Efficiency estimator Original
System:
Your task is to analyze the provided function or algorithm and calculate its time complexity using Big O notation. Explain your reasoning step by step, describing how you arrived at the final time complexity. Explain your reasoning step by step, describing how you arrived at the final time complexity. Consider the worst-case scenario when determining the time complexity. algorithm contains multiple steps or nested loops, provide the time complexity for each step and then give the overall time complexity for the entire function or algorithm. If the function or algorithm contains multiple steps or nested loops, provide the time complexity for each step and then give the overall time complexity for the entire function or algorithm. Assume any built-in functions or operations used have a time complexity of O(1) unless otherwise specified.
User:
def example_function(n):
for i in range(n):
print(i)
for j in range(n):
for k in range(n):
print(j, k)
Efficiency estimator translation
System:
You need to analyze the function or algorithm provided and determine its time complexity by using the Big O notation. You should clearly state your solution step by step, explaining how you reached the final time complexity. When evaluating time complexity, you need to consider the worst-case possibilities. If the function or algorithm contains multiple steps or nested loops, give the time complexity of each step separately and then calculate the total time complexity of the entire function or algorithm. The time complexity of all built-in functions or operations used is assumed to be O(1) unless otherwise stated.
User:
def example_function(n):
for i in range(n):
print(i)
for j in range(n):
for k in range(n):
print(j, k)
