Skip to content

2646. Kth Largest Sum In A Binary Tree

Difficulty: Medium

LeetCode Problem View on GitHub

2646. Kth Largest Sum in a Binary Tree

Medium

You are given the root of a binary tree and a positive integer k.

The level sum in the tree is the sum of the values of the nodes that are on the same level.

Return the kth largest level sum in the tree (not necessarily distinct). If there are fewer than k levels in the tree, return -1.

Note that two nodes are on the same level if they have the same distance from the root.

 

Example 1:

Input: root = [5,8,9,2,1,3,7,4,6], k = 2
Output: 13
Explanation: The level sums are the following:
- Level 1: 5.
- Level 2: 8 + 9 = 17.
- Level 3: 2 + 1 + 3 + 7 = 13.
- Level 4: 4 + 6 = 10.
The 2nd largest level sum is 13.

Example 2:

Input: root = [1,2,null,3], k = 1
Output: 3
Explanation: The largest level sum is 3.

 

Constraints:

  • The number of nodes in the tree is n.
  • 2 <= n <= 105
  • 1 <= Node.val <= 106
  • 1 <= k <= n

Solution

/**
 * Definition for a binary tree node.
 * public class TreeNode {
 *     int val;
 *     TreeNode left;
 *     TreeNode right;
 *     TreeNode() {}
 *     TreeNode(int val) { this.val = val; }
 *     TreeNode(int val, TreeNode left, TreeNode right) {
 *         this.val = val;
 *         this.left = left;
 *         this.right = right;
 *     }
 * }
 */
class Solution {
    public long kthLargestLevelSum(TreeNode root, int k) {
        ArrayList<Long> res = new ArrayList<>();
        Queue<TreeNode> q = new LinkedList<>();
        q.offer(root);
        while (q.size() > 0) {
            int len = q.size();
            long sum = 0;
            for (int i = 0; i < len; i++) {
                if (q.peek().left != null) q.offer(q.peek().left);
                if (q.peek().right != null) q.offer(q.peek().right);
                sum += q.poll().val;
            }
            res.add(sum);
        }
        Collections.sort(res, Collections.reverseOrder());
        if (res.size() < k) return -1;
        return res.get(k - 1);
    }
}

Complexity Analysis

  • Time Complexity: O(?)
  • Space Complexity: O(?)

Approach

Detailed explanation of the approach will be added here