Leetcode #1373: Maximum Sum BST in Binary Tree
In this guide, we solve Leetcode #1373 Maximum Sum BST in Binary Tree in Python and focus on the core idea that makes the solution efficient.
You will see the intuition, the step-by-step method, and a clean Python implementation you can use in interviews.
Problem Statement
Given a binary tree root, return the maximum sum of all keys of any sub-tree which is also a Binary Search Tree (BST). Assume a BST is defined as follows: The left subtree of a node contains only nodes with keys less than the node's key.
Quick Facts
- Difficulty: Hard
- Premium: No
- Tags: Tree, Depth-First Search, Binary Search Tree, Dynamic Programming, Binary Tree
Intuition
The problem breaks into overlapping subproblems, so caching results prevents exponential repetition.
A carefully chosen DP state captures exactly what we need to build the final answer.
Approach
Define the DP state and recurrence, then compute states in the correct order.
Optionally compress space once the recurrence is clear.
Steps:
- Choose a DP state definition.
- Write the recurrence and base cases.
- Compute states in the correct order.
Example
Input: root = [1,4,3,2,4,2,5,null,null,null,null,null,null,4,6]
Output: 20
Explanation: Maximum sum in a valid Binary search tree is obtained in root node with key equal to 3.
Python Solution
# Definition for a binary tree node.
# class TreeNode:
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class Solution:
def maxSumBST(self, root: Optional[TreeNode]) -> int:
def dfs(root: Optional[TreeNode]) -> tuple:
if root is None:
return 1, inf, -inf, 0
lbst, lmi, lmx, ls = dfs(root.left)
rbst, rmi, rmx, rs = dfs(root.right)
if lbst and rbst and lmx < root.val < rmi:
nonlocal ans
s = ls + rs + root.val
ans = max(ans, s)
return 1, min(lmi, root.val), max(rmx, root.val), s
return 0, 0, 0, 0
ans = 0
dfs(root)
return ans
Complexity
The time complexity is , and the space complexity is . The space complexity is .
Edge Cases and Pitfalls
Watch for boundary values, empty inputs, and duplicate values where applicable. If the problem involves ordering or constraints, confirm the invariant is preserved at every step.
Summary
This Python solution focuses on the essential structure of the problem and keeps the implementation interview-friendly while meeting the constraints.