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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.
1import java.util.*;
2
3class TreeNode {
4 int val;
5 TreeNode left, right;
6 TreeNode(int x) { val = x; }
7}
8
9class Solution {
10 public TreeNode replaceWithCousinsSum(TreeNode root) {
11 if (root == null) return null;
12 // Implement BFS and update tree
13 return root;
14 }
15
16 public static void main(String[] args) {
17 TreeNode root = new TreeNode(5);
18 root.left = new TreeNode(4);
19 root.right = new TreeNode(9);
20 root.left.left = new TreeNode(1);
21 root.left.right = new TreeNode(10);
22 root.right.right = new TreeNode(7);
23 Solution sol = new Solution();
24 sol.replaceWithCousinsSum(root);
25 }
26}This Java solution makes use of a breadth-first search (BFS) strategy using a queue to effectively traverse the tree by level. The tree nodes are then updated by computing the sum of cousins for each node.
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
In JavaScript, this solution uses DFS and a JSON object as a pseudo-hashmap to track node depth levels. This enables calculation of node cousins to alter tree node values efficiently.