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The time complexity of the following C function is (assume $n > 0$)

int recursive (int n) {
if(n == 1)
return (1);
else
return (recursive (n-1) + recursive (n-1));
}
1. $O(n)$
2. $O(n \log n)$
3. $O(n^2)$
4. $O(2^n)$
edited | 2.9k views

Option D

int recursive (int n) {
if(n == 1)                      // takes constant time say 'A' time
return (1);                 // takes constant time say 'A' time
else
return (recursive (n-1) + recursive (n-1)); // takes T(n-1) + T(n-1)  time
}

$T(n) = 2T(n-1) +a$   is the recurrence equation found from the pseudo code .

Solving the Recurrence Equation  By Back Substitution Method

$T (n) = 2 T ( n-1 ) +a$ ---------( equation 1 )

$T(n-1) = 2T ( n-2)+a$

$T(n-2) = 2T ( n-3)+a$

We can re write Equation 1 as

\begin{align*}T(n) &= 2 [2T ( n-2)+a ] +a = 4T(n-2)+ 3a \\ &= 2[2T ( n-3)+a] + 3a = 8T (n-3) + 7a \\&\dots \\&= 2^kT(n-k) + (2^{k}-1) a \end{align*}     ------ (Equation 2)

On Substituting Limiting Condition

$T(1) = 1$  implies  $n-k = 1 \\ \implies k= n-1$

Therefore Equation 2 becomes

$2^{ n-1} +(2^{n-1}-1)a = O(2^n)$

edited
You explain well - all points included :)

HTML spacing for alignment is bad- even if we align spaces properly when the screen resolution changes it becomes bad. Latex is better :)
Thank you sir :) . I will try to improve next time.
It will be (2^k)-1
Its similar to tower of hanoi problem

$$T(n)=2T(n-1) + 1$$
T(1)=1

T(2)=2.1 + 1 =3

T(3)=2.3 +1 =7

T(4)=2.7 +1 =15 .... .....

T(n)=2.T(n-1)+ 1

we can see that its a pattern getting formed which is $t(n)=2^n-1$ so it is d $O(2^n)$
@Bhagirathi: why this 1 after 2T(n-1)

@Gate Mm:

T(n) = 2T(n-1)+1. Here +1 is for the base condition if(n==1) return 1. It has the constant time complexity of 1.

No it's not for that. 1 in the recursive equation signifies that some work has been done to divide a problem into two subproblems.

Base condition if(n==1) return 1 is used at the end.
@amitatgateoverflow is correct.

1 or some constant term is there to denote the amount of work to be done to divide the original problem and then combine the solutions to those subproblems to get the final answer.

The recurrence relation from the code is :

T(n) = 2T(n-1) + 1

The above recurrence can be solved easily with the help of Subtract and conquer master's theorem.

Here a=2, b=1 d=0

Since a>1, the third case applies

O(n0 . 2n/1) = O(2n) Option (d)

Another way to visualize this problem.

F(2) = 2 ??? how ??
@Puja Mishra You can find that easily by putting $n = 2$ in the code. The $return$ part in the $else$ block will return $recursive(1) + recursive(1)$.