How to calculate time complexity of function f?
void f(int n)
{
if (n <= 1)
return;
g(n, n / 3);
}
void g(int n, int m)
{
int i = 1;
while (m < n) {
m += i;
i++;
}
f(n / 2);
}
The answer is sqrt(n), but I don't see how...
Thanks
First, note that the the program can be translated now to a single function program, by inlining g(n,m) in f():
void f(int n)
{
if (n <= 1)
return;
m = n/3;
while (m < n) {
m += i;
i++;
}
f(n / 2);
}
The inner loop runs in O(sqrt(n)) iteration, because it starts from n/3, ends with n, and is increased by 1,2,3,... so if we sum it we get:
n/3 + (1 + 2 + ... + i) >= n
We need to solve the above equation to find the final value of i, and we get:
1 + 2 + ... + i >= 2n/3
From sum of arithmetic progression:
i(i+1)/2 >= 2n/3
From the above inequality, we can conclude that indeed i is in O(sqrt(n)).
So, we can denote the complexity as:
T(n) = T(n/2) + O(sqrt(n))
^ ^
recursive step syntatic sugar for some function
which is in O(sqrt(n)).
Now, we can see that:
T(n) = T(n/2) + sqrt(n) = T(n/4) + sqrt(n/2) + sqrt(n) = ... =
= sqrt(1) + ... + sqrt(n/2) + sqrt(n)
And the above sum is in O(sqrt(n))
Let Fn be the time complexity of f(n) and Gn,m be the time complexity of g(n,m).
Gn,m = sqrt(n-m) + Fn / 2
Fn = Gn,n/3 = sqrt(n-n/3) + Fn / 2 = C sqrt(n) + Fn/2
So the answer is sqrt(n).
Related
Trying to analyze the below code snippet.
For the below code can the time complexity be Big O(log n)?. I am new to asymptotic analysis. In the tutorial it says its O( root n).
int p = 0;
for(int i =1;p<=n;i++){
p = p +i;
}
,,,
Variable p is going to take the successive values 1, 1+2, 1+2+3, etc.
This sequence is called the sequence of triangular numbers; you can read more about it on Wikipedia or OEIS.
One thing to be noted is the formula:
1 + 2 + ... + i = i*(i+1)/2
Hence your code could be rewritten under the somewhat equivalent form:
int p = 0;
for (int i = 1; p <= n; i++)
{
p = i * (i + 1) / 2;
}
Or, getting rid of p entirely:
for (int i = 1; (i - 1) * i / 2 <= n; i++)
{
}
Hence your code runs while (i-1)*i <= 2n. You can make the approximation (i-1)*i ≈ i^2 to see that the loop runs for about sqrt(2n) operations.
If you are not satisfied with this approximation, you can solve for i the quadratic equation:
i^2 - i - 2n == 0
You will find that the loop runs while:
i <= (1 + sqrt(1 + 8n)) / 2 == 0.5 + sqrt(2n + 0.125)
I'm working on an algorithm on getting the f(n) of a set 'S'. It is defined as
example: f(4) = S(4) + f(3) + f(2) + f(1) + f(0)
this is my pseudocode
func solve(int k, int[] s, int[] memo)
{
if(k==0) //base case
return s[0]
if(memo[k] == -1)
{
var nTemp = 0
for(int i=0; i < k; i++)
nTemp = (nTemp + solve(i, s, memo))
memo[k] = (nTemp + s[k])
}
return memo[k]
}
I'm not sure about it's time complexity though, I think it is O(n) but i'm not sure.
Let's consider how many operations solve has to perform starting from k = 1:
k = 1: memo[1] = s[0] + s[1] -> 1 sum
k = 2: memo[2] = memo[0] + memo[1] + s[2] -> 2 sums
...
k = m: memo[s] = memo[0] + memo[1] + ... + s[m] -> m sums
So actually, the number of operations is 1 + 2 + .. + k = (k + 1)*k/2 ~ k*k. Hence, the total time complexity is O(k^2).
However, if values up to k-1 are already cached in memo and we need to calculate f(k) then the time complexity is O(k) since it's only about summing up memo[i] such that i<k.
I need to find the complexity of this recursive algorithms, so, i have 3 recursive algorithms and simply just want to know the Big O Notation for them. I think i have the solution for 2 of these algorithms, just wanna check with the community.
int f1(int n)
{
if ( n<= 1)
return (1);
else
return (n *f1(n-1))
}
I think the solution of this is O(n).
int f2 (int n)
{
if(n<=1)
return(1);
else
return(n*f2(n / 2))
}
I think the solution of this is O(Log 2 (n))
int f3
{
int x, i;
if( n <= 1)
return 1;
else
{
x = f3 (n / 2);
for( i = 1 ; i <= n ; i++)
x++;
return x;
}
}
What is the complexity of this recursive algorithm, i don't have the solution for this algorithm, Can you help me?
Your first two answer is correct.
Let's do analysis for your third problem,
for each times, n is divides by 2 and we need to add x for n times,
so the complexity will be
1*n+1*n/2+1*n/4+.....+1=n(1+1/2+1/4+...)=O(n)
#codecrazers answer already covers up how to calculate the complexity step-by-step. But in general the Master-Theorem makes the problem a lot simpler.
To start, lets transform this code
int f3 (int n)
{
int x, i;
if( n <= 1)
return 1;
else
{
x = f3 (n / 2);
for( i = 1 ; i <= n ; i++)
x++;
return x;
}
}
Into a recurrence:
int f(int n)
{
if( n <= 1)
1
else
f(n / 2) + θ(n)
}
So
T(n) = T(n / 2) + θ(n)
T(n <= 1) = 1
Which is case 3, thus yielding
T(n) = θ(n)
I've trying to calculate complexity of an algorithm, but I'm not sure how to do that. I know how to solve simple algorithms, but I'm struggling with recursion.
There is the code of recursion:
static int F(int m, int n)
{
if (n == 0)
return m;
if (m == 0 && n > 0)
return n;
else
return Math.Min((1 + F(m - 1, n)), Math.Min((1 + F(m, n - 1)), (D(m - 1, n - 1) + F(m - 1, n - 1))));
}
Can someone explain me or help me to calculate this function? I've tried googling it but I can find only simple examples.(maybe mine code is also simple?)
Thank you!
I don't know what the D function is in your first piece of code. I'll consider it as a constant function.
The first piece of your code is equivalent to the following one.
static int F(int m, int n)
{
if (n == 0 || m == 0)
return n + m;
else
return Math.Min((1 + F(m - 1, n)), Math.Min((1 + F(m, n - 1)), (D(m - 1, n - 1) + F(m - 1, n - 1))));
}
It's a little difficult to calculate the time complexity of a recursive function with two parameters, but we can estimate it roughly. We have the following equation.
T(n, m) = T(n-1, m) + T(n, m-1) + T(n-1, m-1)
We can discover that the equation is quite similar to the recursive equation of binomial coefficient, but with even larger result. This tells us that the time complexity of the algorithm is an exponential function, which is quite slow.
But actually, you can use some tricks to reduce its time complexity to O(mn).
bool calculated[MAX_M][MAX_N];
int answer[MAX_M][MAX_N]
static int F(int m, int n)
{
if (n == 0 || m == 0)
return n + m;
else
{
if (calculated[m][n] == false)
{
answer[m][n] = Math.Min((1 + F(m - 1, n)), Math.Min((1 + F(m, n - 1)), (D(m - 1, n - 1) + F(m - 1, n - 1))));
calculated[m][n] = true;
}
return answer[m][n];
}
}
I can't quite understand what the second piece of code is going to do as there are lots of functions unprovided in your code. Maybe you can explain that a bit?
Why is the worst time complexity of the following code is O(N)?
/*
* V is sorted
* V.size() = N
* The function is initially called as searchNumOccurrence(V, k, 0, N-1)
*/
int searchNumOccurrence(vector<int> &V, int k, int start, int end) {
if (start > end) return 0;
int mid = (start + end) / 2;
if (V[mid] < k) return searchNumOccurrence(V, k, mid + 1, end);
if (V[mid] > k) return searchNumOccurrence(V, k, start, mid - 1);
return searchNumOccurrence(V, k, start, mid - 1) + 1 + searchNumOccurrence(V, k, mid + 1, end);
}
What's the worst case? the worst case will be that all element are the same and equals to k. Then you have to at least read all elements, which is N. Since most function calls increase the output by 1, there are about N function calls (some returns 0, but they don't spawn new calls). Therefore, the worst time complexity is O(N).
Yes, in the worst case if all the numbers in the array are equal to k, then in this worst case, the recurrence relation shall be:
T(n) = 2*T(n/2)
This translates into O(n).
The last case -
return searchNumOccurrence(V, k, start, mid - 1) + 1 + searchNumOccurrence(V, k, mid + 1, end);
is the bottleneck step.
Assuming all the values in the array are the same, we get the following relation :
T(N) = 2 * T(N/2) + constant
= 4 * T(N/4) + constant ( 2 * constant = another constant )
= 8 * T(N/8) + constant
.....
= N * T(N/N) + constant
= N + constant
= O(N)