Determining complexity for recursive functions Big O notation

Question

I have a Computer Science Midterm tomorrow and I need help determining the complexity of these recursive functions  I know how to solve simple cases  but I am still trying to learn how to solve these harder cases  These were just a few of the example problems that I could not figure out  Any help would be much appreciated and would greatly help in my studies  Thank you      int recursiveFun1 int n        if  n  lt   0          return 1      else         return 1   recursiveFun1 n-1      int recursiveFun2 int n        if  n  lt   0          return 1      else         return 1   recursiveFun2 n-5      int recursiveFun3 int n        if  n  lt   0          return 1      else         return 1   recursiveFun3 n 5      void recursiveFun4 int n  int m  int o        if  n  lt   0                printf   d   d n  m  o             else               recursiveFun4 n-1  m 1  o           recursiveFun4 n-1  m  o 1            int recursiveFun5 int n        for  i   0  i  lt  n  i    2               do something            if  n  lt   0          return 1      else         return 1   recursiveFun5 n-5

User · Answer

One of the best ways I find for approximating the complexity of the recursive algorithm is drawing the recursion tree. Once you have the recursive tree:

Complexity = length of tree from root node to leaf node * number of leaf nodes

The first function will have length of n and number of leaf node 1 so complexity will be n*1 = n
The second function will have the length of n/5 and number of leaf nodes again 1 so complexity will be n/5 * 1 = n/5. It should be approximated to n
For the third function, since n is being divided by 5 on every recursive call, length of recursive tree will be log(n)(base 5), and number of leaf nodes again 1 so complexity will be log(n)(base 5) * 1 = log(n)(base 5)
For the fourth function since every node will have two child nodes, the number of leaf nodes will be equal to (2^n) and length of the recursive tree will be n so complexity will be (2^n) * n. But since n is insignificant in front of (2^n), it can be ignored and complexity can be only said to be (2^n).
For the fifth function, there are two elements introducing the complexity. Complexity introduced by recursive nature of function and complexity introduced by for loop in each function. Doing the above calculation, the complexity introduced by recursive nature of function will be ~ n and complexity due to for loop n. Total complexity will be n*n.

Note: This is a quick and dirty way of calculating complexity(nothing official!). Would love to hear feedback on this. Thanks.

User · Answer

The time complexity  in Big O notation  for each function   int recursiveFun1 int n        if  n  lt   0          return 1      else         return 1   recursiveFun1 n-1      This function is being called recursively n times before reaching the base case so its O n   often called linear   int recursiveFun2 int n        if  n  lt   0          return 1      else         return 1   recursiveFun2 n-5      This function is called n-5 for each time  so we deduct five from n before calling the function  but n-5 is also O n    Actually called order of n 5 times  And  O n 5    O n      int recursiveFun3 int n        if  n  lt   0          return 1      else         return 1   recursiveFun3 n 5      This function is log n  base 5  for every time we divide by 5 before calling the function so its O log n   base 5   often called logarithmic and most often Big O notation and complexity analysis uses base 2   void recursiveFun4 int n  int m  int o        if  n  lt   0                printf  quot  d   d n quot  m  o             else               recursiveFun4 n-1  m 1  o           recursiveFun4 n-1  m  o 1            Here  it s O 2 n   or exponential  since each function call calls itself twice unless it has been recursed n times    int recursiveFun5 int n        for  i   0  i  lt  n  i    2               do something            if  n  lt   0          return 1      else         return 1   recursiveFun5 n-5      And here the for loop takes n 2 since we re increasing by 2  and the recursion takes n 5 and since the for loop is called recursively  therefore  the time complexity is in  n 5     n 2    n 2 10  due to Asymptotic behavior and worst-case scenario considerations or the upper bound that big O is striving for  we are only interested in the largest term so O n 2    Good luck on your midterms

User · Answer

For the case where n  lt   0  T n    O 1   Therefore  the time complexity will depend on when n  gt   0   We will consider the case n  gt   0 in the part below   1   T n    a   T n - 1    where a is some constant   By induction   T n    n   a   T 0    n   a   b   O n    where a  b are some constant   2   T n    a   T n - 5    where a is some constant  By induction   T n    ceil n   5    a   T k    ceil n   5    a   b   O n    where a  b are some constant and k  lt   0  3   T n    a   T n   5    where a is some constant  By induction   T n    a   log5 n    T 0    a   log5 n    b   O log n    where a  b are some constant  4   T n    a   2   T n - 1    where a is some constant  By induction   T n    a   2a   4a         2  n-1    a   T 0    2 n         a   2 n - a   b   2 n         a   b    2 n - a        O 2   n    where a  b are some constant   5   T n    n   2   T n - 5    where n is some constant  Rewrite n   5q   r where q and r are integer and r   0  1  2  3  4  T 5q   r     5q   r    2   T 5    q - 1    r    We have q    n - r    5  and since r  lt  5  we can consider it a constant  so q   O n   By induction   T n    T 5q   r          5q   r    2    5    q - 1    r    2         r   2    T r         5   2    q    q - 1          1     1   2    q   1    r   T r         5   4    q   1    q   1   2    q   1    r   T r         5   4   q 2   5   4   q   1   2   q   r   1   2   r   T r    Since r  lt  4  we can find some constant b so that b  gt   T r   T n    T 5q   r         5   2   q 2    5   4   1   2   r    q   1   2   r   b        5   2   O n   2     5   4   1   2   r    O n    1   2   r   b        O n   2

User · Answer

The key here is to visualise the call tree  Once done that  the complexity is   nodes of the call tree   complexity of other code in the function   the latter term can be computed the same way we do for a normal iterative function   Instead  the total nodes of a complete tree are computed as                    C L - 1                   -------    when C gt 1                    C - 1                    of nodes                                                           L          when C 1   Where C is number of children of each node and L is the number of levels of the tree  root included    It is easy to visualise the tree  Start from the first call  root node  then draw a number of children same as the number of recursive calls in the function  It is also useful to write the parameter passed to the sub-call as  value of the node    So  in the examples above    the call tree here is C   1  L   n 1  Complexity of the rest of function is O 1   Therefore total complexity is L   O 1     n 1    O 1    O n     n     level 1 n-1   level 2 n-2   level 3 n-3   level 4       n levels - gt  L   n     call tree here is C   1  L   n 5  Complexity of the rest of function is O 1   Therefore total complexity is L   O 1     n 5    O 1    O n     n n-5 n-10 n-15       n 5 levels - gt  L   n 5     call tree here is C   1  L   log n   Complexity of the rest of function is O 1   Therefore total complexity is L   O 1    log5 n    O 1    O log n      n n 5 n 5 2 n 5 3       log5 n  levels - gt  L   log5 n      call tree here is C   2  L   n  Complexity of the rest of function is O 1    This time we use the full formula for the number of nodes in the call tree because C   1  Therefore total complexity is  C L-1   C-1    O 1     2 n - 1    O 1    O 2 n                      n                   level 1       n-1             n-1          level 2   n-2     n-2     n-2     n-2          n-3 n-3 n-3 n-3 n-3 n-3 n-3 n-3                                                n levels - gt  L   n     call tree here is C   1  L   n 5  Complexity of the rest of function is O n   Therefore total complexity is L   O 1     n 5    O n    O n 2     n n-5 n-10 n-15       n 5 levels - gt  L   n 5

User · Answer

I see that for the accepted answer  recursivefn5   some folks are having issues with the explanation  so I d try to clarify to the best of my knowledge   The for loop runs for n 2 times because at each iteration  we are increasing i  the counter  by a factor of 2  so say n   10  the for loop will run 10 2   5 times i e when i is 0 2 4 6 and 8 respectively   In the same regard  the recursive call is reduced by a factor of 5 for every time it is called i e it runs for n 5 times  Again assume n   10  the recursive call runs for 10 5   2 times i e when n is 10 and 5 and then it hits the base case and terminates   Calculating the total run time  the for loop runs n 2 times for every time we call the recursive function  since the recursive fxn runs n 5 times  in 2 above  the for loop runs for  n 2     n 5     n 2  10 times  which translates to an overall Big O runtime of O n 2  - ignoring the constant  1 10

User · Answer

We can prove it mathematically which is something I was missing in the above answers   It can dramatically help you understand how to calculate any method  I recommend reading it from top to bottom to fully understand how to do it    T n    T n-1    1 It means that the time it takes for the method to finish is equal to the same method but with n-1 which is T n-1  and we now add   1 because it s the time it takes for the general operations to be completed  except T n-1    Now  we are going to find T n-1  as follow  T n-1    T n-1-1    1  It looks like we can now form a function that can give us some sort of repetition so we can fully understand  We will place the right side of T n-1        instead of T n-1  inside the method T n        which will give us  T n    T n-1-1    1   1 which is T n    T n-2    2 or in other words we need to find our missing k  T n    T n-k    k  The next step is to take n-k and claim that n-k   1 because at the end of the recursion it will take exactly O 1  when n lt  0  From this simple equation we now know that k   n - 1  Let s place k in our final method  T n    T n-k    k which will give us  T n    1   n - 1 which is exactly n or O n   Is the same as 1  You can test it your self and see that you get O n   T n    T n 5    1 as before  the time for this method to finish equals to the time the same method but with n 5 which is why it is bounded to T n 5   Let s find T n 5  like in 1  T n 5    T n 5 5    1 which is T n 5    T n 5 2    1  Let s place T n 5  inside T n  for the final calculation  T n    T n 5 k    k  Again as before  n 5 k   1 which is n   5 k which is exactly as asking what in power of 5  will give us n  the answer is log5n   k  log of base 5   Let s place our findings in T n    T n 5 k    k as follow  T n    1   logn which is O logn  T n    2T n-1    1 what we have here is basically the same as before but this time we are invoking the method recursively 2 times thus we multiple it by 2  Let s find T n-1    2T n-1-1    1 which is T n-1    2T n-2    1  Our next place as before  let s place our finding  T n    2 2T n-2     1   1 which is T n    2 2T n-2    2 that gives us T n    2 kT n-k    k  Let s find k by claiming that n-k   1 which is k   n - 1  Let s place k as follow  T n    2  n-1    n - 1 which is roughly O 2 n  T n    T n-5    n   1 It s almost the same as 4 but now we add n because we have one for loop  Let s find T n-5    T n-5-5    n   1 which is T n-5    T n - 2 5    n   1  Let s place it  T n    T n-2 5    n   n   1   1  which is T n    T n-2 5    2n   2  and for the k  T n    T n-k 5    kn   k  again  n-5k   1 which is n   5k   1 that is roughly n   k  This will give us  T n    T 0    n 2   n which is roughly O n 2     I now recommend reading the rest of the answers which now  will give you a better perspective  Good luck winning those big O s

[recursion] Determining complexity for recursive functions (Big O notation)

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