Leetcode 110 | ✅ Balanced Binary Tree | Optimal O(n) Solution Explained (Python) скачать в хорошем качестве

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

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