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Explain the time complexity of the following code snippet in regards of the Big-O notation:1. for (i=0; i<n; i+=4) {2. for (j=1; j<n; j*=2) {3. for (k=0; k<30; k++) {4. print(“Am I still not 30?!!”);5. }6. print(“Why, God, why? We had a Deal!”);7. for (m=n; m>0; m-=2) {8. print(“Could you BE more dramatic?”);9. }10. }11. }

Question

Explain the time complexity of the following code snippet in regards of the Big-O notation:1. for (i=0; i<n; i+=4) {2. for (j=1; j<n; j*=2) {3. for (k=0; k<30; k++) {4. print(“Am I still not 30?!!”);5. }6. print(“Why, God, why? We had a Deal!”);7. for (m=n; m>0; m-=2) {8. print(“Could you BE more dramatic?”);9. }10. }11. }

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Solution

The time complexity of the given code snippet can be calculated by analyzing each loop individually.

  1. The outermost loop (i=0; i<n; i+=4) runs n/4 times. So, its complexity is O(n).

  2. The second loop (j=1; j<n; j*=2) is a logarithmic loop and runs log(n) times. So, its complexity is O(log(n)).

  3. The third loop (k=0; k<30; k++) is a constant time loop and runs 30 times regardless of the value of n. So, its complexity is O(1).

  4. The innermost loop (m=n; m>0; m-=2) runs n/2 times. So, its complexity is O(n).

Now, combining all these, the total time complexity of the code is O(n) * O(log(n)) * O(1) * O(n) = O(n^2 * log(n)).

This means that the time complexity of the code increases quadratically with the size of the input and also depends on the logarithm of the input size.

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