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The recursive approach naturally aligns with the definition of preorder traversal: visit the root first, then recursively traverse the left subtree, followed by the right subtree.
Time Complexity: O(N) where N is the number of nodes, as each node is visited once. Space Complexity: O(N) in the worst case due to recursion stack space.
1import java.util.ArrayList;
2import java.util.List;
3
4class TreeNode {
5 int val;
6 TreeNode left;
7 TreeNode right;
8 TreeNode(int x) { val = x; }
9}
10
11public class Solution {
12 public List<Integer> preorderTraversal(TreeNode root) {
13 List<Integer> res = new ArrayList<>();
14 preorder(root, res);
15 return res;
16 }
17
18 private void preorder(TreeNode node, List<Integer> res) {
19 if (node == null) return;
20 res.add(node.val);
21 preorder(node.left, res);
22 preorder(node.right, res);
23 }
24}The Java solution uses an ArrayList and a helper method to perform the preorder traversal. The helper method recursively visits nodes and adds their values to the list.
The iterative approach replaces the recursive call stack with an explicit stack. Nodes are processed in preorder, using a stack to maintain traversal state.
Time Complexity: O(N), since each node is visited once. Space Complexity: O(N), for the stack used to store nodes.
1#
In C, we use an array to simulate a stack, manually pushing and popping nodes while traversing the tree. Nodes are visited and added to the result list in prenode order.