Efficiency estimator

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Example output

To analyze the time complexity of the given function example_function(n), we’ll break it down into steps and consider the worst-case scenario.

Step 1: Analyzing the first loop

for i in range(n):
   print(i)

This loop iterates n times and performs a constant-time operation (print(i)) in each iteration. Therefore, the time complexity of this step is O(n).

Step 2: Analyzing the nested loops

for j in range(n):
    for k in range(n):
        print(j, k)

The outer loop iterates n times, and for each iteration, the inner loop also iterates n times. In each iteration of the inner loop, a constant-time operation (print(j, k)) is performed. Therefore, the time complexity of the nested loops is O(n * n) = O(n^2).

Step 3: Combining the time complexities
The function example_function(n) consists of two steps: the first loop and the nested loops. The overall time complexity is the sum of the time complexities of each step.

Time complexity of the first loop: O(n)
Time complexity of the nested loops: O(n^2)

To determine the overall time complexity, we consider the dominant term, which is the term with the highest order of growth. In this case, O(n^2) dominates O(n).

Therefore, the overall time complexity of the function example_function(n) is O(n^2).

In the worst-case scenario, when n is large, the nested loops will dominate the execution time, and the function will have a quadratic time complexity.

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