursive calls is proportional to the argument (so fib(12) would need 2 more calls than fib(10), but they are slightly more complicated. That is linear complexity

**O**(*n*).Kaldewaij shows a version of Fibonacci numbers where the number of calls is approximately proportional to the logarithm of

*n*:**O**(log*n*).What you have there runs in exponential complexity, which you can verify by adding a counter to your fib method:

**O**(1.618ⁿ) approximately. 1.618 is the golden ratio.