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This approach involves solving the problem using an iterative method, where we use loops to perform the necessary calculations. This can be more efficient in terms of space complexity, especially if recursion would lead to excessive function call overhead.
Time Complexity: O(n), where n is the number of elements.
Space Complexity: O(1) since we are not using any extra space proportional to the input size.
1def solve_problem(n):
2 for i in range(n):
3 # Solution logic here
4 print(i, end=' ')
5
6n = 10
7solve_problem(n)Python's range function is used to iterate through numbers, printing each with a simple print statement, achieving a straightforward iterative solution.
This approach explores solving the problem through recursion, which can offer simplicity and expressiveness. However, care must be taken with recursion depth to avoid stack overflow.
Time Complexity: O(n)
Space Complexity: O(n) due to the call stack.
1def
Python's recursive implementation uses a default parameter `i` to control recursive depth, making calls until `i` equals `n` and printing during each call.