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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.
1public class Solution {
2 public static void solveProblem(int n) {
3 for (int i = 0; i < n; i++) {
4 // Solution logic here
5 System.out.print(i + " ");
6 }
7 }
8
9 public static void main(String[] args) {
10 int n = 10;
11 solveProblem(n);
12 }
13}The Java implementation uses a simple loop with System.out.print for printing output. The iterative approach is simple and efficient.
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.
1using System;
2
class Solution {
public static void SolveProblem(int n, int i) {
if (i >= n) return;
// Solution logic here
Console.Write(i + " ");
SolveProblem(n, i + 1);
}
static void Main(string[] args) {
int n = 10;
SolveProblem(n, 0);
}
}The C# recursive method follows the same template, recursively executing until the allowed depth, demonstrating a clear call stack management.