Leetcode 110 | ✅ Balanced Binary Tree | Optimal O(n) Solution Explained (Python)

Описание: In this video, we solve the Balanced Binary Tree problem using an efficient bottom-up recursive approach in Python.

Instead of calculating heights repeatedly (which leads to an O(n²) solution), we use a smart helper function that:
Returns the height of a subtree if it’s balanced
Returns -1 immediately if an imbalance is detected
This allows us to short-circuit early and ensures each node is visited only once.

📌 Key Concepts Covered

What it means for a binary tree to be height-balanced
Why the naive approach is inefficient
How returning -1 helps detect imbalance early
Clean and readable Python implementation

🎯 Why This Solution Is Important

Frequently asked in FAANG & product-based interviews

Demonstrates optimization thinking

Classic example of post-order traversal

👍 Like the video if it helped
📌 Subscribe for more DSA + Interview Prep
💬 Comment if you want an iterative stack-based version
🧠 Approach (Bottom-Up)

Recursively compute left and right subtree heights

If the height difference is more than 1, return -1

Propagate -1 upward to indicate imbalance

Final check: if height ≠ -1, the tree is balanced

🧪 Examples Demonstrated

✔️ Balanced Tree
❌ Unbalanced Tree

⏱ Complexity Analysis

Time Complexity: O(n) — each node is processed once
Space Complexity: O(h) — recursion stack (tree height)


#LeetCode110 #BalancedBinaryTree #BinaryTreeDFS #TreeDP #TreeOptimization #LeetCodeTrees #DSA #InterviewPreparation #FAANGPreparation #CodingInterview #StudyPlacement #TreeProblems #DFSRecursion #DataStructures #LeetCodeSolutions