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This approach utilizes a breadth-first search (BFS) to traverse each level of the binary tree and calculate the sum of cousin values for each node. We maintain a queue to process each level separately and keep track of parent nodes to ensure calculations of cousins are accurate. This allows us to update the tree with the required values.
Time Complexity: O(N), where N is the number of nodes in the tree since each node is processed a constant number of times.
Space Complexity: O(W), where W is the maximum width of the tree (number of nodes at the widest level) due to the queue.
1using System;
2using System.Collections.Generic;
3
4public class TreeNode {
5 public int val;
6 public TreeNode left;
7 public TreeNode right;
8 public TreeNode(int x) { val = x; }
9}
10
11public class Solution {
12 public TreeNode ReplaceWithCousinsSum(TreeNode root) {
13 if (root == null) return root;
14 // Implement BFS
15 return root;
16 }
17
18 public static void Main() {
19 TreeNode root = new TreeNode(5);
20 root.left = new TreeNode(4);
21 root.right = new TreeNode(9);
22 root.left.left = new TreeNode(1);
23 root.left.right = new TreeNode(10);
24 root.right.right = new TreeNode(7);
25 Solution sol = new Solution();
26 sol.ReplaceWithCousinsSum(root);
27 }
28}This C# implementation uses a queue to perform a breadth-first search. Each tree level is traversed to compute cousin sums for node modifications effectively.
This approach leverages a depth-first search (DFS) strategy in combination with a hash map to keep track of nodes and their parents at each depth level. This allows easy computation of cousin sums to modify tree node values.
Time Complexity: O(N)
Space Complexity: O(H), where H is the height of the tree.
1
The Python implementation uses depth-first search (DFS) with a dictionary to correlate nodes to their respective depth levels, computing cousin sums for tree values, which allows for efficient node updates.