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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.
1#include <iostream>
2using namespace std;
3
4void solveProblem(int n) {
5 for (int i = 0; i < n; ++i) {
6 // Implementation of the solution logic here
7 cout << i << " ";
8 }
9}
10
11int main() {
12 int n = 10;
13 solveProblem(n);
14 return 0;
15}In C++, the logic is similar to C, using std::cout for output and looping through each element.
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.