How to check if a tree is a subtree of another

💻 Day 47 of #50DaysOfLeetCode Challenge 🚀 Problem: Binary Tree Maximum Path Sum (Leetcode 124) The goal was to find the maximum path sum in a binary tree — where the path can start and end at any node, not just the root. 🌲 🧠 Approach: I solved it using postorder traversal (Left → Right → Root). At every node, I calculated: 1️⃣ bothpaths → path passing through root (left + right + root) 2️⃣ bestpath → best path going downward from root (max of left or right + root) 3️⃣ onlyroot → when only the root node is part of the path At each step, I updated a global maxsum with the best of these three values. This way, every node contributed the maximum possible sum considering all cases. 🧩 Key Takeaway: 👉 Always think in terms of "What value should I return to parent?" vs "What value should I update globally?" 👉 Binary tree recursion becomes intuitive once you separate these two thoughts.

🚀 Day 124 of #LeetCode Challenge! Problem: Binary Tree Inorder Traversal 💡 My Approach: The goal is to perform an inorder traversal of a binary tree — visiting nodes in the order: Left → Root → Right. Here’s how I implemented it: Use recursion to traverse the tree. Start from the root node. Recursively visit the left subtree, then record the root value, and finally traverse the right subtree. This ensures all nodes are visited in sorted order for a BST. ✨ Example Input: [1, null, 2, 3] Output: [1, 3, 2] 🧠 Key Idea Inorder traversal naturally retrieves nodes in ascending order if the tree is a Binary Search Tree (BST). ⏱ Complexity TypeValueTimeO(N) — each node visited onceSpaceO(H) — recursion stack (H = height of tree) 📎 GitHub Link: https://lnkd.in/gBgJShhT #LeetCode #BinaryTree #InorderTraversal #Recursion #DSA #C++ #ProblemSolving #CodingChallenge #Day124

🚀 Day 125 of #LeetCode Challenge! Problem: Binary Tree Preorder Traversal 💡 My Approach: The goal is to perform a preorder traversal of a binary tree — visiting nodes in the order: Root → Left → Right Here’s the step-by-step logic: Start from the root node. Visit (record) the root value first. Recursively traverse the left subtree, then the right subtree. Store values in a vector as you go. ✨ Example Input: [1, null, 2, 3] Output: [1, 2, 3] 🧠 Key Idea Preorder traversal is useful when you need to copy or serialize a tree — it captures the structure starting from the root. ⏱ Complexity TypeValueTimeO(N) — each node visited onceSpaceO(H) — recursion stack (H = height of tree) 📎 GitHub Link: https://lnkd.in/gZ6GeXzm #LeetCode #BinaryTree #PreorderTraversal #Recursion #DSA #C++ #ProblemSolving #CodingChallenge #Day125

🚀 Day 123 of #LeetCode Challenge! Problem: Convert Sorted Array to Binary Search Tree 💡 My Approach: The task is to convert a sorted array into a height-balanced BST — meaning the depth of the two subtrees of every node should not differ by more than one. Here’s how I approached it: Pick the middle element of the array as the root (ensures balance). Recursively build the left subtree from the left half. Recursively build the right subtree from the right half. This method ensures the tree is balanced and follows the BST property (left < root < right). 🧠 Key Idea The midpoint of the current range always becomes the root, making the BST height-balanced naturally. ✨ Example Input: nums = [-10, -3, 0, 5, 9] Output: A balanced BST like: 0 / \ -3 9 / / -10 5 ⏱ Complexity TypeValueTimeO(N) — each element is processed onceSpaceO(log N) — recursion stack (for balanced BST) 📎 GitHub Link: https://lnkd.in/gDkzKT6A #LeetCode #BinarySearchTree #Recursion #C++ #ProblemSolving #DSA #Day123 #CodingChallenge

🚀 Day 115 of #LeetCode Challenge! Problem: Symmetric Tree 🌳 Concept: Check whether a binary tree is a mirror of itself (i.e., symmetric around its center). 💡 My Approach: Use recursion to compare left and right subtrees. A tree is symmetric if: Left subtree of left node matches right subtree of right node Right subtree of left node matches left subtree of right node Base cases: Both nodes NULL → symmetric ✅ One NULL and one not → not symmetric ❌ Values must match ✨ Example: Input: [1,2,2,3,4,4,3] Output: true ⏱ Time Complexity: O(N) — visiting all nodes 📦 Space Complexity: O(H) — recursion stack (H = height of tree) 📌 Key Insight: This is a classic mirror check — left's left must mirror right's right! 👨💻 GitHub Link: https://lnkd.in/gid6xjvn #LeetCode #BinaryTree #Recursion #SymmetricTree #MirrorTree #DSA #ProblemSolving #C++ #CodingChallenge #Day114

🚀 Day 112 of #LeetCode Challenge! Problem: Diameter of Binary Tree 💡 My Approach: The diameter of a binary tree is the longest path between any two nodes (may or may not pass through the root). The trick is to calculate the height of each subtree while keeping track of the maximum left + right path encountered. Use a helper function height() that: Recursively computes left and right subtree heights. Updates the global diameter as the maximum sum of left and right heights. ✨ Example: Input: [1,2,3,4,5] Output: 3 Explanation: The longest path is [4,2,1,3] or [5,2,1,3] ⏱ Time Complexity: O(N) — visit every node once 💾 Space Complexity: O(H) — recursive stack (H = height of tree) 🌱 Key Insight: At each node, the longest path through it is leftHeight + rightHeight. The overall diameter is the maximum of all such paths. 👨💻 GitHub Link: https://lnkd.in/dvUDMZUC #LeetCode #BinaryTree #Recursion #TreeTraversal #DSA #CodingChallenge #C++ #Day112

🔥 HyperRevision with Structure – Day 87 🔥 🧩 Problem solved today: 1️⃣ Surrounded Regions (LeetCode 130) 💭 Thoughts: Back to Neetcode 150. At first glance, this problem looks just like Number of Islands, but there’s a subtle twist — you need an extra boundary check before marking 'O's as 'X'. The key idea is to start DFS from the border 'O's so that only the inner, fully-surrounded regions are flipped. ⏱️ Time Complexity: O(m × n) 💾 Space Complexity: O(m × n) (due to recursion stack) 📌 GitHub link in comments #HyperRevision #LeetCode #DFS #GraphTraversal #NeetCode150

🚀 Day 127 of #LeetCode Challenge! Problem: Binary Tree Level Order Traversal 💡 My Approach: This problem involves traversing the binary tree level by level — also known as Breadth-First Search (BFS) traversal. Here’s the step-by-step breakdown: Use a queue to store nodes level by level. For each level, store all node values in a temporary list. Push their left and right children into the queue. After finishing one level, add it to the result vector. ✨ Example Input: [3,9,20,null,null,15,7] Output: [[3], [9,20], [15,7]] 🧠 Key Idea This traversal captures the hierarchical structure of the tree perfectly — often used in problems involving level-based processing, like Zigzag traversal or average of levels. ⏱ Complexity TypeValueTimeO(N) — each node visited onceSpaceO(N) — for queue and result storage 📎 GitHub Link: https://lnkd.in/gntMrKbU #LeetCode #BinaryTree #BFS #LevelOrderTraversal #DSA #ProblemSolving #C++ #CodingChallenge #100DaysOfCode #Day127

💡 Day 78 of #100DaysOfCode 💡 🔹 Problem: Find Closest Number to Zero – LeetCode ✨ Approach: Traversed through the array while keeping track of the number closest to zero using absolute difference comparison. Handled ties by preferring the positive number — because even in code, positivity wins 😉 📊 Complexity Analysis: ⏱ Time Complexity: O(n) — single pass through the array 💾 Space Complexity: O(1) — no extra space used ✅ Runtime: 3 ms (Beats 44.80%) ✅ Memory: 46.87 MB 🔑 Key Insight: Even the smallest differences matter — especially when you’re finding what’s closest to zero! ⚡ #LeetCode #100DaysOfCode #DSA #ProblemSolving #CodingChallenge #JavaProgramming #AlgorithmDesign #CodeJourney #StayPositive

🚀 Day 110 of #LeetCode Challenge! Problem: Duplicate Zeros 💡 My Approach: The task is to duplicate every zero in the array in-place, shifting the remaining elements to the right. First, count the total number of zeros to determine the “virtual” extended array size. Then, use two pointers — one (i) for the original array and another (j) for the extended index. Traverse backward: Copy each element from i to j. If it’s a zero, write an additional zero (when within bounds). This avoids overwriting elements and keeps the solution in O(1) extra space. ✨ Example: Input: [1,0,2,3,0,4,5,0] Output: [1,0,0,2,3,0,0,4] ⏱ Time Complexity: O(N) — single pass from the end 💾 Space Complexity: O(1) — done in-place without extra memory 👨💻 GitHub Link: https://lnkd.in/gCuFGw-c #LeetCode #Array #InPlaceAlgorithm #TwoPointers #ProblemSolving #DuplicateZeros #Day110 #DSA #CodingChallenge #C++